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. 2025 Sep 18;12:1629369. doi: 10.3389/fmed.2025.1629369

Table 2.

Overview of the information of the included prediction models.

Author year Variable selection Model development method Calibration method Clinical application Validation method Final predictors Model performance (AUC/C-statistic) Model presentation
Stefano (26) Univariate analysis and multivariate logistic regression analysis Multivariable logistic regression model Hosmer-Lemeshow test NI External validation Killip class (II, III), Killip class (IV), Diabetes, Anterior STEMI, Age > 75 years, eGFR<60 ml/min/1.73m2 D:0.8379(0.802-0.8738);
V:0.84
Risk score
Yinghua (19) Univariate and multivariate regression analyses Multivariate regression analysis Hosmer-Lemeshow test, Calibration plot DCA Internal(self-sampling method) and external validation Age, eGFR, TyG index, PNI D:0.785(0.729-0.841);
V:0.802(0.699-0.905)
Nomogram model
Benjamin (23) Backward selection Multivariable logistic regression model Hosmer-Lemeshow test NI Internal
cross-validation
Age, history of CKD, eGFR, LVEF, LVEDP, whether the patient was hypotensive, whether the patient received an IABP D:0.77(0.70-0.83);
V:0.76(0.70-0.82)
A web-based tool
Yuhei (27) Stepwise backward Multivariable logistic regression model Hosmer-Lemeshow test NI Internal validation Blood sugar(BS) ≥200 mg/dL, high-sensitivity troponin I(hsTnI) >1.6 ng/dL (normal upper limit×50), Albumin ≤3.5 mg/dL, eGFR <45 mL/min/1.73 m2 D:0.754(0.733-0.846);
V:0.754(0.644-0.839)
Risk score
Akaphol (24) Backward elimination Multivariable logistic regression model Hosmer-Lemeshow test, Calibration plot NI Internal validation Age, baseline creatinine, LVEF < 40%, multi-vessel pPCI, treated with thrombus aspiration, inserted IABP, pre and intra-procedural cardiogenic shock, congestive heart failure D:0.78(0.75-0.82);
V:0.75(0.72-0.79)
An online web
Pei-Chun (30) Clinically relevant variables Multivariable logistic model NI NI Internal validation Age, DM, ventilator use, prior AKI, number of intervened
vessels, CKD, IABP use, cardiogenic shock
D:0.874(0.868-0.881);
V:0.8624(0.8515-0.8733)
ADVANCIS Score
Amir (33) Lasso, SHAP Machine learning(NB, LR, CB, LMP, RF) NI NI Internal validation (five-fold cross-validation) LVEF, FPG, creatinine, mean creatinine, eGFR AUC=0.775 Random Forest model
Hang (20) Univariate analysis, LASSO and multivariate logistic regression analysis Multivariate logistic regression analysis Hosmer-Lemeshow test, Calibration curves DCA Internal validation (Bootstrap) DM, LVEF, SII,NT-proBNP, hsCRP D:0.84(0.790-0.890);
V:0.844(0.762-0.926)
Nomogram model
Faysal (28) Univariate analysis,Lasso and multivariate logistic regression analysis Multivariable logistic regression analysis Calibration plot NI Internal validation (A bootstrap of 200 replicates) Age, Hypertension, Hemoglobin, eGFR, Albumin, SIIRI, LVEF, Lesion length, Pain-to-balloon time AUC=0.97 Nomogram model
Kai (21) LASSO regression and multivariable logistic regression analysis Multivariate logistic regression analysis Hosmer–Lemeshow test, Calibration plot DCA Internal validation (Bootstrap internal verification method) Age >75, LVEF, DM, FAR, hsCRP, lymphocyte count D:0.835(0.800-0.871);
V:0.767(0.711-0.824)
Nomogram model
Yue (22) LASSO regression and multivariate analyses Multivariate logistic regression analysis Calibration curves DCA Internal validation (Bootstrap self-sampling method) Subtypes of ACS, age>75, multivessel coronary artery disease, hyperuricemia, LDL-C, TyG index, eGFR D:0.811(0.766-0.844);
V:0.773(0.712-0.829)
Nomogram model
Sukrisd (25) Univariate analysis and multivariate logistic regression analysis Multivariable logistic regression analysis Hosmer-Lemeshow, Calibration plot NI Internal validation(1,000 replicates bootstrapped sampling) Ejection fraction < 40%, Triple-vessel disease, Use of IABP D:0.83(0.76-0.90);
V:0.77(0.68-0.85)
Risk Stratification Score
Kai-Yang (32) Univariate analysis and multivariate logistic regression analysis Multivariate logistic regression analysis Hosmer-Lemeshow test NI Internal validation (bootstrap
method)
Age>75, baseline SCr>1.5 mg/dl, hypotension, the use of IABP V:0.828(0.737-0.920) Risk score
Hui (29) Univariate logistic regression
analysis and multivariate logistic regression analysis
Multivariate logistic regression analysis Hosmer-Lemeshow test, Calibration chart NI External validation Age > 72, ejection fraction of no more than 40%, baseline SCr > 102.7 mmol/L, RDW > 13.15, MDCLs D:0.721(0.652-0.790);
V:0.731(0.624-0.838)
Risk score
Ling (31) Boruta algorithm Machine learning(DT, SVW, RF, KNN, NB, GBM) NI NI Internal validation Ten-fold cross-validation Neutrophil percentage, age, Free triiodothyronine, Preoperation hypotension, SCr, Hemoglobin, LDL-C, Total triglycerides, Brain natriuretic peptide, WBC, HDL-C, Heart rate, BMI, Cardiac troponin I, SBP D:1.000(1.000-1.000);
V:0.82(0.76-0.87)
Random Forest model
Xuejun (18) Univariate logistic regression
analysis and multivariate logistic regression analysis
Multivariate logistic regression analysis Hosmer-Lemeshow test, Calibration plot NI Internal and external validation Hemoglobin, contrast volume >100ml, hypotension before the procedure, eGFR, logBNP, age D:0.775(0.732-0.819);
V:0.715(0.631-0.799)
Nomogram model

NI, no information; D, derivation; V, validation; LVEF, left ventricular ejection fraction; LVEDP, left ventricular end diastolic pressure; IABP, intra-aortic balloon pump; DCA, decision curve analysis; eGFR, estimated glomerular filtration rate; DM, diabetes mellitus; SII, immune-inflammatory index; NT-proBNP, N-terminal pro-brain natriuretic peptide; hsCRP, highly sensitive C-reactive protein; SIIRI, Systemic immune-inflflammation response index; FAR, fibrinogen-to-albumin ratio; LDL-C, low-density lipoprotein cholesterol; TyG, triglyceride-glucose; SCr, serum creatinine; RDW, red blood cell distribution width; MDCLs, middistal segment culprit lesions; HDL-C, High-density lipoprotein cholesterol; BMI, Body mass index; PNI, prognostic nutritional index; SBP, systolic blood pressure; WBC, white blood cell; BNP, B-type natriuretic peptide.