Predictive Value of the Atherogenic Index of Plasma and Systemic Inflammation Response Index for Contrast-Induced Acute Kidney Injury in STEMI Patients Undergoing PCI

Predictive Value of the Atherogenic Index of Plasma and Systemic Inflammation Response Index for Contrast-Induced Acute Kidney Injury in STEMI Patients Undergoing PCI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive Value of the Atherogenic Index of Plasma and Systemic Inflammation Response Index for Contrast-Induced Acute Kidney Injury in STEMI Patients Undergoing PCI Jiahui Ding, Xishen Zhang, Jingkun Jin, Luhong Xu, Jing Zong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7936011/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aimed to investigate the association of the atherogenic index of plasma (AIP) and systemic inflammation response index (SIRI) with contrast-induced acute kidney injury (CI-AKI) in ST-segment elevation myocardial infarction (STEMI) patients undergoing emergency percutaneous coronary intervention (PCI), and to develop a predictive nomogram. Methods We retrospectively analyzed 1080 STEMI patients who underwent emergency PCI. Patients were randomly divided into a training cohort (n = 756) and a validation cohort (n = 324) in a 7:3 ratio. Based on the ESUR criteria, the training cohort was categorized into CI-AKI (n = 136) and non-CI-AKI (n = 620) groups. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors. A nomogram was constructed and validated using ROC curves, calibration plots, and decision curve analysis (DCA). The dose-response relationships were examined using restricted cubic splines (RCS). Results AIP and SIRI levels were significantly higher in the CI-AKI group (both P < 0.001). Multivariate analysis identified AIP (OR = 8.74, 95% CI: 4.53–16.87), SIRI (OR = 1.28, 95% CI: 1.18–1.39), chronic kidney disease, and diuretic use as independent risk factors for CI-AKI. The nomogram incorporating these factors achieved AUCs of 0.853 and 0.873 in the training and validation sets, respectively, with good calibration and clinical utility. RCS analysis revealed a nonlinear dose-response relationship between AIP/SIRI and CI-AKI risk. The combination of AIP and SIRI demonstrated superior predictive performance (AUC = 0.817) than either index alone ( P < 0.001). Conclusion AIP and SIRI are independent risk factors for CI-AKI in STEMI patients after PCI. Their combination improved discrimination. The constructed nomogram provides a practical tool for early risk assessment and identification of high-risk patients. Systemic inflammation response index Atherogenic index of plasma Contrast-induced acute kidney injury Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Acute ST-segment elevation myocardial infarction (STEMI), characterized by myocardial necrosis due to acute coronary artery occlusion, requires prompt reperfusion therapy to improve clinical outcomes[ 1 – 2 ]. Percutaneous coronary intervention (PCI) enables rapid revascularization of the infarct-related artery, effectively restoring myocardial perfusion and reducing mortality[ 3 – 4 ]. However, the administration of iodinated contrast media during PCI can lead to contrast-induced acute kidney injury (CI-AKI)[ 5 – 6 ]. Among STEMI patients, the incidence of CI-AKI reaches 20%–30%, significantly prolonging hospital stays, increasing healthcare costs, and being strongly associated with elevated long-term mortality. It now represents a major cause of iatrogenic renal injury[ 7 – 8 ]. Furthermore, patients who develop CI-AKI face a heightened risk of progressive chronic kidney disease, dialysis dependence, and major adverse cardiovascular events, underscoring its critical long-term consequences. Patients with STEMI are particularly vulnerable to CI-AKI due to a confluence of factors, including hemodynamic instability, systemic inflammatory response triggered by myocardial necrosis, and frequent comorbidities such as diabetes, hypertension, and chronic kidney disease (CKD) which can compromise renal perfusion and tubular repair mechanisms[ 9 – 10 ]. Therefore, early and accurate identification of STEMI patients at high risk for CI-AKI is crucial for implementing timely preventive strategies to reduce both morbidity and mortality. Current risk prediction models for CI-AKI, such as the Mehran score, primarily rely on static parameters including baseline creatinine, age, and diabetes status. These models fail to dynamically capture acute-phase inflammatory activation and disorders of lipid metabolism[ 11 ]. Recent studies have shown that the systemic inflammation response index (SIRI), integrating neutrophil, monocyte, and lymphocyte counts, dynamically reflects the imbalance between pro-inflammatory and anti-inflammatory pathways and is strongly associated with adverse outcomes in patients with coronary artery disease[ 12 ], as these inflammatory cells release mediators that can directly promote renal vascular endothelial injury, microvascular thrombosis, and oxidative stress, providing a clear rationale for its relevance in CI-AKI prediction. The atherogenic index of plasma (AIP), a novel metric of lipid metabolism, not only quantifies impaired reverse cholesterol transport but also correlates with coronary artery calcification, demonstrating superior predictive value compared to conventional lipid parameters[ 13 – 14 ]. Physiologically, AIP reflects the abundance of small, dense LDL particles and triglyceride-rich remnant lipoproteins, which are prone to deposition in the glomeruli and renal tubules. This can induce podocyte apoptosis, tubular epithelial cell damage, and intra-renal inflammation, thereby creating a substrate for heightened susceptibility to contrast-mediated injury. However, the synergistic predictive value of AIP and SIRI for CI-AKI in STEMI patients remains unexplored. Therefore, this study aims to investigate the association of AIP and SIRI with CI-AKI following PCI in this population and to develop a nomogram prediction model, thereby offering new insights for the early prediction of CI-AKI. Materials and Methods Study Population A total of 1080 STEMI patients who underwent emergency PCI at the Cardiovascular Intervention Center of the Affiliated Hospital of Xuzhou Medical University between March 2022 and March 2025 were retrospectively enrolled. The data collection and extraction were completed in July 2025, and the manuscript was prepared thereafter. The current analysis represents the final and complete dataset for this cohort. In accordance with standardized international research protocols, the inclusion criteria were: (1) symptom onset to first medical contact time ≤ 12 hours; (2) meeting indications for emergency revascularization and signed informed consent; (3) fulfilling diagnostic criteria specified in the 2019 Guidelines for the Diagnosis and Treatment of Acute ST-Segment Elevation Myocardial Infarction issued by the Chinese Society of Cardiology[ 15 ]. Exclusion criteria included: (1) baseline estimated glomerular filtration rate (eGFR) 10 mg/L) or autoimmune diseases; (4) New York Heart Association (NYHA) functional class IV; (5) concurrent solid tumors or hematological malignancies; (6) exposure to nephrotoxic agents during the perioperative period (48 hours pre-procedure to 72 hours post-procedure); (7) absence of essential clinical data. Peri-procedural Management All patients received a standardized hydration regimen to mitigate the risk of nephropathy. This comprised an intravenous infusion of 0.9% isotonic saline, initiated 1 hour before the procedure at a rate of 1 mL/kg/h and continued for at least 6 hours thereafter. For patients with left ventricular ejection fraction (LVEF) < 40% or clinical signs of heart failure, the infusion rate was reduced to 0.5 mL/kg/h under continuous hemodynamic monitoring. The low-osmolar, non-ionic contrast agent Iodixanol (Visipaque™, 320 mg I/mL) was used in all procedures, with the total volume administered meticulously recorded for each patient at the discretion of the operating interventional cardiologist. Data Collection and Laboratory Measurements Baseline renal function was assessed using serum creatinine (SCr) and estimated glomerular filtration rate (eGFR) values obtained from venous blood samples collected within 24 hours preceding the PCI procedure. For the calculation of AIP and SIRI, venous blood samples were collected in EDTA-anticoagulant tubes within 15 minutes of the patient's arrival in the catheterization laboratory, prior to contrast agent administration. It should be noted that, given the emergency setting of PCI, blood sampling was performed in a non-fasting state. Complete blood count (CBC), including neutrophil, lymphocyte, and monocyte counts, was performed on an automated hematology analyzer (Sysmex XN-9000). The lipid profile, including triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C), was measured using enzymatic methods on a clinical chemistry analyzer (Beckman Coulter AU5800). All laboratory analyses were conducted in the hospital's central laboratory, which participates in regular internal and external quality control programs. Follow-up blood samples for SCr measurement were collected from all patients at 48 and 72 hours post-PCI to monitor for the development of CI-AKI. The calculation formulas for SIRI and AIP are as follows: SIRI = (monocyte count × neutrophil count) / lymphocyte count[ 16 ]; AIP = log10(TG/HDL-C)[ 17 ]. Interventional Procedure and Medication All percutaneous coronary interventions were performed by a team of experienced cardiology specialists using a digital subtraction angiography (DSA) system. The antithrombotic strategy included a pre-procedural loading dose of aspirin 300 mg combined with either clopidogrel 300 mg or ticagrelor 180 mg. Following the procedure, patients were maintained on dual antiplatelet therapy with aspirin 100 mg/day plus either clopidogrel 75 mg/day or ticagrelor 90 mg twice daily. Post-procedural angiographic data were assessed independently by two radiologists who were blinded to the patients' clinical information. Statistical Analysis Statistical analyses in this study were performed using SPSS 26.0 and R.4.3.2 software. A post-hoc power analysis confirmed that the achieved statistical power exceeded 99% for detecting the observed effect at a significance level of α = 0.05. Normality of continuous variables was assessed using the Shapiro-Wilk test. Normally distributed data are presented as mean ± standard deviation ( x̄±s ), and intergroup comparisons were performed using independent samples t -tests. Non-normally distributed data are described as median (interquartile range) [M(Q1,Q3)], and intergroup differences were analyzed using the Mann-Whitney U test. Categorical variables are expressed as frequency (percentage) [ n (%)], and intergroup comparisons were made using the Chi-square test or Fisher's exact test. Missing data were handled with mean/median imputation for continuous variables (missing rate < 5%) and mode imputation for categorical variables, while extreme outliers were managed using winsorization. Independent risk factors for CI-AKI were identified through multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves were used to evaluate the predictive efficacy of SIRI, AIP, and their combination for CI-AKI. Dose-response relationships between SIRI, AIP and CI-AKI were analyzed using restricted cubic splines (RCS). A nomogram was constructed based on the final model, and the model's clinical net benefit was evaluated using the Hosmer-Lemeshow test, calibration curve, and decision curve analysis (DCA). A P -value < 0.05 was considered statistically significant. Results Comparison of Baseline Clinical Characteristics and Preoperative Laboratory Data between CI-AKI and non-CI-AKI Groups A total of 1,080 eligible patients, including 822 males and 258 females, were included in the analysis, and the cohort was randomly split into a training set (n = 756) and a validation set (n = 324) at a 7:3 ratio. Figure 1 shows the flowchart. This ratio was chosen to optimally balance two key requirements: ensuring a sufficiently large training set for robust and stable model development, while simultaneously reserving an adequate proportion of patients in the validation set to provide a rigorous and unbiased evaluation of the model's generalizability. This split strategy is a widely adopted and methodologically sound practice in clinical prediction model research, particularly for single-center studies with sample sizes similar to ours. According to the definition and diagnostic criteria for CI-AKI established by the European Society of Urogenital Radiology (ESUR): Serum creatinine elevation ≥ 26.5 µmol/L or ≥ 1.5 times the baseline level within 48–72 hours post-procedure; or persistent oliguria [urine output < 0.5 ml/(kg·h) for 6 hours], the training set was divided into non-CI-AKI (n = 620) and CI-AKI groups (n = 136). The CI-AKI group exhibited significantly elevated values compared to the non-CIAKI group in: hypertension prevalence, chronic kidney disease (CKD) prevalence, diuretic use, heart rate, white blood cell count, neutrophil count, monocyte count, platelet count, urea, creatinine, uric acid, cystatin C (Cys C), total cholesterol (TC), SIRI, and AIP ( P < 0.05, Table 1 – 2 ). Table 1 Comparison of basic clinical data between CI-AKI group and non-CI-AKI group Variables CI-AKI (n = 136) Non-CI-AKI(n = 620) P -value Age (years) 74. 97 ± 3.64 68. 86 ± 3.78 <0. 001 Male,n(%) 105(77.21) 472(76.13) 0.785 Heart rate (times/min) 83.54 ± 16.34 80.03 ± 14.14 0.006 Hypertension,n(%) 68(50.00) 262(42.26) 0.034 Hypotension,n(%) 12(8.82) 49(7.90) 0.853 SBP(mmHg) 124.75 ± 19.40 127.32 ± 21.01 0.122 DBP(mmHg) 78.12 ± 15.33 78.41 ± 13.64 0.812 Diabetes,n(%) 39(28.68) 148(23.87) 0.164 CKD,n(%) 9(6.18) 13(2.10) 0.004 Smoke,n(%) 67(49.26) 288(46.45) 0.474 ACEI/ARB,n(%) 67(49.26) 319(51.45) 0.751 Beta-blockers,n(%) 116(85.29) 521(84.03) 0.734 Aspirin,n(%) 135(99.53) 619(99.82) 0.484 Clopidogrel/Ticagrelor,n(%) 136(100.00) 615(99.08) 0.365 Statins,n(%) 135(99.06) 616(99.35) 1.000 Diuretic,n(%) 80(58.82) 190(30.65) <0.001 Killip Classify,n(%) 0.530 1 116(85.92) 540(87.11) 2 9(6.10) 24(3.87) 3 2(1.41) 7(1.10) 4 9(6.57) 49(7.92) Dosage of contrast agent,n(%) 0.473 200ml 3(0.5) 2(0.9) Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; CKD, chronic kidney disease; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor inhibitor; Table 2 Comparison of laboratory data between CI-AKI group and non-CI-AKI group Variables CI-AKI (n = 136) Non-CI-AKI(n = 620) P -value White blood cell count(10^9/L) 11.19 ± 2.90 10.04 ± 2.96 <0.001 Neutrophil count(10^9/L) 9.22 ± 2.64 8.01 ± 2.79 <0.001 Lymphocyte count(10^9/L) 1.38 ± 0.65 1.45 ± 0.78 0.236 Monocyte count(10^9/L) 0.58 ± 0.27 0.51 ± 0.25 <0.001 Red blood cell count(10^9/L) 4.64 ± 0.64 4.56 ± 0.57 0.107 Hemoglobin(g/L) 140.81 ± 19.47 139.46 ± 16.86 0.346 Platelet count(g/L) 224.78 ± 62.56 213.27 ± 59.69 0.019 hs-CRP (mg/L) 3.15 (1.30, 12.02) 3.10 (0.90, 9.17) 0.192 Fibrinogen (g/L) 3.15 ± 1.26 3.01 ± 1.12 0.187 Fibrin Degradation Product(µg/mL) 3.44 ± 4.46 3.66 ± 5.51 0.601 Blood urea (mmol/L) 6.53 ± 3.09 6.06 ± 2.09 0.040 Serum creatinine (µmol /L) 76.44 ± 51.10 66.02 ± 34.30 0.006 Uric acid (µmol/L) 334.60 ± 115.05 313.64 ± 96.88 0.011 Cystatin C (mg/L) 1.01 ± 0.47 0.92 ± 0.25 0.004 eGFR(mL/min) 112.59 ± 43.02 117.49 ± 35.70 0.141 Fasting plasma glucose (mmol/L) 7.20 ± 3.03 6.97 ± 3.02 0.348 Total cholesterol(mmol/L) 4.43 ± 1.00 4.36 ± 1.10 0.426 Triglycerides(mmol/L) 1.79 ± 1.12 1.36 ± 0.74 <0.001 NT- proBNP (pg/mL) 432.00 (116.10, 1564.00) 371.00 (117.00, 1237.00) 0.271 TBIL (µmol/L) 14.50 (10.70, 18.70) 13.80 (9.70, 18.40) 0.244 DBIL (µmol/L) 5.40 (4.20, 6.90) 5.40 (4.10, 7.00) 0.860 SIRI 3.84 (2.46, 5.86) 2.56 (1.68, 3.94) <0.001 AIP 2.17 (2.01, 2.38) 2.04 (1.88, 2.21) <0.001 Abbreviations: hs-CRP, high-sensitivity C-reactive protein; eGFR, estimated glomerular filtration rate; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TBIL, total bilirubin; DBIL, direct bilirubin; SIRI, systemic inflammation response index; AIP, atherogenic index of plasma; Identification of Independent Predictors and Construction of the Prediction Model To construct the predictive model, all variables with a significance level of P < 0.05 in univariate logistic regression were included as candidates for multivariable analysis. The final multivariable logistic regression model, which was statistically significant, identified four independent predictors of CI-AKI: AIP (OR = 8.74, 95% CI: 4.53–16.87, P < 0.001), SIRI (OR = 1.28, 95% CI: 1.18–1.39, P < 0.001), CKD (OR = 3.23, 95% CI: 1.28–8.10, P = 0.013), and diuretic use (OR = 2.97, 95% CI: 2.09–4.22, P < 0.001) (Table 3 ). Based on this final model, a nomogram was constructed to provide a visual tool for individualized risk assessment of CI-AKI (Fig. 2 ). Table 3 Multivariate logistic regression analysis of risk factors for CI-AKI after PCI Variables β SE wald χ 2 OR (95% CI) P -value CKD 1.17 0.47 6.20 3.23(1.28 ~ 8.10) 0.013 Diuretics 1.09 0.18 36.67 2.97(2.09 ~ 4.22) <0.001 SIRI 0.25 0.04 39.06 1.28(1.18 ~ 1.39) <0.001 AIP 2.17 0.34 40.74 8.74(4.53 ~ 16.87) <0.001 Validation and Performance of the Nomogram Prediction Model The predictive performance of the nomogram was rigorously evaluated. In the training set, the model achieved an area under the curve (AUC) of 0.853 (95% CI: 0.811–0.895), demonstrating good discriminative ability. This performance was confirmed in the validation set, with an AUC of 0.873 (95% CI: 0.846–0.900) (Fig. 3 ). Calibration, which assesses the agreement between predicted probabilities and observed outcomes, was excellent. The Hosmer-Lemeshow test yielded nonsignificant results (training set: χ² = 5.812, P = 0.668; validation set: χ² = 7.944, P = 0.439), indicating no significant deviation from perfect fit. This was further supported by the calibration curves, which showed close alignment between predictions and observations (Fig. 4 – 5 ). DCA demonstrated that the nomogram provided a positive net benefit across a wide range of threshold probabilities, confirming its clinical utility for decision-making (Fig. 6 – 7 ). Predictive Value of SIRI and AIP for CI-AKI ROC curve analysis demonstrated that SIRI predicted CI-AKI after PCI in STEMI patients with an AUC of 0.715 (95% CI: 0.682–0.748, P < 0.001), an optimal cutoff value of 2.323, sensitivity of 82.30% and specificity of 52.00%.AIP showed an AUC of 0.732 (95% CI: 0.698–0.767, P < 0.001), with an optimal cutoff of 2.267, sensitivity of 54.60% and specificity of 81.30%. The combination of both biomarkers achieved an AUC of 0.817 (95% CI: 0.788–0.846, P < 0.001), with sensitivity of 71.50% and specificity of 76.10%, superior to either biomarker alone (Fig. 8 ). Dose-Response Relationship Between SIRI, AIP, and CI-AKI After adjusting for potential confounding factors using the RCS model, the analysis results showed that there was a nonlinear dose-response relationship between preoperative SIRI/AIP levels and the occurrence of CI-AKI. When SIRI ≥ 2.803 or AIP ≥ 2.088, the risk of CI-AKI development increased with rising levels of these indices (Fig. 9 – 10 ). Discussion This study established and validated a novel nomogram for predicting CI-AKI risk in STEMI patients undergoing emergency PCI. The core finding is that both the AIP and SIRI serve as powerful, independent predictors of CI-AKI. Their combination demonstrated synergistic predictive value, achieving a significantly higher AUC than either marker alone. The final model, which also incorporated CKD and diuretic use, exhibited robust discriminative ability, good calibration, and promising clinical utility across both training and validation cohorts. Our findings on the prognostic value of SIRI align with the established pathophysiological role of inflammation in CI-AKI. The elevated SIRI reflects a state of neutrophil activation, monocyte infiltration, and relative lymphopenia, creating a pro-inflammatory milieu that can aggravate renal injury through oxidative stress and endothelial dysfunction[ 18 ]. Myeloperoxidase and reactive oxygen species released from neutrophils directly compromise mitochondrial function in renal tubular epithelial cells. Macrophages differentiated from monocytes secrete pro-inflammatory cytokines such as IL-1β and TNF-α, accelerating renal interstitial fibrosis[ 19 ]. Lymphocytopenia diminishes anti-inflammatory responses, collectively establishing a vicious cycle of amplified inflammatory cascades. This inflammatory microenvironment may activate the TLR4/NF-κB signaling pathway, upregulating angiotensin II expression in renal tissues, ultimately inducing renal hemodynamic disturbances and oxidative stress injury[ 20 ]. This is consistent with previous studies linking systemic inflammation to adverse renal outcomes in CAD patients. Similarly, the strong independent association of AIP with CI-AKI extends its known role in cardiovascular risk stratification. Functioning as a novel quantitative biomarker for dyslipidemia, the predictive superiority of AIP arises from its sensitive detection of heterogeneity in atherogenic lipoprotein remnants. Its pathogenic mechanism involves the deposition of triglyceride-enriched lipoprotein remnants in the glomerular basement membrane, triggering podocyte apoptosis, while concurrently disrupting the tight junction barrier of renal tubular epithelial cells, exacerbating protein leakage. The key innovation of our work lies in demonstrating the synergistic effect of AIP and SIRI. This interaction is biologically plausible, as inflammation and dyslipidemia often form a vicious cycle; for instance, inflammatory cytokines like IL-6 can inhibit lipoprotein lipase, exacerbating triglyceride-rich lipoprotein accumulation[ 21 – 22 ]. This interplay likely explains why their combined use substantially improved predictive performance, a gap that traditional static models like the Mehran score fail to address. Meanwhile, the high clinical feasibility of SIRI and AIP is a key advantage, as both are derived from routine blood tests (CBC and lipid profile) and can be calculated rapidly at no additional cost. This makes them uniquely practical for immediate risk stratification in the emergency PCI setting, unlike novel biomarkers that are often unavailable or delayed. Consequently, this study provides a valuable complement to the existing framework for CI-AKI risk assessment. By dynamically integrating acute-phase inflammatory (SIRI) and lipid metabolic (AIP) dysregulation, our model addresses a critical limitation of conventional scores that rely predominantly on static baseline parameters. The constructed nomogram translates these complex biochemical insights into a practical, visual tool for bedside use. This enables clinicians to proactively identify high-risk patients before PCI, facilitating timely and personalized preventive measures, such as optimized hydration protocols, stringent contrast volume limitation, or targeted anti-inflammatory/antioxidant strategies, thereby moving towards precision medicine in peri-procedural care. Limitations Several limitations of this study must be acknowledged. First, its single-center, retrospective design inherently carries risks of selection bias and unmeasured confounding, despite our multivariate adjustments. Second, the sample size, though sufficient for statistical power, remains limited, and the generalizability of our findings requires external validation. Third, non-fasting blood sampling, necessitated by the emergency setting, might have introduced minimal variability in lipid parameters. Finally, our model utilized baseline SIRI and AIP, whereas tracking their dynamic changes post-PCI might offer even greater prognostic insight. Future research should prioritize large-scale, multicenter prospective studies to validate and refine this model. Incorporating longitudinal data on SIRI and AIP trends, alongside exploration of other novel biomarkers and genetic factors, will be crucial. Furthermore, interventional trials are needed to determine whether risk stratification using this nomogram can effectively guide targeted therapies and ultimately improve hard clinical outcomes. Conclusions AIP and SIRI are independent risk factors for CI-AKI after PCI in STEMI patients, and their combination significantly improves predictive efficacy. The constructed nomogram model demonstrates good discriminatory ability and clinical applicability, providing a novel strategy for early prevention of CI-AKI. Abbreviations AIP Atherogenic index of plasma SIRI Systemic inflammation response index CI-AKI Contrast-induced acute kidney injury STEMI ST-segment elevation myocardial infarction PCI Percutaneous coronary intervention CKD Chronic kidney disease eGFR Estimated glomerular filtration rate ROC Receiver operating characteristic AUC Area under the curve DCA Decision curve analysis RCS Restricted cubic splines. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (approval number: XYFY2022-KL122-01). The need for written informed consent was waived by the ethics committee due to the retrospective nature of the study. Consent for publication Not applicable. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Authors' contributions J.D. and X.Z. contributed equally to this work. They were responsible for the study conceptualization, data curation, formal analysis, and writing of the original draft. J.J. assisted with methodology, software validation, and investigation. L.X., J.Z., and S.L. were responsible for data collection, project administration, and resources. Y.L. and W.L. supervised the study, reviewed and edited the final manuscript. Y.L. and W.L. also served as corresponding authors, overseeing the project administration and correspondence. All authors have read and approved the final version of the manuscript. Acknowledgements Not applicable. 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progression of non-culprit coronary lesion is related to higher SII,SIRI,and PIV in patients with ACS.Medicine.2024,103(52):e41094.doi: 10.1097/MD.0000000000041094 Liu Z,Zheng L.Associations between SII,SIRI,and cardiovascular disease in obese individuals:a nationwide cross-sectional analysis.Frontiers in Cardiovascular Medicine.2024,111361088-1361088.doi: 10.3389/FCVM.2024.1361088 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7936011","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543437897,"identity":"010065f7-ecd0-4874-a99c-8d5914ef47ed","order_by":0,"name":"Jiahui Ding","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Ding","suffix":""},{"id":543437898,"identity":"7dc38f7b-372b-4c14-aa9d-051a26028dd0","order_by":1,"name":"Xishen Zhang","email":"","orcid":"","institution":"Xuzhou Medical 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1","display":"","copyAsset":false,"role":"figure","size":106789,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of the study\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7936011/v1/50e14cde6705cbc834108e46.jpeg"},{"id":95807181,"identity":"e71b72c5-5274-455c-a0e0-cb3008e1cd35","added_by":"auto","created_at":"2025-11-13 08:48:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62605,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the risk of CI-AKI after PCI\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7936011/v1/86efbb3b0ff0004491c6dfe5.png"},{"id":95819056,"identity":"f893902c-7013-49d3-8298-e2ff34aeed96","added_by":"auto","created_at":"2025-11-13 10:37:46","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81804,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the nomogram in training set and validation set\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7936011/v1/e10ae2a6ba7732f55ad8be22.jpeg"},{"id":95807261,"identity":"a72d7412-ed75-484d-b8d7-947e40e957c0","added_by":"auto","created_at":"2025-11-13 08:48:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80501,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the nomogram in the training set\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7936011/v1/54d5df15f72f783aded4ddc8.png"},{"id":95807080,"identity":"d939ed2c-8d45-4bad-aa63-2841a8cd5a82","added_by":"auto","created_at":"2025-11-13 08:48:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":77863,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the nomogram in the validation set\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7936011/v1/d69a4bb54871f39a0030eef2.png"},{"id":95806931,"identity":"a2753578-2672-4ff8-9ec6-ee0b2c5704ab","added_by":"auto","created_at":"2025-11-13 08:48:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":59707,"visible":true,"origin":"","legend":"\u003cp\u003eDCA of the nomogram in the training set\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7936011/v1/0468a5a10005c25c395853fd.png"},{"id":95806720,"identity":"e87374cc-2edd-4978-a445-94a17ece9a22","added_by":"auto","created_at":"2025-11-13 08:47:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":60552,"visible":true,"origin":"","legend":"\u003cp\u003eDCA of the nomogram in the validation set\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7936011/v1/d8702b0c9eabe3187c3d17f9.png"},{"id":95806926,"identity":"9669d1f4-9c81-4441-b116-53520f1d5146","added_by":"auto","created_at":"2025-11-13 08:48:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1115669,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves comparing individual and combined predictive value of AIP and SIRI for CI-AKI\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7936011/v1/b475265151d99669fb5b1023.png"},{"id":95806722,"identity":"feaa4e17-c79e-4f6b-9357-875960613e21","added_by":"auto","created_at":"2025-11-13 08:47:50","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":46415,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of nonlinear association between SIRI and CI-AKI\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7936011/v1/a1d6cc3377262c1c0f7fb3ab.png"},{"id":95807033,"identity":"dbfd07e4-be21-4e61-98aa-cfa8ae33dca8","added_by":"auto","created_at":"2025-11-13 08:48:04","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":49887,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of nonlinear association between AIP and CI-AKI\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7936011/v1/3111fa4ab9ba9fa82384174f.png"},{"id":97897625,"identity":"683afad7-e981-4e8b-b1af-7c3636a0f2d7","added_by":"auto","created_at":"2025-12-10 15:38:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1512708,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7936011/v1/c6303ebd-d420-4c26-b3cb-a1e6cf66aa73.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of the Atherogenic Index of Plasma and Systemic Inflammation Response Index for Contrast-Induced Acute Kidney Injury in STEMI Patients Undergoing PCI","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute ST-segment elevation myocardial infarction (STEMI), characterized by myocardial necrosis due to acute coronary artery occlusion, requires prompt reperfusion therapy to improve clinical outcomes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Percutaneous coronary intervention (PCI) enables rapid revascularization of the infarct-related artery, effectively restoring myocardial perfusion and reducing mortality[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the administration of iodinated contrast media during PCI can lead to contrast-induced acute kidney injury (CI-AKI)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among STEMI patients, the incidence of CI-AKI reaches 20%\u0026ndash;30%, significantly prolonging hospital stays, increasing healthcare costs, and being strongly associated with elevated long-term mortality. It now represents a major cause of iatrogenic renal injury[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, patients who develop CI-AKI face a heightened risk of progressive chronic kidney disease, dialysis dependence, and major adverse cardiovascular events, underscoring its critical long-term consequences.\u003c/p\u003e\u003cp\u003ePatients with STEMI are particularly vulnerable to CI-AKI due to a confluence of factors, including hemodynamic instability, systemic inflammatory response triggered by myocardial necrosis, and frequent comorbidities such as diabetes, hypertension, and chronic kidney disease (CKD) which can compromise renal perfusion and tubular repair mechanisms[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, early and accurate identification of STEMI patients at high risk for CI-AKI is crucial for implementing timely preventive strategies to reduce both morbidity and mortality.\u003c/p\u003e\u003cp\u003eCurrent risk prediction models for CI-AKI, such as the Mehran score, primarily rely on static parameters including baseline creatinine, age, and diabetes status. These models fail to dynamically capture acute-phase inflammatory activation and disorders of lipid metabolism[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent studies have shown that the systemic inflammation response index (SIRI), integrating neutrophil, monocyte, and lymphocyte counts, dynamically reflects the imbalance between pro-inflammatory and anti-inflammatory pathways and is strongly associated with adverse outcomes in patients with coronary artery disease[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], as these inflammatory cells release mediators that can directly promote renal vascular endothelial injury, microvascular thrombosis, and oxidative stress, providing a clear rationale for its relevance in CI-AKI prediction. The atherogenic index of plasma (AIP), a novel metric of lipid metabolism, not only quantifies impaired reverse cholesterol transport but also correlates with coronary artery calcification, demonstrating superior predictive value compared to conventional lipid parameters[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Physiologically, AIP reflects the abundance of small, dense LDL particles and triglyceride-rich remnant lipoproteins, which are prone to deposition in the glomeruli and renal tubules. This can induce podocyte apoptosis, tubular epithelial cell damage, and intra-renal inflammation, thereby creating a substrate for heightened susceptibility to contrast-mediated injury.\u003c/p\u003e\u003cp\u003eHowever, the synergistic predictive value of AIP and SIRI for CI-AKI in STEMI patients remains unexplored. Therefore, this study aims to investigate the association of AIP and SIRI with CI-AKI following PCI in this population and to develop a nomogram prediction model, thereby offering new insights for the early prediction of CI-AKI.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eA total of 1080 STEMI patients who underwent emergency PCI at the Cardiovascular Intervention Center of the Affiliated Hospital of Xuzhou Medical University between March 2022 and March 2025 were retrospectively enrolled. The data collection and extraction were completed in July 2025, and the manuscript was prepared thereafter. The current analysis represents the final and complete dataset for this cohort. In accordance with standardized international research protocols, the inclusion criteria were: (1) symptom onset to first medical contact time\u0026thinsp;\u0026le;\u0026thinsp;12 hours; (2) meeting indications for emergency revascularization and signed informed consent; (3) fulfilling diagnostic criteria specified in the 2019 Guidelines for the Diagnosis and Treatment of Acute ST-Segment Elevation Myocardial Infarction issued by the Chinese Society of Cardiology[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Exclusion criteria included: (1) baseline estimated glomerular filtration rate (eGFR)\u0026thinsp;\u0026lt;\u0026thinsp;15 mL/min/1.73m\u0026sup2;; (2) contraindications such as iodine contrast allergy or hyperthyroidism; (3) active inflammation status including active infection (C-reactive protein\u0026thinsp;\u0026gt;\u0026thinsp;10 mg/L) or autoimmune diseases; (4) New York Heart Association (NYHA) functional class IV; (5) concurrent solid tumors or hematological malignancies; (6) exposure to nephrotoxic agents during the perioperative period (48 hours pre-procedure to 72 hours post-procedure); (7) absence of essential clinical data.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePeri-procedural Management\u003c/h3\u003e\n\u003cp\u003eAll patients received a standardized hydration regimen to mitigate the risk of nephropathy. This comprised an intravenous infusion of 0.9% isotonic saline, initiated 1 hour before the procedure at a rate of 1 mL/kg/h and continued for at least 6 hours thereafter. For patients with left ventricular ejection fraction (LVEF)\u0026thinsp;\u0026lt;\u0026thinsp;40% or clinical signs of heart failure, the infusion rate was reduced to 0.5 mL/kg/h under continuous hemodynamic monitoring. The low-osmolar, non-ionic contrast agent Iodixanol (Visipaque\u0026trade;, 320 mg I/mL) was used in all procedures, with the total volume administered meticulously recorded for each patient at the discretion of the operating interventional cardiologist.\u003c/p\u003e\n\u003ch3\u003eData Collection and Laboratory Measurements\u003c/h3\u003e\n\u003cp\u003eBaseline renal function was assessed using serum creatinine (SCr) and estimated glomerular filtration rate (eGFR) values obtained from venous blood samples collected within 24 hours preceding the PCI procedure. For the calculation of AIP and SIRI, venous blood samples were collected in EDTA-anticoagulant tubes within 15 minutes of the patient's arrival in the catheterization laboratory, prior to contrast agent administration. It should be noted that, given the emergency setting of PCI, blood sampling was performed in a non-fasting state. Complete blood count (CBC), including neutrophil, lymphocyte, and monocyte counts, was performed on an automated hematology analyzer (Sysmex XN-9000). The lipid profile, including triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C), was measured using enzymatic methods on a clinical chemistry analyzer (Beckman Coulter AU5800). All laboratory analyses were conducted in the hospital's central laboratory, which participates in regular internal and external quality control programs. Follow-up blood samples for SCr measurement were collected from all patients at 48 and 72 hours post-PCI to monitor for the development of CI-AKI. The calculation formulas for SIRI and AIP are as follows: SIRI = (monocyte count \u0026times; neutrophil count) / lymphocyte count[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]; AIP\u0026thinsp;=\u0026thinsp;log10(TG/HDL-C)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eInterventional Procedure and Medication\u003c/h3\u003e\n\u003cp\u003eAll percutaneous coronary interventions were performed by a team of experienced cardiology specialists using a digital subtraction angiography (DSA) system. The antithrombotic strategy included a pre-procedural loading dose of aspirin 300 mg combined with either clopidogrel 300 mg or ticagrelor 180 mg. Following the procedure, patients were maintained on dual antiplatelet therapy with aspirin 100 mg/day plus either clopidogrel 75 mg/day or ticagrelor 90 mg twice daily. Post-procedural angiographic data were assessed independently by two radiologists who were blinded to the patients' clinical information.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses in this study were performed using SPSS 26.0 and R.4.3.2 software. A post-hoc power analysis confirmed that the achieved statistical power exceeded 99% for detecting the observed effect at a significance level of α\u0026thinsp;=\u0026thinsp;0.05. Normality of continuous variables was assessed using the Shapiro-Wilk test. Normally distributed data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u003cem\u003ex̄\u0026plusmn;s\u003c/em\u003e), and intergroup comparisons were performed using independent samples \u003cem\u003et\u003c/em\u003e-tests. Non-normally distributed data are described as median (interquartile range) [M(Q1,Q3)], and intergroup differences were analyzed using the Mann-Whitney \u003cem\u003eU\u003c/em\u003e test. Categorical variables are expressed as frequency (percentage) [\u003cem\u003en\u003c/em\u003e(%)], and intergroup comparisons were made using the Chi-square test or Fisher's exact test. Missing data were handled with mean/median imputation for continuous variables (missing rate\u0026thinsp;\u0026lt;\u0026thinsp;5%) and mode imputation for categorical variables, while extreme outliers were managed using winsorization. Independent risk factors for CI-AKI were identified through multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves were used to evaluate the predictive efficacy of SIRI, AIP, and their combination for CI-AKI. Dose-response relationships between SIRI, AIP and CI-AKI were analyzed using restricted cubic splines (RCS). A nomogram was constructed based on the final model, and the model's clinical net benefit was evaluated using the Hosmer-Lemeshow test, calibration curve, and decision curve analysis (DCA). A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eComparison of Baseline Clinical Characteristics and Preoperative Laboratory Data between CI-AKI and non-CI-AKI Groups\u003c/h2\u003e\u003cp\u003eA total of 1,080 eligible patients, including 822 males and 258 females, were included in the analysis, and the cohort was randomly split into a training set (n\u0026thinsp;=\u0026thinsp;756) and a validation set (n\u0026thinsp;=\u0026thinsp;324) at a 7:3 ratio. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the flowchart. This ratio was chosen to optimally balance two key requirements: ensuring a sufficiently large training set for robust and stable model development, while simultaneously reserving an adequate proportion of patients in the validation set to provide a rigorous and unbiased evaluation of the model's generalizability. This split strategy is a widely adopted and methodologically sound practice in clinical prediction model research, particularly for single-center studies with sample sizes similar to ours. According to the definition and diagnostic criteria for CI-AKI established by the European Society of Urogenital Radiology (ESUR): Serum creatinine elevation\u0026thinsp;\u0026ge;\u0026thinsp;26.5 \u0026micro;mol/L or \u0026ge;\u0026thinsp;1.5 times the baseline level within 48\u0026ndash;72 hours post-procedure; or persistent oliguria [urine output\u0026thinsp;\u0026lt;\u0026thinsp;0.5 ml/(kg\u0026middot;h) for 6 hours], the training set was divided into non-CI-AKI (n\u0026thinsp;=\u0026thinsp;620) and CI-AKI groups (n\u0026thinsp;=\u0026thinsp;136). The CI-AKI group exhibited significantly elevated values compared to the non-CIAKI group in: hypertension prevalence, chronic kidney disease (CKD) prevalence, diuretic use, heart rate, white blood cell count, neutrophil count, monocyte count, platelet count, urea, creatinine, uric acid, cystatin C (Cys C), total cholesterol (TC), SIRI, and AIP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\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\u003eComparison of basic clinical data between CI-AKI group and non-CI-AKI group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCI-AKI (n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-CI-AKI(n\u0026thinsp;=\u0026thinsp;620)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74. 97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68. 86\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0. 001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105(77.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e472(76.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate (times/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.54\u0026thinsp;\u0026plusmn;\u0026thinsp;16.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.03\u0026thinsp;\u0026plusmn;\u0026thinsp;14.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\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\u003e68(50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e262(42.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypotension,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12(8.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49(7.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124.75\u0026thinsp;\u0026plusmn;\u0026thinsp;19.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127.32\u0026thinsp;\u0026plusmn;\u0026thinsp;21.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.12\u0026thinsp;\u0026plusmn;\u0026thinsp;15.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.41\u0026thinsp;\u0026plusmn;\u0026thinsp;13.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.812\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39(28.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148(23.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.164\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\u003e9(6.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13(2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67(49.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e288(46.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACEI/ARB,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67(49.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e319(51.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta-blockers,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116(85.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e521(84.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.734\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspirin,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135(99.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e619(99.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClopidogrel/Ticagrelor,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136(100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e615(99.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.365\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\u003e135(99.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e616(99.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiuretic,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80(58.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e190(30.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKillip Classify,n(%)\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\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116(85.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e540(87.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(6.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24(3.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7(1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(6.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49(7.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDosage of contrast agent,n(%)\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\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;100ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107(80.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e529(85.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100\u0026thinsp;~\u0026thinsp;200ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26(19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89(13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;200ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3(0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; CKD, chronic kidney disease; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor inhibitor;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\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\u003eComparison of laboratory data between CI-AKI group and non-CI-AKI group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCI-AKI (n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-CI-AKI(n\u0026thinsp;=\u0026thinsp;620)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell count(10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil count(10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte count(10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte count(10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell count(10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140.81\u0026thinsp;\u0026plusmn;\u0026thinsp;19.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139.46\u0026thinsp;\u0026plusmn;\u0026thinsp;16.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e224.78\u0026thinsp;\u0026plusmn;\u0026thinsp;62.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e213.27\u0026thinsp;\u0026plusmn;\u0026thinsp;59.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehs-CRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.15 (1.30, 12.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.10 (0.90, 9.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFibrinogen (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFibrin Degradation Product(\u0026micro;g/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood urea (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum creatinine (\u0026micro;mol /L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76.44\u0026thinsp;\u0026plusmn;\u0026thinsp;51.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.02\u0026thinsp;\u0026plusmn;\u0026thinsp;34.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334.60\u0026thinsp;\u0026plusmn;\u0026thinsp;115.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e313.64\u0026thinsp;\u0026plusmn;\u0026thinsp;96.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCystatin C (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR(mL/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112.59\u0026thinsp;\u0026plusmn;\u0026thinsp;43.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117.49\u0026thinsp;\u0026plusmn;\u0026thinsp;35.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFasting plasma glucose (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNT- proBNP (pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e432.00 (116.10, 1564.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e371.00 (117.00, 1237.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBIL (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.50 (10.70, 18.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.80 (9.70, 18.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBIL (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.40 (4.20, 6.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.40 (4.10, 7.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.84 (2.46, 5.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.56 (1.68, 3.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.17 (2.01, 2.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.04 (1.88, 2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: hs-CRP, high-sensitivity C-reactive protein; eGFR, estimated glomerular filtration rate; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TBIL, total bilirubin; DBIL, direct bilirubin; SIRI, systemic inflammation response index; AIP, atherogenic index of plasma;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIdentification of Independent Predictors and Construction of the Prediction Model\u003c/h3\u003e\n\u003cp\u003eTo construct the predictive model, all variables with a significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate logistic regression were included as candidates for multivariable analysis. The final multivariable logistic regression model, which was statistically significant, identified four independent predictors of CI-AKI: AIP (OR\u0026thinsp;=\u0026thinsp;8.74, 95% CI: 4.53\u0026ndash;16.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SIRI (OR\u0026thinsp;=\u0026thinsp;1.28, 95% CI: 1.18\u0026ndash;1.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CKD (OR\u0026thinsp;=\u0026thinsp;3.23, 95% CI: 1.28\u0026ndash;8.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), and diuretic use (OR\u0026thinsp;=\u0026thinsp;2.97, 95% CI: 2.09\u0026ndash;4.22, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on this final model, a nomogram was constructed to provide a visual tool for individualized risk assessment of CI-AKI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression analysis of risk factors for CI-AKI after PCI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ewald \u003cem\u003eχ\u003c/em\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.23(1.28\u0026thinsp;~\u0026thinsp;8.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiuretics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.97(2.09\u0026thinsp;~\u0026thinsp;4.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.28(1.18\u0026thinsp;~\u0026thinsp;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.74(4.53\u0026thinsp;~\u0026thinsp;16.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eValidation and Performance of the Nomogram Prediction Model\u003c/h2\u003e\u003cp\u003eThe predictive performance of the nomogram was rigorously evaluated. In the training set, the model achieved an area under the curve (AUC) of 0.853 (95% CI: 0.811\u0026ndash;0.895), demonstrating good discriminative ability. This performance was confirmed in the validation set, with an AUC of 0.873 (95% CI: 0.846\u0026ndash;0.900) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Calibration, which assesses the agreement between predicted probabilities and observed outcomes, was excellent. The Hosmer-Lemeshow test yielded nonsignificant results (training set: χ\u0026sup2; = 5.812, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.668; validation set: χ\u0026sup2; = 7.944, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.439), indicating no significant deviation from perfect fit. This was further supported by the calibration curves, which showed close alignment between predictions and observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). DCA demonstrated that the nomogram provided a positive net benefit across a wide range of threshold probabilities, confirming its clinical utility for decision-making (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePredictive Value of SIRI and AIP for CI-AKI\u003c/h2\u003e\u003cp\u003eROC curve analysis demonstrated that SIRI predicted CI-AKI after PCI in STEMI patients with an AUC of 0.715 (95% CI: 0.682\u0026ndash;0.748, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), an optimal cutoff value of 2.323, sensitivity of 82.30% and specificity of 52.00%.AIP showed an AUC of 0.732 (95% CI: 0.698\u0026ndash;0.767, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an optimal cutoff of 2.267, sensitivity of 54.60% and specificity of 81.30%. The combination of both biomarkers achieved an AUC of 0.817 (95% CI: 0.788\u0026ndash;0.846, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with sensitivity of 71.50% and specificity of 76.10%, superior to either biomarker alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDose-Response Relationship Between SIRI, AIP, and CI-AKI\u003c/h2\u003e\u003cp\u003eAfter adjusting for potential confounding factors using the RCS model, the analysis results showed that there was a nonlinear dose-response relationship between preoperative SIRI/AIP levels and the occurrence of CI-AKI. When SIRI\u0026thinsp;\u0026ge;\u0026thinsp;2.803 or AIP\u0026thinsp;\u0026ge;\u0026thinsp;2.088, the risk of CI-AKI development increased with rising levels of these indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study established and validated a novel nomogram for predicting CI-AKI risk in STEMI patients undergoing emergency PCI. The core finding is that both the AIP and SIRI serve as powerful, independent predictors of CI-AKI. Their combination demonstrated synergistic predictive value, achieving a significantly higher AUC than either marker alone. The final model, which also incorporated CKD and diuretic use, exhibited robust discriminative ability, good calibration, and promising clinical utility across both training and validation cohorts.\u003c/p\u003e\u003cp\u003eOur findings on the prognostic value of SIRI align with the established pathophysiological role of inflammation in CI-AKI. The elevated SIRI reflects a state of neutrophil activation, monocyte infiltration, and relative lymphopenia, creating a pro-inflammatory milieu that can aggravate renal injury through oxidative stress and endothelial dysfunction[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Myeloperoxidase and reactive oxygen species released from neutrophils directly compromise mitochondrial function in renal tubular epithelial cells. Macrophages differentiated from monocytes secrete pro-inflammatory cytokines such as IL-1β and TNF-α, accelerating renal interstitial fibrosis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Lymphocytopenia diminishes anti-inflammatory responses, collectively establishing a vicious cycle of amplified inflammatory cascades. This inflammatory microenvironment may activate the TLR4/NF-κB signaling pathway, upregulating angiotensin II expression in renal tissues, ultimately inducing renal hemodynamic disturbances and oxidative stress injury[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This is consistent with previous studies linking systemic inflammation to adverse renal outcomes in CAD patients. Similarly, the strong independent association of AIP with CI-AKI extends its known role in cardiovascular risk stratification. Functioning as a novel quantitative biomarker for dyslipidemia, the predictive superiority of AIP arises from its sensitive detection of heterogeneity in atherogenic lipoprotein remnants. Its pathogenic mechanism involves the deposition of triglyceride-enriched lipoprotein remnants in the glomerular basement membrane, triggering podocyte apoptosis, while concurrently disrupting the tight junction barrier of renal tubular epithelial cells, exacerbating protein leakage.\u003c/p\u003e\u003cp\u003eThe key innovation of our work lies in demonstrating the synergistic effect of AIP and SIRI. This interaction is biologically plausible, as inflammation and dyslipidemia often form a vicious cycle; for instance, inflammatory cytokines like IL-6 can inhibit lipoprotein lipase, exacerbating triglyceride-rich lipoprotein accumulation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This interplay likely explains why their combined use substantially improved predictive performance, a gap that traditional static models like the Mehran score fail to address. Meanwhile, the high clinical feasibility of SIRI and AIP is a key advantage, as both are derived from routine blood tests (CBC and lipid profile) and can be calculated rapidly at no additional cost. This makes them uniquely practical for immediate risk stratification in the emergency PCI setting, unlike novel biomarkers that are often unavailable or delayed.\u003c/p\u003e\u003cp\u003eConsequently, this study provides a valuable complement to the existing framework for CI-AKI risk assessment. By dynamically integrating acute-phase inflammatory (SIRI) and lipid metabolic (AIP) dysregulation, our model addresses a critical limitation of conventional scores that rely predominantly on static baseline parameters. The constructed nomogram translates these complex biochemical insights into a practical, visual tool for bedside use. This enables clinicians to proactively identify high-risk patients before PCI, facilitating timely and personalized preventive measures, such as optimized hydration protocols, stringent contrast volume limitation, or targeted anti-inflammatory/antioxidant strategies, thereby moving towards precision medicine in peri-procedural care.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eSeveral limitations of this study must be acknowledged. First, its single-center, retrospective design inherently carries risks of selection bias and unmeasured confounding, despite our multivariate adjustments. Second, the sample size, though sufficient for statistical power, remains limited, and the generalizability of our findings requires external validation. Third, non-fasting blood sampling, necessitated by the emergency setting, might have introduced minimal variability in lipid parameters. Finally, our model utilized baseline SIRI and AIP, whereas tracking their dynamic changes post-PCI might offer even greater prognostic insight. Future research should prioritize large-scale, multicenter prospective studies to validate and refine this model. Incorporating longitudinal data on SIRI and AIP trends, alongside exploration of other novel biomarkers and genetic factors, will be crucial. Furthermore, interventional trials are needed to determine whether risk stratification using this nomogram can effectively guide targeted therapies and ultimately improve hard clinical outcomes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAIP and SIRI are independent risk factors for CI-AKI after PCI in STEMI patients, and their combination significantly improves predictive efficacy. The constructed nomogram model demonstrates good discriminatory ability and clinical applicability, providing a novel strategy for early prevention of CI-AKI.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAtherogenic index of plasma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSIRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic inflammation response index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI-AKI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eContrast-induced acute kidney injury\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTEMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eST-segment elevation myocardial infarction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePercutaneous coronary intervention\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic kidney disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEstimated glomerular filtration rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDecision curve analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRestricted cubic splines.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (approval number: XYFY2022-KL122-01). The need for written informed consent was waived by the ethics committee due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.D. and X.Z. contributed equally to this work. They were responsible for the study conceptualization, data curation, formal analysis, and writing of the original draft.\u003c/p\u003e\n\u003cp\u003eJ.J. assisted with methodology, software validation, and investigation. L.X., J.Z., and S.L. were responsible for data collection, project administration, and resources. Y.L. and W.L. supervised the study, reviewed and edited the final manuscript. Y.L. and W.L. also served as corresponding authors, overseeing the project administration and correspondence. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZinoviev R,Kumar A,Huded P C,et al.Association of a Comprehensive ST-Segment-Elevation Myocardial Infarction Protocol With Key Process Metrics Among Patients Transferred for Primary Percutaneous Coronary Intervention.Journal of the American Heart Association.2025,14(9):e034054.doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/JAHA.123.034054\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.123.034054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChe W,Jin Y,Chang S,et al.Prediction of myocardial ischemia\u0026ndash;reperfusion injury post-PCI:role of sST2 levels in 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class=\"RefSource\"\u003e10.3389/FCVM.2024.1361088\u003c/span\u003e\u003cspan address=\"10.3389/FCVM.2024.1361088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Systemic inflammation response index, Atherogenic index of plasma, Contrast-induced acute kidney injury, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7936011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7936011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aimed to investigate the association of the atherogenic index of plasma (AIP) and systemic inflammation response index (SIRI) with contrast-induced acute kidney injury (CI-AKI) in ST-segment elevation myocardial infarction (STEMI) patients undergoing emergency percutaneous coronary intervention (PCI), and to develop a predictive nomogram.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe retrospectively analyzed 1080 STEMI patients who underwent emergency PCI. Patients were randomly divided into a training cohort (n\u0026thinsp;=\u0026thinsp;756) and a validation cohort (n\u0026thinsp;=\u0026thinsp;324) in a 7:3 ratio. Based on the ESUR criteria, the training cohort was categorized into CI-AKI (n\u0026thinsp;=\u0026thinsp;136) and non-CI-AKI (n\u0026thinsp;=\u0026thinsp;620) groups. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors. A nomogram was constructed and validated using ROC curves, calibration plots, and decision curve analysis (DCA). The dose-response relationships were examined using restricted cubic splines (RCS).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAIP and SIRI levels were significantly higher in the CI-AKI group (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate analysis identified AIP (OR\u0026thinsp;=\u0026thinsp;8.74, 95% CI: 4.53\u0026ndash;16.87), SIRI (OR\u0026thinsp;=\u0026thinsp;1.28, 95% CI: 1.18\u0026ndash;1.39), chronic kidney disease, and diuretic use as independent risk factors for CI-AKI. The nomogram incorporating these factors achieved AUCs of 0.853 and 0.873 in the training and validation sets, respectively, with good calibration and clinical utility. RCS analysis revealed a nonlinear dose-response relationship between AIP/SIRI and CI-AKI risk. The combination of AIP and SIRI demonstrated superior predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.817) than either index alone (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAIP and SIRI are independent risk factors for CI-AKI in STEMI patients after PCI. Their combination improved discrimination. The constructed nomogram provides a practical tool for early risk assessment and identification of high-risk patients.\u003c/p\u003e","manuscriptTitle":"Predictive Value of the Atherogenic Index of Plasma and Systemic Inflammation Response Index for Contrast-Induced Acute Kidney Injury in STEMI Patients Undergoing PCI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 07:55:23","doi":"10.21203/rs.3.rs-7936011/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a12c3202-1ec7-4696-8e1a-61a9ffd3a2ac","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-10T08:53:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 07:55:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7936011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7936011","identity":"rs-7936011","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}