The Association Between Atherogenic Index of Plasma and Postoperative Delirium in Cardiac Surgery Patients: An Analysis of the MIMIC-IV Database

The Association Between Atherogenic Index of Plasma and Postoperative Delirium in Cardiac Surgery Patients: An Analysis of the MIMIC-IV Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Association Between Atherogenic Index of Plasma and Postoperative Delirium in Cardiac Surgery Patients: An Analysis of the MIMIC-IV Database Xiaqing Zhang, Mingliang Xing, Afen Zhang, Rongzhi Zheng, Huiru Hu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8514238/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study aimed to investigate the relationship between the Atherogenic Index of Plasma (AIP) and the risk of Postoperative Delirium (POD) in patients undergoing cardiac surgery. Methods A retrospective cohort study was conducted using data from the MIMIC-IV 2.2 with patients who underwent cardiac surgery. AIP was calculated as log₁₀(TG/HDL-C). PSM was used in a 1:1 ratio to balance baseline characteristics (age, sex, race) between delirium and non-delirium groups. Multivariable logistic regression models were employed to assess the independent association between AIP (analyzed as both a continuous and categorical variable in quartiles) and POD, with adjustments for demographics, comorbidities, laboratory parameters, vital signs, medication use, and surgical details. RCS were used to explore nonlinearity. Subgroup and mediation analyses were also performed. Results Among 6,067 eligible patients, 1,565 (25.8%) developed POD. After PSM, 3,130 patients were analyzed. Multivariable regression revealed a significant positive association between AIP and POD (OR: 1.622, 95% CI: 1.455–1.81, P < 0.001). Quartile stratification revealed that higher AIP levels were associated with an increased risk of POD (OR: 1.708, 95% CI: 1.399–2.088, P < 0.001). In the fully adjusted model (Model 3), AIP remained significantly associated with POD (OR: 1.435, 95% CI: 1.249–1.652, P < 0.001). RCS analysis revealed a significant nonlinear relationship between AIP and POD ( P < 0.001), consistent across gender subgroups. Subgroup analysis revealed no significant interactions. Mediation analysis indicated that hemoglobin (Hb), red cell distribution width (RDW), albumin (Alb), and respiratory rate (RR) partially mediated the association between AIP and POD. Conclusion A higher AIP is independently associated with an increased risk of POD in patients undergoing cardiac surgery, exhibiting a nonlinear relationship. This association is partially mediated by several metabolic and inflammatory markers. AIP may serve as a valuable and easily obtainable predictive biomarker for POD risk stratification in this patient population. Postoperative Delirium Cardiac Surgery Atherogenic Index of Plasma MIMIC-IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Postoperative delirium (POD) is a frequent complication after surgery, particularly in patients aged 65 and older, with an incidence rate as high as 20%-50% following major surgeries such as cardiac procedures 1 . POD is not only linked to extended hospital stays, higher healthcare costs, but also serves as an independent risk factor for long-term cognitive decline and increased postoperative mortality 2, 3 . In a prospective cohort study of 560 elderly patients without dementia undergoing major elective surgery, postoperative delirium was significantly associated with accelerated cognitive decline over a 72-month follow-up period. The delirium group exhibited a markedly steeper cognitive decline slope, comparable to that observed in patients diagnosed with Alzheimer's disease within five years 4 . Although its pathophysiological mechanisms involve multiple pathways such as neuroinflammation, oxidative stress, and neurotransmitter imbalances, effective prevention and treatment options remain limited. Consequently, the current focus of POD management has shifted toward preoperative risk prediction and stratification, aiming to improve postoperative outcomes by identifying high-risk patients early and implementing targeted interventions 5 . Within this context, identifying convenient and reliable preoperative predictive biomarkers has become a research hotspot. The relationship between lipid dysregulations and POD has gradually attracted the attention of researchers in recent years. The brain is one of the body's most lipid-rich organs, with lipids playing a critical role in maintaining neuronal membrane integrity, synaptic function, and signal transduction 6 . Both preclinical and clinical studies have revealed that POD patients often exhibit specific lipid profile dysregulation, such as alterations in levels of omega-3 polyunsaturated fatty acids, cholesterol, and sphingolipids 7, 8 . These lipid dysregulations may increase the brain's vulnerability to perioperative stress by exacerbating neuroinflammation, compromising blood-brain barrier integrity, and disrupting energy metabolism, ultimately triggering delirium 9 . However, the predictive power of individual blood lipid markers for disease, like triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C), is restricted and frequently yields inconsistent outcomes. The Atherogenic Index of Plasma (AIP), defined as log₁₀ [TG/HDL-C], comprehensively reflects the balance between atherogenic and protective lipoproteins. It represents a superior cardiovascular metabolic risk predictor compared to individual markers 10 . Notably, recent evidence indicates a link between AIP and cognitive function. A nationwide longitudinal study found that higher AIP levels are significantly associated with increased risk of cognitive impairment in middle-aged and elderly individuals 11 , suggesting that AIP may as a potential neurocognitive risk biomarker. Nevertheless, the relationship between AIP and POD is still remains unclear. Considering the significant overlap between the dyslipidemia represented by AIP and the potential pathophysiological mechanisms of POD, we hypothesize that elevated preoperative AIP independently increases the risk of POD in cardiac surgery patients. To validate this hypothesis, this study utilizes the large public database MIMIC-IV to systematically investigate the association between preoperative AIP and POD in cardiac surgery, aiming to provide a novel, readily accessible laboratory marker for perioperative risk stratification. Methods 1. Data Source This research utilized a retrospective cohort design, sourcing data from the Medical Information from Critical Care (MIMIC-IV) database (version 2.2) to obtain research data 12 . MIMIC-IV is a publicly accessible and one of the most comprehensive intensive care databases in critical care medicine. The dataset comprises comprehensive clinical data for patients hospitalized at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2019, encompassing length of stay, laboratory test outcomes, medication regimens, vital signs, and other pertinent clinical information. The Institutional Review Boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) granted permission to use the MIMIC-IV database for this research. 2. Ethics and Data Privacy This research data has been de-identified to uphold ethical standards and protect patient privacy, with all necessary measures implemented to ensure confidentiality. The Institutional Review Board at Beth Israel Deaconess Medical Center waived the informed consent requirement due to the data being de-identified. Author Xiaqing Zhang successfully completed the National Institutes of Health (NIH) online course “Protection of Human Research Participants” (ID: 15008001) and was therefore authorized to access the MIMIC-IV database for data extraction. 3. Data Inclusion and Exclusion Criteria The MIMIC-IV database contains clinical data from 180,733 patients admitted to the hospital's intensive care unit (ICU) between 2012 and 2019. This study involved patients identified through International Classification of Diseases, Ninth or Tenth Revision (ICD-9/10) codes as having undergone cardiac surgery or experienced delirium (Supplementary Table S1). From the initial cohort of 180,733 patients, we excluded cases for the following reasons: (1) removal of non-cardiac surgery diagnoses (N = 170,086), (2) removal of non-delirium diagnoses (N = 4,052), and (3) removal of cases lacking baseline data (N = 528). The final study cohort comprised 60,67 patients, including 15,65 delirium patients and 45,02 non-delirium patients. (Fig. 1 ) 4. Data Extraction Clinical data were retrieved from the MIMIC-IV database using Structured Query Language (SQL). The extracted data encompassed demographic details, vital signs, essential laboratory parameters, and significant comorbidities. Specifically, demographic details included age, gender, and ethnicity. Vital signs comprise systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), respiratory rate (RR), and pulse oximetry oxygen saturation (SpO2). Laboratory parameters primarily include hematologic indicators, hepatic and renal function markers, and electrolyte levels. Additionally, we documented significant comorbidities potentially linked to cardiac surgery and POD, such as hypertension, diabetes, congestive heart failure, and myocardial infarction and so on. And we also extracted data on the type of cardiac surgery and the use of anesthetic sedatives potentially related to POD occurrence. For all variables, we utilized the first recorded value after patient admission. 5. Definition of AIP AIP is determined by assessing plasma levels of high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) 10 . The AIP is calculated using the formula: log₁₀ [TG (mmol/L) / HDL-C (mmol/L)]. 6. Statistical Analysis Data processing and analysis were conducted using R software (version 4.1.1). Continuous variables with non-normal distribution are described using medians and interquartile ranges (IQR) for baseline characteristic analyses. Categorical variables are reported as counts and weighted percentages. The Wilcoxon rank-sum test assessed differences in continuous variables across AIP quartiles, while the Rao-Scott chi-square test evaluated weighted percentages of categorical variables. Propensity score matching (PSM) was conducted in a 1:1 ratio based on sex, age, and race to ensure comparable baseline data distribution between the delirium and non-delirium groups, minimizing baseline data impact on research outcomes. Odds ratios (OR) and 95% confidence intervals (CI) were derived using logistic regression models. Covariate selection was guided by established associations with POD, clinically significant baseline differences between groups ( P < 0.01), and clinical relevance. AIP was incorporated into the regression models both as a continuous variable and as a categorical variable. As a continuous variable, the raw AIP value was entered into the model. The original AIP values were divided into quartiles based on the interquartile range, ranked from low to high. These quartiles were labeled as the first quartile (Q1), second quartile (Q2), third quartile (Q3), and fourth quartile (Q4), and were further evaluated as categorical variables. When AIP was treated as a categorical variable, Q1 was used as the reference group. Three distinct statistical models were constructed: Model 1 represented the unadjusted model. Model 2 was adjusted for the following factors: ICU length of stay, in-hospital mortality, comorbidities, sedative drug use, and surgery category. Model 3 was further adjusted based on Model 2 by incorporating laboratory biochemical parameters and vital signs. Survey weights were applied to all regressions, and continuous covariates with non-normal distributions were transformed using weighted quartiles. Restricted cubic spline (RCS) analysis was employed to explore potential non-linear relationships between AIP and the risk of POD. Subgroup analysis was conducted to assess whether the association between AIP and POD after cardiac surgery differed across various subgroups, with results visualized using forest plots. Interaction analysis was performed to evaluate potential interactions between each subgroup and AIP. P -values were corrected for the False Discovery Rate (FDR). A two-sided P -value < 0.05 was considered statistically significant. Results 1. Baseline Characteristics of Patients From an analysis of 180,733 patients in the MIMIC-IV database, 6,067 patients met the study's criteria and were included. Figure 1 shows the flow diagram of the patient selection process. with 1,565 patients (25.8%) experiencing POD during hospitalization. After PSM, the analysis included 3,130 patients, evenly divided into two groups of 1,565 each, with 415 patients (19.8%) experiencing POD. Post-PSM, all standardized mean differences (SMDs) were all < 0.1, demonstrating comparable baseline variable distributions between the two groups. Baseline characteristics are presented in Table 1 . Table 1 Baseline Characteristics of Patients Variable Before PSM p -value 2 After PSM p -value 2 Overall Non-Delirium Delirium Overall Non-Delirium Delirium N = 6,067 1 N = 4,502 1 N = 1,565 1 N = 3,130 1 N = 1,565 1 N = 1,565 1 Age 69 (10) 69 (10) 69 (10) 0.13 69 (10) 68 (10) 69 (10) 0.1 Gender 0.034 > 0.99 Male 3,753(61.9%) 2,820 (63%) 933 (60%) 1,866(60%) 933 (60%) 933 (60%) Female 2,314(38.1%) 1,682 (37%) 632 (40%) 1,264(40%) 632 (40%) 632 (40%) Race 0.15 0.38 Asian 109 (1.8%) 87 (1.9%) 22 (1.4%) 52 (1.7%) 30 (1.9%) 22 (1.4%) Black 276 (4.5%) 194 (4.3%) 82 (5.2%) 150 (4.8%) 68 (4.3%) 82 (5.2%) White 4,586(75.6%) 3,422 (76%) 1,164(74%) 2,348(75%) 1,184 (76%) 1,164(74%) Others 1,096(18.1%) 799 (18%) 297 (19%) 580 (19%) 283 (18%) 297 (19%) Cerebrovascular disease < 0.001 < 0.001 No 5,690 (94%) 4,346 (97%) 1,344(86%) 2,855(91%) 1,511 (97%) 1,344(86%) Yes 377 (6.2%) 156 (3.5%) 221 (14%) 275 (8.8%) 54 (3.5%) 221 (14%) Neurological disease < 0.001 < 0.001 No 5,937 (98%) 4,452 (99%) 1,485(95%) 3,034(97%) 1,549 (99%) 1,485(95%) Yes 130 (2.1%) 50 (1.1%) 80 (5.1%) 96 (3.1%) 16 (1.0%) 80 (5.1%) Respiratory system disease < 0.001 < 0.001 No 5,207 (86%) 3,967 (88%) 1,240(79%) 2,616(84%) 1,376 (88%) 1,240(79%) Yes 860 (14%) 535 (12%) 325 (21%) 514 (16%) 189 (12%) 325 (21%) Diabetes 2,414 (40%) 1,688 (37%) 726 (46%) < 0.001 1,345(43%) 619 (40%) 726 (46%) < 0.001 Hypertension 2,370 (39%) 1,703 (38%) 667 (43%) < 0.001 1,279(41%) 612 (39%) 667 (43%) 0.046 Congestive Heart Failure 1,143 (19%) 705 (16%) 438 (28%) < 0.001 686 (22%) 248 (16%) 438 (28%) < 0.001 Myocardial Infarction 1,856 (31%) 1,276 (28%) 580 (37%) < 0.001 1,014(32%) 434 (28%) 580 (37%) < 0.001 Kindey disease 269 (4.4%) 120 (2.7%) 149 (9.5%) < 0.001 189 (6.0%) 40 (2.6%) 149 (9.5%) < 0.001 Liver disease 130 (2.1%) 91 (2.0%) 39 (2.5%) 0.27 68 (2.2%) 29 (1.9%) 39 (2.5%) 0.22 Obstructive sleep apnea 977 (16%) 670 (15%) 307 (20%) < 0.001 530 (17%) 223 (14%) 307 (20%) < 0.001 Surgical category < 0.001 < 0.001 Aortic replacement 390 (6.4%) 126 (2.8%) 264 (17%) 312(10%) 48 (3.1%) 264 (17%) Combined cardiac surgery 1,797 (30%) 1,407 (31%) 390 (25%) 873 (28%) 483 (31%) 390 (25%) GABA 939 (15%) 728 (16%) 211 (13%) 471 (15%) 260 (17%) 211 (13%) Valve surgery 2,941 (48%) 2,241 (50%) 700 (45%) 1,474(47%) 774 (49%) 700 (45%) Dexmedetomidine 576 (9.5%) 289 (6.4%) 287 (18%) < 0.001 386 (12%) 99 (6.3%) 287 (18%) < 0.001 Benzodiazepines 7 (0.1%) 3 (< 0.1%) 4 (0.3%) 0.078 5 (0.2%) 1 (< 0.1%) 4 (0.3%) 0.37 Propofol 5,484 (90%) 4,210 (94%) 1,274(81%) < 0.001 2,739(88%) 1,465 (94%) 1,274(81%) < 0.001 LDL(mmol/L) 82 (37) 81 (36) 83 (40) 0.31 82 (37) 81 (35) 83 (40) 0.41 Hb(g/L) 11.52 (2.09) 11.72 (2.05) 10.95(2.08) < 0.001 11.27(2.08) 11.58 (2.02) 10.95(2.08) < 0.001 RDW(%) 14.90 (2.10) 14.73 (1.96) 15.38(2.39) < 0.001 15.06(2.21) 14.74 (1.97) 15.38(2.39) < 0.001 Lymphocyte(×10⁹/L) 1.81 (2.91) 1.82 (2.46) 1.80 (3.95) < 0.001 1.80 (3.12) 1.80 (1.97) 1.80 (3.95) < 0.001 Monocyte(×10⁹/L) 0.71 (0.40) 0.70 (0.39) 0.74 (0.43) 0.001 0.72 (0.42) 0.70 (0.41) 0.74 (0.43) 0.001 Platelet(×10⁹/L) 225 (105) 224 (100) 226 (117) 0.11 226 (109) 225 (101) 226 (117) 0.1 Neutrophil(×10⁹/L) 7.9 (5.1) 7.7 (4.9) 8.4 (5.6) 0.003 8.1 (5.3) 7.8 (4.9) 8.4 (5.6) 0.02 WBC(×10⁹/L) 9.7 (4.3) 9.5 (4.2) 10.2 (4.8) < 0.001 9.8 (4.5) 9.4 (4.1) 10.2 (4.8) < 0.001 BUN(mmol/L) 26 (18) 24 (16) 30 (22) < 0.001 27 (19) 24 (16) 30 (22) < 0.001 UA(µmol/L) 6.45 (2.37) 6.38 (2.28) 6.65 (2.59) 0.006 6.49 (2.44) 6.34 (2.28) 6.65 (2.59) 0.006 Cr(µmol/L) 1.31 (1.07) 1.25 (0.99) 1.49 (1.27) < 0.001 1.37 (1.17) 1.25 (1.04) 1.49 (1.27) < 0.001 Cl⁻(mmol/L) 101.6 (5.1) 101.8 (4.9) 100.8 (5.5) < 0.001 101.3 (5.3) 101.8 (4.9) 100.8 (5.5) < 0.001 Total Cholesterol(mmol/L) 155 (49) 154 (46) 158 (57) 0.96 156 (51) 154 (44) 158 (57) 0.76 AG(mmol/L) 14.5 (3.3) 14.4 (3.3) 14.6 (3.5) 0.041 14.6 (3.4) 14.5 (3.3) 14.6 (3.5) 0.43 Alb(g/L) 3.80 (0.60) 3.88 (0.55) 3.59 (0.69) < 0.001 3.73 (0.64) 3.88 (0.54) 3.59 (0.69) < 0.001 TG(mmol/L) 131 (144) 120 (59) 162 (263) < 0.001 142 (191) 121 (58) 162 (263) < 0.001 HDL(mmol/L) 48 (15) 49 (15) 46 (15) < 0.001 47 (15) 48 (15) 46 (15) 0.003 Glu(mmol/L) 124 (45) 122 (43) 132 (49) < 0.001 128 (47) 123 (45) 132 (49) < 0.001 SBP(mmHg) 113 (19) 113 (19) 114 (21) 0.69 113 (20) 112 (19) 114 (21) 0.34 DBP(mmHg) 57 (12) 57 (12) 56 (14) 0.17 56 (13) 56 (11) 56 (14) 0.32 HR(t/min) 82 (15) 82 (15) 83 (16) 0.065 82 (16) 81 (15) 83 (16) 0.042 RR(t/min) 17.9 (6.0) 17.4 (5.8) 19.3 (6.3) < 0.001 18.4 (6.2) 17.5 (6.0) 19.3 (6.3) < 0.001 SpO 2 (%) 97.59 (2.94) 97.71 (2.82) 97.25(3.22) < 0.001 97.49(3.03) 97.74 (2.81) 97.25(3.22) < 0.001 ICU length of stay 7 (11) 4 (4) 16 (18) < 0.001 10 (14) 4 (4) 16 (18) < 0.001 In-hospital death 153 (2.5%) 90 (2.0%) 63 (4.0%) < 0.001 98 (3.1%) 35 (2.2%) 63 (4.0%) 0.004 AIP 0.90 (0.63) 0.84 (0.58) 1.05 (0.73) < 0.001 0.96 (0.67) 0.86 (0.58) 1.05 (0.73) < 0.001 Notes: LDL: Low - Density Lipoprotein; Hb: Hemoglobin; RDW: Red Cell Distribution Width; WBC: White Blood Cell; BUN: Blood Urea Nitrogen; UA: Uric Acid; Cr: Creatinine; Cl⁻: Chloride; AG: Anion Gap; Alb: Albumin; TG: Triglyceride; HDL: High - Density Lipoprotein; Glu: Glucose; SBP: Blood Pressure Systolic; DBP: Blood Pressure Diastolic; HR: Heart Rate; RR: Respiratory Rate; SpO2: Pulse Oximetry Oxygen Saturation; AIP: Atherogenic Index of Plasma. 1.Mean (sd) or Frequency (%); 2.Pearson’s Chi-squared test; Wilcoxon rank sum test; Fisher’s exact test. Post-matching showed there were no significant differences in age, sex, or racial distribution between the two groups ( P > 0.05). However, persistent differences in clinical indicators and comorbidities, suggesting delirium patients exhibited greater disease severity and more intricate clinical profiles. For instance, patients in the delirium group had more preoperative comorbidities than the non-delirium group, including cardiovascular and cerebrovascular diseases, neurological disorders, respiratory diseases, diabetes, hypertension, renal disease, and obstructive sleep apnea ( P < 0.05). The proportion of patients undergoing aortic valve replacement was higher in the delirium group (17% vs 3.1%). Dexmedetomidine increases the incidence of POD (18% vs 6.3%), whereas propofol has the opposite effect (81% vs 94%). Laboratory tests revealed that Hemoglobin (Hb), Chloride (Cl⁻), Albumin (Alb), HDL and SpO 2 were significantly lower in delirium group ( P < 0.05). In contrast, Red Cell Distribution Width (RDW), Lymphocyte, Monocyte, Neutrophil, White Blood Cell (WBC), Blood Urea Nitrogen (BUN), Uric Acid (UA), Creatinine (Cr), TG, Glucose (Glu), and AIP levels were significantly elevated ( P < 0.05). Although several variables exhibited statistically significant differences between the two groups ( P < 0.05), some of these differences were not clinically meaningful. 2. Association Between POD and AIP in Cardiac Surgery Patients The association between AIP and POD in cardiac surgery patients was analyzed using a multiple linear regression model (Table 2 ). Model 1 (without covariates) showed a positive correlation between AIP and POD (OR: 1.622, 95% CI: 1.455–1.81, P < 0.001). Furthermore, AIP quartile stratification revealed a significant difference between the Q4 group (OR: 1.708, 95% CI: 1.399–2.088, P < 0.001) and the Q1 group, indicating that higher AIP levels are associated with an higher risk of POD. Model 2 (after adjusting for covariates including ICU length of stay, in-hospital mortality, comorbidities [cerebrovascular disease, neurological disease, respiratory system disease, congestive heart failure, diabetes, hypertension, renal disease, myocardial infarction, obstructive sleep apnea], dexmedetomidine, propofol, and surgery category) further analyzed the relationship, showing that AIP remained significantly associated with POD (OR: 1.431, 95% CI: 1.247–1.644, P < 0.001). The Q4 quartile group demonstrated a significantly higher risk of POD compared to the Q1 quartile group (OR: 1.396, 95% CI: 1.089–1.792, P = 0.0085). Model 3 (further adjusting for Hb, RDW, lymphocytes, monocytes, neutrophils, WBC, BUN, UA, Cr, Cl⁻, Alb, Glu, RR, SpO 2 , and other clinical variables based on Model 2) was also observed a significant positive correlation between AIP and POD (OR: 1.435, 95% CI: 1.249–1.652, P < 0.001). In Model 3, the Q4 group demonstrated a notably higher risk of POD compared to the Q1 group (OR: 1.38, 95% CI: 1.073–1.777, P = 0.0122). Trend P -values showed significant trends with P < 0.001 in Model 1, P = 0.0141 in Model 2, and P = 0.0156 in Model 3. Table 2 The relationship between AIP and delirium after cardiac surgery. Characterisitic Model 1 Model 2 Model 3 OR 95%CI P -value OR 95%CI P -value OR 95%CI P -value AIP continuous 1.622 (1.455,1.81) < 0.001 1.431 (1.247,1.644) < 0.001 1.435 (1.249,1.652) < 0.001 AIP quantile Q1(low)(< 0.41) 781(24.95%) Ref Ref Ref Ref Ref Ref Q2( 0.41-< 0.88) 781(24.95%) 1.086 (0.89,1.325) 0.4167 1.06 (0.829,1.354) 0.6437 1.041 (0.812,1.335) 0.7518 Q3( 0.41-< 0.88) 785(25.08%) 1.191 (0.977,1.453) 0.0844 1.049 (0.817,1.346) 0.7077 1.06 (0.823,1.366) 0.6509 Q4(high)(≥ 1.4) 783(25.02%) 1.708 (1.399,2.088) < 0.001 1.396 (1.089,1.792) 0.0085 1.38 (1.073,1.777) 0.0122 P for trend < 0.001 0.0141 0.0156 OR, odds ratio; CI, confidence intervals The model 1 was the crude model. The model 2 was adjusted by ICU length of stay, In-hospital death, Cerebrovascular disease, Neurological disease, Respiratory system disease, Congestive Heart Failure, Diabetes, Hypertension, Kidney disease, Myocardial Infarction, Obstructive sleep apnea, Dexmedetomidine, Propofol, Surgical category The model 3 was further adjusted by ICU length of stay, In-hospital death, Cerebrovascular disease, Neurological disease, Respiratory system disease, Congestive Heart Failure, Diabetes, Hypertension, Kidney disease, Myocardial Infarction, Obstructive sleep apnea, Dexmedetomidine, Propofol, Surgical category, Hemoglobin, Red Cell Distribution Width, Lymphocyte, Monocyte, Neutrophil, White Blood Cell, Blood Urea Nitrogen, Uric Acid, Creatinine, Chloride, Albumin, Glucose, Respiratory Rate, Pulse Oximetry Oxygen Saturation. 3. RCS Curve for the Association Between AIP and POD RCS curves were employed to examine the relationship between AIP and POD in cardiac surgery patients, adjusting for all relevant covariates (Fig. 2 ). The results revealed a notable nonlinear association between AIP and POD in cardiac surgery patients ( P for non-linearity < 0.001, Fig. 2 A). In gender subgroup analysis, a significant nonlinear relationship between AIP and POD observed in the male group ( P for non-linearity < 0.001, Fig. 2 B). In the female subgroup, AIP also showed a significant nonlinear relationship with POD ( P for non-linearity = 0.015, Fig. 2 C). These results further confirm the important role of AIP in the occurrence of POD in cardiac surgery patients and suggest that gender may play a moderating role in this relationship. 2.4 Relationship Between AIP and Baseline Characteristics of Subgroups During subgroup analysis, multiple variables were evaluated, including ICU length of stay, cerebrovascular disease, neurological disorders, respiratory diseases, congestive heart failure, diabetes, hypertension, renal disease, obstructive sleep apnea, dexmedetomidine, propofol, and surgical category. The results indicated no significant interactions were observed among these subgroups ( P for interaction > 0.05 for all) (Fig. 3 ). This further confirms that the association between AIP and POD remains consistent across subgroups. 2.5 Association of Relevant Indicators with AIP and POD in Cardiac Surgery Table 3 presents the association between AIP and relevant indicators, analyzed under three models—Model 1, Model 2, and Model 3—with a focus on indicators with P < 0.05. Among these, Alb showed a statistically significant association with AIP across all three models ( P < 0.001 for all), with β values of -0.254, -0.179, and − 0.177 respectively. and the progressively narrowing 95% CI indicate a strong negative correlation between AIP and Alb, with stable results. Cr exhibited P values of 0.001, 0.014, and 0.031 across the three models, with decreasing β values, suggesting a positive correlation between AIP and Cr that may weaken with model adjustments. BUN showed P < 0.05 in all three models (< 0.001, 0.007, 0.016), indicating a positive correlation. WBC, Monocyte, Hb, RR, Neutrophil, and RDW showed strong and statistically significant associations with AIP ( P < 0.001 for all). A significant association was also found for Lymphocyte count in Model 1 ( P = 0.021) and Model 2 ( P = 0.047). Cl⁻ exhibited a P value of 0.043 only in Model 3, with no significant correlation in other models. UA and SpO 2 exhibited P > 0.05 across all models, indicating no significant association between AIP and these variables. Table 3 The associations between AIP and related indicators Variable β value 95% CI P -value Glu Model1 0.001 (0,0.003) 0.058 Model2 0.001 (-0.001,0.002) 0.22 Model3 0.001 (-0.001,0.002) 0.257 Alb Model1 -0.254 (-0.354,-0.154) < 0.001 Model2 -0.179 (-0.285,-0.074) 0.001 Model3 -0.177 (-0.283,-0.071) 0.001 Cl⁻ Model1 0.007 (-0.005,0.019) 0.223 Model2 0.012 (-0.001,0.024) 0.062 Model3 0.012 (0,0.025) 0.043 Cr Model1 0.099 (0.041,0.157) 0.001 Model2 0.073 (0.015,0.132) 0.014 Model3 0.065 (0.006,0.124) 0.031 UA Model1 0.012 (-0.014,0.038) 0.358 Model2 0.006 (-0.02,0.032) 0.64 Model3 0.004 (-0.022,0.03) 0.752 BUN Model1 0.007 (0.003,0.01) < 0.001 Model2 0.005 (0.001,0.008) 0.007 Model3 0.004 (0.001,0.008) 0.016 WBC Model1 0.039 (0.025,0.053) < 0.001 Model2 0.036 (0.022,0.051) < 0.001 Model3 0.036 (0.022,0.05) < 0.001 Monocyte Model1 0.354 (0.2,0.508) < 0.001 Model2 0.294 (0.139,0.45) < 0.001 Model3 0.294 (0.137,0.451) < 0.001 Lymphocyte Model1 0.022 (0.003,0.045) 0.021 Model2 0.02 (0,0.044) 0.047 Model3 0.019 (-0.001,0.043) 0.06 Hb Model1 -0.065 (-0.095,-0.035) < 0.001 Model2 -0.046 (-0.077,-0.015) 0.004 Model3 -0.048 (-0.079,-0.017) 0.002 SpO 2 Model1 -0.011 (-0.032,0.009) 0.276 Model2 -0.005 (-0.026,0.015) 0.608 Model3 -0.004 (-0.025,0.016) 0.693 RR Model1 0.018 (0.008,0.028) < 0.001 Model2 0.012 (0.001,0.023) 0.025 Model3 0.012 (0.001,0.023) 0.025 Neutrophil Model1 0.032 (0.02,0.045) < 0.001 Model2 0.028 (0.016,0.041) < 0.001 Model3 0.028 (0.016,0.041) < 0.001 RDW Model1 0.052 (0.024,0.08) < 0.001 Model2 0.035 (0.006,0.064) 0.017 Model3 0.033 (0.004,0.062) 0.025 The model 1 was the crude model. The model 2 was adjusted by ICU length of stay, In-hospital death. The model 3 was adjusted by ICU length of stay, In-hospital death, Cerebrovascular disease, Neurological disease, Respiratory system disease, Congestive Heart Failure, Diabetes, Hypertension, Kidney disease, Myocardial Infarction, Obstructive sleep apnea, Dexmedetomidine, Propofol, Surgical category. Notes: Glu: Glucose; Alb: Albumin; Cl⁻: Chloride; Cr: Creatinine; UA: Uric Acid; BUN: Blood Urea Nitrogen; WBC: White Blood Cell; Hb: Hemoglobin; SpO 2 : Pulse Oximetry Oxygen Saturation; RR: Respiratory Rate; RDW: Red Cell Distribution Width. 2.6 Analysis of the Mediating Role in Delirium after Cardiac Surgery. We performed a mediation analysis to explore the influence of metabolic-related indicators on the relationship between AIP and POD after cardiac surgery, as indicated by our analysis of relevant indicators (Fig. 4 ). The findings revealed that with Hb as the mediating variable, the indirect effect of AIP on POD was − 0.002 (95% CI: -0.005 to -0.00039, P = 0.04); The total effect was 0.0593 ( P < 0.001), the mediation proportion was − 3.37%, and the direct effect was 0.0613 (95% CI: 0.0453 to 0.0777, P < 0.001) (Fig. 4 A). With RDW as the mediating variable, the indirect effect of AIP on POD was 0.0015 (95% CI: 0.0004 to 0.0031, P < 0.001). The total effect was 0.0615 ( P < 0.001), the mediation proportion was 2.09%, and the direct effect was 0.06 (95% CI: 0.0343 to 0.0817, P < 0.001) (Fig. 4 B). With Alb as the mediating variable, the indirect effect was − 0.0035 (95% CI: -0.0064 to -0.0011, P < 0.001). The total effect was 0.0566 ( P < 0.001), the mediation proportion was 6.18%, and the direct effect was 0.0601 (95% CI: 0.0388 to 0.0763, P < 0.001) (Fig. 4 C). Finally, with RR as the mediator, the indirect effect of AIP on delirium was 0.0015 (95% CI: 0.0001 to 0.0035, P = 0.04). The total effect was 0.0649 ( P < 0.001), the mediation proportion was 2.11%, and the direct effect was 0.0634 (95% CI: 0.0455 to 0.0821, P < 0.001) (Fig. 4 D). See Supplementary Table S2 for details. Discussion This research is the first exploration of the relationship between the AIP index and POD. Results indicate that within a large-scale cardiac surgery cohort, that a preoperatively elevated AIP is independently associated with an higher risk of POD. This association remains robust after multivariate adjustment, and RCS analysis reveals a significant nonlinear “J-shaped” relationship between the two groups. Our findings suggest that preoperative dyslipidemia may represent a key underlying mechanism contributing to increased postoperative brain vulnerability and susceptibility to delirium, providing a novel biomarker for identifying high-risk patients. AIP serves as an effective predictive tool for assessing preoperative POD risk, aiding clinical anesthesiologists in identifying high-risk patients and guiding perioperative management. Lipids are widely distributed throughout the brain and body, with their various subtypes playing crucial roles in maintaining homeostasis and supporting cognitive development. Perioperative dyslipidemia can be regarded as a risk factor for neurological dysfunction. Changes in lipid homeostasis and metabolism can impact neuroinflammation, oxidative stress, and blood-brain barrier(BBB) integrity, potentially contributing to the onset and progression of delirium 9 . Cardiac surgery may contribute to pathological changes such as intracerebral microembolism formation, cerebral hypoperfusion, decreased cerebral oxygen saturation, and inflammatory cytokine release, collectively forming the pathophysiological basis for the high incidence of POD following such procedures 13 . Notably, a study identified 51 differentially expressed metabolites between elderly patients with and without POD after cardiopulmonary bypass (CPB) cardiac surgery, mainly enriched at the lipid and lipoprotein molecular levels 7 . This finding indicates a strong link between POD following cardiac surgery and acute changes in lipid metabolism. Furthermore, preoperative administration of lipid-lowering drugs like simvastatin and fenofibrate has demonstrated potential protective effects against CPB-induced cognitive dysfunction and neuronal integrity impairment, with drugs are believed to exert their effects by protecting endothelial function and reducing inflammatory responses 14 . AIP, as a simple indicator calculable through routine lipid testing (TG and HDL-C), sensitively assesses overall lipid profiles and lipoprotein patterns. It serves as a robust biomarker for predicting atherosclerotic cardiovascular disease, diabetes, and prediabetes by reflecting the balance between atherogenic and protective lipoproteins 15 – 17 . Recent studies have further linked AIP to neurodegenerative disorders. Studies show a positive correlation between elevated AIP and the severity of white matter hyperintensities (WMH). WMH typically indicate microvascular ischemic changes in the brain, with clinical manifestations ranging from asymptomatic to cognitive impairment, dementia, or cerebrovascular accidents, including ischemic stroke and transient ischemic attacks 18 . Notably, conditions that increase cerebrovascular event risk (e.g., hypertension, atrial fibrillation, history of stroke) are themselves risk factors for POD 19 , 20 . Although overt postoperative stroke is rare, imaging studies reveal that approximately 7–10% of elderly surgical patients exhibit evidence of asymptomatic cerebral ischemia, with these patients showing more than double the risk of POD 21 , 22 . Concurrently, several cohort studies across various populations indicate a link between elevated AIP and the onset of cognitive impairment and dementia in middle-aged and elderly individuals 23 , 24 , suggesting the need for further research into the relationship between AIP and perioperative cognitive outcomes. The underlying regulatory mechanisms linking AIP and POD remain unclear, potentially involving multiple intertwined pathophysiological pathways. Elevated AIP not only reflects lipid metabolism disorders but is also closely associated with systemic inflammation and oxidative stress, both of which are core drivers of POD 25 . Elevated AIP typically indicates a relative increase in small dense low-density lipoproteins (sdLDL), these lipoproteins are more susceptible to oxidative modification and readily penetrate the vascular endothelium into the arterial wall, triggering macrophage phagocytosis and foam cell formation. This process initiates and amplifies inflammatory responses within the arterial wall 26 . This lipid-driven state of low-grade chronic inflammation can be dramatically amplified under the stress of surgical trauma, leading to a cytokine storm that accelerates adverse effects on central nervous system function. Conversely, HDL-C exerts multifaceted neuroprotective effects, including potent anti-inflammatory and antioxidant functions, as well as promoting the clearance of β-amyloid (Aβ) within the brain 27 , 28 . Some studies suggest that higher HDL-C levels may suppress neuropathological inflammation and prevent cognitive impairment by modulating biochemical pathways through anti-inflammatory actions such as inhibiting antigen-presenting cell maturation and regulating lymphocyte inflammatory responses 28 . Elevated AIP levels are frequently accompanied by reduced HDL-C, decreased cholesterol reverse transport, increased lipid accumulation, and enhanced proinflammatory effects 29 . Consistent with existing studies, our results show that low HDL-C levels are associated with a higher risk of POD 30 . Oxidative stress represents another critical pathological mechanism in delirium development, characterized by reactive oxygen species (ROS) production and disruption of the brain's antioxidant defense system 31 , 32 . Higher oxidative stress may harms cellular structures such as lipids, proteins, and DNA, often resulting in neuronal impairment or cell death 33 . Elevated TG levels constitute another critical component of increased AIP. High TG levels impair brain health through multiple pathways, including inducing vascular endothelial dysfunction, exacerbating oxidative stress, and subsequently compromising BBB integrity. Increased BBB permeability facilitates the entry of peripheral inflammatory cells, cytokines, and harmful metabolic byproducts into the central nervous system 9 . Furthermore, hypertriglyceridemia may promote abnormal lipid deposition in the brain, impair cerebral blood flow autoregulation, and potentially accelerate Alzheimer's disease (AD)-related protein pathologies by affecting Aβ and tau metabolism 34 , 35 . A retrospective study revealed an association between elevated TG levels and increased delirium severity in elderly patients following hip fracture surgery 36 . Therefore, AIP, as a composite indicator, may simultaneously capture dual risks: “weakened protective factors (e.g., HDL-C)” and “enhanced detrimental factors (e.g., atherogenicity).” This enables it to more accurately reflect the combined risks of neuroinflammation, oxidative stress, and cerebrovascular injury faced during surgical stress. This dual-risk capture may explain why AIP outperforms single lipid markers in predicting POD. We performed subgroup analyses on various known or suspected risk factors for POD to further confirm the robustness of the association between AIP and POD. These included ICU length of stay, pre-existing comorbidities (e.g., cerebrovascular disease, neurological disease), anesthetic agents (e.g., dexmedetomidine, propofol), and surgery category. The findings indicated a consistent association between AIP and POD across all predefined subgroups, with no significant interactions detected. This indicates that AIP's predictive capability for POD is unaffected by these diverse important clinical factors. It further supports AIP's potential as a universal biomarker, whose predictive value applies across patient populations with varying underlying disease states, different surgical types, and diverse anesthesia management strategies. Mediation analysis further suggests a potential mediating pathway linking AIP to POD. We found that RD and Hb partially mediated the relationship between AIP and POD. Elevated AIP may promote inflammation and oxidative stress, impairing erythropoiesis and shortening red blood cell lifespan, leading to increased RDW and anemia 37 , 38 . This, in turn, causes insufficient oxygen supply to brain tissue. Inadequate oxygen transport may trigger alterations in cerebral blood flow, hypoperfusion, and impaired glucose metabolism, ultimately resulting in cerebrovascular lesions, accelerated amyloid deposition, and disruption of the blood-brain barrier, thereby increasing susceptibility to POD 39 . Furthermore, multiple population studies indicate that low Alb may be an independent risk factor for cognitive impairment and dementia 40 . The negative correlation between AIP and serum Alb and its mediating role suggest that metabolic disorders represented by AIP may coexist with malnutrition/inflammatory states, collectively exacerbating patients' physiological vulnerability. Currently, no effective treatment measures have been proven to prevent POD. Therefore, understanding its multiple risk factors is crucial for promoting early detection and prevention, which is the primary objective and clinical significance of our study. This research is strengthened by its use of a large database, rigorous statistical adjustments, and innovative machine learning interpretation methods. Our findings indicate that higher preoperative AIP levels correlate with increased risk of POD in patients, exhibiting a nonlinear relationship. AIP can be incorporated into preoperative alert systems to implement enhanced monitoring and multimodal prevention strategies. Nonetheless, this research presents certain limitations. First, as a retrospective study, despite thorough comprehensive adjustments, the impact of unmeasured confounders like baseline cognitive function, social support levels, and surgical details cannot be completely excluded. Second, data originated from a single center, necessitating prospective validation of conclusions across diverse populations and healthcare institutions. Additionally, we utilized only initial admission laboratory data, failing to dynamically observe the association between perioperative AIP changes and POD. Conclusion In summary, this study establishes for the first time that preoperative AIP serves as a potential risk biomarker for POD. As an easily calculable composite lipid indicator, AIP offers a novel perspective for preoperative risk stratification and provides a new target for early screening and intervention of POD. This suggests that regulating blood lipids (reducing AIP) may help prevent postoperative cognitive impairment. Future research should focus on validating this association in prospective cohorts, exploring its underlying mechanisms, and developing potential intervention strategies to prevent POD by improving perioperative lipid homeostasis. Abbreviations AIP Atherogenic Index of Plasma POD Postoperative Delirium TG Triglycerides HDL-C High-density Lipoprotein Cholesterol MIMIC-IV Medical Information from Critical Care BIDMC Beth Israel Deaconess Medical Center NIH National Institutes of Health ICU Intensive Care Unit ICD-9/10 International Classification of Diseases, Ninth or Tenth Revision SBP Systolic Blood Pressure DBP Diastolic Blood Pressure HR Heart Rate RR Respiratory Rate SpO 2 Pulse Oximetry Oxygen Saturation IQR Interquartile Ranges PSM Propensity Score Matching OR Odds Ratios CI Confidence Intervals RCS Restricted Cubic Spline SMDs Standardized Mean Differences Hb Hemoglobin Cl Chloride Alb Albumin RDW Red Cell Distribution Width WBC White Blood Cell BUN Blood Urea Nitrogen UA Uric Acid Cr Creatinine Glu Glucose BBB Blood-Brain Barrier CPB Cardiopulmonary Bypass WMH White Matter Hyperintensities sdLDL Small Dense Low-density Lipoproteins Aβ β-amyloid ROS Reactive Oxygen Species AD Alzheimer's Disease Declarations Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author Contribution All authors contributed to the study conception and design. Writing - original draft preparation: Xiaqing Zhang, Mingliang Xing, Afen Zhang, Rongzhi Zheng, Huiru Hu, Xianming Zeng; Writing - review and editing, visualization: Xiaqing Zhang, Mingliang Xing, Huiru Hu; Conceptualization: Xiaqing Zhang, Mingliang Xing, Xianming Zeng; Methodology: Afen Zhang, Huiru Hu; Formal analysis and investigation:Xiaqing Zhang, Mingliang Xing; Resources: Xianming Zeng; Supervision: Rongzhi Zheng, Xianming Zeng, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. Data Availability This research utilized a retrospective cohort design, sourcing data from the Medical Information from Critical Care (MIMIC-IV) database (version 2.2) to obtain research data. MIMIC-IV is a publicly accessible and one of the most comprehensive intensive care databases in critical care medicine. The dataset comprises comprehensive clinical data for patients hospitalized at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2019, encompassing length of stay, laboratory test outcomes, medication regimens, vital signs, and other pertinent clinical information. The Institutional Review Boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) granted permission to use the MIMIC-IV database for this research. Author Xiaqing Zhang successfully completed the National Institutes of Health (NIH) online course “Protection of Human Research Participants” (ID: 15008001) and was therefore authorized to access the MIMIC-IV database for data extraction. References Brown CH, Laflam A, Max L, et al. The Impact of Delirium After Cardiac Surgical Procedures on Postoperative Resource Use. Ann Thorac Surg. 2016;101(5):1663–9. Yan E, Veitch M, Saripella A, et al. Association between postoperative delirium and adverse outcomes in older surgical patients: A systematic review and meta-analysis. J Clin Anesth. 2023;90:111221. Evered L, Silbert B, Knopman DS, et al. Recommendations for the nomenclature of cognitive change associated with anaesthesia and surgery—2018. Br J Anaesth. 2018;121(5):1005–12. Kunicki ZJ, Ngo LH, Marcantonio ER, et al. Six-Year Cognitive Trajectory in Older Adults Following Major Surgery and Delirium. JAMA Intern Med. 2023;183(5):442–50. Jin Z, Hu J, Ma D. Postoperative delirium: perioperative assessment, risk reduction, and management. Br J Anaesth. 2020;125(4):492–504. Cecilia V, Cheryl W, Laura C. HDL and cholesterol handling in the brain. Cardiovascular Res. 2014;103(3):405–13. Huang H, Han J, Li Y, et al. Early Serum Metabolism Profile of Post-operative Delirium in Elderly Patients Following Cardiac Surgery With Cardiopulmonary Bypass. Front Aging Neurosci. 2022;14:857902. Qian G, Wang Y, Peluso I. Serum Metabolomics of Early Postoperative Cognitive Dysfunction in Elderly Patients Using Liquid Chromatography and Q-TOF Mass Spectrometry. Oxidative Med Cell Longev. 2020;2020:1–11. Shaw A, Teng R, Fasina T, et al. Lipid dysregulation and delirium in older adults: A review of the current evidence and future directions. Brain Res Bull. 2025;224:111299. Dobiásová M. FJ. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem 34(7):583–8. Zhou J, Han H, Bai W. Association between atherogenic index of plasma and cognitive impairment in middle-aged and older adults: results from CHARLS. Front Aging Neurosci. 2025;17:1506973. Johnson AEW, Bulgarelli L, Shen L, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):1. Karu I, Taal G, Zilmer K, et al. Inflammatory/oxidative stress during the first week after different types of cardiac surgery. Scandinavian Cardiovasc J. 2010;44(2):119–24. Ouk T, Amr G, Azzaoui R, et al. Lipid-lowering drugs prevent neurovascular and cognitive consequences of cardiopulmonary bypass. Vascul Pharmacol. 2016;80:59–66. Fernández-Macías JC, Ochoa-Martínez AC, Varela-Silva JA, Pérez-Maldonado IN. Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses. Arch Med Res. 2019;50(5):285–94. Fu L, Zhou Y, Sun J, et al. Atherogenic index of plasma is associated with major adverse cardiovascular events in patients with type 2 diabetes mellitus. Cardiovasc Diabetol. 2021;20(1):201. Zheng Y, Li C, Yang J, et al. Atherogenic index of plasma for non-diabetic, coronary artery disease patients after percutaneous coronary intervention: a prospective study of the long-term outcomes in China. Cardiovasc Diabetol. 2022;21(1):29. Kwon H-S, Ko J-S, Lee J-H, et al. A Positive Association between the Atherogenic Index of Plasma and White Matter Hyperintensity. Korean J Family Med. 2022;43(3):193–8. Guo Y, Jia P, Zhang J, et al. Prevalence and risk factors of postoperative delirium in elderly hip fracture patients. J Int Med Res. 2016;44(2):317–27. Oliveira FR, Oliveira VH, Oliveira ÍM, et al. Hypertension, mitral valve disease, atrial fibrillation and low education level predict delirium and worst outcome after cardiac surgery in older adults. BMC Anesthesiol. 2018;18(1):15. Mrkobrada M, Hill MD, Chan MTV, et al. Covert stroke after non-cardiac surgery: a prospective cohort study. Br J Anaesth. 2016;117(2):191–7. Mrkobrada M, Chan MTV, Cowan D, et al. Perioperative covert stroke in patients undergoing non-cardiac surgery (NeuroVISION): a prospective cohort study. Lancet. 2019;394(10203):1022–9. Huang P, Ren G, Wang Y, et al. The Association Between the Atherogenic Index of Plasma and Cognitive Function: Evidence From the NHANES 2011–2014. Brain Behav. 2025;15(8):e70768. Lin J, Li Y, Li H. Association between atherogenic index of plasma and dementia: A longitudinal observational study. J Alzheimer’s Disease. 2025;106(1):197–205. Zhu X-W, Deng F-Y, Lei S-F. Meta-analysis of Atherogenic Index of Plasma and other lipid parameters in relation to risk of type 2 diabetes mellitus. Prim Care Diabetes. 2015;9(1):60–7. Mitsis A, Kadoglou NPE, Lambadiari V, et al. Prognostic role of inflammatory cytokines and novel adipokines in acute myocardial infarction: An updated and comprehensive review. Cytokine. 2022;153:155848. Farhangrazi ZS, Ying H, Bu G, et al. High density lipoprotein decreases beta-amyloid toxicity in cortical cell culture. NeuroReport. 1997;8(5):1127–30. Morris G, Puri BK, Bortolasci CC, et al. The role of high-density lipoprotein cholesterol, apolipoprotein A and paraoxonase-1 in the pathophysiology of neuroprogressive disorders. Neurosci Biobehavioral Reviews. 2021;125:244–63. Yvan-Charvet L, Pagler T, Gautier EL, et al. ATP-binding cassette transporters and HDL suppress hematopoietic stem cell proliferation. Science. 2010;328(5986):1689–93. Feinkohl I, Janke J, Slooter AJC, et al. Metabolic syndrome and the risk of postoperative delirium and postoperative cognitive dysfunction: a multi-centre cohort study. Br J Anaesth. 2023;131(2):338–47. Pang Y, Li Y, Zhang Y, et al. Effects of inflammation and oxidative stress on postoperative delirium in cardiac surgery. Front Cardiovasc Med. 2022;9:1049600. Aguilar-Hernández L, Alejandre R, César Morales-Medina J, et al. Cellular mechanisms in brain aging: Focus on physiological and pathological aging. J Chem Neuroanat. 2023;128:102210. Lopez MG, Hughes CG, DeMatteo A, et al. Intraoperative Oxidative Damage and Delirium after Cardiac Surgery. Anesthesiology. 2020;132(3):551–61. Lin Y, Peng X, Lin X, et al. Potential Value of Serum Lipid in the Identication of Postoperative Delirium Undergoing Knee/Hip Arthroplasty: The Perioperative Neurocognitive Disorder and Biomarker Lifestyle Study. Front Psychiatry. 2022;13:870317. Burgess BL, McIsaac SA, Naus KE, et al. Elevated plasma triglyceride levels precede amyloid deposition in Alzheimer’s disease mouse models with abundant Aβ in plasma. Neurobiol Dis. 2006;24(1):114–27. Chen Y, Guo Y, Tong G, et al. Combined nutritional status and activities of daily living disability is associated with one-year mortality after hip fracture surgery for geriatric patients: a retrospective cohort study. Aging Clin Exp Res. 2024;36(1):127. Zhou Y, Zhao X, Tang Y, et al. Erythropoietin Inhibits the Increase of Pulmonary Labile Zinc and the Expression of Inflammatory Mediators Following Subarachnoid Hemorrhage in Rats. Neurocrit Care. 2015;24(3):472–80. Payne RS, Goldbart A, Gozal D, Schurr A. Effect of intermittent hypoxia on long-term potentiation in rat hippocampal slices. Brain Res. 2004;1029(2):195–9. Fornal M, Wizner B, Cwynar M, et al. Association of red blood cell distribution width, inflammation markers and morphological as well as rheological erythrocyte parameters with target organ damage in hypertension. Clin Hemorheol Microcirc. 2014;56(4):325–35. Hu Y, Lin D, Song M, et al. Sex and race differences in the association of albumin with cognitive function in older adults. Brain Behav. 2024;14(2):e3435. Additional Declarations No competing interests reported. Supplementary Files SuppTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8514238","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573350253,"identity":"8a7ce56c-821d-42e1-b197-5f03d2b6fade","order_by":0,"name":"Xiaqing Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaqing","middleName":"","lastName":"Zhang","suffix":""},{"id":573350254,"identity":"2db9da91-734a-4e81-b7ba-bdd227d5f98b","order_by":1,"name":"Mingliang Xing","email":"","orcid":"","institution":"Tang Du Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mingliang","middleName":"","lastName":"Xing","suffix":""},{"id":573350255,"identity":"30920a59-7517-408c-aa21-a1f6ed0ba892","order_by":2,"name":"Afen Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Afen","middleName":"","lastName":"Zhang","suffix":""},{"id":573350256,"identity":"41a022a8-b1a9-4dee-8e8d-7e9df1b1c0ef","order_by":3,"name":"Rongzhi Zheng","email":"","orcid":"","institution":"Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rongzhi","middleName":"","lastName":"Zheng","suffix":""},{"id":573350257,"identity":"a27bccb8-eed7-4249-8e44-bf14b0706f59","order_by":4,"name":"Huiru Hu","email":"","orcid":"","institution":"Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Huiru","middleName":"","lastName":"Hu","suffix":""},{"id":573350258,"identity":"35de0c9b-3b2c-477c-82da-ce6f3a425c66","order_by":5,"name":"Xianming Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACNvb+D4f/VEjU87M3H3yQUFFDWAsfzwHDAzxnbBIke44lGzw4c4ywFjmJBOMDvG1pCQY3ctQkH7YwE+EwngMJByTYDucBtbBVJDawMfC3dycQ8EvDgQMGPIeLJc+8PXYjcYcMg8SZsxsI2HKw4UCCxGHGvuN5aTcSz7AxGEjkEtAikcwAtOYwY8OBHLOCxDZmYrSkMRxsSEhLnHAix4yBOC08ZxgOMxywMQYFskTCmWM8BP0i397D/Jnxn4QcKCo//qiokeNv78WvBQPwkKZ8FIyCUTAKRgFWAAB+VVIOvAv3VQAAAABJRU5ErkJggg==","orcid":"","institution":"Xi'an International Medical Center Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xianming","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2026-01-04 15:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8514238/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8514238/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100366753,"identity":"27a8bf05-5c9b-4dd2-927f-6cd4224afc87","added_by":"auto","created_at":"2026-01-16 07:56:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":587343,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptXianmingZeng.docx","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/9099c9eab93ecc201f9e8609.docx"},{"id":100136261,"identity":"5de0c93d-4321-4370-8a68-2c2915118961","added_by":"auto","created_at":"2026-01-13 10:45:05","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9315,"visible":true,"origin":"","legend":"","description":"","filename":"2c31660536154bdc9b67a6b1011a7d86.json","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/3ba5440357b6825653e4fc50.json"},{"id":100366337,"identity":"ce3d620b-3381-46ab-8bde-b23bc1967c4c","added_by":"auto","created_at":"2026-01-16 07:56:14","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":192023,"visible":true,"origin":"","legend":"","description":"","filename":"2c31660536154bdc9b67a6b1011a7d861enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/fae9a5eb6df34ff080601fab.xml"},{"id":100136259,"identity":"dcff0e80-cca6-4770-8448-85282e3a12a0","added_by":"auto","created_at":"2026-01-13 10:45:05","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":232081,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/e1a2e279dfd5dd7140442c68.jpeg"},{"id":100367197,"identity":"90f6cc97-57f9-41da-b8e4-bd85a2c220b4","added_by":"auto","created_at":"2026-01-16 07:56:50","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1629600,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/0e2120d498f44c5976f23a5c.jpeg"},{"id":100136264,"identity":"dd7d5dfb-3e11-42ef-b450-642edb4e8a01","added_by":"auto","created_at":"2026-01-13 10:45:05","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5199988,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/89335485ee83ef0458736589.jpeg"},{"id":100367994,"identity":"c226a78a-df59-4008-b441-49668414e79c","added_by":"auto","created_at":"2026-01-16 07:57:30","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":311273,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/eef619d9cf3d69e94e034de6.jpeg"},{"id":100368102,"identity":"5221c003-8bcf-4d3e-8279-c70cc60d92fa","added_by":"auto","created_at":"2026-01-16 07:57:36","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100067,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/8644f48d207637f162dd4424.png"},{"id":100368175,"identity":"b0b65dee-1a9b-4794-a74e-65cff4be9e8a","added_by":"auto","created_at":"2026-01-16 07:57:40","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17553,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/65d3c59073712742bd811d17.png"},{"id":100136266,"identity":"54a74084-26e2-4168-9c91-35a1eb3ea460","added_by":"auto","created_at":"2026-01-13 10:45:05","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87687,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/958d93d90dca775fe59f2b55.png"},{"id":100366569,"identity":"25dc3887-f588-400d-b709-353dcf03187d","added_by":"auto","created_at":"2026-01-16 07:56:22","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73661,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/e7978c566b9e329ace5ee39c.png"},{"id":100366679,"identity":"3ca737cd-278f-441b-b9a6-8c34420694b4","added_by":"auto","created_at":"2026-01-16 07:56:28","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":186116,"visible":true,"origin":"","legend":"","description":"","filename":"2c31660536154bdc9b67a6b1011a7d861structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/0b63cffdb1f1668026f22f33.xml"},{"id":100136260,"identity":"1dac1780-df90-4e4b-b0fa-0753d61f7322","added_by":"auto","created_at":"2026-01-13 10:45:05","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":201341,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/dea1cf3fc59f21aff11b1ca8.html"},{"id":100367447,"identity":"9da285b4-8dd0-4712-baa5-6532ee15c63f","added_by":"auto","created_at":"2026-01-16 07:57:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":119912,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Patient Selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/19293eff1045085e4d277ca3.png"},{"id":100367454,"identity":"afaa65cf-6776-40f1-b384-3e432d7bdae5","added_by":"auto","created_at":"2026-01-16 07:57:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82566,"visible":true,"origin":"","legend":"\u003cp\u003eRCS Curve for the Association Between AIP and POD\u003c/p\u003e\n\u003cp\u003eNote: the red bold line indicating the odds ratio and the shaded area representing the 95% CI\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/cd3e0a7b882cb71855ab463d.png"},{"id":100366966,"identity":"822d7273-bb50-4516-933e-c7e22e209fc3","added_by":"auto","created_at":"2026-01-16 07:56:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":200073,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of subgroup analysis.\u003c/p\u003e\n\u003cp\u003eNote: Covariates: hemoglobin, red blood cell distribution width, lymphocyte count, monocyte count, neutrophil count, white blood cell count, blood urea nitrogen, uric acid, creatinine, chloride, albumin, glucose, respiratory rate, and pulse oximetry oxygen saturation.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/a3aff59dbc41b89029216e9d.png"},{"id":100136251,"identity":"5453c712-6b79-48b6-b419-480cbba14ae1","added_by":"auto","created_at":"2026-01-13 10:45:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":100893,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the Mediating Role in Delirium after Cardiac Surgery.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/791c4b96bbdb1b79a646478e.png"},{"id":101752149,"identity":"19003996-1a7f-4580-be71-d17de8a3fef7","added_by":"auto","created_at":"2026-02-03 10:25:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1880648,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/6ffd94d3-5d10-4818-b71e-8038c8ca0c7d.pdf"},{"id":100367434,"identity":"759c02e8-c461-44ff-a506-f83b9e6a7b8a","added_by":"auto","created_at":"2026-01-16 07:57:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19459,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8514238/v1/567d6affead1884872ce962e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association Between Atherogenic Index of Plasma and Postoperative Delirium in Cardiac Surgery Patients: An Analysis of the MIMIC-IV Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePostoperative delirium (POD) is a frequent complication after surgery, particularly in patients aged 65 and older, with an incidence rate as high as 20%-50% following major surgeries such as cardiac procedures\u003csup\u003e1\u003c/sup\u003e. POD is not only linked to extended hospital stays, higher healthcare costs, but also serves as an independent risk factor for long-term cognitive decline and increased postoperative mortality\u003csup\u003e2, 3\u003c/sup\u003e. In a prospective cohort study of 560 elderly patients without dementia undergoing major elective surgery, postoperative delirium was significantly associated with accelerated cognitive decline over a 72-month follow-up period. The delirium group exhibited a markedly steeper cognitive decline slope, comparable to that observed in patients diagnosed with Alzheimer\u0026apos;s disease within five years\u003csup\u003e4\u003c/sup\u003e. Although its pathophysiological mechanisms involve multiple pathways such as neuroinflammation, oxidative stress, and neurotransmitter imbalances, effective prevention and treatment options remain limited. Consequently, the current focus of POD management has shifted toward preoperative risk prediction and stratification, aiming to improve postoperative outcomes by identifying high-risk patients early and implementing targeted interventions\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWithin this context, identifying convenient and reliable preoperative predictive biomarkers has become a research hotspot. The relationship between lipid dysregulations and POD has gradually attracted the attention of researchers in recent years. The brain is one of the body\u0026apos;s most lipid-rich organs, with lipids playing a critical role in maintaining neuronal membrane integrity, synaptic function, and signal transduction\u003csup\u003e6\u003c/sup\u003e. Both preclinical and clinical studies have revealed that POD patients often exhibit specific lipid profile dysregulation, such as alterations in levels of omega-3 polyunsaturated fatty acids, cholesterol, and sphingolipids\u003csup\u003e7, 8\u003c/sup\u003e. These lipid dysregulations may increase the brain\u0026apos;s vulnerability to perioperative stress by exacerbating neuroinflammation, compromising blood-brain barrier integrity, and disrupting energy metabolism, ultimately triggering delirium\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, the predictive power of individual blood lipid markers for disease, like triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C), is restricted and frequently yields inconsistent outcomes. The Atherogenic Index of Plasma (AIP), defined as log₁₀ [TG/HDL-C], comprehensively reflects the balance between atherogenic and protective lipoproteins. It represents a superior cardiovascular metabolic risk predictor compared to individual markers\u003csup\u003e10\u003c/sup\u003e. Notably, recent evidence indicates a link between AIP and cognitive function. A nationwide longitudinal study found that higher AIP levels are significantly associated with increased risk of cognitive impairment in middle-aged and elderly individuals\u003csup\u003e11\u003c/sup\u003e, suggesting that AIP may as a potential neurocognitive risk biomarker.\u003c/p\u003e\n\u003cp\u003eNevertheless, the relationship between AIP and POD is still remains unclear. Considering the significant overlap between the dyslipidemia represented by AIP and the potential pathophysiological mechanisms of POD, we hypothesize that elevated preoperative AIP independently increases the risk of POD in cardiac surgery patients. To validate this hypothesis, this study utilizes the large public database MIMIC-IV to systematically investigate the association between preoperative AIP and POD in cardiac surgery, aiming to provide a novel, readily accessible laboratory marker for perioperative risk stratification.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e1. Data Source\u003c/h2\u003e\n\u003cp\u003eThis research utilized a retrospective cohort design, sourcing data from the Medical Information from Critical Care (MIMIC-IV) database (version 2.2) to obtain research data\u003csup\u003e12\u003c/sup\u003e. MIMIC-IV is a publicly accessible and one of the most comprehensive intensive care databases in critical care medicine. The dataset comprises comprehensive clinical data for patients hospitalized at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2019, encompassing length of stay, laboratory test outcomes, medication regimens, vital signs, and other pertinent clinical information. The Institutional Review Boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) granted permission to use the MIMIC-IV database for this research.\u003c/p\u003e\n\u003ch3\u003e2. Ethics and Data Privacy\u003c/h3\u003e\n\u003cp\u003eThis research data has been de-identified to uphold ethical standards and protect patient privacy, with all necessary measures implemented to ensure confidentiality. The Institutional Review Board at Beth Israel Deaconess Medical Center waived the informed consent requirement due to the data being de-identified. Author Xiaqing Zhang successfully completed the National Institutes of Health (NIH) online course \u0026ldquo;Protection of Human Research Participants\u0026rdquo; (ID: 15008001) and was therefore authorized to access the MIMIC-IV database for data extraction.\u003c/p\u003e\n\u003ch3\u003e3. Data Inclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eThe MIMIC-IV database contains clinical data from 180,733 patients admitted to the hospital's intensive care unit (ICU) between 2012 and 2019. This study involved patients identified through International Classification of Diseases, Ninth or Tenth Revision (ICD-9/10) codes as having undergone cardiac surgery or experienced delirium (Supplementary Table S1). From the initial cohort of 180,733 patients, we excluded cases for the following reasons: (1) removal of non-cardiac surgery diagnoses (N\u0026thinsp;=\u0026thinsp;170,086), (2) removal of non-delirium diagnoses (N\u0026thinsp;=\u0026thinsp;4,052), and (3) removal of cases lacking baseline data (N\u0026thinsp;=\u0026thinsp;528). The final study cohort comprised 60,67 patients, including 15,65 delirium patients and 45,02 non-delirium patients. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e4. Data Extraction\u003c/h3\u003e\n\u003cp\u003eClinical data were retrieved from the MIMIC-IV database using Structured Query Language (SQL). The extracted data encompassed demographic details, vital signs, essential laboratory parameters, and significant comorbidities.\u003c/p\u003e \u003cp\u003eSpecifically, demographic details included age, gender, and ethnicity. Vital signs comprise systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), respiratory rate (RR), and pulse oximetry oxygen saturation (SpO2). Laboratory parameters primarily include hematologic indicators, hepatic and renal function markers, and electrolyte levels. Additionally, we documented significant comorbidities potentially linked to cardiac surgery and POD, such as hypertension, diabetes, congestive heart failure, and myocardial infarction and so on. And we also extracted data on the type of cardiac surgery and the use of anesthetic sedatives potentially related to POD occurrence. For all variables, we utilized the first recorded value after patient admission.\u003c/p\u003e\n\u003ch3\u003e5. Definition of AIP\u003c/h3\u003e\n\u003cp\u003eAIP is determined by assessing plasma levels of high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The AIP is calculated using the formula: log₁₀ [TG (mmol/L) / HDL-C (mmol/L)].\u003c/p\u003e\n\u003ch3\u003e6. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eData processing and analysis were conducted using R software (version 4.1.1). Continuous variables with non-normal distribution are described using medians and interquartile ranges (IQR) for baseline characteristic analyses. Categorical variables are reported as counts and weighted percentages. The Wilcoxon rank-sum test assessed differences in continuous variables across AIP quartiles, while the Rao-Scott chi-square test evaluated weighted percentages of categorical variables.\u003c/p\u003e \u003cp\u003ePropensity score matching (PSM) was conducted in a 1:1 ratio based on sex, age, and race to ensure comparable baseline data distribution between the delirium and non-delirium groups, minimizing baseline data impact on research outcomes. Odds ratios (OR) and 95% confidence intervals (CI) were derived using logistic regression models. Covariate selection was guided by established associations with POD, clinically significant baseline differences between groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and clinical relevance. AIP was incorporated into the regression models both as a continuous variable and as a categorical variable. As a continuous variable, the raw AIP value was entered into the model. The original AIP values were divided into quartiles based on the interquartile range, ranked from low to high. These quartiles were labeled as the first quartile (Q1), second quartile (Q2), third quartile (Q3), and fourth quartile (Q4), and were further evaluated as categorical variables. When AIP was treated as a categorical variable, Q1 was used as the reference group. Three distinct statistical models were constructed: Model 1 represented the unadjusted model. Model 2 was adjusted for the following factors: ICU length of stay, in-hospital mortality, comorbidities, sedative drug use, and surgery category. Model 3 was further adjusted based on Model 2 by incorporating laboratory biochemical parameters and vital signs. Survey weights were applied to all regressions, and continuous covariates with non-normal distributions were transformed using weighted quartiles.\u003c/p\u003e \u003cp\u003eRestricted cubic spline (RCS) analysis was employed to explore potential non-linear relationships between AIP and the risk of POD. Subgroup analysis was conducted to assess whether the association between AIP and POD after cardiac surgery differed across various subgroups, with results visualized using forest plots. Interaction analysis was performed to evaluate potential interactions between each subgroup and AIP. \u003cem\u003eP\u003c/em\u003e-values were corrected for the False Discovery Rate (FDR). A two-sided \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Baseline Characteristics of Patients\u003c/h3\u003e\n\u003cp\u003eFrom an analysis of 180,733 patients in the MIMIC-IV database, 6,067 patients met the study's criteria and were included. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the flow diagram of the patient selection process. with 1,565 patients (25.8%) experiencing POD during hospitalization. After PSM, the analysis included 3,130 patients, evenly divided into two groups of 1,565 each, with 415 patients (19.8%) experiencing POD. Post-PSM, all standardized mean differences (SMDs) were all \u0026lt;\u0026thinsp;0.1, demonstrating comparable baseline variable distributions between the two groups. Baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBefore PSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eAfter PSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Delirium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDelirium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-Delirium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDelirium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;6,067\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,502\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,565\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;3,130\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,565\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,565\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,753(61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,820 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e933 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,866(60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e933 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e933 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,314(38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,682 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e632 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,264(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e632 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e632 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,586(75.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,422 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,164(74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,348(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,184 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,164(74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,096(18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e799 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e297 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e580 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e283 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e297 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,690 (94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,346 (97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,344(86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,855(91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,511 (97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,344(86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e275 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e221 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,937 (98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,452 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,485(95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,034(97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,549 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,485(95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory system disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,207 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,967 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,240(79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,616(84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,376 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,240(79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e860 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e535 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e325 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e514 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e189 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e325 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,414 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,688 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e726 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,345(43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e619 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e726 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,370 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,703 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e667 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,279(41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e612 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e667 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive Heart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,143 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e705 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e438 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e686 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e248 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e438 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial Infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,856 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,276 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e580 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,014(32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e434 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e580 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKindey disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e189 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e149 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstructive sleep apnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e977 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e670 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e307 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e530 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e223 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e307 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic replacement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e390 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e264 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e312(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e264 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined cardiac surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,797 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,407 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e390 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e873 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e483 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e390 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGABA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e939 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e728 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e471 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e260 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e211 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValve surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,941 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,241 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e700 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,474(47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e774 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e700 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDexmedetomidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e576 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e289 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e287 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e386 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e287 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBenzodiazepines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePropofol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,484 (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,210 (94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,274(81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,739(88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,465 (94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,274(81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.52 (2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.72 (2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.95(2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.27(2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.58 (2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.95(2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.90 (2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.73 (1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.38(2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.06(2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.74 (1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.38(2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte(\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81 (2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82 (2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80 (3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.80 (3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.80 (1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.80 (3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte(\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet(\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e226 (117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e226 (109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e225 (101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e226 (117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil(\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.9 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.1 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.8 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.4 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC(\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.2 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.8 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.4 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.2 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.45 (2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.38 (2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.65 (2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.49 (2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.34 (2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.65 (2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31 (1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37 (1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.25 (1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49 (1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl⁻(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.6 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.8 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.8 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101.3 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e101.8 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.8 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e154 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e158 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.5 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.6 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.6 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.5 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.6 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.80 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.88 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.59 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.73 (0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.88 (0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.59 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e142 (191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e121 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e162 (263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e123 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e132 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e112 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e114 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR(t/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR(t/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.9 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.4 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.4 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.5 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.59 (2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.71 (2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.25(3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.49(3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.74 (2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.25(3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU length of stay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.05 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNotes: LDL: Low - Density Lipoprotein; Hb: Hemoglobin; RDW: Red Cell Distribution Width; WBC: White Blood Cell; BUN: Blood Urea Nitrogen; UA: Uric Acid; Cr: Creatinine; Cl⁻: Chloride; AG: Anion Gap; Alb: Albumin; TG: Triglyceride; HDL: High - Density Lipoprotein; Glu: Glucose; SBP: Blood Pressure Systolic; DBP: Blood Pressure Diastolic; HR: Heart Rate; RR: Respiratory Rate; SpO2: Pulse Oximetry Oxygen Saturation; AIP: Atherogenic Index of Plasma. 1.Mean (sd) or Frequency (%); 2.Pearson\u0026rsquo;s Chi-squared test; Wilcoxon rank sum test; Fisher\u0026rsquo;s exact test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePost-matching showed there were no significant differences in age, sex, or racial distribution between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, persistent differences in clinical indicators and comorbidities, suggesting delirium patients exhibited greater disease severity and more intricate clinical profiles. For instance, patients in the delirium group had more preoperative comorbidities than the non-delirium group, including cardiovascular and cerebrovascular diseases, neurological disorders, respiratory diseases, diabetes, hypertension, renal disease, and obstructive sleep apnea (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The proportion of patients undergoing aortic valve replacement was higher in the delirium group (17% vs 3.1%). Dexmedetomidine increases the incidence of POD (18% vs 6.3%), whereas propofol has the opposite effect (81% vs 94%). Laboratory tests revealed that Hemoglobin (Hb), Chloride (Cl⁻), Albumin (Alb), HDL and SpO\u003csub\u003e2\u003c/sub\u003e were significantly lower in delirium group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, Red Cell Distribution Width (RDW), Lymphocyte, Monocyte, Neutrophil, White Blood Cell (WBC), Blood Urea Nitrogen (BUN), Uric Acid (UA), Creatinine (Cr), TG, Glucose (Glu), and AIP levels were significantly elevated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although several variables exhibited statistically significant differences between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), some of these differences were not clinically meaningful.\u003c/p\u003e\n\u003ch3\u003e2. Association Between POD and AIP in Cardiac Surgery Patients\u003c/h3\u003e\n\u003cp\u003eThe association between AIP and POD in cardiac surgery patients was analyzed using a multiple linear regression model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Model 1 (without covariates) showed a positive correlation between AIP and POD (OR: 1.622, 95% CI: 1.455\u0026ndash;1.81, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, AIP quartile stratification revealed a significant difference between the Q4 group (OR: 1.708, 95% CI: 1.399\u0026ndash;2.088, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the Q1 group, indicating that higher AIP levels are associated with an higher risk of POD. Model 2 (after adjusting for covariates including ICU length of stay, in-hospital mortality, comorbidities [cerebrovascular disease, neurological disease, respiratory system disease, congestive heart failure, diabetes, hypertension, renal disease, myocardial infarction, obstructive sleep apnea], dexmedetomidine, propofol, and surgery category) further analyzed the relationship, showing that AIP remained significantly associated with POD (OR: 1.431, 95% CI: 1.247\u0026ndash;1.644, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Q4 quartile group demonstrated a significantly higher risk of POD compared to the Q1 quartile group (OR: 1.396, 95% CI: 1.089\u0026ndash;1.792, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0085). Model 3 (further adjusting for Hb, RDW, lymphocytes, monocytes, neutrophils, WBC, BUN, UA, Cr, Cl⁻, Alb, Glu, RR, SpO\u003csub\u003e2\u003c/sub\u003e, and other clinical variables based on Model 2) was also observed a significant positive correlation between AIP and POD (OR: 1.435, 95% CI: 1.249\u0026ndash;1.652, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In Model 3, the Q4 group demonstrated a notably higher risk of POD compared to the Q1 group (OR: 1.38, 95% CI: 1.073\u0026ndash;1.777, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0122). Trend \u003cem\u003eP\u003c/em\u003e-values showed significant trends with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in Model 1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0141 in Model 2, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0156 in Model 3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe relationship between AIP and delirium after cardiac surgery.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCharacterisitic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP continuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.455,1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.247,1.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(1.249,1.652)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP quantile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1(low)(\u0026lt;\u0026thinsp;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e781(24.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2( 0.41-\u0026lt; 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e781(24.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.89,1.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.829,1.354)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.6437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.812,1.335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.7518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3( 0.41-\u0026lt; 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e785(25.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.977,1.453)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.817,1.346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.823,1.366)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.6509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4(high)(\u0026ge;\u0026thinsp;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e783(25.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.399,2.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.089,1.792)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(1.073,1.777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eOR, odds ratio; CI, confidence intervals\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eThe model 1 was the crude model.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eThe model 2 was adjusted by ICU length of stay, In-hospital death, Cerebrovascular disease, Neurological disease, Respiratory system disease, Congestive Heart Failure, Diabetes, Hypertension, Kidney disease, Myocardial Infarction, Obstructive sleep apnea, Dexmedetomidine, Propofol, Surgical category\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eThe model 3 was further adjusted by ICU length of stay, In-hospital death, Cerebrovascular disease, Neurological disease, Respiratory system disease, Congestive Heart Failure, Diabetes, Hypertension, Kidney disease, Myocardial Infarction, Obstructive sleep apnea, Dexmedetomidine, Propofol, Surgical category, Hemoglobin, Red Cell Distribution Width, Lymphocyte, Monocyte, Neutrophil, White Blood Cell, Blood Urea Nitrogen, Uric Acid, Creatinine, Chloride, Albumin, Glucose, Respiratory Rate, Pulse Oximetry Oxygen Saturation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e3. RCS Curve for the Association Between AIP and POD\u003c/h3\u003e\n\u003cp\u003eRCS curves were employed to examine the relationship between AIP and POD in cardiac surgery patients, adjusting for all relevant covariates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results revealed a notable nonlinear association between AIP and POD in cardiac surgery patients (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In gender subgroup analysis, a significant nonlinear relationship between AIP and POD observed in the male group (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In the female subgroup, AIP also showed a significant nonlinear relationship with POD (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.015, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). These results further confirm the important role of AIP in the occurrence of POD in cardiac surgery patients and suggest that gender may play a moderating role in this relationship.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Relationship Between AIP and Baseline Characteristics of Subgroups\u003c/h2\u003e \u003cp\u003eDuring subgroup analysis, multiple variables were evaluated, including ICU length of stay, cerebrovascular disease, neurological disorders, respiratory diseases, congestive heart failure, diabetes, hypertension, renal disease, obstructive sleep apnea, dexmedetomidine, propofol, and surgical category. The results indicated no significant interactions were observed among these subgroups (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This further confirms that the association between AIP and POD remains consistent across subgroups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Association of Relevant Indicators with AIP and POD in Cardiac Surgery\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the association between AIP and relevant indicators, analyzed under three models\u0026mdash;Model 1, Model 2, and Model 3\u0026mdash;with a focus on indicators with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Among these, Alb showed a statistically significant association with AIP across all three models (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all), with β values of -0.254, -0.179, and \u0026minus;\u0026thinsp;0.177 respectively. and the progressively narrowing 95% CI indicate a strong negative correlation between AIP and Alb, with stable results. Cr exhibited \u003cem\u003eP\u003c/em\u003e values of 0.001, 0.014, and 0.031 across the three models, with decreasing β values, suggesting a positive correlation between AIP and Cr that may weaken with model adjustments. BUN showed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in all three models (\u0026lt;\u0026thinsp;0.001, 0.007, 0.016), indicating a positive correlation. WBC, Monocyte, Hb, RR, Neutrophil, and RDW showed strong and statistically significant associations with AIP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all). A significant association was also found for Lymphocyte count in Model 1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) and Model 2 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047). Cl⁻ exhibited a \u003cem\u003eP\u003c/em\u003e value of 0.043 only in Model 3, with no significant correlation in other models. UA and SpO\u003csub\u003e2\u003c/sub\u003e exhibited \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 across all models, indicating no significant association between AIP and these variables.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe associations between AIP and related indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0,0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.001,0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.001,0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.354,-0.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.285,-0.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.283,-0.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl⁻\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.005,0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.001,0.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0,0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.041,0.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.015,0.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.006,0.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.014,0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.02,0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.022,0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.003,0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.001,0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.001,0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.025,0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.022,0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.022,0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.2,0.508)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.139,0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.137,0.451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.003,0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0,0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.001,0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.095,-0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.077,-0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.079,-0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.032,0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.026,0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.025,0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.008,0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.001,0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.001,0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.02,0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.016,0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.016,0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.024,0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.006,0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.004,0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe model 1 was the crude model.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe model 2 was adjusted by ICU length of stay, In-hospital death.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe model 3 was adjusted by ICU length of stay, In-hospital death, Cerebrovascular disease, Neurological disease, Respiratory system disease, Congestive Heart Failure, Diabetes, Hypertension, Kidney disease, Myocardial Infarction, Obstructive sleep apnea, Dexmedetomidine, Propofol, Surgical category.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Glu: Glucose; Alb: Albumin; Cl⁻: Chloride; Cr: Creatinine; UA: Uric Acid; BUN: Blood Urea Nitrogen; WBC: White Blood Cell; Hb: Hemoglobin; SpO\u003csub\u003e2\u003c/sub\u003e: Pulse Oximetry Oxygen Saturation; RR: Respiratory Rate; RDW: Red Cell Distribution Width.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Analysis of the Mediating Role in Delirium after Cardiac Surgery.\u003c/h2\u003e \u003cp\u003eWe performed a mediation analysis to explore the influence of metabolic-related indicators on the relationship between AIP and POD after cardiac surgery, as indicated by our analysis of relevant indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The findings revealed that with Hb as the mediating variable, the indirect effect of AIP on POD was \u0026minus;\u0026thinsp;0.002 (95% CI: -0.005 to -0.00039, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04); The total effect was 0.0593 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the mediation proportion was \u0026minus;\u0026thinsp;3.37%, and the direct effect was 0.0613 (95% CI: 0.0453 to 0.0777, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). With RDW as the mediating variable, the indirect effect of AIP on POD was 0.0015 (95% CI: 0.0004 to 0.0031, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The total effect was 0.0615 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the mediation proportion was 2.09%, and the direct effect was 0.06 (95% CI: 0.0343 to 0.0817, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). With Alb as the mediating variable, the indirect effect was \u0026minus;\u0026thinsp;0.0035 (95% CI: -0.0064 to -0.0011, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The total effect was 0.0566 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the mediation proportion was 6.18%, and the direct effect was 0.0601 (95% CI: 0.0388 to 0.0763, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Finally, with RR as the mediator, the indirect effect of AIP on delirium was 0.0015 (95% CI: 0.0001 to 0.0035, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). The total effect was 0.0649 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the mediation proportion was 2.11%, and the direct effect was 0.0634 (95% CI: 0.0455 to 0.0821, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). See Supplementary Table S2 for details.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research is the first exploration of the relationship between the AIP index and POD. Results indicate that within a large-scale cardiac surgery cohort, that a preoperatively elevated AIP is independently associated with an higher risk of POD. This association remains robust after multivariate adjustment, and RCS analysis reveals a significant nonlinear \u0026ldquo;J-shaped\u0026rdquo; relationship between the two groups. Our findings suggest that preoperative dyslipidemia may represent a key underlying mechanism contributing to increased postoperative brain vulnerability and susceptibility to delirium, providing a novel biomarker for identifying high-risk patients. AIP serves as an effective predictive tool for assessing preoperative POD risk, aiding clinical anesthesiologists in identifying high-risk patients and guiding perioperative management.\u003c/p\u003e \u003cp\u003eLipids are widely distributed throughout the brain and body, with their various subtypes playing crucial roles in maintaining homeostasis and supporting cognitive development. Perioperative dyslipidemia can be regarded as a risk factor for neurological dysfunction. Changes in lipid homeostasis and metabolism can impact neuroinflammation, oxidative stress, and blood-brain barrier(BBB) integrity, potentially contributing to the onset and progression of delirium \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Cardiac surgery may contribute to pathological changes such as intracerebral microembolism formation, cerebral hypoperfusion, decreased cerebral oxygen saturation, and inflammatory cytokine release, collectively forming the pathophysiological basis for the high incidence of POD following such procedures\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Notably, a study identified 51 differentially expressed metabolites between elderly patients with and without POD after cardiopulmonary bypass (CPB) cardiac surgery, mainly enriched at the lipid and lipoprotein molecular levels\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This finding indicates a strong link between POD following cardiac surgery and acute changes in lipid metabolism. Furthermore, preoperative administration of lipid-lowering drugs like simvastatin and fenofibrate has demonstrated potential protective effects against CPB-induced cognitive dysfunction and neuronal integrity impairment, with drugs are believed to exert their effects by protecting endothelial function and reducing inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAIP, as a simple indicator calculable through routine lipid testing (TG and HDL-C), sensitively assesses overall lipid profiles and lipoprotein patterns. It serves as a robust biomarker for predicting atherosclerotic cardiovascular disease, diabetes, and prediabetes by reflecting the balance between atherogenic and protective lipoproteins \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Recent studies have further linked AIP to neurodegenerative disorders. Studies show a positive correlation between elevated AIP and the severity of white matter hyperintensities (WMH). WMH typically indicate microvascular ischemic changes in the brain, with clinical manifestations ranging from asymptomatic to cognitive impairment, dementia, or cerebrovascular accidents, including ischemic stroke and transient ischemic attacks\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Notably, conditions that increase cerebrovascular event risk (e.g., hypertension, atrial fibrillation, history of stroke) are themselves risk factors for POD\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Although overt postoperative stroke is rare, imaging studies reveal that approximately 7\u0026ndash;10% of elderly surgical patients exhibit evidence of asymptomatic cerebral ischemia, with these patients showing more than double the risk of POD\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Concurrently, several cohort studies across various populations indicate a link between elevated AIP and the onset of cognitive impairment and dementia in middle-aged and elderly individuals\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, suggesting the need for further research into the relationship between AIP and perioperative cognitive outcomes.\u003c/p\u003e \u003cp\u003eThe underlying regulatory mechanisms linking AIP and POD remain unclear, potentially involving multiple intertwined pathophysiological pathways. Elevated AIP not only reflects lipid metabolism disorders but is also closely associated with systemic inflammation and oxidative stress, both of which are core drivers of POD\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Elevated AIP typically indicates a relative increase in small dense low-density lipoproteins (sdLDL), these lipoproteins are more susceptible to oxidative modification and readily penetrate the vascular endothelium into the arterial wall, triggering macrophage phagocytosis and foam cell formation. This process initiates and amplifies inflammatory responses within the arterial wall\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This lipid-driven state of low-grade chronic inflammation can be dramatically amplified under the stress of surgical trauma, leading to a cytokine storm that accelerates adverse effects on central nervous system function. Conversely, HDL-C exerts multifaceted neuroprotective effects, including potent anti-inflammatory and antioxidant functions, as well as promoting the clearance of β-amyloid (Aβ) within the brain\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Some studies suggest that higher HDL-C levels may suppress neuropathological inflammation and prevent cognitive impairment by modulating biochemical pathways through anti-inflammatory actions such as inhibiting antigen-presenting cell maturation and regulating lymphocyte inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Elevated AIP levels are frequently accompanied by reduced HDL-C, decreased cholesterol reverse transport, increased lipid accumulation, and enhanced proinflammatory effects\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Consistent with existing studies, our results show that low HDL-C levels are associated with a higher risk of POD\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Oxidative stress represents another critical pathological mechanism in delirium development, characterized by reactive oxygen species (ROS) production and disruption of the brain's antioxidant defense system\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Higher oxidative stress may harms cellular structures such as lipids, proteins, and DNA, often resulting in neuronal impairment or cell death\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Elevated TG levels constitute another critical component of increased AIP. High TG levels impair brain health through multiple pathways, including inducing vascular endothelial dysfunction, exacerbating oxidative stress, and subsequently compromising BBB integrity. Increased BBB permeability facilitates the entry of peripheral inflammatory cells, cytokines, and harmful metabolic byproducts into the central nervous system\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Furthermore, hypertriglyceridemia may promote abnormal lipid deposition in the brain, impair cerebral blood flow autoregulation, and potentially accelerate Alzheimer's disease (AD)-related protein pathologies by affecting Aβ and tau metabolism\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. A retrospective study revealed an association between elevated TG levels and increased delirium severity in elderly patients following hip fracture surgery\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Therefore, AIP, as a composite indicator, may simultaneously capture dual risks: \u0026ldquo;weakened protective factors (e.g., HDL-C)\u0026rdquo; and \u0026ldquo;enhanced detrimental factors (e.g., atherogenicity).\u0026rdquo; This enables it to more accurately reflect the combined risks of neuroinflammation, oxidative stress, and cerebrovascular injury faced during surgical stress. This dual-risk capture may explain why AIP outperforms single lipid markers in predicting POD.\u003c/p\u003e \u003cp\u003eWe performed subgroup analyses on various known or suspected risk factors for POD to further confirm the robustness of the association between AIP and POD. These included ICU length of stay, pre-existing comorbidities (e.g., cerebrovascular disease, neurological disease), anesthetic agents (e.g., dexmedetomidine, propofol), and surgery category. The findings indicated a consistent association between AIP and POD across all predefined subgroups, with no significant interactions detected. This indicates that AIP's predictive capability for POD is unaffected by these diverse important clinical factors. It further supports AIP's potential as a universal biomarker, whose predictive value applies across patient populations with varying underlying disease states, different surgical types, and diverse anesthesia management strategies.\u003c/p\u003e \u003cp\u003eMediation analysis further suggests a potential mediating pathway linking AIP to POD. We found that RD and Hb partially mediated the relationship between AIP and POD. Elevated AIP may promote inflammation and oxidative stress, impairing erythropoiesis and shortening red blood cell lifespan, leading to increased RDW and anemia\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. This, in turn, causes insufficient oxygen supply to brain tissue. Inadequate oxygen transport may trigger alterations in cerebral blood flow, hypoperfusion, and impaired glucose metabolism, ultimately resulting in cerebrovascular lesions, accelerated amyloid deposition, and disruption of the blood-brain barrier, thereby increasing susceptibility to POD\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Furthermore, multiple population studies indicate that low Alb may be an independent risk factor for cognitive impairment and dementia\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The negative correlation between AIP and serum Alb and its mediating role suggest that metabolic disorders represented by AIP may coexist with malnutrition/inflammatory states, collectively exacerbating patients' physiological vulnerability.\u003c/p\u003e \u003cp\u003eCurrently, no effective treatment measures have been proven to prevent POD. Therefore, understanding its multiple risk factors is crucial for promoting early detection and prevention, which is the primary objective and clinical significance of our study. This research is strengthened by its use of a large database, rigorous statistical adjustments, and innovative machine learning interpretation methods. Our findings indicate that higher preoperative AIP levels correlate with increased risk of POD in patients, exhibiting a nonlinear relationship. AIP can be incorporated into preoperative alert systems to implement enhanced monitoring and multimodal prevention strategies. Nonetheless, this research presents certain limitations. First, as a retrospective study, despite thorough comprehensive adjustments, the impact of unmeasured confounders like baseline cognitive function, social support levels, and surgical details cannot be completely excluded. Second, data originated from a single center, necessitating prospective validation of conclusions across diverse populations and healthcare institutions. Additionally, we utilized only initial admission laboratory data, failing to dynamically observe the association between perioperative AIP changes and POD.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study establishes for the first time that preoperative AIP serves as a potential risk biomarker for POD. As an easily calculable composite lipid indicator, AIP offers a novel perspective for preoperative risk stratification and provides a new target for early screening and intervention of POD. This suggests that regulating blood lipids (reducing AIP) may help prevent postoperative cognitive impairment. Future research should focus on validating this association in prospective cohorts, exploring its underlying mechanisms, and developing potential intervention strategies to prevent POD by improving perioperative lipid homeostasis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAtherogenic Index of Plasma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePostoperative Delirium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMIMIC-IV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedical Information from Critical Care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIDMC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBeth Israel Deaconess Medical Center\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Institutes of Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-9/10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Diseases, Ninth or Tenth Revision\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRespiratory Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSpO\u003csub\u003e2\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePulse Oximetry Oxygen Saturation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Ranges\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePropensity Score Matching\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRestricted Cubic Spline\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandardized Mean Differences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCl\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChloride\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAlb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRDW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed Cell Distribution Width\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite Blood Cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBUN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood Urea Nitrogen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUric Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGlu\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBBB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood-Brain Barrier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiopulmonary Bypass\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWMH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite Matter Hyperintensities\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esdLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSmall Dense Low-density Lipoproteins\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAβ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eβ-amyloid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReactive Oxygen Species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlzheimer's Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Writing - original draft preparation: Xiaqing Zhang, Mingliang Xing, Afen Zhang, Rongzhi Zheng, Huiru Hu, Xianming Zeng; Writing - review and editing, visualization: Xiaqing Zhang, Mingliang Xing, Huiru Hu; Conceptualization: Xiaqing Zhang, Mingliang Xing, Xianming Zeng; Methodology: Afen Zhang, Huiru Hu; Formal analysis and investigation:Xiaqing Zhang, Mingliang Xing; Resources: Xianming Zeng; Supervision: Rongzhi Zheng, Xianming Zeng, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThis research utilized a retrospective cohort design, sourcing data from the Medical Information from Critical Care (MIMIC-IV) database (version 2.2) to obtain research data. MIMIC-IV is a publicly accessible and one of the most comprehensive intensive care databases in critical care medicine. The dataset comprises comprehensive clinical data for patients hospitalized at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2019, encompassing length of stay, laboratory test outcomes, medication regimens, vital signs, and other pertinent clinical information. The Institutional Review Boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) granted permission to use the MIMIC-IV database for this research. Author Xiaqing Zhang successfully completed the National Institutes of Health (NIH) online course \u0026ldquo;Protection of Human Research Participants\u0026rdquo; (ID: 15008001) and was therefore authorized to access the MIMIC-IV database for data extraction.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrown CH, Laflam A, Max L, et al. The Impact of Delirium After Cardiac Surgical Procedures on Postoperative Resource Use. Ann Thorac Surg. 2016;101(5):1663\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan E, Veitch M, Saripella A, et al. Association between postoperative delirium and adverse outcomes in older surgical patients: A systematic review and meta-analysis. J Clin Anesth. 2023;90:111221.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvered L, Silbert B, Knopman DS, et al. Recommendations for the nomenclature of cognitive change associated with anaesthesia and surgery\u0026mdash;2018. Br J Anaesth. 2018;121(5):1005\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKunicki ZJ, Ngo LH, Marcantonio ER, et al. Six-Year Cognitive Trajectory in Older Adults Following Major Surgery and Delirium. JAMA Intern Med. 2023;183(5):442\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin Z, Hu J, Ma D. Postoperative delirium: perioperative assessment, risk reduction, and management. Br J Anaesth. 2020;125(4):492\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCecilia V, Cheryl W, Laura C. HDL and cholesterol handling in the brain. Cardiovascular Res. 2014;103(3):405\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang H, Han J, Li Y, et al. Early Serum Metabolism Profile of Post-operative Delirium in Elderly Patients Following Cardiac Surgery With Cardiopulmonary Bypass. Front Aging Neurosci. 2022;14:857902.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian G, Wang Y, Peluso I. Serum Metabolomics of Early Postoperative Cognitive Dysfunction in Elderly Patients Using Liquid Chromatography and Q-TOF Mass Spectrometry. Oxidative Med Cell Longev. 2020;2020:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaw A, Teng R, Fasina T, et al. Lipid dysregulation and delirium in older adults: A review of the current evidence and future directions. Brain Res Bull. 2025;224:111299.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobi\u0026aacute;sov\u0026aacute; M. FJ. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem 34(7):583\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou J, Han H, Bai W. Association between atherogenic index of plasma and cognitive impairment in middle-aged and older adults: results from CHARLS. Front Aging Neurosci. 2025;17:1506973.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson AEW, Bulgarelli L, Shen L, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaru I, Taal G, Zilmer K, et al. Inflammatory/oxidative stress during the first week after different types of cardiac surgery. Scandinavian Cardiovasc J. 2010;44(2):119\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuk T, Amr G, Azzaoui R, et al. Lipid-lowering drugs prevent neurovascular and cognitive consequences of cardiopulmonary bypass. Vascul Pharmacol. 2016;80:59\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Mac\u0026iacute;as JC, Ochoa-Mart\u0026iacute;nez AC, Varela-Silva JA, P\u0026eacute;rez-Maldonado IN. Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses. Arch Med Res. 2019;50(5):285\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu L, Zhou Y, Sun J, et al. Atherogenic index of plasma is associated with major adverse cardiovascular events in patients with type 2 diabetes mellitus. Cardiovasc Diabetol. 2021;20(1):201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Y, Li C, Yang J, et al. Atherogenic index of plasma for non-diabetic, coronary artery disease patients after percutaneous coronary intervention: a prospective study of the long-term outcomes in China. Cardiovasc Diabetol. 2022;21(1):29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwon H-S, Ko J-S, Lee J-H, et al. A Positive Association between the Atherogenic Index of Plasma and White Matter Hyperintensity. Korean J Family Med. 2022;43(3):193\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Y, Jia P, Zhang J, et al. Prevalence and risk factors of postoperative delirium in elderly hip fracture patients. J Int Med Res. 2016;44(2):317\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliveira FR, Oliveira VH, Oliveira \u0026Iacute;M, et al. Hypertension, mitral valve disease, atrial fibrillation and low education level predict delirium and worst outcome after cardiac surgery in older adults. BMC Anesthesiol. 2018;18(1):15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMrkobrada M, Hill MD, Chan MTV, et al. Covert stroke after non-cardiac surgery: a prospective cohort study. Br J Anaesth. 2016;117(2):191\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMrkobrada M, Chan MTV, Cowan D, et al. Perioperative covert stroke in patients undergoing non-cardiac surgery (NeuroVISION): a prospective cohort study. Lancet. 2019;394(10203):1022\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang P, Ren G, Wang Y, et al. The Association Between the Atherogenic Index of Plasma and Cognitive Function: Evidence From the NHANES 2011\u0026ndash;2014. Brain Behav. 2025;15(8):e70768.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin J, Li Y, Li H. Association between atherogenic index of plasma and dementia: A longitudinal observational study. J Alzheimer\u0026rsquo;s Disease. 2025;106(1):197\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu X-W, Deng F-Y, Lei S-F. Meta-analysis of Atherogenic Index of Plasma and other lipid parameters in relation to risk of type 2 diabetes mellitus. Prim Care Diabetes. 2015;9(1):60\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitsis A, Kadoglou NPE, Lambadiari V, et al. Prognostic role of inflammatory cytokines and novel adipokines in acute myocardial infarction: An updated and comprehensive review. Cytokine. 2022;153:155848.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarhangrazi ZS, Ying H, Bu G, et al. High density lipoprotein decreases beta-amyloid toxicity in cortical cell culture. NeuroReport. 1997;8(5):1127\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris G, Puri BK, Bortolasci CC, et al. The role of high-density lipoprotein cholesterol, apolipoprotein A and paraoxonase-1 in the pathophysiology of neuroprogressive disorders. Neurosci Biobehavioral Reviews. 2021;125:244\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYvan-Charvet L, Pagler T, Gautier EL, et al. ATP-binding cassette transporters and HDL suppress hematopoietic stem cell proliferation. Science. 2010;328(5986):1689\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeinkohl I, Janke J, Slooter AJC, et al. Metabolic syndrome and the risk of postoperative delirium and postoperative cognitive dysfunction: a multi-centre cohort study. Br J Anaesth. 2023;131(2):338\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePang Y, Li Y, Zhang Y, et al. Effects of inflammation and oxidative stress on postoperative delirium in cardiac surgery. Front Cardiovasc Med. 2022;9:1049600.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAguilar-Hern\u0026aacute;ndez L, Alejandre R, C\u0026eacute;sar Morales-Medina J, et al. Cellular mechanisms in brain aging: Focus on physiological and pathological aging. J Chem Neuroanat. 2023;128:102210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez MG, Hughes CG, DeMatteo A, et al. Intraoperative Oxidative Damage and Delirium after Cardiac Surgery. Anesthesiology. 2020;132(3):551\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin Y, Peng X, Lin X, et al. Potential Value of Serum Lipid in the Identication of Postoperative Delirium Undergoing Knee/Hip Arthroplasty: The Perioperative Neurocognitive Disorder and Biomarker Lifestyle Study. Front Psychiatry. 2022;13:870317.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess BL, McIsaac SA, Naus KE, et al. Elevated plasma triglyceride levels precede amyloid deposition in Alzheimer\u0026rsquo;s disease mouse models with abundant Aβ in plasma. Neurobiol Dis. 2006;24(1):114\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Guo Y, Tong G, et al. Combined nutritional status and activities of daily living disability is associated with one-year mortality after hip fracture surgery for geriatric patients: a retrospective cohort study. Aging Clin Exp Res. 2024;36(1):127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Zhao X, Tang Y, et al. Erythropoietin Inhibits the Increase of Pulmonary Labile Zinc and the Expression of Inflammatory Mediators Following Subarachnoid Hemorrhage in Rats. Neurocrit Care. 2015;24(3):472\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePayne RS, Goldbart A, Gozal D, Schurr A. Effect of intermittent hypoxia on long-term potentiation in rat hippocampal slices. Brain Res. 2004;1029(2):195\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFornal M, Wizner B, Cwynar M, et al. Association of red blood cell distribution width, inflammation markers and morphological as well as rheological erythrocyte parameters with target organ damage in hypertension. Clin Hemorheol Microcirc. 2014;56(4):325\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Y, Lin D, Song M, et al. Sex and race differences in the association of albumin with cognitive function in older adults. Brain Behav. 2024;14(2):e3435.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Postoperative Delirium, Cardiac Surgery, Atherogenic Index of Plasma, MIMIC-IV","lastPublishedDoi":"10.21203/rs.3.rs-8514238/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8514238/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the relationship between the Atherogenic Index of Plasma (AIP) and the risk of Postoperative Delirium (POD) in patients undergoing cardiac surgery.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted using data from the MIMIC-IV 2.2 with patients who underwent cardiac surgery. AIP was calculated as log₁₀(TG/HDL-C). PSM was used in a 1:1 ratio to balance baseline characteristics (age, sex, race) between delirium and non-delirium groups. Multivariable logistic regression models were employed to assess the independent association between AIP (analyzed as both a continuous and categorical variable in quartiles) and POD, with adjustments for demographics, comorbidities, laboratory parameters, vital signs, medication use, and surgical details. RCS were used to explore nonlinearity. Subgroup and mediation analyses were also performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 6,067 eligible patients, 1,565 (25.8%) developed POD. After PSM, 3,130 patients were analyzed. Multivariable regression revealed a significant positive association between AIP and POD (OR: 1.622, 95% CI: 1.455\u0026ndash;1.81, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Quartile stratification revealed that higher AIP levels were associated with an increased risk of POD (OR: 1.708, 95% CI: 1.399\u0026ndash;2.088, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the fully adjusted model (Model 3), AIP remained significantly associated with POD (OR: 1.435, 95% CI: 1.249\u0026ndash;1.652, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RCS analysis revealed a significant nonlinear relationship between AIP and POD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent across gender subgroups. Subgroup analysis revealed no significant interactions. Mediation analysis indicated that hemoglobin (Hb), red cell distribution width (RDW), albumin (Alb), and respiratory rate (RR) partially mediated the association between AIP and POD.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA higher AIP is independently associated with an increased risk of POD in patients undergoing cardiac surgery, exhibiting a nonlinear relationship. This association is partially mediated by several metabolic and inflammatory markers. AIP may serve as a valuable and easily obtainable predictive biomarker for POD risk stratification in this patient population.\u003c/p\u003e","manuscriptTitle":"The Association Between Atherogenic Index of Plasma and Postoperative Delirium in Cardiac Surgery Patients: An Analysis of the MIMIC-IV Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 10:44:58","doi":"10.21203/rs.3.rs-8514238/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f55e772e-923d-4f2b-b6ef-f8fe10ebbf5a","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T11:23:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 10:44:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8514238","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8514238","identity":"rs-8514238","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}