This was a multicenter, observational study of the data of patients treated between January 2016 and December 2018 at the Hebei General Hospital, Baoding First Central Hospital, and Cangzhou Central Hospital. All patients were meeting the diagnostic criteria of acute STEMI and underwent primary PCI according to current guidelines [25]. The exclusion criteria were: (1) STEMI but no primary PCI; or (2) acute non-STEMI or unstable angina.

The patients who were retrospectively included constituted the training set (January 2016 to June 2018). The validation set contained patients who were prospectively enrolled (July 2018 to December 2018) according to the same criteria so as to avoid modeling bias caused by similar populations. The study was approved by the ethics committees of Hebei General Hospital, Baoding First Central Hospital, and Cangzhou Central Hospital. The patients and their immediate family members consented to receive a primary intervention. The written consent was obtained from study participants and their immediate family.

General data (age, sex, body mass index (BMI), smoking, and alcohol consumption), past medical history (including coronary heart disease, history of angina pectoris, hypertension, type 2 diabetes mellitus, myocardial infarction, cerebral infarction, chronic kidney disease, coronary intervention, atrial fibrillation, and cerebral hemorrhage), vital signs at admission (body temperature, pulse, respiratory rate, systolic blood pressure, diastolic blood pressure, Killip classification, and location of the myocardial infarction), auxiliary examinations (white blood cell count, neutrophil count, eosinophil count, basophil count, red blood cell count, hemoglobin, platelet count, serum potassium, serum sodium, serum chlorine, alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine, uric acid, cholesterol, triglycerides (TG), low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C), very low-density lipoprotein (VLDL-C), random blood glucose, creatine kinase MB (CK-MB) peak, and left ventricular ejection fraction (EF)), chest pain data, interventions (culprit vessels, location, diameter, length, vessel number of lesions, treatment of non-culprit vessels or not, preoperative TIMI flow, presence or absence of collateral circulation, syntax score, grading of thrombus, thrombus aspiration, number of, grading of postoperative TIMI flow, IABP, intraoperative slow flow, intraoperative ventricular tachycardia, intraoperative ventricular fibrillation, and intraoperative cardiac tamponade), stents (most of the stents used in this study were EXCEL drug-coated stents (Jiwei Medical Products Co., Ltd.) and all other stents were drug-coated stents), medications (administration of β-blocker, angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACEI/ARB), aldosterone, diuretic, nicorandil, and calcium channel blocker or not 3 months before admission), and administration of the above medications after admission were collected from the medical charts.

In-hospital mortality was defined as all-cause mortality during hospitalization. The assessment of the left ventricular function by transthoracic echocardiography after STEMI was confirmed. The patient was in the supine position, and according to the frontier approaches of the American Society of Echocardiography, at least three consecutive cardiac cycles were used to measure the internal dimensions of the left ventricle (i.e., the end-systolic diameter and the end-diastolic diameter). LVEF was calculated as follows:

Chest pain data included symptom-to-door time, symptom-to-antiplatelet administration time, symptom-to-anticoagulant administration time, symptom-to-balloon time, first medical contact-to-antiplatelet administration time, first medical contact-to-anticoagulant administration time, and first medical contact-to-balloon time. The time of chest pain onset was determined by asking the patient to and consulting the family accompanying the patient. After admission, ECG and blood sampling were done within 10 min. According to the presence or not of a Q wave, the dynamic evolution of ST-T, and whether the blood myoglobin and CK-MB levels were elevated, self-reported chest pain onset time was validated. The balloon expansion time was determined based on Chinese chest pain data provided by the center.

Killip class I included the patients with no clinical signs of heart failure. Killip class II included the patients with AMI complicated by left heart failure, with moist rales of both lungs being less than 50% of the lung field. Killip class III included the patients with AMI complicated with acute pulmonary edema, with large, small, dry, and moist rales of the whole lung. Killip class IV included the patients with AMI complicated with hemodynamic changes at different degrees or stages, such as cardiogenic shock [26].

TIMI 0 flow (no perfusion) referred to the absence of any antegrade flow beyond the coronary occlusion. TIMI 1 flow (penetration without perfusion) referred to faint antegrade coronary flow beyond the occlusion, with an incomplete filling of the distal coronary bed. TIMI 2 flow (partial reperfusion) referred to delayed or sluggish antegrade flow with complete filling of the distal territory. TIMI 3 flow was a normal flow that filled the distal coronary bed completely [27].

The thrombus score was assessed after the guidewire passed through the lesion (but before balloon dilatation). A significant filling defect in the lumen could be seen, which was visible in multiple angles of angiography and persistently present over multiple cardiac cycles, and after excluding the interlayer of the inner membrane caused by the guidewire in the false lumen. The thrombus score was graded as 0: no thrombus; 1: haziness; 2: definite thrombus < 1/2 vessel diameter; 3: definite thrombus 1/2 to 2 vessel diameters; 4: definite thrombus > 2 vessel diameters; 5: assessing thrombus was impossible due to vascular occlusion [28].

The least absolute shrinkage and selection operator (LASSO) method was used to select the features that were the most significantly associated with the outcome (in-hospital mortality). Then, a regression model was built using the selected variables [29]. Originally proposed for linear regression models, this method minimizes the residual sum of squares, subject to the sum of the absolute value of the coefficients being less than a tuning parameter (λ). For the binary logistic regression model, the residual sum of squares was replaced by the negative log-likelihood. If λ was large, there was no effect on the estimated regression parameters, but as λ was smaller, some coefficients were shrunk to zero [30, 31]. Then, the λ value was selected for which the cross-validation error was the smallest. Finally, the model was re-fitted using all available observations and the selected λ. Thus, most of the coefficients of the covariates were reduced to zero, and the remaining non-zero coefficients were selected by LASSO. The variable factor of a non-zero coefficient was defined as a mortality risk predictor. Therefore, in the present study, the mortality risk score for each patient was calculated by a linear combination of predictors that were weighted by their respective coefficients. The performance of the nomogram was evaluated in terms of discrimination and calibration. Discrimination was quantified using the area under the receiver operating characteristic (ROC) curve. The extent of over- and underestimation was graphically described using calibration plots. Decision curve analysis (DCA) was used to evaluate the net benefit of the model [32‐34].

A total of 1485 acute STEMI patients who underwent primary PCI were included in this study, including 1169 in the training set (95 (8.1%) dead patients and 1074 (91.9%) survivors) and 316 (25 (7.9%) dead patients and 291 (92.1%) survivors) in the validation set. The clinical characteristics of the patients in the two sets are shown in Table 1. The proportions of males were 74.9% and 71.2% in the training and validation sets, respectively, for a ratio of 1.05 between the two sets. The hospital stay was 10.9 ± 3.6 days in the training set and 10.3 ± 3.3 days in the validation set (P = 0.394).

Based on the LASSO analysis, sex, Killip classification, LMCAD, thrombus score, postoperative TIMI flow grading, intraoperative slow flow, IABP, administration of β-blocker, ACEI/ARB, SDT, SBT, syntax score, EF, and CK-MB peak were selected as predictors for the development of the mortality risk prediction model (Fig. 1a,b).

The mortality risk score was calculated as follows:in which male sex was scored as 1 and female sex as 2; for LMCAD, intraoperative slow flow, IABP, administration of β-blocker, and ACEI/ARB, yes was scored as 1, no as 0; Killip classification was scored as 1–4; the thrombus score was scored as 0–5; postoperative TIMI flow grading was scored as 0–3; and the remaining variables were continuous variables. This equation was used to design a nomogram (Fig. 2).

The prediction model was validated in the training and validation sets. The C-statistic in the training set was 0.987 (Fig. 3a), indicating that the nomogram had good discrimination. The prediction accuracy of the nomogram was evaluated by the Hosmer–Lemeshow chi-square statistic calibration method and revealed P = 0.722, indicating that the nomogram had good prediction accuracy. The C-statistic in the validation set was 0.984 (Fig. 3b), indicating that the nomogram had good discrimination. The prediction accuracy of the nomogram revealed P = 0.669, indicating that the nomogram had good prediction accuracy. Figure 4a shows the ROC curve in the training set (AUC = 0.987, 95% confidence interval (CI): 0.981–0.994, P = 0.003). Sensitivity was 97.9%, specificity was 91.6%. Figure 4b shows the ROC curve in the validation set (AUC = 0.990, 95% CI: 0.987–0.998, P = 0.007). Sensitivity was 94.7%, specificity was 95.1%. Figure 5 shows the DCA of the prediction nomogram. DCA reveals that the nomogram can achieve good net benefit.

The supplementary materials present an example of how to calculate the score using the nomogram (Additional file 1).

Table 2 shows that the proportions of patients with random blood glucose levels > 10 mmol/L was 29.5% in the mortality group and 22.2% in the survival group (P = 0.103). In addition, among 240 patients with diabetes, there were no significant differences in the proportions of those treated by diet management alone, oral hypoglycemic drugs, and insulin with or without oral hypoglycemic drugs (P = 0.813).