Prognostic value of serum creatinine trajectories on ICU mortality in patients with acute myocardial infarction: a longitudinal retrospective cohort study

Prognostic value of serum creatinine trajectories on ICU mortality in patients with acute myocardial infarction: a longitudinal retrospective cohort study | 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 Prognostic value of serum creatinine trajectories on ICU mortality in patients with acute myocardial infarction: a longitudinal retrospective cohort study Shuyang Dai, Bingjie Li, Zongshan Zhang, Gaoli Zhang, Poshi Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9036677/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Acute kidney injury (AKI) commonly complicates acute myocardial infarction (AMI) and markedly increases mortality risk. However, the prognostic value of dynamic serum creatinine trajectories in AMI patients admitted to the intensive care unit (ICU) remains insufficiently characterized. Methods This retrospective cohort study used the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. We included adults aged ≥ 18 years admitted to the ICU with AMI who had at least four serum creatinine measurements within 96 hours of admission. Latent growth mixture modeling (LGMM) identified distinct creatinine trajectory subgroups. The primary outcomes were all-cause mortality at 30 days and 365 days after ICU admission. Cox proportional hazards regression assessed the independent association between trajectory class and mortality, adjusting for demographics, comorbidities, disease severity scores, and therapeutic interventions. Results Among 1,153 patients meeting inclusion criteria, LGMM identified three distinct creatinine trajectories: Class 1 (stable-low, n = 394, 34.2%), Class 2 (moderate-ascending, n = 436, 37.8%), and Class 3 (high-rapid rising, n = 323, 28.0%). Model fit statistics demonstrated optimal discrimination, with average posterior probabilities > 0.77 for all classes. Kaplan-Meier analysis revealed statistically significant differences in survival curves among trajectory groups (P < 0.001). In fully adjusted Cox models, Class 3 demonstrated significantly increased mortality risk compared with Class 1 at both 30 days (HR = 2.00, 95%CI: 1.18–3.40, P = 0.010) and 365 days (HR = 1.73, 95%CI: 1.07–2.79, P = 0.026). Class 2 showed elevated risk after adjustment but did not achieve statistical significance. Conclusion Serum creatinine trajectories derived by LGMM offer independent prognostic information on both short-term and long-term mortality in ICU patients with AMI. Dynamic monitoring of these creatinine patterns may improve risk stratification beyond conventional single-timepoint assessments of renal function. serum creatinine trajectory acute myocardial infarction MIMIC-IV database all-cause mortality prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 1.Introduction Acute myocardial infarction (AMI) is a major cause of morbidity and mortality globally, with around 3 million cases each year in developed nations[ 1 , 2 ]. Despite progress in reperfusion strategies and drug therapies, AMI patients in intensive care units (ICUs) still face high mortality risks, especially when acute kidney injury (AKI) complicates their condition[ 3 , 4 ]. The combination of cardiac and renal dysfunction, known as cardiorenal syndrome, impacts 20–30% of AMI patients and is linked to significantly poorer outcomes[ 5 ]. Serum creatinine, a commonly used biomarker for renal function, has shown prognostic significance in cardiovascular diseases[ 6 ]. Traditional methods that depend on single or absolute creatinine values often miss the dynamic changes in renal function during the critical early phase of AMI[ 7 ]. Recent studies indicate that creatinine trajectories—patterns of change over time—offer better prognostic information than static measurements[ 8 – 10 ]. In populations with sepsis and heart failure, specific creatinine trajectory patterns have been linked to varying mortality risks, with worsening trajectories indicating adverse outcomes[ 11 ]. Previous studies have explored creatinine trajectories in heart surgery and sepsis populations[ 12 , 13 ], but data specifically focusing on AMI patients in the ICU are limited. The variability in creatinine changes among this group and their distinct prognostic implications are not well understood. Latent growth mixture modeling (LGMM) represents a robust statistical methodology capable of identifying unobserved subgroups with similar longitudinal outcome patterns while accounting for individual heterogeneity[ 14 ]. This approach has been successfully applied to characterize lactate trajectories in sepsis-associated acute lung injury and creatinine trajectories in ischemic stroke, revealing distinct phenotypes with divergent clinical outcomes[ 8 , 15 ]. Nevertheless, the prognostic value of creatinine trajectory patterns in AMI patients requiring intensive care remains unreported. We hypothesized that distinct longitudinal creatinine trajectories exist within AMI populations and that these patterns maintain independent associations with mortality after adjustment for established risk factors. Accordingly, this study aimed to: (1) identify homogeneous subgroups of AMI patients based on early ICU creatinine trajectories using LGMM; (2) characterize the clinical and demographic features of these trajectory groups; and (3) evaluate their independent prognostic significance for 30-day and 365-day mortality. 2. Materials and methods 2.1 Data source This retrospective cohort study utilized the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database[ 16 ]. MIMIC-IV comprises de-identified comprehensive clinical data from over 190,000 ICU admissions at Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) between 2008 and 2022, including high-resolution physiological measurements, laboratory results, diagnostic codes, and survival data. Database access was obtained after completion of required training modules (CITI Program, protocol number: 14012091). 2.2 Study population Inclusion criteria were: age ≥ 18 years; ICU admission with primary diagnosis of AMI (identified through International Classification of Diseases, Ninth and Tenth Revision codes); and at least four serum creatinine measurement records within 96 hours of ICU admission. Exclusion criteria were: (1) pre-existing end-stage renal disease requiring chronic dialysis; (2) history of renal transplantation; (3) ICU length of stay < 72 hours; (4) absence of creatinine measurement data within the specified time window; and (5) missing critical demographic or outcome data. 2.3 Data collection Clinical and demographic data were extracted from the MIMIC-IV database using PostgreSQL: (1) Demographic information: age, sex and race; (2)Laboratory parameters: full blood count (including white blood cells [WBC], red blood cells [RBC], haemoglobin and platelets), metabolic panel (creatinine, potassium, sodium, fasting blood glucose [FBG],anion gap, chloride); (3)Comorbidities: hypertension, type 2 diabetes mellitus (T2DM), dyslipidaemia, pneumonia, acute kidney injury (AKI), chronic kidney disease (CKD), stroke, chronic obstructive pulmonary disease (COPD); (4) Therapeutic interventions: use of vasoactive agents, antibiotics; (5) Prognostic scores: Sequential Organ Failure Assessment score(SOFA), Acute Physiology Score III (APS III), Simplified Acute Physiology Score II (SAPS II), Oxford Acute Severity of Illness Score (OASIS), CCI(Charlson Comorbidity Index). For variables with missing values not exceeding 10%, multiple imputation was used to impute the missing values. 2.4 Outcome The primary outcome was the all-cause mortality rate within 30 days after ICU admission, and the secondary outcome was the all-cause mortality rate within 365 days after ICU admission. 2.5 Trajectory Modeling Latent growth mixture modeling (LGMM) was performed using the lcmm package (version 4.3.1) in R software to analyze serum creatinine trajectories within 96 hours of admission[ 14 ]. LGMM assumes that the population consists of discrete latent classes with distinct longitudinal trajectories, with each class defined by its specific growth parameters. Models with 1–6 latent classes were fitted using linear functional forms with random intercepts and slopes. Model selection was based on Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), and entropy. Lower BIC and AIC values indicated better fit, while entropy approaching 1.0 suggested clearer class separation. Average posterior probability (AvePP) for each class was also examined, with values > 0.70 considered indicative of adequate classification precision. The final number of classes was determined based on statistical fit indices, clinical interpretability, and class prevalence (> 5% of total population to avoid clinically meaningless subgroups)[ 17 ]. 2.5 Statistical analysis Continuous variables are presented as median (interquartile range) [M (IQR)], and categorical variables as frequency (percentage) [n (%)]. Baseline characteristics across trajectory groups were compared using Kruskal-Wallis tests for continuous variables and χ² tests or Fisher's exact tests for categorical variables. Survival analysis was conducted using Kaplan-Meier methods, with log-rank tests comparing unadjusted survival distributions among trajectory classes. Cox proportional hazards regression models were constructed to evaluate the independent association between creatinine trajectories and mortality. The proportional hazards assumption was verified through Schoenfeld residual analysis. Three hierarchical models were constructed: Model 1 (unadjusted); Model 2 (adjusted for sex, age, race, and BMI); and Model 3 (fully adjusted: Model 2 covariates plus hypertension, pneumonia, stroke, CKD, T2DM, hyperlipidemia, COPD, hemoglobin, erythrocytes, leukocytes, anion gap, fasting glucose, serum chloride, serum potassium, serum sodium, baseline creatinine, platelets, SOFA score, SAPS II score, APS III score, OASIS score, CCI score, invasive ventilation, antibiotics, and vasoactive medications). The stable-low trajectory group (Class 1) served as the reference. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated. Subgroup analyses were performed according to age (< 65 years vs. ≥65 years), sex, and diabetes mellitus to assess potential effect modification. Interaction terms were tested in the fully adjusted model. All tests were two-sided, with P < 0.05 considered statistically significant. All statistical analyses were conducted utilising the R software(version4.4.0) and DecisionLinnc(version 1.1.5.8), with a P-value of less than 0.05 being considered statistically significant. 3.Results 3.1 Patient Selection and Model Identification Among 5,726 AMI admissions identified in the MIMIC-IV database, 1,153 met inclusion criteria (Fig. 1 ). Table 1 a presents model fit statistics for LGMM solutions with 1–6 classes. The 3-class model achieved optimal balance between statistical fit and clinical interpretability, with BIC of 2,787.66 and entropy of 0.651. Although the 4-class model showed slightly lower BIC (2,764.78), the 3-class solution provided clearer clinical distinction and met the criterion of average posterior probability > 0.70. Therefore, the 3-class model was selected as the final solution. Table 1 b demonstrates that average posterior probabilities for the 3-class model ranged from 0.778 to 0.948, all exceeding the 0.70 threshold, indicating excellent classification certainty. 3.2 Baseline Characteristics and Clinical Phenotypes across trajectory groups The three identified trajectories exhibited the following characteristics (Fig. 2 ). Class 1 (stable-low): 394 patients (34.2%). Class 2 (moderate-ascending): 436 patients (37.8%). Class 3 (high-rapid rising): 323 patients (28.0%). Table 2 shows baseline differences across trajectory groups. Class 3 patients were oldest [75 (67-82.5) years] and most severely ill (highest SOFA, APS III, SAPS II, and CCI scores, all P < 0.001). Class 1 had more females (49.0% vs. 25.9% and 29.4%, P < 0.001) and the lowest AKI incidence (82.7% no AKI vs. 10.2% in Class 3), while CKD prevalence increased progressively across classes (P < 0.001). Class 3 demonstrated worse metabolic profiles: higher anion gap, potassium, and FBG, but lower hemoglobin (all P < 0.001)(Table 2 ). 3.3 Survival analysis Mortality increased stepwise across trajectory classes. Thirty-day mortality: Class 1, 10.4%; Class 2, 14.7%; Class 3, 24.2% (P < 0.001). One-year mortality followed a similar pattern: 12.7%, 19.0%, and 28.2%, respectively (P < 0.001). Kaplan-Meier survival curves demonstrated statistically significant differences among the three groups for both 30-day (log-rank test P < 0.001) and 365-day mortality (log-rank test P < 0.001), with Class 3 showing the steepest decline in survival probability (Fig. 3 ). 3.4 Cox Regression Analysis Table 3 presents multivariable Cox regression results. In the unadjusted model (Model 1), both Class 2 (HR = 1.44, 95%CI: 0.97–2.13) and Class 3 (HR = 2.55, 95%CI: 1.74–3.72) showed increased 30-day mortality risk compared with Class 1, though Class 2 did not achieve statistical significance. After adjusting for demographic factors (Model 2), Class 2 became significantly associated with 30-day mortality (HR = 1.56, 95%CI: 1.04–2.35, P = 0.033), while Class 3 demonstrated stronger association (HR = 2.78, 95%CI: 1.87–4.15, P < 0.001). In the fully adjusted model (Model 3), Class 3 remained independently associated with 30-day mortality (HR = 2.00, 95%CI: 1.18–3.40, P = 0.010), whereas Class 2 lost statistical significance (HR = 1.21, 95%CI: 0.78–1.95, P = 0.377). Similar patterns were observed for 365-day mortality: Class 3 maintained significant association after full adjustment (HR = 1.73, 95%CI: 1.07–2.79, P = 0.026), while Class 2 did not (HR = 1.27, 95%CI: 0.84–1.92, P = 0.263). 3.5 Subgroup analysis Figure 4 presents subgroup analyses for 30-day and 365-day mortality. The association between Class 3 and mortality remained consistent across most subgroups, including age stratification, sex, CKD status, and diabetes mellitus. 4. Discussion We used LGMM to identify three distinct serum creatinine trajectory subgroups among ICU-admitted AMI patients. The high-rapid rising trajectory (Class 3) independently predicted higher 30-day and 365-day all-cause mortality after adjustment for comprehensive clinical covariates. The stable-low trajectory (Class 1) was associated with the most favourable prognosis. The moderate-ascending trajectory (Class 2) showed a nonsignificant increase in mortality risk after full adjustment. These results indicate that dynamic serum creatinine trajectories offer superior prognostic stratification compared with single-timepoint measurements. Serum creatinine levels hold significant prognostic value in patients admitted to the ICU. Numerous studies have confirmed their strong association with mortality, AKI, the need for kidney replacement therapy (KRT), and long-term adverse outcomes[ 18 , 19 ]. The dynamic change trend of serum creatinine also has important prognostic significance[ 20 ]. The strong prognostic value of the high-rapidly rising creatinine trajectory is consistent with the pathophysiology of cardiorenal interaction in AMI[ 21 ]. Reduced cardiac output and systemic hypoperfusion caused by AMI-induced cardiac dysfunction directly impair renal blood flow and trigger AKI[ 22 ]. Persistent and rapid creatinine increases in Class 3 patients likely indicate ongoing renal hypoperfusion compounded by neurohormonal activation and iatrogenic insults such as contrast exposure during coronary angiography and use of nephrotoxic medications [ 23 ]. A rapid creatinine rise also functions as a surrogate for systemic hypoperfusion and early multiorgan dysfunction, both established predictors of mortality in critically ill patients[ 24 ]. Class 3 patients in this study had the highest SOFA, APS III, and CCI scores and the greatest prevalence of AKI (89.78%) and chronic kidney disease (CKD, 58.51%), further linking severe renal deterioration to overall disease severity in AMI. The moderate-ascending creatinine trajectory (Class 2) was associated with higher 30-day mortality in the demographically adjusted model (Model 2) and lost significance after full adjustment (Model 3). This indicates that the excess mortality risk in Class 2 is largely mediated by confounders such as advanced age, comorbidities, and illness severity. Class 2 patients had higher rates of hypertension, T2DM, and AKI than Class 1, and these conditions are established predictors of poor outcomes in AMI[ 25 ]. The result implies that moderate creatinine elevation after AMI may be reversible with timely, targeted management of underlying risk factors. By contrast, the high–rapid rising trajectory (Class 3) retained prognostic significance after full adjustment, indicating that rapid, severe renal decline is an independent risk factor. The study's use of LGMM to characterise creatinine trajectories in AMI patients is a key strength. Conventional measures of renal function often miss the dynamic shifts that occur during the early phase of AMI[ 7 ]. LGMM, by contrast, detects subgroups with distinct longitudinal patterns while accounting for individual heterogeneity[ 14 ]. Previous work in sepsis, ischemic stroke, and cardiac surgery patients showed that creatinine trajectory phenotypes predict outcomes more effectively than single measurements[ 8 , 13 ]. We have extended this approach to AMI patients admitted to the ICU, and the average posterior probability for each of the three trajectory classes exceeded 0.77, demonstrating high reliability. The three-class solution also offers clear clinical interpretability and is better suited to risk stratification than more complex models. The three trajectory subgroups differed markedly in baseline clinical characteristics, thereby supporting the classification's clinical relevance. Class 1 patients exhibited the highest proportion of females (49.0%), the lowest incidence of AKI and CKD, and the most favourable metabolic profile (lower anion gap, potassium, and fasting blood glucose), consistent with prior reports that female sex and preserved renal function confer protection in AMI prognosis[ 26 ](29). Patients in Class 3 were the oldest (median age 75 years) and exhibited the poorest metabolic and haematological indicators (e.g. lowest haemoglobin, highest anion gap). These findings may reflect age-related decline in renal function, together with greater systemic inflammation and tissue hypoperfusion[ 27 ](30). The stepwise rise in CKD prevalence across classes (3.55% in Class 1, 24.77% in Class 2, 58.51% in Class 3) further indicates that preexisting renal dysfunction predicts rapid creatinine elevation in AMI, aligning with the GRACE study's conclusion that CKD is an independent risk factor for mortality in acute coronary syndrom strong link between a high–rapid rising creatinine trajectory and mortality[ 28 ]. The prognostic value of Class 3 was consistent across age groups, sex, BMI, and COPD status, showing that rapid creatinine elevation is a widely applicable adverse prognostic marker among ICU-admitted AMI patients. Patients with hypertension and T2DM had an even higher mortality risk in Class 3, suggesting exacerbated cardiorenal interactions: hypertension causes renal arteriosclerosis and reduces renal perfusion reserve, while T2DM causes diabetic nephropathy and microvascular dysfunction, both increasing renal vulnerability to AMI-induced hypoperfusion[ 29 ]. These results show that AMI patients with hypertension or T2DM who show rapid creatinine elevation need more intensive monitoring and intervention. This study has several important clinical results. First, we should check the serum creatinine level of patients with AMI every 96 hours after they are admitted to the ICU. This can be done using LGMM-based trajectory analysis, which can identify high-risk individuals (Class 3) who need urgent protection for their heart and kidneys. Second, for Class 3 patients, measures to improve blood flow to the kidneys (for example, using the right amount of fluids and support for the heart to keep it working well) and avoiding things that can harm the kidneys (for example, using the right amount of contrast media and antibiotics that are bad for the kidneys) may improve short- and long-term outcomes. Thirdly, the fact that the moderate-ascending trajectory (Class 2) is no longer important after full adjustment suggests that early treatment of things that can be changed (like blood sugar and blood pressure control) may stop moderate kidney damage and lower the risk of death in this group. We acknowledge several limitations. Firstly, this retrospective cohort study used the MIMIC-IV database and may be affected by residual confounding and selection bias despite extensive covariate adjustment. Secondly, we analysed only serum creatinine measurements within 96 hours of ICU admission, so longer-term creatinine trajectories and their significance were not assessed. Thirdly, MIMIC-IV derives from a single U.S. centre, and the findings' generalisability to other ethnic groups and multicentre populations requires confirmation in international, multicentre studies. Fourthly, we did not collect data on reperfusion strategies or on the timing of contrast media use; both factors could influence creatinine trajectories and were not included in the adjustment model. Fifthly, our focus was limited to serum creatinine, so the combined value of creatinine trajectories with other renal biomarkers remains to be determined. Future research should pursue several directions. First, prospective multicenter studies are needed to validate the prognostic value of creatinine trajectories in ICU-admitted AMI patients and to determine optimal interventions for high-risk trajectory subgroups. Second, combining creatinine trajectories with other clinical and biochemical markers (e.g., cardiac troponin, NT-proBNP, renal biomarkers) could yield a more accurate prognostic model for AMI. Third, investigating the genetic and molecular mechanisms that underlie distinct creatinine trajectory phenotypes may reveal novel therapeutic targets. Fourth, extending the observation window for creatinine trajectories would clarify their long-term prognostic value (e.g., 5-year mortality) and their relationship to chronic kidney disease progression among AMI survivors. In conclusion, using LGMM this study identified three distinct serum creatinine trajectory subgroups among ICU-admitted AMI patients and found that a high–rapid rising creatinine trajectory independently predicts increased 30-day and 365-day all-cause mortality. Dynamic monitoring and trajectory analysis of serum creatinine during the initial 96 hours of ICU admission improve risk stratification beyond single-timepoint renal assessments and support individualized cardiorenal protection strategies in clinical practice. Abbreviations AKI Acute kidney injury AMI Acute myocardial infarction APS III Acute Physiology Score III AvePP Average posterior probability BIC Bayesian Information Criterion BMI Body mass index CCI Charlson Comorbidity Index CI Confidence interval CKD Chronic kidney disease COPD Chronic obstructive pulmonary disease FBG Fasting blood glucose HR Hazard ratio ICU Intensive care unit IQR Interquartile range LGMM Latent growth mixture modeling MIMIC-IV Medical Information Mart for Intensive Care IV OASIS Oxford Acute Severity of Illness Score RBC Red blood cells SAPS II Simplified Acute Physiology Score II SOFA Sequential Organ Failure Assessment score T2DM Type 2 diabetes mellitus WBC White blood cells Declarations Ethics approval and consent to participate Not applicable. This retrospective study used de-identified data from the MIMIC-IV database, which has been approved by the Institutional Review Board (IRB) of Beth Israel Deaconess Medical Center (IRB No.: 2019P000002). Consent for publication All the authors gave their written consent to publication. Competing interests The authors declare no competing interests.. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Poshi Xu designed and conceptualized this study, Shuyang Dai analyzed the data and wrote the manuscript, Bingjie Li drew the images, and Zongshan Zhang and Gaoli Zhang checked the manuscript. Acknowledgements We sincerely thank the MIMIC-IV database team for providing the clinical data required for this study, and the Biostatistics Research Center of Fuwai Central China Cardiovascular Hospital for the guidance on statistical modeling and analysis. Data Availability All relevant data supporting the findings of this study are available from https://physionet.org/content/mimiciv/3.1/. 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Physical Function and Mortality in Older Adults with Chronic Kidney Disease. Clin J Am Soc Nephrol. 2024;19:1253–62. https://doi.org/10.2215/CJN.0000000000000515 . Fox KAA, Dabbous OH, Goldberg RJ, Pieper KS, Eagle KA, Van de Werf F, et al. Prediction of risk of death and myocardial infarction in the six months after presentation with acute coronary syndrome: prospective multinational observational study (GRACE). BMJ. 2006;333:1091. https://doi.org/10.1136/bmj.38985.646481.55 . Wang Y, Feng L, Wang Y, Li B, Liang N, Song X, et al. Cardiorenal protective effects of glucagon-like peptide-1 receptor agonists in chronic kidney disease: a systematic review and meta-analysis. Ren Fail. 2026;48:2620155. https://doi.org/10.1080/0886022X.2026.2620155 . Tables Table 1 a. Model fit statistics for latent class growth mixed models of creatinine trajectories G Log likelihood AIC BIC Entropy class1 (%) Class2 (%) Clas3 (%) Clas4 (%) Class5 (%) Class6 (%) 1 -1872.46 3756.93 3787.23 1 100 0 2 -1422.61 2865.23 2915.72 0.655 63.83 36.17 3 -1344.48 2716.96 2787.66 0.651 34.17 37.81 28.01 4 -1318.94 2673.88 2764.78 0.658 26.19 24.20 22.29 27.32 5 -1308.99 2661.99 2773.09 0.644 21.51 24.72 14.57 28.97 10.23 6 -1303.47 2658.94 2790.24 0.649 26.71 9.28 5.29 23.50 22.81 12.40 G: The number of classes in the model; AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion. Table 1b Average Posterior Probabilities (AvePP) for Latent Creatinine Trajectory Classes. Class APP Class 1 0.948 Class 2 0.846 Class 3 0.778 Average posterior probabilities for each latent class derived from the three-trajectory latent growth mixture model. All values exceed the recommended threshold of 0.7, indicating excellent classification certainty and model adequacy. Table 2 Clinical characteristics and outcomes by creatinine trajectory in AMI patients Variables Total(n = 1153) Class 1(n = 394) Class 2(n = 436) Class 3(n = 323) P Age (years) 72 (62–80) 68 (59–77) 73 (63–80) 75 (67-82.5) < 0.001 Gender, n(%) Female 401 (34.78) 193 (48.98) 113 (25.92) 95 (29.41) < 0.001 Male 752 (65.22) 201 (51.02) 323 (74.08) 228 (70.59) Race (%) Black 74 (6.42) 16 (4.06) 38 (8.72) 20 (6.19) 0.045 White 922 (79.97) 323 (81.98) 347 (79.59) 252 (78.02) Others 157 (13.62) 55 (13.96) 51 (11.70) 51 (15.79) BMI 27.317 (23.927–31.25) 26.396 (23.443–30.252) 27.855 (24.292–31.657) 27.624 (24.248–32.438) < 0.001 SOFA 6 (4–9) 5 (3–7) 6 (4–9) 8 (5–10) < 0.001 APSIII 46 (35–59) 37 (29–51) 44 (34.75-58) 54 (46–67) < 0.001 SAPS II 40 (32–49) 35 (29–43) 40 (33–49) 45 (39–53) < 0.001 OASIS 34 (29–40) 33 (28–39) 34 (29–40) 36 (30–42) < 0.001 CCI 6 (5–8) 5 (4–6) 6 (5–8) 8 (6–10) < 0.001 Hypertension (%) No 778 (67.48) 223 (56.60) 292 (66.97) 263 (81.42) < 0.001 Yes 375 (32.52) 171 (43.40) 144 (33.03) 60 (18.58) AKI(%) No 509 (44.15) 326 (82.74) 150 (34.40) 33 (10.22) < 0.001 Yes 644 (55.85) 68 (17.26) 286 (65.60) 290 (89.78) Pneumonia (%) No 754 (65.39) 269 (68.27) 281 (64.45) 204 (63.16) 0.312 Yes 399 (34.61) 125 (31.73) 155 (35.55) 119 (36.84) Stroke (%) No 1046 (90.72) 367 (93.15) 393 (90.14) 286 (88.54) 0.093 Yes 107 (9.28) 27 (6.85) 43 (9.86) 37 (11.46) CKD(%) No 842 (73.03) 380 (96.45) 328 (75.23) 134 (41.49) < 0.001 Yes 311 (26.97) 14 (3.55) 108 (24.77) 189 (58.51) T2DM (%) No 672 (58.28) 281 (71.32) 247 (56.65) 144 (44.58) < 0.001 Yes 481 (41.72) 113 (28.68) 189 (43.35) 179 (55.42) Hyperlipidemia (%) No 530 (45.97) 190 (48.22) 185 (42.43) 155 (47.99) 0.171 Yes 623 (54.03) 204 (51.78) 251 (57.57) 168 (52.01) COPD (%) No 942 (81.70) 318 (80.71) 370 (84.86) 254 (78.64) 0.074 Yes 211 (18.30) 76 (19.29) 66 (15.14) 69 (21.36) Hemoglobin (g/dL) 10.6 (8.7–12.5) 10.9 (9.2–12.5) 10.9 (8.8-12.925) 9.9 (8.3-11.85) < 0.001 Platelets (×103/µL) 189 (137–256) 196.5 (144-265.75) 180.5 (134.75-240.25) 186 (135–257) 0.043 RBC (×106/µL) 3.6 (2.96–4.21) 3.7 (3.062–4.188) 3.65 (2.95–4.34) 3.45 (2.85–4.04) 0.003 WBC (×103/µL) 12.8 (9.3–17.7) 13 (9.15–17.5) 12.5 (9.1–17.7) 12.8 (9.6-17.55) 0.902 Anion gap 14 (12–17) 13 (11–16) 14 (12–16) 16 (14–19) < 0.001 Chloride(mEq/L) 104 (100–108) 105 (101–108) 105 (101–108) 104 (98.5–107) 0.009 FBG (mg/dL) 145 (114–202) 131 (108-176.75) 150 (115–202) 169 (126-234.5) < 0.001 Potassium (mEq/L) 4.2 (3.9–4.7) 4.1 (3.8–4.5) 4.3 (3.9–4.7) 4.4 (3.9–4.9) < 0.001 Sodium (mEq/L) 138 (136–141) 138 (136–140) 138 (136–141) 138 (135–141) 0.730 Invasive ventilation (%) No 57 (4.94) 16 (4.06) 18 (4.13) 23 (7.12) 0.104 Yes 1096 (95.06) 378 (95.94) 418 (95.87) 300 (92.88) Antibiotic drugs (%) No 116 (10.06) 38 (9.64) 43 (9.86) 35 (10.84) 0.857 Yes 1037 (89.94) 356 (90.36) 393 (90.14) 288 (89.16) Vasoactive drugs(%) No 189 (16.39) 77 (19.54) 68 (15.60) 44 (13.62) 0.088 Yes 964 (83.61) 317 (80.46) 368 (84.40) 279 (86.38) 30-day mortality(%) 183 (15.87) 41 (10.41) 64 (14.68) 78 (24.15) < 0.001 365-day mortality(%) 224 (19.43) 50 (12.69) 83 (19.04) 91 (28.17) < 0.001 BMI, body mass index; SOFA, Sequential Organ Failure Assessment score; APS III, Acute Physiology Score III; SAPS II, Simplified Acute Physiology Score II; OASIS, Oxford Acute Severity of Illness Score; CCI, Charlson Comorbidity Index; AKI, acute kidney injury; CKD, chronic kidney disease; T2DM, type 2 diabetes mellitus; COPD, chronic obstructive pulmonary disease; RBC, red blood cells; WBC, white blood cells; FBG, fasting blood glucose. Table 3 Cox Regression Analysis of Different Creatinine Trajectories with 30-Day and 365-Day Mortality Outcome Class Model 1 Model 2 Model 3 HR (95% CI) P HR (95% CI) P HR (95% CI) P 30-day mortality Class 1 Ref Ref Ref Class 2 1.436(0.97–2.125) 0.07 1.559(1.035–2.348) 0.033 1.213(0.776–1.952) 0.377 Class 3 2.546(1.744–3.716) < 0.001 2.782(1.867–4.415) < 0.001 2.002(1.178-3.4) 0.01 365-day mortality Class 1 Ref Ref Ref Class 2 1.541(1.085–2.188) 0.016 1.651(1.144–2.382) 0.007 1.267(0.837–1.918) 0.263 Class 3 2.469(1.748–3.486) < 0.001 2.645(1.839–3.804) < 0.001 1.728(1.068–2.793) 0.026 Model 1: unadjusted crude model. Model 2: adjusted for sex, age, race, BMI. Model 3: adjusted for factors in Model 2 and hypertension, pneumonia, AKI, stroke, CKD, T2DM, hyperlipidemia, COPD, hemoglobin, RBC, WBC, anion gap, FBG, Chloride, potassium, sodium, serum creatinine, platelet, SOFA, SAPS II, APS III, OASIS, CCI, invasive ventilation, antibiotic drugs, vasoactive drugs. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor invited by journal 09 Mar, 2026 Editor assigned by journal 06 Mar, 2026 Submission checks completed at journal 06 Mar, 2026 First submitted to journal 05 Mar, 2026 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. 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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-9036677","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617532379,"identity":"b3f1568b-45ad-4dd8-8cdc-cbf2d99040d3","order_by":0,"name":"Shuyang Dai","email":"","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital, Zhengzhou University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuyang","middleName":"","lastName":"Dai","suffix":""},{"id":617532380,"identity":"0ae4bf82-5833-4f78-9e70-9a2c16deab97","order_by":1,"name":"Bingjie Li","email":"","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital, Zhengzhou University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bingjie","middleName":"","lastName":"Li","suffix":""},{"id":617532382,"identity":"e20bd648-b4c0-43b0-b250-259c4ae751c2","order_by":2,"name":"Zongshan Zhang","email":"","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital, Zhengzhou University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zongshan","middleName":"","lastName":"Zhang","suffix":""},{"id":617532383,"identity":"2224a726-1756-4e60-9764-38a4c5f632d4","order_by":3,"name":"Gaoli Zhang","email":"","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital, Zhengzhou University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gaoli","middleName":"","lastName":"Zhang","suffix":""},{"id":617532384,"identity":"938f66ae-a970-436a-bb6b-8c700a6c7b7a","order_by":4,"name":"Poshi Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBAC++PNBx98/GPDzM/eQKyeM8eSDWc2pLFL9hwgVsuNHDNh3obD/AYzEojUwTgjwYxx5o40aQPJxxtvMNTYRBPUwszzIO3BxzM2xubSacUWDMfSchsIaWFjTzhuOIMtLdlydo6ZBGPDYcJaeBgS26R52A7Xb7h5hkgtEhzJbNK8bYeZDW7wEKnFgOcYs+GMM2nMkj1AvyQQ4xcD9v6PDz5UgKLy8MYbH2psCGtB0S6RQIpyiBZSdYyCUTAKRsHIAADcg0JhMwA4FgAAAABJRU5ErkJggg==","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital, Zhengzhou University People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Poshi","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2026-03-05 06:38:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9036677/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9036677/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106533218,"identity":"503ffad6-7e83-4f95-8015-1eef4e30d07e","added_by":"auto","created_at":"2026-04-09 14:56:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39429,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the selection of patients. MIMIC-IV: Medical Information Mart for Intensive Care IV; ICU: intensive care unit.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9036677/v1/11cee807aa11f16482f88d15.jpg"},{"id":106533160,"identity":"96d56881-afeb-4af1-b2dd-fd60abb52725","added_by":"auto","created_at":"2026-04-09 14:56:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53772,"visible":true,"origin":"","legend":"\u003cp\u003eTrajectories of serum creatinine levels in AMI patients\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9036677/v1/aeb176892daf66ebd94ce2e1.jpg"},{"id":106533213,"identity":"5b345f2c-23ca-46cd-bcd5-6bb5985f183d","added_by":"auto","created_at":"2026-04-09 14:56:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69237,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves comparing creatinine trajectory categories . (A) Death within 30-day all-cause mortality; (B) Death within 365-day all-cause mortality.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9036677/v1/3999b8cd05fb41f57623c9d4.jpg"},{"id":106533217,"identity":"a5f46d7e-6e9d-48d3-9dd5-9a9203006217","added_by":"auto","created_at":"2026-04-09 14:56:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":147248,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association of serum creatinine trajectories with mortality of AMI\u003cstrong\u003e.\u003c/strong\u003e (A) 30-day mortality, (B) 365-day mortality.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9036677/v1/1792e12c7ffea298cfbfebbb.jpg"},{"id":106533406,"identity":"dbb11ab3-2e66-4546-9881-35cd01eebb30","added_by":"auto","created_at":"2026-04-09 14:57:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1351939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9036677/v1/3a7564a1-5f16-4453-a6ec-f6ec563ad7e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic value of serum creatinine trajectories on ICU mortality in patients with acute myocardial infarction: a longitudinal retrospective cohort study","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eAcute myocardial infarction (AMI) is a major cause of morbidity and mortality globally, with around 3\u0026nbsp;million cases each year in developed nations[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite progress in reperfusion strategies and drug therapies, AMI patients in intensive care units (ICUs) still face high mortality risks, especially when acute kidney injury (AKI) complicates their condition[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The combination of cardiac and renal dysfunction, known as cardiorenal syndrome, impacts 20\u0026ndash;30% of AMI patients and is linked to significantly poorer outcomes[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSerum creatinine, a commonly used biomarker for renal function, has shown prognostic significance in cardiovascular diseases[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Traditional methods that depend on single or absolute creatinine values often miss the dynamic changes in renal function during the critical early phase of AMI[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recent studies indicate that creatinine trajectories\u0026mdash;patterns of change over time\u0026mdash;offer better prognostic information than static measurements[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In populations with sepsis and heart failure, specific creatinine trajectory patterns have been linked to varying mortality risks, with worsening trajectories indicating adverse outcomes[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have explored creatinine trajectories in heart surgery and sepsis populations[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], but data specifically focusing on AMI patients in the ICU are limited. The variability in creatinine changes among this group and their distinct prognostic implications are not well understood.\u003c/p\u003e \u003cp\u003eLatent growth mixture modeling (LGMM) represents a robust statistical methodology capable of identifying unobserved subgroups with similar longitudinal outcome patterns while accounting for individual heterogeneity[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This approach has been successfully applied to characterize lactate trajectories in sepsis-associated acute lung injury and creatinine trajectories in ischemic stroke, revealing distinct phenotypes with divergent clinical outcomes[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Nevertheless, the prognostic value of creatinine trajectory patterns in AMI patients requiring intensive care remains unreported.\u003c/p\u003e \u003cp\u003eWe hypothesized that distinct longitudinal creatinine trajectories exist within AMI populations and that these patterns maintain independent associations with mortality after adjustment for established risk factors. Accordingly, this study aimed to: (1) identify homogeneous subgroups of AMI patients based on early ICU creatinine trajectories using LGMM; (2) characterize the clinical and demographic features of these trajectory groups; and (3) evaluate their independent prognostic significance for 30-day and 365-day mortality.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study utilized the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. MIMIC-IV comprises de-identified comprehensive clinical data from over 190,000 ICU admissions at Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) between 2008 and 2022, including high-resolution physiological measurements, laboratory results, diagnostic codes, and survival data. Database access was obtained after completion of required training modules (CITI Program, protocol number: 14012091).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study population\u003c/h2\u003e \u003cp\u003eInclusion criteria were: age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; ICU admission with primary diagnosis of AMI (identified through International Classification of Diseases, Ninth and Tenth Revision codes); and at least four serum creatinine measurement records within 96 hours of ICU admission. Exclusion criteria were: (1) pre-existing end-stage renal disease requiring chronic dialysis; (2) history of renal transplantation; (3) ICU length of stay\u0026thinsp;\u0026lt;\u0026thinsp;72 hours; (4) absence of creatinine measurement data within the specified time window; and (5) missing critical demographic or outcome data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection\u003c/h2\u003e \u003cp\u003eClinical and demographic data were extracted from the MIMIC-IV database using PostgreSQL: (1) Demographic information: age, sex and race; (2)Laboratory parameters: full blood count (including white blood cells [WBC], red blood cells [RBC], haemoglobin and platelets), metabolic panel (creatinine, potassium, sodium, fasting blood glucose [FBG],anion gap, chloride); (3)Comorbidities: hypertension, type 2 diabetes mellitus (T2DM), dyslipidaemia, pneumonia, acute kidney injury (AKI), chronic kidney disease (CKD), stroke, chronic obstructive pulmonary disease (COPD); (4) Therapeutic interventions: use of vasoactive agents, antibiotics; (5) Prognostic scores: Sequential Organ Failure Assessment score(SOFA), Acute Physiology Score III (APS III), Simplified Acute Physiology Score II (SAPS II), Oxford Acute Severity of Illness Score (OASIS), CCI(Charlson Comorbidity Index). For variables with missing values not exceeding 10%, multiple imputation was used to impute the missing values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Outcome\u003c/h2\u003e \u003cp\u003eThe primary outcome was the all-cause mortality rate within 30 days after ICU admission, and the secondary outcome was the all-cause mortality rate within 365 days after ICU admission.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Trajectory Modeling\u003c/h2\u003e \u003cp\u003eLatent growth mixture modeling (LGMM) was performed using the lcmm package (version 4.3.1) in R software to analyze serum creatinine trajectories within 96 hours of admission[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. LGMM assumes that the population consists of discrete latent classes with distinct longitudinal trajectories, with each class defined by its specific growth parameters.\u003c/p\u003e \u003cp\u003eModels with 1\u0026ndash;6 latent classes were fitted using linear functional forms with random intercepts and slopes. Model selection was based on Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), and entropy. Lower BIC and AIC values indicated better fit, while entropy approaching 1.0 suggested clearer class separation. Average posterior probability (AvePP) for each class was also examined, with values\u0026thinsp;\u0026gt;\u0026thinsp;0.70 considered indicative of adequate classification precision. The final number of classes was determined based on statistical fit indices, clinical interpretability, and class prevalence (\u0026gt;\u0026thinsp;5% of total population to avoid clinically meaningless subgroups)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as median (interquartile range) [M (IQR)], and categorical variables as frequency (percentage) [n (%)]. Baseline characteristics across trajectory groups were compared using Kruskal-Wallis tests for continuous variables and χ\u0026sup2; tests or Fisher's exact tests for categorical variables.\u003c/p\u003e \u003cp\u003eSurvival analysis was conducted using Kaplan-Meier methods, with log-rank tests comparing unadjusted survival distributions among trajectory classes. Cox proportional hazards regression models were constructed to evaluate the independent association between creatinine trajectories and mortality. The proportional hazards assumption was verified through Schoenfeld residual analysis.\u003c/p\u003e \u003cp\u003eThree hierarchical models were constructed: Model 1 (unadjusted); Model 2 (adjusted for sex, age, race, and BMI); and Model 3 (fully adjusted: Model 2 covariates plus hypertension, pneumonia, stroke, CKD, T2DM, hyperlipidemia, COPD, hemoglobin, erythrocytes, leukocytes, anion gap, fasting glucose, serum chloride, serum potassium, serum sodium, baseline creatinine, platelets, SOFA score, SAPS II score, APS III score, OASIS score, CCI score, invasive ventilation, antibiotics, and vasoactive medications). The stable-low trajectory group (Class 1) served as the reference. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated.\u003c/p\u003e \u003cp\u003eSubgroup analyses were performed according to age (\u0026lt;\u0026thinsp;65 years vs. \u0026ge;65 years), sex, and diabetes mellitus to assess potential effect modification. Interaction terms were tested in the fully adjusted model. All tests were two-sided, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. All statistical analyses were conducted utilising the R software(version4.4.0) and DecisionLinnc(version 1.1.5.8), with a P-value of less than 0.05 being considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient Selection and Model Identification\u003c/h2\u003e \u003cp\u003eAmong 5,726 AMI admissions identified in the MIMIC-IV database, 1,153 met inclusion criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea presents model fit statistics for LGMM solutions with 1\u0026ndash;6 classes. The 3-class model achieved optimal balance between statistical fit and clinical interpretability, with BIC of 2,787.66 and entropy of 0.651. Although the 4-class model showed slightly lower BIC (2,764.78), the 3-class solution provided clearer clinical distinction and met the criterion of average posterior probability\u0026thinsp;\u0026gt;\u0026thinsp;0.70. Therefore, the 3-class model was selected as the final solution. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb demonstrates that average posterior probabilities for the 3-class model ranged from 0.778 to 0.948, all exceeding the 0.70 threshold, indicating excellent classification certainty.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Baseline Characteristics and Clinical Phenotypes across trajectory groups\u003c/h2\u003e \u003cp\u003eThe three identified trajectories exhibited the following characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Class 1 (stable-low): 394 patients (34.2%). Class 2 (moderate-ascending): 436 patients (37.8%). Class 3 (high-rapid rising): 323 patients (28.0%). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows baseline differences across trajectory groups. Class 3 patients were oldest [75 (67-82.5) years] and most severely ill (highest SOFA, APS III, SAPS II, and CCI scores, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Class 1 had more females (49.0% vs. 25.9% and 29.4%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the lowest AKI incidence (82.7% no AKI vs. 10.2% in Class 3), while CKD prevalence increased progressively across classes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Class 3 demonstrated worse metabolic profiles: higher anion gap, potassium, and FBG, but lower hemoglobin (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Survival analysis\u003c/h2\u003e \u003cp\u003eMortality increased stepwise across trajectory classes. Thirty-day mortality: Class 1, 10.4%; Class 2, 14.7%; Class 3, 24.2% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). One-year mortality followed a similar pattern: 12.7%, 19.0%, and 28.2%, respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Kaplan-Meier survival curves demonstrated statistically significant differences among the three groups for both 30-day (log-rank test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 365-day mortality (log-rank test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Class 3 showing the steepest decline in survival probability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Cox Regression Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents multivariable Cox regression results. In the unadjusted model (Model 1), both Class 2 (HR\u0026thinsp;=\u0026thinsp;1.44, 95%CI: 0.97\u0026ndash;2.13) and Class 3 (HR\u0026thinsp;=\u0026thinsp;2.55, 95%CI: 1.74\u0026ndash;3.72) showed increased 30-day mortality risk compared with Class 1, though Class 2 did not achieve statistical significance. After adjusting for demographic factors (Model 2), Class 2 became significantly associated with 30-day mortality (HR\u0026thinsp;=\u0026thinsp;1.56, 95%CI: 1.04\u0026ndash;2.35, P\u0026thinsp;=\u0026thinsp;0.033), while Class 3 demonstrated stronger association (HR\u0026thinsp;=\u0026thinsp;2.78, 95%CI: 1.87\u0026ndash;4.15, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the fully adjusted model (Model 3), Class 3 remained independently associated with 30-day mortality (HR\u0026thinsp;=\u0026thinsp;2.00, 95%CI: 1.18\u0026ndash;3.40, P\u0026thinsp;=\u0026thinsp;0.010), whereas Class 2 lost statistical significance (HR\u0026thinsp;=\u0026thinsp;1.21, 95%CI: 0.78\u0026ndash;1.95, P\u0026thinsp;=\u0026thinsp;0.377). Similar patterns were observed for 365-day mortality: Class 3 maintained significant association after full adjustment (HR\u0026thinsp;=\u0026thinsp;1.73, 95%CI: 1.07\u0026ndash;2.79, P\u0026thinsp;=\u0026thinsp;0.026), while Class 2 did not (HR\u0026thinsp;=\u0026thinsp;1.27, 95%CI: 0.84\u0026ndash;1.92, P\u0026thinsp;=\u0026thinsp;0.263).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Subgroup analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents subgroup analyses for 30-day and 365-day mortality. The association between Class 3 and mortality remained consistent across most subgroups, including age stratification, sex, CKD status, and diabetes mellitus.\u003c/p\u003e"},{"header":"4. Discussion","content":" \u003cp\u003eWe used LGMM to identify three distinct serum creatinine trajectory subgroups among ICU-admitted AMI patients. The high-rapid rising trajectory (Class 3) independently predicted higher 30-day and 365-day all-cause mortality after adjustment for comprehensive clinical covariates. The stable-low trajectory (Class 1) was associated with the most favourable prognosis. The moderate-ascending trajectory (Class 2) showed a nonsignificant increase in mortality risk after full adjustment. These results indicate that dynamic serum creatinine trajectories offer superior prognostic stratification compared with single-timepoint measurements.\u003c/p\u003e \u003cp\u003eSerum creatinine levels hold significant prognostic value in patients admitted to the ICU. Numerous studies have confirmed their strong association with mortality, AKI, the need for kidney replacement therapy (KRT), and long-term adverse outcomes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The dynamic change trend of serum creatinine also has important prognostic significance[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The strong prognostic value of the high-rapidly rising creatinine trajectory is consistent with the pathophysiology of cardiorenal interaction in AMI[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Reduced cardiac output and systemic hypoperfusion caused by AMI-induced cardiac dysfunction directly impair renal blood flow and trigger AKI[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Persistent and rapid creatinine increases in Class 3 patients likely indicate ongoing renal hypoperfusion compounded by neurohormonal activation and iatrogenic insults such as contrast exposure during coronary angiography and use of nephrotoxic medications [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A rapid creatinine rise also functions as a surrogate for systemic hypoperfusion and early multiorgan dysfunction, both established predictors of mortality in critically ill patients[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Class 3 patients in this study had the highest SOFA, APS III, and CCI scores and the greatest prevalence of AKI (89.78%) and chronic kidney disease (CKD, 58.51%), further linking severe renal deterioration to overall disease severity in AMI.\u003c/p\u003e \u003cp\u003eThe moderate-ascending creatinine trajectory (Class 2) was associated with higher 30-day mortality in the demographically adjusted model (Model 2) and lost significance after full adjustment (Model 3). This indicates that the excess mortality risk in Class 2 is largely mediated by confounders such as advanced age, comorbidities, and illness severity. Class 2 patients had higher rates of hypertension, T2DM, and AKI than Class 1, and these conditions are established predictors of poor outcomes in AMI[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The result implies that moderate creatinine elevation after AMI may be reversible with timely, targeted management of underlying risk factors. By contrast, the high\u0026ndash;rapid rising trajectory (Class 3) retained prognostic significance after full adjustment, indicating that rapid, severe renal decline is an independent risk factor.\u003c/p\u003e \u003cp\u003eThe study's use of LGMM to characterise creatinine trajectories in AMI patients is a key strength. Conventional measures of renal function often miss the dynamic shifts that occur during the early phase of AMI[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. LGMM, by contrast, detects subgroups with distinct longitudinal patterns while accounting for individual heterogeneity[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Previous work in sepsis, ischemic stroke, and cardiac surgery patients showed that creatinine trajectory phenotypes predict outcomes more effectively than single measurements[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We have extended this approach to AMI patients admitted to the ICU, and the average posterior probability for each of the three trajectory classes exceeded 0.77, demonstrating high reliability. The three-class solution also offers clear clinical interpretability and is better suited to risk stratification than more complex models.\u003c/p\u003e \u003cp\u003eThe three trajectory subgroups differed markedly in baseline clinical characteristics, thereby supporting the classification's clinical relevance. Class 1 patients exhibited the highest proportion of females (49.0%), the lowest incidence of AKI and CKD, and the most favourable metabolic profile (lower anion gap, potassium, and fasting blood glucose), consistent with prior reports that female sex and preserved renal function confer protection in AMI prognosis[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e](29). Patients in Class 3 were the oldest (median age 75 years) and exhibited the poorest metabolic and haematological indicators (e.g. lowest haemoglobin, highest anion gap). These findings may reflect age-related decline in renal function, together with greater systemic inflammation and tissue hypoperfusion[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e](30). The stepwise rise in CKD prevalence across classes (3.55% in Class 1, 24.77% in Class 2, 58.51% in Class 3) further indicates that preexisting renal dysfunction predicts rapid creatinine elevation in AMI, aligning with the GRACE study's conclusion that CKD is an independent risk factor for mortality in acute coronary syndrom strong link between a high\u0026ndash;rapid rising creatinine trajectory and mortality[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The prognostic value of Class 3 was consistent across age groups, sex, BMI, and COPD status, showing that rapid creatinine elevation is a widely applicable adverse prognostic marker among ICU-admitted AMI patients. Patients with hypertension and T2DM had an even higher mortality risk in Class 3, suggesting exacerbated cardiorenal interactions: hypertension causes renal arteriosclerosis and reduces renal perfusion reserve, while T2DM causes diabetic nephropathy and microvascular dysfunction, both increasing renal vulnerability to AMI-induced hypoperfusion[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These results show that AMI patients with hypertension or T2DM who show rapid creatinine elevation need more intensive monitoring and intervention.\u003c/p\u003e \u003cp\u003eThis study has several important clinical results. First, we should check the serum creatinine level of patients with AMI every 96 hours after they are admitted to the ICU. This can be done using LGMM-based trajectory analysis, which can identify high-risk individuals (Class 3) who need urgent protection for their heart and kidneys. Second, for Class 3 patients, measures to improve blood flow to the kidneys (for example, using the right amount of fluids and support for the heart to keep it working well) and avoiding things that can harm the kidneys (for example, using the right amount of contrast media and antibiotics that are bad for the kidneys) may improve short- and long-term outcomes. Thirdly, the fact that the moderate-ascending trajectory (Class 2) is no longer important after full adjustment suggests that early treatment of things that can be changed (like blood sugar and blood pressure control) may stop moderate kidney damage and lower the risk of death in this group.\u003c/p\u003e \u003cp\u003eWe acknowledge several limitations. Firstly, this retrospective cohort study used the MIMIC-IV database and may be affected by residual confounding and selection bias despite extensive covariate adjustment. Secondly, we analysed only serum creatinine measurements within 96 hours of ICU admission, so longer-term creatinine trajectories and their significance were not assessed. Thirdly, MIMIC-IV derives from a single U.S. centre, and the findings' generalisability to other ethnic groups and multicentre populations requires confirmation in international, multicentre studies. Fourthly, we did not collect data on reperfusion strategies or on the timing of contrast media use; both factors could influence creatinine trajectories and were not included in the adjustment model. Fifthly, our focus was limited to serum creatinine, so the combined value of creatinine trajectories with other renal biomarkers remains to be determined. Future research should pursue several directions. First, prospective multicenter studies are needed to validate the prognostic value of creatinine trajectories in ICU-admitted AMI patients and to determine optimal interventions for high-risk trajectory subgroups. Second, combining creatinine trajectories with other clinical and biochemical markers (e.g., cardiac troponin, NT-proBNP, renal biomarkers) could yield a more accurate prognostic model for AMI. Third, investigating the genetic and molecular mechanisms that underlie distinct creatinine trajectory phenotypes may reveal novel therapeutic targets. Fourth, extending the observation window for creatinine trajectories would clarify their long-term prognostic value (e.g., 5-year mortality) and their relationship to chronic kidney disease progression among AMI survivors.\u003c/p\u003e \u003cp\u003eIn conclusion, using LGMM this study identified three distinct serum creatinine trajectory subgroups among ICU-admitted AMI patients and found that a high\u0026ndash;rapid rising creatinine trajectory independently predicts increased 30-day and 365-day all-cause mortality. Dynamic monitoring and trajectory analysis of serum creatinine during the initial 96 hours of ICU admission improve risk stratification beyond single-timepoint renal assessments and support individualized cardiorenal protection strategies in clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute kidney injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute myocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPS III\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Physiology Score III\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAvePP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAverage posterior probability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCharlson Comorbidity Index\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 interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFasting blood glucose\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\u003eHazard ratio\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\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGMM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLatent growth mixture modeling\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 Mart for Intensive Care IV\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOASIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOxford Acute Severity of Illness Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed blood cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAPS II\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSimplified Acute Physiology Score II\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSequential Organ Failure Assessment score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eType 2 diabetes mellitus\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 cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable. This retrospective study used de-identified data from the MIMIC-IV database, which has been approved by the Institutional Review Board (IRB) of Beth Israel Deaconess Medical Center (IRB No.: 2019P000002).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eAll the authors gave their written consent to publication.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests..\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePoshi Xu designed and conceptualized this study, Shuyang Dai analyzed the data and wrote the manuscript, Bingjie Li drew the images, and Zongshan Zhang and Gaoli Zhang checked the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe sincerely thank the MIMIC-IV database team for providing the clinical data required for this study, and the Biostatistics Research Center of Fuwai Central China Cardiovascular Hospital for the guidance on statistical modeling and analysis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll relevant data supporting the findings of this study are available from https://physionet.org/content/mimiciv/3.1/. Access to the MIMIC-IV database requires completion of the National Institutes of Health (NIH) training course and CITI program certification\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u0026ndash;2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76:2982\u0026ndash;3021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacc.2020.11.010\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2020.11.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReed GW, Rossi JE, Cannon CP. Acute myocardial infarction. 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colname=\"c2\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eclass1\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eClass2\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eClas3\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eClas4\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003eClass5\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003eClass6\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-1872.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3756.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e3787.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-1422.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2865.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2915.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e63.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e36.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-1344.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2716.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2787.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e34.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e37.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e28.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-1318.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2673.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2764.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e26.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e24.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e22.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e27.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-1308.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2661.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2773.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e21.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e24.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e14.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e28.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e10.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-1303.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2658.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2790.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e26.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e23.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e22.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e12.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eG: The number of classes in the model; AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1b\u0026nbsp;\u003c/strong\u003eAverage Posterior Probabilities (AvePP) for Latent Creatinine Trajectory Classes.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAPP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eClass 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eClass 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eClass 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAverage posterior probabilities for each latent class derived from the three-trajectory latent growth mixture model. All values exceed the recommended threshold of 0.7, indicating excellent classification certainty and model adequacy.\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eClinical characteristics and outcomes by creatinine trajectory in AMI patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;1153)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eClass 1(n\u0026thinsp;=\u0026thinsp;394)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eClass 2(n\u0026thinsp;=\u0026thinsp;436)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eClass 3(n\u0026thinsp;=\u0026thinsp;323)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e72 (62\u0026ndash;80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e68 (59\u0026ndash;77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e73 (63\u0026ndash;80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e75 (67-82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGender, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e401 (34.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e193 (48.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e113 (25.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e95 (29.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e752 (65.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e201 (51.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e323 (74.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e228 (70.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRace (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e74 (6.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16 (4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e38 (8.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e20 (6.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e922 (79.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e323 (81.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e347 (79.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e252 (78.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e157 (13.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e55 (13.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e51 (11.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e51 (15.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e27.317 (23.927\u0026ndash;31.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e26.396 (23.443\u0026ndash;30.252)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e27.855 (24.292\u0026ndash;31.657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e27.624 (24.248\u0026ndash;32.438)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6 (4\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5 (3\u0026ndash;7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6 (4\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8 (5\u0026ndash;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAPSIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e46 (35\u0026ndash;59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e37 (29\u0026ndash;51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e44 (34.75-58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e54 (46\u0026ndash;67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSAPS II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e40 (32\u0026ndash;49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e35 (29\u0026ndash;43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e40 (33\u0026ndash;49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e45 (39\u0026ndash;53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOASIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e34 (29\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e33 (28\u0026ndash;39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e34 (29\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e36 (30\u0026ndash;42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6 (5\u0026ndash;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5 (4\u0026ndash;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6 (5\u0026ndash;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8 (6\u0026ndash;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e778 (67.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e223 (56.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e292 (66.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e263 (81.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e375 (32.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e171 (43.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e144 (33.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e60 (18.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAKI(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e509 (44.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e326 (82.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e150 (34.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e33 (10.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e644 (55.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e68 (17.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e286 (65.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e290 (89.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePneumonia (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e754 (65.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e269 (68.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e281 (64.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e204 (63.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e399 (34.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e125 (31.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e155 (35.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e119 (36.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eStroke (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1046 (90.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e367 (93.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e393 (90.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e286 (88.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e107 (9.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e27 (6.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e43 (9.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e37 (11.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCKD(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e842 (73.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e380 (96.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e328 (75.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e134 (41.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e311 (26.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14 (3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e108 (24.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e189 (58.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eT2DM (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e672 (58.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e281 (71.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e247 (56.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e144 (44.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e481 (41.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e113 (28.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e189 (43.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e179 (55.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHyperlipidemia (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e530 (45.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e190 (48.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e185 (42.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e155 (47.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e623 (54.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e204 (51.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e251 (57.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e168 (52.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCOPD (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e942 (81.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e318 (80.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e370 (84.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e254 (78.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e211 (18.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e76 (19.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e66 (15.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e69 (21.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10.6 (8.7\u0026ndash;12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10.9 (9.2\u0026ndash;12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e10.9 (8.8-12.925)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e9.9 (8.3-11.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePlatelets (\u0026times;103/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e189 (137\u0026ndash;256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e196.5 (144-265.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e180.5 (134.75-240.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e186 (135\u0026ndash;257)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRBC (\u0026times;106/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.6 (2.96\u0026ndash;4.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.7 (3.062\u0026ndash;4.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.65 (2.95\u0026ndash;4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.45 (2.85\u0026ndash;4.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWBC (\u0026times;103/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e12.8 (9.3\u0026ndash;17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13 (9.15\u0026ndash;17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e12.5 (9.1\u0026ndash;17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e12.8 (9.6-17.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAnion gap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e14 (12\u0026ndash;17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13 (11\u0026ndash;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e14 (12\u0026ndash;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16 (14\u0026ndash;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChloride(mEq/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e104 (100\u0026ndash;108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e105 (101\u0026ndash;108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e105 (101\u0026ndash;108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e104 (98.5\u0026ndash;107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFBG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e145 (114\u0026ndash;202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e131 (108-176.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e150 (115\u0026ndash;202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e169 (126-234.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePotassium (mEq/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.2 (3.9\u0026ndash;4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.1 (3.8\u0026ndash;4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4.3 (3.9\u0026ndash;4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.4 (3.9\u0026ndash;4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSodium (mEq/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e138 (136\u0026ndash;141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e138 (136\u0026ndash;140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e138 (136\u0026ndash;141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e138 (135\u0026ndash;141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eInvasive ventilation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e57 (4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16 (4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e18 (4.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e23 (7.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1096 (95.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e378 (95.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e418 (95.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e300 (92.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAntibiotic drugs (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e116 (10.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e38 (9.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e43 (9.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e35 (10.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1037 (89.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e356 (90.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e393 (90.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e288 (89.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVasoactive drugs(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e189 (16.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e77 (19.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e68 (15.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e44 (13.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e964 (83.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e317 (80.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e368 (84.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e279 (86.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e30-day mortality(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e183 (15.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e41 (10.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e64 (14.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e78 (24.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e365-day mortality(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e224 (19.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e50 (12.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e83 (19.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e91 (28.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eBMI, body mass index; SOFA, Sequential Organ Failure Assessment score; APS III, Acute Physiology Score III; SAPS II, Simplified Acute Physiology Score II; OASIS, Oxford Acute Severity of Illness Score; CCI, Charlson Comorbidity Index; AKI, acute kidney injury; CKD, chronic kidney disease; T2DM, type 2 diabetes mellitus; COPD, chronic obstructive pulmonary disease; RBC, red blood cells; WBC, white blood cells; FBG, fasting blood glucose.\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCox Regression Analysis of Different Creatinine Trajectories with 30-Day and 365-Day Mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e30-day mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eClass 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eClass 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.436(0.97\u0026ndash;2.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.559(1.035\u0026ndash;2.348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.213(0.776\u0026ndash;1.952)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eClass 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.546(1.744\u0026ndash;3.716)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.782(1.867\u0026ndash;4.415)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.002(1.178-3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e365-day mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eClass 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eClass 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.541(1.085\u0026ndash;2.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.651(1.144\u0026ndash;2.382)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.267(0.837\u0026ndash;1.918)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eClass 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.469(1.748\u0026ndash;3.486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.645(1.839\u0026ndash;3.804)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.728(1.068\u0026ndash;2.793)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eModel 1: unadjusted crude model. Model 2: adjusted for sex, age, race, BMI. Model 3: adjusted for factors in Model 2 and hypertension, pneumonia, AKI, stroke, CKD, T2DM, hyperlipidemia, COPD, hemoglobin, RBC, WBC, anion gap, FBG, Chloride, potassium, sodium, serum creatinine, platelet, SOFA, SAPS II, APS III, OASIS, CCI, invasive ventilation, antibiotic drugs, vasoactive drugs.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"serum creatinine, trajectory, acute myocardial infarction, MIMIC-IV database, all-cause mortality, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-9036677/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9036677/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcute kidney injury (AKI) commonly complicates acute myocardial infarction (AMI) and markedly increases mortality risk. However, the prognostic value of dynamic serum creatinine trajectories in AMI patients admitted to the intensive care unit (ICU) remains insufficiently characterized.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study used the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. We included adults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years admitted to the ICU with AMI who had at least four serum creatinine measurements within 96 hours of admission. Latent growth mixture modeling (LGMM) identified distinct creatinine trajectory subgroups. The primary outcomes were all-cause mortality at 30 days and 365 days after ICU admission. Cox proportional hazards regression assessed the independent association between trajectory class and mortality, adjusting for demographics, comorbidities, disease severity scores, and therapeutic interventions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 1,153 patients meeting inclusion criteria, LGMM identified three distinct creatinine trajectories: Class 1 (stable-low, n\u0026thinsp;=\u0026thinsp;394, 34.2%), Class 2 (moderate-ascending, n\u0026thinsp;=\u0026thinsp;436, 37.8%), and Class 3 (high-rapid rising, n\u0026thinsp;=\u0026thinsp;323, 28.0%). Model fit statistics demonstrated optimal discrimination, with average posterior probabilities\u0026thinsp;\u0026gt;\u0026thinsp;0.77 for all classes. Kaplan-Meier analysis revealed statistically significant differences in survival curves among trajectory groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In fully adjusted Cox models, Class 3 demonstrated significantly increased mortality risk compared with Class 1 at both 30 days (HR\u0026thinsp;=\u0026thinsp;2.00, 95%CI: 1.18\u0026ndash;3.40, P\u0026thinsp;=\u0026thinsp;0.010) and 365 days (HR\u0026thinsp;=\u0026thinsp;1.73, 95%CI: 1.07\u0026ndash;2.79, P\u0026thinsp;=\u0026thinsp;0.026). Class 2 showed elevated risk after adjustment but did not achieve statistical significance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSerum creatinine trajectories derived by LGMM offer independent prognostic information on both short-term and long-term mortality in ICU patients with AMI. Dynamic monitoring of these creatinine patterns may improve risk stratification beyond conventional single-timepoint assessments of renal function.\u003c/p\u003e","manuscriptTitle":"Prognostic value of serum creatinine trajectories on ICU mortality in patients with acute myocardial infarction: a longitudinal retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 14:54:19","doi":"10.21203/rs.3.rs-9036677/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-12T00:59:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45480718896017714610646955580263597546","date":"2026-04-09T15:29:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331884361019445147670516219723569115002","date":"2026-04-02T12:07:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T10:01:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-09T10:37:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T11:15:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-06T11:11:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-03-05T06:31:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37c6356e-2be5-4449-b35e-6ab2bf48dfac","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T14:54:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 14:54:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9036677","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9036677","identity":"rs-9036677","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}