Predictive Value of Inflammatory Prognostic Index for Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography and /or Percutaneous Coronary Intervention

Predictive Value of Inflammatory Prognostic Index for Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography and /or Percutaneous Coronary Intervention | 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 Article Predictive Value of Inflammatory Prognostic Index for Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography and /or Percutaneous Coronary Intervention Yan Jiang, Baolin Luo, Yaqin Chen, Yanchun Peng, Wen Lu, Liangwan Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4096614/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Aims The purpose of this study was to investigate the relationship between IPI levels and Contrast-Induced Nephropathy (CIN) risk and postoperative clinical outcomes in patients undergoing coronary angiography (CAG) and/ or percutaneous coronary intervention (PCI). Methods A total of 3,340 consecutive patients who underwent CAG and/or PCI between May 2017 and December 2022 were enrolled in this study. Based on their baseline IPI levels, patients were categorized into four groups. Clinical characteristics and postoperative outcomes were compared among these groups. In-hospital outcomes focused on CIN risk, repeated revascularization, major bleeding, and major adverse cardiovascular events (MACE), while the long-term outcome examined the all-cause readmission rate. Results Quartile analysis found a significant link between IPI levels and CIN risk, notably in the highest quartile ( p < 0.001). Even after adjusting for baseline factors, this association remained significant, with an adjusted Odds Ratio (aOR) of 2.33 (95%CI 1.50–3.64; p = 0.001). Notably, baseline IPI level emerged as an independent predictor of severe arrhythmia, with aOR of 0.50 (95%CI 0.35–0.69; p < 0.001), particularly driven by the highest quartile. Furthermore, a significant correlation between IPI and acute myocardial infarction was observed ( p < 0.001), which remained significant post-adjustment. Conclusions For patients undergoing CAG and/or PCI, baseline IPI levels can independently predict clinical prognosis. As a comprehensive inflammation indicator, IPI effectively identifies high-risk patients post-procedure. This study underscores IPI's potential to assist medical professionals in making more precise clinical decisions, ultimately reducing mortality and readmission rates linked to cardiovascular disease (CVD). Health sciences/Cardiology/Cardiovascular biology Health sciences/Cardiology/Interventional cardiology Health sciences/Diseases/Cardiovascular diseases/Acute coronary syndromes/Myocardial infarction Inflammatory Prognostic Index Prognosis Coronary Angiography Percutaneous Coronary Intervention Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cardiovascular disease (CVD) is a prevalent chronic condition globally, with China experiencing a steady rise in its incidence, now estimated at 330 million patients annually 1 . Coronary heart disease (CHD) is particularly prevalent and associated with high mortality rates 2 . Consequently, coronary angiography (CAG) and percutaneous coronary intervention (PCI) are crucial diagnostic and treatment modalities. However, despite PCI's benefits for long-term prognosis, risks like contrast-induced nephropathy and major adverse cardiovascular events (MACE) remain significant 3 , 4 , contributing to increased mortality and readmission rates. Contrast-induced nephropathy (CIN) refers to acute kidney injury following exposure to iodinated contrast agents. It typically occurs 1–3 days post-exposure, associated with poorer prognosis including increased hospitalization rates and costs 5 , 6 . Incidence can range from 20–25% in patients undergoing CAG and/or PCI, reaching 40% in high-risk cohorts 7 , 8 . While the exact cause of CIN variability remains unclear, enhanced inflammatory response is considered significant 9 , 10 . Identifying inflammatory markers predicting CIN is crucial to mitigate complications. Neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), and serum albumin (ALB) are established indicators of inflammation and atherosclerosis pathogenesis. Combining these into an inflammatory prognostic index (IPI) offers a more comprehensive assessment of inflammatory status. IPI has shown clinical significance in various fields but lacks evidence in predicting CIN and its relationship with clinical outcomes in CAG and/or PCI patients is unclear. NLR 11 , CRP 13 , and ALB 14 indicate systemic inflammation and atherosclerosis development. However, individual markers lack completeness. Building upon this rationale, some scholars have proposed an IPI 15 . IPI combines CRP, NLR, and ALB, offering a better reflection of patients' inflammatory status. Initially used in predicting outcomes for cancer patients 16 , its significance in cardiovascular contexts, like cardiac surgery 18 , has been recognized. Yet, its predictive value for CIN in CAG and/or PCI patients and its link to clinical outcomes remain unexplored. This study aims to investigate the role of IPI in predicting CIN risk and clinical outcomes in patients undergoing CAG and/or PCI, offering a practical predictive method. This could assist healthcare professionals in making more precise decisions, potentially lowering mortality and readmission rates in CHD patients. Methods Study design and population We collected retrospective data from 16,759 patients undergoing CAG and/or PCI at Fujian Medical University Union Hospital between May 2017 and December 2022. Exclusion criteria were age < 18, missing or limited serum creatinine data, on-pump CABG, contrast agent allergies, ongoing dialysis, and missing data or discharge status. 3,340 patients were included for analysis. The study was approved by the hospital's Ethics Committee and followed the Declaration of Helsinki. Data collection and clinical definition We obtained baseline data from the Electronic Medical Records System of Fujian Medical University Union Hospital, which primarily comprised sociodemographic information, admission and discharge diagnoses, results of laboratory and imaging tests, medication details, procedural characteristics, and discharge outcomes. Laboratory tests were conducted at the Laboratory Center of Fujian Medical University Union Hospital. Venous blood samples were collected after a fasting period of over 8 hours, with water deprivation during the final 3–4 hours of the fast. Hypertension, diabetes mellitus (DM) and stroke were defined using the 10th Revision Codes of the International Classification of Diseases (ICD-10) 19 . The guidelines for the prevention and treatment of dyslipidemia in Chinese adults (2016) 20 , were used to define the diagnosis of hyperlipemia. The diagnostic criteria of chronic kidney disease (CKD) were based on Guidelines for the Screening, Diagnosis and Prevention of Chronic kidney Disease in China (2017) 21 , estimated glomerular filtration rate (eGFR) < 60mL/min/1.73m 2 and calculated with MDRD formula 22 . CIN was defined as an absolute increase 44.2 µmol/L (0.5 mg/dL) or a relative increase of 25% in serum creatinine level from baseline within 48 to 72 h after intravascular use of iodinated contrast agents. Measurements Venous blood samples collected within 24 hours of admission were used to measure neutrophils (N), lymphocytes (L), ALB, and CRP. These parameters were used to calculate two inflammatory biomarkers: the neutrophil-to-lymphocyte ratio (NLR = N/L) and the inflammatory prognostic index (IPI = CRP×NLR/ALB). Baseline values for other laboratory parameters were determined based on recent preoperative serum creatinine levels and additional indicators. Outcomes measured In-hospital outcomes included CIN risk, repeated revascularization, major bleeding, and MACE. Repeated revascularization meant placing additional stents, excluding the initial one. Major bleeding included intracranial, upper, and lower gastrointestinal hemorrhage. MACE covered postoperative AMI, acute heart failure, stroke, severe arrhythmia, and all-cause in-hospital mortality. AMI was diagnosed with serum enzyme elevation and symptoms. AHF involved sudden symptoms due to cardiac dysfunction. Severe arrhythmias included atrioventricular block, atrial flutter or fibrillation, and ventricular flutter or fibrillation. Data were from discharge summaries and postoperative records. Long-term endpoint was all-cause readmission rate, confirmed through medical records or communication. Follow-up averaged 1 year. Statistical analysis In order to assess the impact of IPI levels on clinical outcomes in patients, we divided IPI into four groups based on quartiles. Enumeration data were expressed as numbers and percentage values (%), analyzed using the χ 2 test.When χ 2 test test conditions were not met, Fisher exact test was used, and values of P < 0.05 (two-sided) were considered significant. Shapiro-Wilk test was used to test the normality of continuous variables. Continuous variables were expressed as mean ± standard deviation (for normal distribution) and analyzed using ANOVA analysis, or as median and interquartile range (IQR) for non-normal distribution, the Mann-Whitney U test was used. First, univariate logistic regression analysis was used to determine the potential risk factors for in-hospital mortality ( P < 0.05), and then multivariate logistic regression analysis was used to confirm that the previously significant variables were independent factors ( P < 0.05). Multivariate Cox proportional hazard regression and Kaplan-Meier survival curve were used to evaluate all-cause readmission rate. All statistical analyses were performed using SPSS version 26.0 software (IBM, Armonk, New York, USA). Results Baseline characteristics Among the 3,340 patients who underwent CAG and/or PCI, the average age was 63.96 ± 10.53 years, with 922 (27.6%) being female, and 1950 (58.4%) undergoing PCI. The mean IPI level in these patients was 0.74 [interquartile range: 0.11, 1.94]. Patients were stratified into four groups based on baseline IPI levels (Group 1,≤0.11, n = 835; Group 2, 0.12–0.74, n = 835; Group 3, 0.75–1.93, n = 835; Group 4, ≥ 1.94, n = 835). Notably, higher IPI levels were associated with increased incidence of valvular heart disease (VHD) (41.7%, p < 0.001), heart failure (HF) (6.3%, p < 0.001), liver dysfunction (11.4%, p < 0.001), and chronic kidney disease (CKD) (16.6%, p < 0.001). Conversely, the incidence of hypertension (50.2%, p < 0.001), diabetes mellitus (DM) (27.4%, p < 0.001), hyperlipidemia (21.2%, p < 0.001), and acute coronary syndrome (ACS) (39.8%, p < 0.001) was lower in patients with higher IPI levels. Furthermore, patients in the highest IPI quartile had a higher prevalence of atrial fibrillation (AF) (23.5%, p < 0.001), pulmonary hypertension (17.8%, p < 0.001), and pulmonary infection (65.4%, p < 0.001), and tended to have longer hospital stays ( p < 0.001). However, they had a lower history of myocardial infarction (2.3%, p < 0.001) and PCI (7.1%, p < 0.001). It is worth noting that with increasing IPI levels, patients' admission systolic and diastolic blood pressures, as well as left ventricular ejection fraction (LVEF), were negatively correlated ( p < 0.001). Patients in group 4 exhibited elevated preoperative creatinine levels ( p < 0.001) and baseline urea nitrogen levels ( p < 0.001). However, these patients also displayed a tendency toward reduced glomerular filtration rates ( p < 0.001). Conversely, individuals with higher IPI levels tended to have elevated absolute neutrophil counts ( p < 0.001) and white blood cell counts (WBC) ( p < 0.001), but diminished absolute lymphocyte counts ( p < 0.001). Moreover, the NLR was notably higher among subjects with IPI levels in the second and fourth quartiles ( p < 0.001). Additionally, patients with IPI levels in the highest quartile exhibited decreased red blood cell count (RBC) levels ( p < 0.001), concomitant with reductions in ALP ( p < 0.001) and hemoglobin (Hb) levels ( p < 0.001), while CRP ( p < 0.001) and blood glucose ( p < 0.001) levels increased. Patients in group 4 had higher levels of preoperative creatinine ( p < 0.001) and baseline urea nitrogen ( p < 0.001), but these patients also tended to have lower glomerular filtration rate ( p < 0.001). Individuals with higher IPI levels tended to have higher absolute neutrophil counts ( p < 0.001) and white blood cell counts (WBC) ( p < 0.001), but had lower absolute lymphocyte counts ( p < 0.001). At the same time, NLR was higher in subjects with IPI levels in the second and fourth quartiles ( p < 0.001). In addition, patients with IPI levels in the highest quartile had lower RBC levels ( p < 0.001), accompanied by a decrease in ALP( p < 0.001), hemoglobin (Hb) ( p < 0.001), while CRP ( p < 0.001) and blood glucose ( p < 0.001) levels increased. In terms of medication, we observed that the use of diuretics increased in tandem with higher IPI levels. Conversely, the use of other medications such as calcium channel blockers, angiotensin converting enzyme inhibitors, angiotensin receptor blockers, angiotensin receptor enkephalinase inhibitors (ACEI/ARB/ARNI),β-blockers, dual antiplatelet therapy (DAPT), and statins exhibited an inverse relationship with IPI levels ( p < 0.001). Regarding surgical characteristics, it is noteworthy that patients in the highest quartile of IPI levels had the highest number of infarcted arteries ( p < 0.001). Conversely, patients with IPI levels in the lowest quartile were more likely to receive hydration therapy ( p < 0.001) and undergo repeat angiography within seven days ( p < 0.001), with a greater amount of contrast agent used. Furthermore, there was a significant negative correlation between IPI levels and the likelihood of undergoing PCI (80.4 vs 65.7 vs 49.2 vs 38.2%, p < 0.001), indicating that patients with lower IPI levels were more inclined to undergo PCI. More detailed baseline information is provided in Table 1 . Table 1 Baseline characteristics in patients undergoing CAG and/or PCI. Inflammatory Prognostic Index Quartile Variables Quartile 1 (≤ 0.11) Quartile 2 (0.12–0.74) Quartile 3 (0.75–1.93) Quartile 4 (≥ 1.94) P -value (n = 835) (n = 835) (n = 835) (n = 835) Demographic and clinical characteristics Age, years, M (SD) 63.49 ± 10.31 64.54 ± 10.92 63.77 ± 10.38 64.04 ± 10.50 0.209 Female sex, n (%) 200 (24.0) 235 (28.1) 576 (30.4) 622 (27.9) 0.029 Current smoking, n (%) 278 (33.3) 276 (33.1) 248 (29.7) 253 (30.3) 0.222 Current drinking, n (%) 599 (7.1) 58 (6.9) 51 (6.1) 66 (7.9) 0.509 SBP, mm Hg, M (SD) 131.80 ± 19.06 129.84 ± 19.62 127.22 ± 20.09 125.55 ± 21.10 < 0.001 DBP, mm Hg, M (SD) 79.28 ± 12.69 78.19 ± 12.42 78.08 ± 27.16 75.57 ± 13.54 < 0.001 BMI, kg/m 2 , M (SD) 24.22 ± 3.37 24.34 ± 2.98 23.85 ± 3.10 23.52 ± 3.24 < 0.001 LOS, days, M (SD) 7.88 ± 6.12 9.66 ± 7.09 12.29 ± 9.01 16.15 ± 10.97 < 0.001 EF (%), M (SD) 62.68 ± 9.74 59.41 ± 11.87 59.55 ± 12.63 57.32 ± 13.11 < 0.001 Prior MI, n (%) 105 (12.6) 58 (6.9) 27 (3.2) 19 (2.3) < 0.001 Prior CVA, n (%) 55 (6.6) 52 (6.2) 40 (4.8) 59 (7.1) 0.245 Prior PCI, n (%) 20 (2.4) 20 (2.4) 10 (1.2) 13 (1.6) 0.174 Prior CABG, n (%) 215 (25.8) 141 (16.9) 96 (11.5) 59 (7.1) < 0.001 Complication Hypertension, n (%) 533 (63.8) 492 (58.9) 430 (51.5) 419 (50.2) < 0.001 DM, n (%) 320 (38.3) 276 (33.1) 231(27.7) 229 (27.4) < 0.001 Hyperlipemia, n (%) 291 (34.9) 285 (34.1) 189 (22.6) 143 (17.1) < 0.001 CKD, n (%) 53 (6.3) 65 (7.8) 86 (10.3) 139 (16.6) < 0.001 Hepatic insufficiency, n (%) 39 (4.7) 60 (7.2) 65 (7.8) 95 (11.4) < 0.001 ACS, n (%) 580 (69.5) 509 (61.0) 354 (42.4) 332 (39.8) < 0.001 Pulmonary infection, n (%) 115 (13.8) 235 (28.1) 420 (50.3) 546 (65.4) < 0.001 Valvular heart disease, n (%) 131 (15.7) 198 (23.7) 279 (33.4) 348 (41.7) < 0.001 AF, n (%) 81 (9.7) 130 (15.6) 146 (17.5) 196 (23.5) < 0.001 PAH, n (%) 26 (3.1) 64 (7.7) 130 (15.6) 149 (17.8) < 0.001 HF, n (%) 17 (2.0) 34 (4.1) 23 (2.8) 53 (6.3) < 0.001 Medications before procedures Diuretics, n (%) 139 (16.6) 250 (29.9) 342 (41.0) 452 (54.1) < 0.001 CCB, n (%) 275 (32.9) 202 (24.2) 167 (20.0) 126 (15.1) < 0.001 ACEI/ARB/ARNI, n (%) 326 (39.0) 266 (31.9) 211 (25.3) 189 (22.6) < 0.001 β-blockers, n (%) 470 (56.3) 426 (51.0) 408 (48.9) 340 (40.7) < 0.001 Statins, n (%) 715 (85.6) 588 (70.4) 469 (56.2) 370 (44.3) < 0.001 DAPT, n (%) 725 (86.8) 589 (70.5) 444 (53.2) 353 (42.3) < 0.001 Procedural characteristics PCI, n (%) 671 (80.4) 549(65.7) 411(49.2) 319(38.2) < 0.001 Number of infarcted arteries 2.70 ± 0.79 2.75 ± 0.76 2.65 ± 0.83 2.75 ± 0.82 < 0.001 Stent number 1.51 ± 0.82 1.59 ± 0.86 1.62 ± 0.92 1.65 ± 0.95 0.094 Hydration therapy, n (%) 806 (96.5) 764 (91.5) 732 (87.7) 743 (89.0) < 0.001 Repeated angiography(7 days) 689 (82.5) 586 (70.2) 463 (55.4) 367 (44.0) < 0.001 Contrast volume, ml, M (SD) 347.97 ± 143.76 305.69 ± 165.52 301.45 ± 153.54 254.60 ± 174.38 < 0.001 Baseline chemistry ALP, g/L, M (SD) 40.99 ± 3.59 39.91 ± 3.74 39.35 ± 4.31 36.86 ± 5.38 < 0.001 UREA, mmol/L, M (SD) 5.74 ± 2.21 6.11 ± 3.19 6.39 ± 3.41 7.25 ± 4.54 < 0.001 Scr, µmol/L, M (SD) 80.29 ± 29.72 86.78 ± 48.50 91.83 ± 70.86 106.11 ± 104.56 < 0.001 Hemoglobin, g/L, M (SD) 137.01 ± 17.07 133.50 ± 17.66 132.33 ± 18.49 125.53 ± 22.98 < 0.001 NE, 10^9/L, M (SD) 3.86 ± 1.46 4.66 ± 2.19 4.62 ± 2.25 6.50 ± 3.52 < 0.001 WBC, 10^9/L, M (SD) 6.48 ± 1.83 7.30 ± 2.55 7.18 ± 2.52 8.77 ± 3.67 < 0.001 Lymphocyte, 10^9/L, M (SD) 1.97 ± 0.73 1.92 ± 0.95 1.85 ± 0.65 1.49 ± 0.64 < 0.001 RBC, 10^12/L, M (SD) 4.51 ± 0.58 4.43 ± 0.62 4.42 ± 0.65 4.23 ± 0.84 < 0.001 Platelet, 10^9/L, M (SD) 218.79 ± 57.53 222.87 ± 70.99 222.73 ± 70.25 224.73 ± 81.19 0.295 TC, mmol/L, M (SD) 4.19 ± 1.43 4.29 ± 1.13 4.33 ± 1.11 4.24 ± 1.19 0.126 GFR, mL/min, M (SD) 78.93 ± 23.57 75.33 ± 23.71 72.90 ± 22.78 71.63 ± 21.75 < 0.001 Blood glucose, mmol/L, M (SD) 5.97 ± 2.41 6.14 ± 2.50 5.91 ± 2.33 6.40 ± 3.10 0.002 NLR, MED (IQR) 1.93 (1.45,2.62) 2.35 (1.63, 3.33) 2.22 (1.73,3.18) 3.81 (2.61,6.72) < 0.001 Hs-CRP, mg/L, MED (IQR) 0.80 (0.44, 1.28) 4.61 (2.97, 8.42) 24.35 (15.06,24.35) 43.00 (24.35,88.05) < 0.001 Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; LOS, length of stay; EF, ejection fraction; MI, myocardial infarction; CVA, cerebrovascular accident; PCI, percutaneous coronary intervention; CABG, coronary artery bypass graft surgery; DM, diabetes mellitus; CKD, chronic kidney disease; ACS, acute coronary syndrome; AF, atrial fibrillation; PAH, pulmonary artery hypertension; HF, heart failure; CCB, Calcium Channel Blockers; ACEI or ARB or ARNI, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers or angiotensin receptor–neprilysin inhibitors; DAPT, dual antiplatelet therapy; ALP, albumin; Scr, serum creatinine; NE, neutrophiles; WBC, White blood cells; RBC, red blood cells; TC, total cholesterol; GFR,glomeruar filtration rate; NLR, neutrophil to lymphocyte ratio; Hs-CRP, high-sensitivity C-reactive protein IPI as a predictor of CIN risk and clinical outcome In the quartile analysis, the distribution of IPI levels was as follows: ≤0.11 (4.2%), 0.12–0.74 (9.7%), 0.75–1.93 (16.5%), and ≥ 1.94 (22.2%). These results reveal a strong positive correlation between high IPI levels and the risk of CIN. Even after adjusting for baseline confounders in model 2, this difference remained significant ( p = 0.001). Notably, variables such as absolute neutrophil count, white blood cell count, absolute lymphocyte count, and hemoglobin were excluded from the model due to collinearity (variance inflation factor [VIF] > 5). Subgroup analysis indicated that male patients faced a 1.44 times higher risk of CIN compared to females. Moreover, PCI and DAPT were identified as protective factors against CIN ( p < 0.05), while preoperative CKD, pulmonary infection, diuretic use, NYHA class III, and glomerular filtration rate emerged as independent risk factors ( p < 0.05) (Fig. 1 ). Furthermore, a significant positive correlation was observed between patients' IPI levels and all-cause in-hospital mortality (0.2% vs 1.1% vs 1.3% vs 3.4%, p < 0.001), with the highest quartile driving this association. However, multivariate logistic regression analysis failed to confirm this relationship (adjusted odds ratio [aOR] 2.50; 95% CI 0.49–12.78; p = 0.348). Additionally, there were no significant differences in the incidence of repeated revascularization and major bleeding across different IPI levels ( p > 0.05). These conclusions were further supported by adjusted results. In addition, different baseline IPI levels were found to be independently associated with postoperative MACE, including arrhythmia and acute myocardial infarction (AMI), in patients undergoing CAG and/or PCI ( p < 0.001). Specifically, the incidence of arrhythmia was highest among patients with baseline IPI levels in the highest quartile, although no significant difference was initially observed between the groups (3.2%, p > 0.05). However, subsequent multivariate analysis revealed significant differences between the groups (aOR 0.50; 95% CI 0.35–0.69; p < 0.001)(Fig. 2 ). Similarly, baseline IPI levels were independently associated with the risk of AMI in patients (p < 0.001), with this association further confirmed after adjusting for covariates (aOR 3.00; 95% CI 1.89–4.76; p 0.05) (Table 2 ). Table 2 The risk of CIN and clinical outcomes in patients undergoing CAG and/or PCI. Inflammatory Prognostic Index Quartile Quartile 1 (≤ 0.11) Quartile 2 (0.12–0.74) Quartile 3 (0.75–1.93) Quartile 4 (≥ 1.94) P -value In-hospital outcomes Model (n = 835) (n = 835) (n = 835) (n = 835) CIN CIN 35 (4.2) 81 (9.7) 138 (16.5) 185 (22.2) < 0.001 Non-CIN 800 (95.8) 754 (90.3) 697 (83.5) 650 (77.8) NA Model 1 Reference 2.40 (1.59–3.62) 4.35 (2.96–6.40) 6.48 (4.44–9.45) < 0.001 Model 2 Reference 1.67 (1.06–2.64) 2.16 (1.39–3.35) 2.33 (1.50–3.64) 0.001 Repeated revascularization Repeated revascularization 49(5.9) 37 (4.4) 40 (4.8) 30 (3.6) 0.172 Non-Repeated revascularization 786(94.1) 798 (95.6) 795 (95.2) 805 (96.4) NA Model 1 Reference 0.75 (0.48–1.17) 0.83 (0.54–1.27) 0.60 (0.38–0.96) 0.194 Model 2 Reference 0.60 (0.36-1.00) 1.00 (0.59–1.70) 0.63 (0.33–1.20) 0.129 Hematorrhea 大出血 22 (2.6) 23 (2.8) 24 (2.9) 37 (4.4) 0.121 非大出血 813 (97.4) 812 (97.2) 811 (97.1) 789 (95.6) NA Model 1 Reference 1.03 (0.57–1.87) 1.11 (0.62-2.00) 1.71 (1.00-2.93) 0.129 Model 2 Reference 1.01 (0.55–1.88) 0.84 (0.43–1.64) 1.08 (0.55–2.12) 0.873 MACE AMI AMI 138 (16.5) 262 (31.4) 188 (22.5) 269 (32.2) < 0.001 Non-AMI 697 (83.5) 573 (68.6) 647 (77.5) 566 (67.8) NA Model 1 Reference 2.39 (1.89–3.03) 1.54 (1.20–1.96) 2.49 (1.97–3.15) < 0.001 Model 2 Reference 1.97 (1.37–2.83) 1.68 (1.12–2.52) 3.00 (1.89–4.76) < 0.001 Stroke Stroke 94 (11.3) 97 (11.6) 85 (10.2) 101 (12.1) 0.646 Non-Stroke 741 (88.7) 738 (88.4) 750 (89.8) 734 (87.9) NA Model 1 Reference 0.98 (0.72–1.34) 0.90 (0.65–1.23) 1.06 (0.78–1.44) 0.762 Model 2 Reference 1.00 (0.72–1.40) 0.81 (0.56–1.16) 0.95 (0.64–1.40) 0.601 Severe arrhythmia Severe arrhythmia 285 (34.1) 285 (34.1) 273 (32.7) 306 (36.6) 0.392 Non-Severe arrhythmia 550 (65.9) 550 (65.9) 562 (67.3) 529 (63.4) NA Model 1 Reference 0.96 (0.78–1.18) 0.92 (0.75–1.13) 1.09 (0.89–1.34) 0.378 Model 2 Reference 0.73 (0.56–0.95) 0.61 (0.46–0.81) 0.50 (0.35–0.69) < 0.001 Acute heart failure Acute heart failure 1 (0.1) 2 (0.2) 5 (0.6) 16 (1.9) < 0.001 Non-Acute heart failure 834 (99.9) 833 (99.8) 830 (99.4) 819 (98.1) NA Model 1 Reference 1.95 (0.18–21.55) 4.93 (0.57–42.33) 16.03 (2.12–121.20) 0.001 Model 2 Reference 0.98 (0.08–12.24) 1.10 (0.10-12.33) 2.86 (0.28–29.05) 0.457 All-cause in-hospital mortality All-cause in-hospital mortality 2 (0.2) 9 (1.1) 11 (1.3) 28 (3.4) < 0.001 Non-All-cause in-hospital mortality 833 (99.8) 826 (98.9) 824 (98.7) 807 (96.6) NA Model 1 Reference 4.45 (0.96–20.69) 5.52 (1.22–24.98) 14.31 (3.40-60.28) < 0.001 Model 2 Reference 3.89 (0.76–20.04) 1.89 (0.35–10.35) 2.50 (0.49–12.78) 0.348 Long-term outcome All-cause readmission All-cause readmission 387 (46.3) 323 (38.7) 209 (25.0) 207 (24.8) < 0.001 Non-All-cause readmission 448 (53.7) 512 (61.3) 626 (75.0) 628 (75.2) NA Model 1 Reference 1.09 (0.92–1.30) 1.13 (0.93–1.38) 1.47 (1.21–1.79) 0.002 Model 2 Reference 1.03 (0.85–1.25) 1.08 (0.86–1.36) 1.14 (0.88–1.46) 0.780 Abbreviations: CIN, contrast-induced nephropathy; AMI, acute myocardial infarction; MACE, major adverse cardiovascular events; Model 1 was adjusted for age and sex; Model 2 was adjusted for Model 1 plus other risk factors During the 1-year follow-up, 1095 out of 3340 patients were readmitted (32.8%). According to the results of univariate analysis, a higher baseline IPI level was associated with a lower rate of all-cause readmission ( p < 0.001); however, multivariate analysis did not confirm this result. Kaplan-Meier curves for the all-cause readmission rate as shown below(Fig. 4 )(Table 2 ). Discussion This study is the first to examine the link between IPI levels and the risk of CIN and clinical outcomes in patients having CAG and/or PCI. Higher IPI levels were independently associated with increased CIN risk, and baseline IPI levels predicted severe arrhythmia and AMI. These findings suggest that IPI could be a useful prognostic marker in this patient population. CIN is a complication that often arises in patients undergoing CAG and/or PCI, with notable morbidity and lethality 23 . Its development is likely associated with factors such as immunity, oxidative stress, and inflammatory responses 24 . While the exact pathophysiological mechanism remains uncertain, it is hypothesized to involve the cytotoxicity of contrast agents on renal tubules, leading to aggravated renal vasoconstriction, endothelial dysfunction, renal medulla ischemia, and hypoxia, ultimately resulting in renal tubular cell injury and death. Our analysis of 3340 patients undergoing CAG and/or PCI revealed a clear relationship between IPI levels and the risk of CIN. Particularly, patients in the highest quartile of IPI levels, indicative of more severe inflammatory responses, demonstrated a significantly elevated risk of CIN ( p < 0.001). This association persisted even after adjusting for baseline confounders, consistent with prior studies 25 . Additionally, factors such as endothelial dysfunction, oxidative stress, and renal vasoconstriction have been identified as potential contributors to CIN 26 . Furthermore, subgroup analysis indicated that PCI treatment and preoperative use of DAPT were protective against CIN, whereas preoperative CKD and diuretic use increased the risk. These findings are in line with existing research 27 , 28 , suggesting that PCI may improve renal hemodynamics, while diuretic use may exacerbate renal tubular toxicity. Identifying high-risk individuals is crucial for implementing targeted interventions. An increasing body of research suggests that inflammation plays a crucial role in the development and destabilization of atherosclerotic plaques, highlighting its significance in CAD 30 – 32 . Our study, focusing on patients undergoing CAG and/or PCI, revealed a notable association between baseline levels of the IPI and postoperative MACE, including AMI and severe arrhythmia ( p < 0.05). Specifically, patients in the highest quartile of IPI demonstrated the highest incidence of AMI, a finding that remained significant even after adjusting for baseline confounders ( p < 0.001). Subsequent subgroup analysis identified hyperlipidemia and the extent of coronary artery involvement as independent risk factors for AMI, while preoperative DAPT emerged as a protective factor, consistent with previous studies 25 . The severity of the inflammatory response, as reflected by IPI levels, correlates with the magnitude of AMI, which is closely linked to immune-inflammatory responses and oxidative stress 34 . The intensity of the inflammatory response during AMI affects infarct size and subsequent ventricular remodeling 35 , serving as an indicator of AMI severity to some extent. Interestingly, while initially no difference in severe arrhythmia incidence was observed among patients with varying IPI levels ( p > 0.05), this association became significant after adjustment ( p < 0.001). Furthermore, subgroup analysis identified atrial fibrillation as an independent risk factor for severe arrhythmia in patients. However, existing research on the relationship between inflammation and arrhythmia risk presents conflicting conclusions 36 , 37 , likely due to variations in study populations and designs. Therefore, future studies with longer follow-up periods and larger, multicenter samples are needed to obtain more definitive evidence on the association between inflammatory factors and arrhythmias. Coronary stent implantation is the primary method for revascularization in patients with CHD 23 . In-stent restenosis (ISR) is a significant postoperative complication that has attracted considerable attention. The pathogenesis of ISR is complex, involving vascular endothelial cell injury, excessive proliferation and migration of vascular smooth muscle cells (VSMC), in-stent intimal hyperplasia, in-stent thrombosis, and persistent vascular inflammation. Previous studies have reported that 17–32% of patients with stent implantation develop ISR, typically occurring 6 to 12 months after PCI 38 . However, existing research predominantly focuses on the long-term association between individual inflammatory factor levels and ISR, overlooking the exploration of the relationship between IPI levels and repeated revascularization. Our study findings indicate no significant association between IPI and repeated revascularization (p > 0.05). This contrasts with some prior research conclusions 39 , possibly due to population differences in inflammatory responses and the duration of inflammation persistence. Further investigations are necessary to clarify these inconsistencies and comprehensively understand the role of inflammation in ISR and repeated revascularization. After nearly 1 year of follow-up in this study, a significant correlation was observed between IPI and the all-cause readmission rate. However, after adjusting for covariates, Cox regression analysis did not provide further evidence to support this relationship ( p > 0.05). The study findings indicated that the risk of all-cause readmission in male patients was 1.3 times higher than that in female patients ( p > 0.05), consistent with findings from other studies 17 . This could be attributed to the higher mortality rate and incidence of adverse cardiac events among elderly male patients post-PCI compared to females 12 . Additionally, elderly male patients tend to have higher rates of smoking, drinking, and being overweight compared to females. Furthermore, research by domestic scholars has shown that elderly male patients exhibit lower compliance with chronic disease treatment and medication than elderly female patients 33 , leading to a higher risk of all-cause readmission in male patients. Additionally, we observed significantly lower all-cause readmission rates among patients who underwent PCI compared to those who did not ( p < 0.001). This highlights the importance of focusing on patients who do not receive PCI treatment and ensuring timely PCI intervention based on the disease condition. Limitations Our study has limitations. It's a single-center retrospective study, not multicenter. Future research should prioritize multicenter prospective studies for broader insights. Also, we lack long-term follow-up data, focusing mainly on short-term in-hospital prognosis. Lastly, IPI levels may change during hospitalization, so future studies should monitor them dynamically. Despite limitations, our study is the largest to date and the first to explore IPI levels' association with patient prognosis in coronary angiography and/or percutaneous coronary intervention. Conclusion This study is the first to investigate the IPI in patients undergoing CAG and/or PCI. We found that IPI is significantly associated with clinical prognosis, predicting the risk of CIN and postoperative outcomes independently. Its affordability and ease of use make it a valuable tool for early intervention strategies. Moving forward, more structured trials with increased participant involvement are needed to confirm our findings. Declarations Acknowledgments We appreciate the financial support of the Fujian Provincial Finance Department for this study. Author contribution All authors contributed to: (1) substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, and, (3) final approval of the version to be published. Ethics approval Ethical Approval was obtained from the Fujian Medical University Union Hospital ethics committee (2023KY032) prior to data collection. Informed consent Informed consent was provided by all participants. Sources of Funding This study was supported by the fund of Fujian Provincial Clinical Research Center for Cardiovascular Diseases Heart Center of Fujian Medical University[2021-76]. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Data availability The datasets used and analysed during the current study are available from the corresponding author upon reasonable request. References The Writing Committee of the Report on Cardiovascular Health Diseases in China. Interpretation of Report on Cardiovascular Health and Diseases in China 2022[J]. Chin J Cardiovasc Med. 2023,28(04):297–312. Zhou M, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 [published correction appears in Lancet. 2020;396(10243):26]. Lancet. 2019;394(10204):1145–1158. TESTA L, et al.Unprotected left main revascularization:Percutaneous coronary intervention versus coronary artery bypass.An updated systematicreview and meta-analysis of randomised controlled trials[J].PLoS One,2017,12(6):e0179060. PATEL K V,PANDEY A,DE LEMOS J A. Conceptual Framework for Addressing ResidualAtherosclerotic Cardiovascular Disease Risk in the Era of Precision Medicine[J].Circulation,2018,137(24):2551–3. Zuo T, et al. Hyperuricemia and contrast-induced acute kidney injury: A systematic review and meta-analysis[J]. Int J Cardiol, 2016,224:286–294. Azzalini L, Spagnoli V and Ly H Q.Contrast-Induced Nephropathy: From Pathophysiology to Preventive Strategies[J]. Can J Cardiol, 2016, 32(2): 247–55. Barbieri L, et al. Gender Difference in the Risk of Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography or Percutaneous Coronary Intervention. Angiology. 2017;68(6):542–546. Barbieri L, et al. Uric acid levels and the risk of Contrast Induced Nephropathy in patients undergoing coronary angiography or PCI. Nutr Metab Cardiovasc Dis. 2015;25(2):181–186. Wong PCY, Li Z, Guo J, Zhang A. Pathophysiology of contrast- induced nephropathy. Int J Cardiol. 2012;158:186–92. 5. Liu Y, et al. High-sensitivity C-reactive protein predicts contrast-induced nephropathy after primary percutaneous coronary intervention. J Nephrol. 2012;25:332–40. 6. Altun B, et al.The relationship between high-sensitive troponin T, neutrophil lymphocyte ratio and SYNTAX Score[J].Scand J Clin Lab Invest, 2014, 74 (2):108–115. Yu YM, et al. Influence of sex difference on the therapeutic prognosis in aged patients with coronary heart disease receiving percutaneous coronary intervention[J]. Journal of Interventional Radiology, 2018,27(11):1075–1079. WATT D G,HORGAN P G,MCMILLAN D C. Routine clinical markers of the magnitude of the systemic inflammatory response after elective operation:a systematic review[J].Surgery,2015,157(2):362–380. Fracassi F, et al. Optical coherence tomography and C-reactive protein in risk stratification of acute coronary syndromes[J]. Int J Cardiol, 2019, 286:7–12. DIRICAN N, et al. A new inflammatory prognostic index,based on C-reactive protein,the neutrophil to lymphocyte ratio and serum albumin is useful for predicting prognosis in non-small cell lung cancer cases[J].Asian Pacific Journal of Cancer Prevention,2016,17(12):5101–5106. Şaylık F, Çınar T, Akbulut T, Selçuk M. Serum Uric Acid to Albumin Ratio Can Predict Contrast-Induced Nephropathy in ST-Elevation Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention. Angiology. 2023;74(1):70–78. Yang JL. Construction and application of a nomogram for predicting readmission risk within 6 months in elderly patients with coronary heart disease[D].Jiangsu University, 2023. Dirican N, et al. A New Inflammatory Prognostic Index, Based on C-reactive Protein, the Neutrophil to Lymphocyte Ratio and Serum Albumin is Useful for Predicting Prognosis in Non-Small Cell Lung Cancer Cases. Asian Pac J Cancer Prev. 2016;17(12):5101–5106. American Medical Association. The Complete Official Codebook: ICD-10-CM. USA: Optum360, LLC; 2016. Zhu JR, et al. Guidelines for prevention and treatment of dyslipidemia in adults in China (Revised Edition 2016). Chin Circ J. 2016;31:937–53. Expert Group on Early Detection, Gao X, Mei CL. Guideline for screening, diagnosis, prevention and treatment of chronic kidney disease. Chin J Pract Intern Med. 2017;37:28–34. Levey AS, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate [published correction appears in Ann Intern Med. 2008;149(7):519] [published correction appears in Ann Intern Med. 2021;174(4):584]. Ann Intern Med. 2006;145(4):247–254. Cui KY, Lyu SZ, Zhang M, Song XT, Yuan F, Xu F. Drug-Eluting Balloon versus New-Generation Drug-Eluting Stent for the Treatment of In-Stent Restenosis: An Updated Systematic Review and Meta-Analysis. Chin Med J (Engl). 2018;131(5):600–607. He H, Chen XR, Chen YQ, Niu TS, Liao YM. Prevalence and Predictors of Contrast-Induced Nephropathy (CIN) in Patients with ST-Segment Elevation Myocardial Infarction (STEMI) Undergoing Percutaneous Coronary Intervention (PCI): A Meta-Analysis. J Interv Cardiol. 2019;2019:2750173. Published 2019 Aug 25. Zhang H, et al. Predictive Value of Novel Inflammatory Indexes for In-hospital Outcomes of Patients With Acute Myocardial Infarction[J].Chinese Circulation Journal,2023,38(04):414–420. Mccullough PA, et al. Risk prediction of contrast-induced nephropathy[J].Am J Cardiol, 2006, 98 (6A):27K-36K. Karauzum I, et al. The Utility of Systemic Immune-Inflammation Index for Predicting Contrast-Induced Nephropathy in Patients with ST-Segment Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention. Cardiorenal Med. 2022;12(2):71–80. Neyra JA, et al. Contrast-induced acute kidney injury following coronary angiography: a cohort study of hospitalized patients with or without chronic kidney disease. Nephrol Dial Transplant. 2013;28:1463–71. Qiu QN, Li XY, Wang ZF, Ye YR, Yan QR, et al. Risk factors of contrast induced nephropathy in elderly patients with coronary artery disease. Chin J Clin Med. 2022;29: 813–7. Pearson T.A., et al. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003;107(3):499–511. Ishikawa T., et al. Possible contribution of C-reactive protein within coronary plaque to increasing its own plasma levels across coronary circulation. Am. J. Cardiol. 2004;93(5):611–614. Ridker PM. Clinical application of C-reactive protein for cardiovascular disease detection and prevention. Circulation. 2003;107(3):363–369. LAI L, DING SQ, ZHONG ZQ, HU MH, ZHENG F. Research progress of medication literacy in patients with coronary heart disease[J].Chinese Journal of Nursing, 2020,55(8):1276–1280. Cui YL, Yu Y. Relationship and Cinical Significance of Inflammation and Oxidative Stress in Acute Myocardial Infarction[J].China Modern Doctor, 2011,49(31):157–158 + 160. Ong SB, et al. Inflammation following acute myocardial infarction: Multiple players, dynamic roles, and novel therapeutic opportunities. Pharmacol Ther. 2018;186:73–87. Blangy H, et al. Serum BNP, hs-C-reactive protein, procollagen to assess the risk of ventricular tachycardia in ICD recipients after myocardial infarction. Europace. 2007;9(9):724–729. Szydlowski L, Skierska A, Markiewicz-Loskot G, Mazurek B, Morka A, Undas A. The role of Interleukin-6, its – 174 G > C polymorphism and C-reactive protein in idiopathic cardiac arrhythmias in children. Adv Med Sci. 2013;58(2):320–325. Lee M S, Banka G. In-stent Restenosis[J]. Interv Cardiol Clin, 2016,5(2):211–220. Yang YS, et al.The relationship between systemic immune inflammatory index, NHR and in-stent restenosis in patients with coronary heart disease[J].Chinese Journal of Integrative Medicine on Cardio-Cerebrovascular Disease,2023,21(17):3221–3225. Additional Declarations No competing interests reported. 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Fuzhou","correspondingAuthor":false,"prefix":"","firstName":"Baolin","middleName":"","lastName":"Luo","suffix":""},{"id":285017612,"identity":"e02a84a5-1c06-4b9a-aed9-6b3e4789457f","order_by":2,"name":"Yaqin Chen","email":"","orcid":"","institution":"School of Nursing, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yaqin","middleName":"","lastName":"Chen","suffix":""},{"id":285017613,"identity":"74875de9-ec7d-47e2-8689-fa43928e8a38","order_by":3,"name":"Yanchun Peng","email":"","orcid":"","institution":"Department of Nursing, Fujian Medical University Union Hospital, Fuzhou","correspondingAuthor":false,"prefix":"","firstName":"Yanchun","middleName":"","lastName":"Peng","suffix":""},{"id":285017614,"identity":"3123897a-33a9-41f0-a17b-8cf4aafc3bce","order_by":4,"name":"Wen Lu","email":"","orcid":"","institution":"School of Nursing, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Lu","suffix":""},{"id":285017618,"identity":"acabb957-188e-48b4-9aa8-c48ab75f108e","order_by":5,"name":"Liangwan Chen","email":"","orcid":"","institution":"Department of Cardiovascular Surgery, Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liangwan","middleName":"","lastName":"Chen","suffix":""},{"id":285017620,"identity":"09a02b66-b5f7-47dd-85b7-717865f74fe4","order_by":6,"name":"Yanjuan Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIie3QsQrCMBCA4YtCu5y6nrTYV6gUioOgjxIXO7l3rEu65BHEx3BOETrlDVwEZ/eM1vYBPDfB/FOG+wh3AD7fDxZWJgDA1SIIa8MjaHpC2RSt5BLoCNDuTNuUSYTNHxhToQgkuPLCINAWGSIdVHQ0QtvbZ7KBaxv1JDZyLBSDIDTqTYqAZMolwy/yG9Lu5yekpeqO3LB2QbI5PfU6Seq6ubuSQYAMjCZ6eBvGfNesAuEcb9bn8/n+tBewKDZkAojotQAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Cardiovascular Surgery, Fujian Medical University Union Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yanjuan","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-03-14 02:07:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4096614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4096614/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-66880-7","type":"published","date":"2024-07-09T22:02:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53875889,"identity":"7789e213-eaa9-4312-81a6-52270a743f8b","added_by":"auto","created_at":"2024-04-01 16:37:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":318348,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4096614/v1/f91fec4fe3266b53bba5328b.jpg"},{"id":53877390,"identity":"1d1d6f45-ffed-4bc7-ab9c-a7f526766509","added_by":"auto","created_at":"2024-04-01 16:45:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":306209,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4096614/v1/9e4cbac9ab734c8130b53e77.jpg"},{"id":53875890,"identity":"df04dedd-d2da-4c91-9938-41c8103351d4","added_by":"auto","created_at":"2024-04-01 16:37:27","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":358345,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4096614/v1/85c1d5660402eb7a4c4eb12d.jpg"},{"id":53877389,"identity":"66c43aeb-03bf-489c-af80-acd015c4c2ff","added_by":"auto","created_at":"2024-04-01 16:45:27","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":275511,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4096614/v1/f2e986a13a52b65414f94e06.jpg"},{"id":60193661,"identity":"058bf783-6d77-4987-9011-84235271db8d","added_by":"auto","created_at":"2024-07-12 22:02:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2209579,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4096614/v1/8556fd23-d022-4ff4-a085-fd99dc6cde4b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of Inflammatory Prognostic Index for Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography and /or Percutaneous Coronary Intervention","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) is a prevalent chronic condition globally, with China experiencing a steady rise in its incidence, now estimated at 330\u0026nbsp;million patients annually\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Coronary heart disease (CHD) is particularly prevalent and associated with high mortality rates\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Consequently, coronary angiography (CAG) and percutaneous coronary intervention (PCI) are crucial diagnostic and treatment modalities. However, despite PCI's benefits for long-term prognosis, risks like contrast-induced nephropathy and major adverse cardiovascular events (MACE) remain significant\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, contributing to increased mortality and readmission rates.\u003c/p\u003e \u003cp\u003eContrast-induced nephropathy (CIN) refers to acute kidney injury following exposure to iodinated contrast agents. It typically occurs 1\u0026ndash;3 days post-exposure, associated with poorer prognosis including increased hospitalization rates and costs\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Incidence can range from 20\u0026ndash;25% in patients undergoing CAG and/or PCI, reaching 40% in high-risk cohorts\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. While the exact cause of CIN variability remains unclear, enhanced inflammatory response is considered significant\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Identifying inflammatory markers predicting CIN is crucial to mitigate complications. Neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), and serum albumin (ALB) are established indicators of inflammation and atherosclerosis pathogenesis. Combining these into an inflammatory prognostic index (IPI) offers a more comprehensive assessment of inflammatory status. IPI has shown clinical significance in various fields but lacks evidence in predicting CIN and its relationship with clinical outcomes in CAG and/or PCI patients is unclear.\u003c/p\u003e \u003cp\u003eNLR\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, CRP\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and ALB\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e indicate systemic inflammation and atherosclerosis development. However, individual markers lack completeness. Building upon this rationale, some scholars have proposed an IPI\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. IPI combines CRP, NLR, and ALB, offering a better reflection of patients' inflammatory status. Initially used in predicting outcomes for cancer patients\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, its significance in cardiovascular contexts, like cardiac surgery\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, has been recognized. Yet, its predictive value for CIN in CAG and/or PCI patients and its link to clinical outcomes remain unexplored.\u003c/p\u003e \u003cp\u003eThis study aims to investigate the role of IPI in predicting CIN risk and clinical outcomes in patients undergoing CAG and/or PCI, offering a practical predictive method. This could assist healthcare professionals in making more precise decisions, potentially lowering mortality and readmission rates in CHD patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eWe collected retrospective data from 16,759 patients undergoing CAG and/or PCI at Fujian Medical University Union Hospital between May 2017 and December 2022. Exclusion criteria were age\u0026thinsp;\u0026lt;\u0026thinsp;18, missing or limited serum creatinine data, on-pump CABG, contrast agent allergies, ongoing dialysis, and missing data or discharge status. 3,340 patients were included for analysis. The study was approved by the hospital's Ethics Committee and followed the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection and clinical definition\u003c/h2\u003e \u003cp\u003eWe obtained baseline data from the Electronic Medical Records System of Fujian Medical University Union Hospital, which primarily comprised sociodemographic information, admission and discharge diagnoses, results of laboratory and imaging tests, medication details, procedural characteristics, and discharge outcomes. Laboratory tests were conducted at the Laboratory Center of Fujian Medical University Union Hospital. Venous blood samples were collected after a fasting period of over 8 hours, with water deprivation during the final 3\u0026ndash;4 hours of the fast.\u003c/p\u003e \u003cp\u003eHypertension, diabetes mellitus (DM) and stroke were defined using the 10th Revision Codes of the International Classification of Diseases (ICD-10)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The guidelines for the prevention and treatment of dyslipidemia in Chinese adults (2016)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, were used to define the diagnosis of hyperlipemia. The diagnostic criteria of chronic kidney disease (CKD) were based on Guidelines for the Screening, Diagnosis and Prevention of Chronic kidney Disease in China (2017)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, estimated glomerular filtration rate (eGFR)\u0026thinsp;\u0026lt;\u0026thinsp;60mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e and calculated with MDRD formula\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. CIN was defined as an absolute increase 44.2 \u0026micro;mol/L (0.5 mg/dL) or a relative increase of 25% in serum creatinine level from baseline within 48 to 72 h after intravascular use of iodinated contrast agents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements\u003c/h2\u003e \u003cp\u003eVenous blood samples collected within 24 hours of admission were used to measure neutrophils (N), lymphocytes (L), ALB, and CRP. These parameters were used to calculate two inflammatory biomarkers: the neutrophil-to-lymphocyte ratio (NLR\u0026thinsp;=\u0026thinsp;N/L) and the inflammatory prognostic index (IPI\u0026thinsp;=\u0026thinsp;CRP\u0026times;NLR/ALB). Baseline values for other laboratory parameters were determined based on recent preoperative serum creatinine levels and additional indicators.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes measured\u003c/h2\u003e \u003cp\u003eIn-hospital outcomes included CIN risk, repeated revascularization, major bleeding, and MACE. Repeated revascularization meant placing additional stents, excluding the initial one. Major bleeding included intracranial, upper, and lower gastrointestinal hemorrhage. MACE covered postoperative AMI, acute heart failure, stroke, severe arrhythmia, and all-cause in-hospital mortality. AMI was diagnosed with serum enzyme elevation and symptoms. AHF involved sudden symptoms due to cardiac dysfunction. Severe arrhythmias included atrioventricular block, atrial flutter or fibrillation, and ventricular flutter or fibrillation. Data were from discharge summaries and postoperative records. Long-term endpoint was all-cause readmission rate, confirmed through medical records or communication. Follow-up averaged 1 year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn order to assess the impact of IPI levels on clinical outcomes in patients, we divided IPI into four groups based on quartiles. Enumeration data were expressed as numbers and percentage values (%), analyzed using the χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e test.When χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e test test conditions were not met, Fisher exact test was used, and values of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided) were considered significant. Shapiro-Wilk test was used to test the normality of continuous variables. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (for normal distribution) and analyzed using ANOVA analysis, or as median and interquartile range (IQR) for non-normal distribution, the Mann-Whitney U test was used. First, univariate logistic regression analysis was used to determine the potential risk factors for in-hospital mortality (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and then multivariate logistic regression analysis was used to confirm that the previously significant variables were independent factors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate Cox proportional hazard regression and Kaplan-Meier survival curve were used to evaluate all-cause readmission rate. All statistical analyses were performed using SPSS version 26.0 software (IBM, Armonk, New York, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eAmong the 3,340 patients who underwent CAG and/or PCI, the average age was 63.96\u0026thinsp;\u0026plusmn;\u0026thinsp;10.53 years, with 922 (27.6%) being female, and 1950 (58.4%) undergoing PCI. The mean IPI level in these patients was 0.74 [interquartile range: 0.11, 1.94]. Patients were stratified into four groups based on baseline IPI levels (Group 1,\u0026le;0.11, n\u0026thinsp;=\u0026thinsp;835; Group 2, 0.12\u0026ndash;0.74, n\u0026thinsp;=\u0026thinsp;835; Group 3, 0.75\u0026ndash;1.93, n\u0026thinsp;=\u0026thinsp;835; Group 4, \u0026ge;\u0026thinsp;1.94, n\u0026thinsp;=\u0026thinsp;835). Notably, higher IPI levels were associated with increased incidence of valvular heart disease (VHD) (41.7%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), heart failure (HF) (6.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), liver dysfunction (11.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and chronic kidney disease (CKD) (16.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, the incidence of hypertension (50.2%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), diabetes mellitus (DM) (27.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hyperlipidemia (21.2%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and acute coronary syndrome (ACS) (39.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was lower in patients with higher IPI levels. Furthermore, patients in the highest IPI quartile had a higher prevalence of atrial fibrillation (AF) (23.5%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), pulmonary hypertension (17.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and pulmonary infection (65.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and tended to have longer hospital stays (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, they had a lower history of myocardial infarction (2.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PCI (7.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It is worth noting that with increasing IPI levels, patients' admission systolic and diastolic blood pressures, as well as left ventricular ejection fraction (LVEF), were negatively correlated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003ePatients in group 4 exhibited elevated preoperative creatinine levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and baseline urea nitrogen levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, these patients also displayed a tendency toward reduced glomerular filtration rates (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, individuals with higher IPI levels tended to have elevated absolute neutrophil counts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and white blood cell counts (WBC) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but diminished absolute lymphocyte counts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, the NLR was notably higher among subjects with IPI levels in the second and fourth quartiles (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, patients with IPI levels in the highest quartile exhibited decreased red blood cell count (RBC) levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), concomitant with reductions in ALP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and hemoglobin (Hb) levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while CRP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and blood glucose (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) levels increased.\u003c/p\u003e \u003cp\u003ePatients in group 4 had higher levels of preoperative creatinine (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and baseline urea nitrogen (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but these patients also tended to have lower glomerular filtration rate (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Individuals with higher IPI levels tended to have higher absolute neutrophil counts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and white blood cell counts (WBC) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but had lower absolute lymphocyte counts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At the same time, NLR was higher in subjects with IPI levels in the second and fourth quartiles (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, patients with IPI levels in the highest quartile had lower RBC levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), accompanied by a decrease in ALP(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hemoglobin (Hb) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while CRP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and blood glucose (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) levels increased.\u003c/p\u003e \u003cp\u003eIn terms of medication, we observed that the use of diuretics increased in tandem with higher IPI levels. Conversely, the use of other medications such as calcium channel blockers, angiotensin converting enzyme inhibitors, angiotensin receptor blockers, angiotensin receptor enkephalinase inhibitors (ACEI/ARB/ARNI),β-blockers, dual antiplatelet therapy (DAPT), and statins exhibited an inverse relationship with IPI levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding surgical characteristics, it is noteworthy that patients in the highest quartile of IPI levels had the highest number of infarcted arteries (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, patients with IPI levels in the lowest quartile were more likely to receive hydration therapy (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and undergo repeat angiography within seven days (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a greater amount of contrast agent used. Furthermore, there was a significant negative correlation between IPI levels and the likelihood of undergoing PCI (80.4 vs 65.7 vs 49.2 vs 38.2%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that patients with lower IPI levels were more inclined to undergo PCI. More detailed baseline information is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics in patients undergoing CAG and/or PCI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eInflammatory Prognostic Index Quartile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003cp\u003e(\u0026le;\u0026thinsp;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003cp\u003e(0.12\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003cp\u003e(0.75\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003cp\u003e(\u0026ge;\u0026thinsp;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic and clinical characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.49\u0026thinsp;\u0026plusmn;\u0026thinsp;10.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.54\u0026thinsp;\u0026plusmn;\u0026thinsp;10.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e63.77\u0026thinsp;\u0026plusmn;\u0026thinsp;10.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e576 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e622 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e248 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e253 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e599 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e51 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mm Hg, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.80\u0026thinsp;\u0026plusmn;\u0026thinsp;19.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.84\u0026thinsp;\u0026plusmn;\u0026thinsp;19.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e127.22\u0026thinsp;\u0026plusmn;\u0026thinsp;20.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125.55\u0026thinsp;\u0026plusmn;\u0026thinsp;21.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mm Hg, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.28\u0026thinsp;\u0026plusmn;\u0026thinsp;12.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.19\u0026thinsp;\u0026plusmn;\u0026thinsp;12.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e78.08\u0026thinsp;\u0026plusmn;\u0026thinsp;27.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.57\u0026thinsp;\u0026plusmn;\u0026thinsp;13.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e23.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS, days, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.88\u0026thinsp;\u0026plusmn;\u0026thinsp;6.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12.29\u0026thinsp;\u0026plusmn;\u0026thinsp;9.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF (%), M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.68\u0026thinsp;\u0026plusmn;\u0026thinsp;9.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.41\u0026thinsp;\u0026plusmn;\u0026thinsp;11.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e59.55\u0026thinsp;\u0026plusmn;\u0026thinsp;12.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.32\u0026thinsp;\u0026plusmn;\u0026thinsp;13.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior MI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e27 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior CVA, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e40 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior PCI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e10 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior CABG, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e96 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e533 (63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e492 (58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e430 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e419 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e231(27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e229 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e189 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e86 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic insufficiency, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e65 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACS, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e580 (69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e509 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e354 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e332 (39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary infection, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e420 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e546 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValvular heart disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e279 (33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e348 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e146 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e196 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAH, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e130 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e149 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e23 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications before procedures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretics, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e342 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e452 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCB, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e167 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI/ARB/ARNI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e211 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e189 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-blockers, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e470 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e426 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e408 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e340 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e715 (85.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e588 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e469 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e370 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAPT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e725 (86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e589 (70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e444 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e353 (42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcedural characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e671 (80.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e549(65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e411(49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e319(38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of infarcted arteries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStent number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydration\u0026nbsp;therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e806 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e764 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e732 (87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e743 (89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepeated angiography(7 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e689 (82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e586 (70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e463 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e367 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContrast volume, ml, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e347.97\u0026thinsp;\u0026plusmn;\u0026thinsp;143.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e305.69\u0026thinsp;\u0026plusmn;\u0026thinsp;165.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e301.45\u0026thinsp;\u0026plusmn;\u0026thinsp;153.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e254.60\u0026thinsp;\u0026plusmn;\u0026thinsp;174.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline chemistry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP, g/L, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e39.35\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.86\u0026thinsp;\u0026plusmn;\u0026thinsp;5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUREA, mmol/L, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr, \u0026micro;mol/L, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.29\u0026thinsp;\u0026plusmn;\u0026thinsp;29.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.78\u0026thinsp;\u0026plusmn;\u0026thinsp;48.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e91.83\u0026thinsp;\u0026plusmn;\u0026thinsp;70.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106.11\u0026thinsp;\u0026plusmn;\u0026thinsp;104.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.01\u0026thinsp;\u0026plusmn;\u0026thinsp;17.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133.50\u0026thinsp;\u0026plusmn;\u0026thinsp;17.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e132.33\u0026thinsp;\u0026plusmn;\u0026thinsp;18.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125.53\u0026thinsp;\u0026plusmn;\u0026thinsp;22.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNE, 10^9/L, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.50\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, 10^9/L, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte, 10^9/L, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC, 10^12/L, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet, 10^9/L, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218.79\u0026thinsp;\u0026plusmn;\u0026thinsp;57.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222.87\u0026thinsp;\u0026plusmn;\u0026thinsp;70.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e222.73\u0026thinsp;\u0026plusmn;\u0026thinsp;70.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e224.73\u0026thinsp;\u0026plusmn;\u0026thinsp;81.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFR, mL/min, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.93\u0026thinsp;\u0026plusmn;\u0026thinsp;23.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.33\u0026thinsp;\u0026plusmn;\u0026thinsp;23.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e72.90\u0026thinsp;\u0026plusmn;\u0026thinsp;22.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.63\u0026thinsp;\u0026plusmn;\u0026thinsp;21.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose, mmol/L, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR, MED (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93 (1.45,2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35 (1.63, 3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.22 (1.73,3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.81 (2.61,6.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs-CRP, mg/L, MED (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80 (0.44, 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.61 (2.97, 8.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e24.35 (15.06,24.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.00 (24.35,88.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; LOS, length of stay; EF, ejection fraction; MI, myocardial infarction; CVA, cerebrovascular accident; PCI, percutaneous coronary intervention; CABG, coronary artery bypass graft surgery; DM, diabetes mellitus; CKD, chronic kidney disease; ACS, acute coronary syndrome; AF, atrial fibrillation; PAH, pulmonary artery hypertension; HF, heart failure; CCB, Calcium Channel Blockers; ACEI or ARB or ARNI, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers or angiotensin receptor\u0026ndash;neprilysin inhibitors; DAPT, dual antiplatelet therapy; ALP, albumin; Scr, serum creatinine; NE, neutrophiles; WBC, White blood cells; RBC, red blood cells; TC, total cholesterol; GFR,glomeruar filtration rate; NLR, neutrophil to lymphocyte ratio; Hs-CRP, high-sensitivity C-reactive protein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIPI as a predictor of CIN risk and clinical outcome\u003c/h2\u003e \u003cp\u003eIn the quartile analysis, the distribution of IPI levels was as follows: \u0026le;0.11 (4.2%), 0.12\u0026ndash;0.74 (9.7%), 0.75\u0026ndash;1.93 (16.5%), and \u0026ge;\u0026thinsp;1.94 (22.2%). These results reveal a strong positive correlation between high IPI levels and the risk of CIN. Even after adjusting for baseline confounders in model 2, this difference remained significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Notably, variables such as absolute neutrophil count, white blood cell count, absolute lymphocyte count, and hemoglobin were excluded from the model due to collinearity (variance inflation factor [VIF]\u0026thinsp;\u0026gt;\u0026thinsp;5). Subgroup analysis indicated that male patients faced a 1.44 times higher risk of CIN compared to females. Moreover, PCI and DAPT were identified as protective factors against CIN (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while preoperative CKD, pulmonary infection, diuretic use, NYHA class III, and glomerular filtration rate emerged as independent risk factors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, a significant positive correlation was observed between patients' IPI levels and all-cause in-hospital mortality (0.2% vs 1.1% vs 1.3% vs 3.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the highest quartile driving this association. However, multivariate logistic regression analysis failed to confirm this relationship (adjusted odds ratio [aOR] 2.50; 95% CI 0.49\u0026ndash;12.78; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.348). Additionally, there were no significant differences in the incidence of repeated revascularization and major bleeding across different IPI levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These conclusions were further supported by adjusted results.\u003c/p\u003e \u003cp\u003eIn addition, different baseline IPI levels were found to be independently associated with postoperative MACE, including arrhythmia and acute myocardial infarction (AMI), in patients undergoing CAG and/or PCI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, the incidence of arrhythmia was highest among patients with baseline IPI levels in the highest quartile, although no significant difference was initially observed between the groups (3.2%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, subsequent multivariate analysis revealed significant differences between the groups (aOR 0.50; 95% CI 0.35\u0026ndash;0.69; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, baseline IPI levels were independently associated with the risk of AMI in patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with this association further confirmed after adjusting for covariates (aOR 3.00; 95% CI 1.89\u0026ndash;4.76; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, there was no significant difference observed between IPI levels and the risk of stroke (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe risk of CIN and clinical outcomes in patients undergoing CAG and/or PCI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eInflammatory Prognostic Index Quartile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003cp\u003e(\u0026le;\u0026thinsp;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003cp\u003e(0.12\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003cp\u003e(0.75\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003cp\u003e(\u0026ge;\u0026thinsp;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIn-hospital outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCIN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e185 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-CIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e800 (95.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e754 (90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e697 (83.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e650 (77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40 (1.59\u0026ndash;3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.35 (2.96\u0026ndash;6.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.48 (4.44\u0026ndash;9.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67 (1.06\u0026ndash;2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.16 (1.39\u0026ndash;3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.33 (1.50\u0026ndash;3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRepeated revascularization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRepeated revascularization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Repeated revascularization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e786(94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e798 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e795 (95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e805 (96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.48\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83 (0.54\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60 (0.38\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60 (0.36-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.59\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63 (0.33\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHematorrhea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e大出血\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e非大出血\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e813 (97.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e812 (97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e811 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e789 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.57\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 (0.62-2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.71 (1.00-2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.55\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84 (0.43\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08 (0.55\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMACE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e188 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e269 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-AMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e697 (83.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e573 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e647 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e566 (67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.39 (1.89\u0026ndash;3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54 (1.20\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.49 (1.97\u0026ndash;3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.97 (1.37\u0026ndash;2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68 (1.12\u0026ndash;2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.00 (1.89\u0026ndash;4.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e741 (88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e738 (88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e750 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e734 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.72\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 (0.65\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06 (0.78\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.72\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81 (0.56\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95 (0.64\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere arrhythmia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere arrhythmia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e285 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e273 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e306 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Severe arrhythmia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e550 (65.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e550 (65.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e562 (67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e529 (63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.78\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92 (0.75\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09 (0.89\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 (0.56\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61 (0.46\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50 (0.35\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Acute heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e834 (99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e833 (99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e830 (99.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e819 (98.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.95 (0.18\u0026ndash;21.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.93 (0.57\u0026ndash;42.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.03 (2.12\u0026ndash;121.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.08\u0026ndash;12.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10 (0.10-12.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.86 (0.28\u0026ndash;29.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause in-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll-cause in-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-All-cause in-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e833 (99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e826 (98.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e824 (98.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e807 (96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.45 (0.96\u0026ndash;20.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.52 (1.22\u0026ndash;24.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.31 (3.40-60.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.89 (0.76\u0026ndash;20.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.89 (0.35\u0026ndash;10.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.50 (0.49\u0026ndash;12.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLong-term outcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause\u0026nbsp;readmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll-cause\u0026nbsp;readmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e387 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e209 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e207 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-All-cause\u003c/p\u003e \u003cp\u003ereadmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e448 (53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e512 (61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e626 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e628 (75.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.92\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13 (0.93\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.47 (1.21\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.85\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 (0.86\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14 (0.88\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: CIN, contrast-induced nephropathy; AMI, acute myocardial infarction; MACE, major adverse cardiovascular events; Model 1 was adjusted for age and sex; Model 2 was adjusted for Model 1 plus other risk factors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring the 1-year follow-up, 1095 out of 3340 patients were readmitted (32.8%). According to the results of univariate analysis, a higher baseline IPI level was associated with a lower rate of all-cause readmission (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); however, multivariate analysis did not confirm this result. Kaplan-Meier curves for the all-cause readmission rate as shown below(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to examine the link between IPI levels and the risk of CIN and clinical outcomes in patients having CAG and/or PCI. Higher IPI levels were independently associated with increased CIN risk, and baseline IPI levels predicted severe arrhythmia and AMI. These findings suggest that IPI could be a useful prognostic marker in this patient population.\u003c/p\u003e \u003cp\u003eCIN is a complication that often arises in patients undergoing CAG and/or PCI, with notable morbidity and lethality\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Its development is likely associated with factors such as immunity, oxidative stress, and inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. While the exact pathophysiological mechanism remains uncertain, it is hypothesized to involve the cytotoxicity of contrast agents on renal tubules, leading to aggravated renal vasoconstriction, endothelial dysfunction, renal medulla ischemia, and hypoxia, ultimately resulting in renal tubular cell injury and death. Our analysis of 3340 patients undergoing CAG and/or PCI revealed a clear relationship between IPI levels and the risk of CIN. Particularly, patients in the highest quartile of IPI levels, indicative of more severe inflammatory responses, demonstrated a significantly elevated risk of CIN (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association persisted even after adjusting for baseline confounders, consistent with prior studies\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Additionally, factors such as endothelial dysfunction, oxidative stress, and renal vasoconstriction have been identified as potential contributors to CIN\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Furthermore, subgroup analysis indicated that PCI treatment and preoperative use of DAPT were protective against CIN, whereas preoperative CKD and diuretic use increased the risk. These findings are in line with existing research\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, suggesting that PCI may improve renal hemodynamics, while diuretic use may exacerbate renal tubular toxicity. Identifying high-risk individuals is crucial for implementing targeted interventions.\u003c/p\u003e \u003cp\u003eAn increasing body of research suggests that inflammation plays a crucial role in the development and destabilization of atherosclerotic plaques, highlighting its significance in CAD\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Our study, focusing on patients undergoing CAG and/or PCI, revealed a notable association between baseline levels of the IPI and postoperative MACE, including AMI and severe arrhythmia (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, patients in the highest quartile of IPI demonstrated the highest incidence of AMI, a finding that remained significant even after adjusting for baseline confounders (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subsequent subgroup analysis identified hyperlipidemia and the extent of coronary artery involvement as independent risk factors for AMI, while preoperative DAPT emerged as a protective factor, consistent with previous studies\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The severity of the inflammatory response, as reflected by IPI levels, correlates with the magnitude of AMI, which is closely linked to immune-inflammatory responses and oxidative stress\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The intensity of the inflammatory response during AMI affects infarct size and subsequent ventricular remodeling\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, serving as an indicator of AMI severity to some extent. Interestingly, while initially no difference in severe arrhythmia incidence was observed among patients with varying IPI levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), this association became significant after adjustment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, subgroup analysis identified atrial fibrillation as an independent risk factor for severe arrhythmia in patients. However, existing research on the relationship between inflammation and arrhythmia risk presents conflicting conclusions\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, likely due to variations in study populations and designs. Therefore, future studies with longer follow-up periods and larger, multicenter samples are needed to obtain more definitive evidence on the association between inflammatory factors and arrhythmias.\u003c/p\u003e \u003cp\u003eCoronary stent implantation is the primary method for revascularization in patients with CHD\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In-stent restenosis (ISR) is a significant postoperative complication that has attracted considerable attention. The pathogenesis of ISR is complex, involving vascular endothelial cell injury, excessive proliferation and migration of vascular smooth muscle cells (VSMC), in-stent intimal hyperplasia, in-stent thrombosis, and persistent vascular inflammation. Previous studies have reported that 17\u0026ndash;32% of patients with stent implantation develop ISR, typically occurring 6 to 12 months after PCI\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, existing research predominantly focuses on the long-term association between individual inflammatory factor levels and ISR, overlooking the exploration of the relationship between IPI levels and repeated revascularization. Our study findings indicate no significant association between IPI and repeated revascularization (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This contrasts with some prior research conclusions\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, possibly due to population differences in inflammatory responses and the duration of inflammation persistence. Further investigations are necessary to clarify these inconsistencies and comprehensively understand the role of inflammation in ISR and repeated revascularization.\u003c/p\u003e \u003cp\u003eAfter nearly 1 year of follow-up in this study, a significant correlation was observed between IPI and the all-cause readmission rate. However, after adjusting for covariates, Cox regression analysis did not provide further evidence to support this relationship (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The study findings indicated that the risk of all-cause readmission in male patients was 1.3 times higher than that in female patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), consistent with findings from other studies\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This could be attributed to the higher mortality rate and incidence of adverse cardiac events among elderly male patients post-PCI compared to females\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Additionally, elderly male patients tend to have higher rates of smoking, drinking, and being overweight compared to females. Furthermore, research by domestic scholars has shown that elderly male patients exhibit lower compliance with chronic disease treatment and medication than elderly female patients\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, leading to a higher risk of all-cause readmission in male patients. Additionally, we observed significantly lower all-cause readmission rates among patients who underwent PCI compared to those who did not (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This highlights the importance of focusing on patients who do not receive PCI treatment and ensuring timely PCI intervention based on the disease condition.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur study has limitations. It's a single-center retrospective study, not multicenter. Future research should prioritize multicenter prospective studies for broader insights. Also, we lack long-term follow-up data, focusing mainly on short-term in-hospital prognosis. Lastly, IPI levels may change during hospitalization, so future studies should monitor them dynamically. Despite limitations, our study is the largest to date and the first to explore IPI levels' association with patient prognosis in coronary angiography and/or percutaneous coronary intervention.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study is the first to investigate the IPI in patients undergoing CAG and/or PCI. We found that IPI is significantly associated with clinical prognosis, predicting the risk of CIN and postoperative outcomes independently. Its affordability and ease of use make it a valuable tool for early intervention strategies. Moving forward, more structured trials with increased participant involvement are needed to confirm our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the financial support of the\u0026nbsp;Fujian Provincial Finance Department\u0026nbsp;for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to: (1) substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, and, (3) final approval of the version to be published.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical Approval was obtained from the Fujian Medical University Union Hospital ethics committee (2023KY032) prior to data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was provided by all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of Funding\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was supported by the fund of\u0026nbsp;Fujian Provincial Clinical Research Center for Cardiovascular Diseases Heart Center of Fujian Medical University[2021-76].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Conflicting Interests \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThe Writing Committee of the Report on Cardiovascular Health Diseases in China. Interpretation of Report on Cardiovascular Health and Diseases in China 2022[J]. Chin J Cardiovasc Med. 2023,28(04):297\u0026ndash;312.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou M, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017 [published correction appears in Lancet. 2020;396(10243):26]. Lancet. 2019;394(10204):1145\u0026ndash;1158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTESTA L, et al.Unprotected left main revascularization:Percutaneous coronary intervention versus coronary artery bypass.An updated systematicreview and meta-analysis of randomised controlled trials[J].PLoS One,2017,12(6):e0179060.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePATEL K V,PANDEY A,DE LEMOS J A. Conceptual Framework for Addressing ResidualAtherosclerotic Cardiovascular Disease Risk in the Era of Precision Medicine[J].Circulation,2018,137(24):2551\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo T, et al. Hyperuricemia and contrast-induced acute kidney injury: A systematic review and meta-analysis[J]. Int J Cardiol, 2016,224:286\u0026ndash;294.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzzalini L, Spagnoli V and Ly H Q.Contrast-Induced Nephropathy: From Pathophysiology to Preventive Strategies[J]. Can J Cardiol, 2016, 32(2): 247\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbieri L, et al. Gender Difference in the Risk of Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography or Percutaneous Coronary Intervention. Angiology. 2017;68(6):542\u0026ndash;546.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbieri L, et al. 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Inflammation following acute myocardial infarction: Multiple players, dynamic roles, and novel therapeutic opportunities. Pharmacol Ther. 2018;186:73\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlangy H, et al. Serum BNP, hs-C-reactive protein, procollagen to assess the risk of ventricular tachycardia in ICD recipients after myocardial infarction. Europace. 2007;9(9):724\u0026ndash;729.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzydlowski L, Skierska A, Markiewicz-Loskot G, Mazurek B, Morka A, Undas A. The role of Interleukin-6, its \u0026ndash;\u0026thinsp;174 G\u0026thinsp;\u0026gt;\u0026thinsp;C polymorphism and C-reactive protein in idiopathic cardiac arrhythmias in children. Adv Med Sci. 2013;58(2):320\u0026ndash;325.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee M S, Banka G. In-stent Restenosis[J]. Interv Cardiol Clin, 2016,5(2):211\u0026ndash;220.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang YS, et al.The relationship between systemic immune inflammatory index, NHR and in-stent restenosis in patients with coronary heart disease[J].Chinese Journal of Integrative Medicine on Cardio-Cerebrovascular Disease,2023,21(17):3221\u0026ndash;3225.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Inflammatory Prognostic Index, Prognosis, Coronary Angiography, Percutaneous Coronary Intervention","lastPublishedDoi":"10.21203/rs.3.rs-4096614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4096614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eThe purpose of this study was to investigate the relationship between IPI levels and Contrast-Induced Nephropathy (CIN) risk and postoperative clinical outcomes in patients undergoing coronary angiography (CAG) and/ or percutaneous coronary intervention (PCI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 3,340 consecutive patients who underwent CAG and/or PCI between May 2017 and December 2022 were enrolled in this study. Based on their baseline IPI levels, patients were categorized into four groups. Clinical characteristics and postoperative outcomes were compared among these groups. In-hospital outcomes focused on CIN risk, repeated revascularization, major bleeding, and major adverse cardiovascular events (MACE), while the long-term outcome examined the all-cause readmission rate.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eQuartile analysis found a significant link between IPI levels and CIN risk, notably in the highest quartile (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Even after adjusting for baseline factors, this association remained significant, with an adjusted Odds Ratio (aOR) of 2.33 (95%CI 1.50\u0026ndash;3.64; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Notably, baseline IPI level emerged as an independent predictor of severe arrhythmia, with aOR of 0.50 (95%CI 0.35\u0026ndash;0.69; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), particularly driven by the highest quartile. Furthermore, a significant correlation between IPI and acute myocardial infarction was observed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which remained significant post-adjustment.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFor patients undergoing CAG and/or PCI, baseline IPI levels can independently predict clinical prognosis. As a comprehensive inflammation indicator, IPI effectively identifies high-risk patients post-procedure. This study underscores IPI's potential to assist medical professionals in making more precise clinical decisions, ultimately reducing mortality and readmission rates linked to cardiovascular disease (CVD).\u003c/p\u003e","manuscriptTitle":"Predictive Value of Inflammatory Prognostic Index for Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography and /or Percutaneous Coronary Intervention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 16:37:22","doi":"10.21203/rs.3.rs-4096614/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-08T07:18:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-07T08:19:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"aa1fe711-5b18-4d03-ad0c-446fe7e26a72","date":"2024-04-20T12:28:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-20T11:28:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"dd1b0aec-a4c5-4cb6-9308-0d464fa6481e","date":"2024-04-19T13:05:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-09T14:29:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-08T06:03:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-28T09:16:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-26T08:50:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-14T02:06:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b901a7f-a07a-46cf-931c-9b0532afee83","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30006186,"name":"Health sciences/Cardiology/Cardiovascular biology"},{"id":30006187,"name":"Health sciences/Cardiology/Interventional cardiology"},{"id":30006188,"name":"Health sciences/Diseases/Cardiovascular diseases/Acute coronary syndromes/Myocardial infarction"}],"tags":[],"updatedAt":"2024-07-12T22:02:34+00:00","versionOfRecord":{"articleIdentity":"rs-4096614","link":"https://doi.org/10.1038/s41598-024-66880-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-07-09 22:02:34","publishedOnDateReadable":"July 9th, 2024"},"versionCreatedAt":"2024-04-01 16:37:22","video":"","vorDoi":"10.1038/s41598-024-66880-7","vorDoiUrl":"https://doi.org/10.1038/s41598-024-66880-7","workflowStages":[]},"version":"v1","identity":"rs-4096614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4096614","identity":"rs-4096614","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}