A Novel Predictive Model for In-Hospital Mortality Based on a Combination of Multiple Blood Variables in Patients with ST-Segment-Elevation Myocardial Infarction

Abstract

In emergency clinical settings, it may be beneficial to use rapidly measured objective variables for the risk assessment for patient outcome. This study sought to develop an easy-to-measure and objective risk-score prediction model for in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI). A total of 1027 consecutive STEMI patients were recruited and divided into derivation (n = 669) and validation (n = 358) cohorts. A risk-score model was created based on the combination of blood test parameters obtained immediately after admission. In the derivation cohort, multivariate analysis showed that the following 5 variables were significantly associated with in-hospital death: estimated glomerular filtration rate <45 mL/min/1.73 m², platelet count <15 × 10⁴/μL, albumin ≤3.5 g/dL, high-sensitivity troponin I >1.6 ng/mL, and blood sugar ≥200 mg/dL. The risk score was weighted for those variables according to their odds ratios. An incremental change in the scores was significantly associated with elevated in-hospital mortality (p < 0.001). Receiver operating characteristic curve analysis showed adequate discrimination between patients with and without in-hospital death (derivation cohort: area under the curve (AUC) 0.853; validation cohort: AUC 0.879), and there was no significant difference in the AUC values between the laboratory-based and Global Registry of Acute Coronary Events (GRACE) score (p = 0.721). Thus, our laboratory-based model might be helpful in objectively and accurately predicting in-hospital mortality in STEMI patients.

Keywords: ST-segment-elevation myocardial infarction; in-hospital mortality; risk score.

Figure 1

Flow diagram of patient enrollment in the study. PCI, percutaneous coronary intervention; STEMI, ST-segment elevation myocardial infarction.

Figure 2

Distribution of risk scores and risk of in-hospital mortality (A) derivation cohort and (B) validation cohort.

Figure 3

Risk stratification for predicting in-hospital mortality. (A) derivation cohort and (B) validation cohort. The low-, moderate-, and high-risk groups have scores assigned as follows: 0–1, 2–4, and ≥5, respectively.

Figure 4

Receiver operating characteristic (ROC) curves of laboratory risk score. (A) Area under the curve (AUC) was 0.853 (95% confidence interval (CI) 0.782–0.904) for derivation cohort. (B) AUC was 0.879 (95% CI 0.791–0.933) for validation cohort.

Figure 5

Comparison between laboratory risk score and Global Registry of Acute Coronary Events (GRACE) risk score. (A) Area under the curves (AUCs) of laboratory risk score (red) and GRACE risk score (blue) in validation cohort were 0.879 (95% confidence interval (CI) 0.790–0.931) and 0.891 (95% CI 0.773–0.952), respectively, and the difference was not significant. (B) Substratification by the combined laboratory parameter risk scores of all patients stratified according to GRACE risk scores. Table below graph shows in-hospital mortality for each stratified population. AUC, area under the curve; CI, confidence interval; GRACE, Global Registry of Acute Coronary Events.