Key result
A personalized nomogram incorporating biomarkers and PCI status predicts 30-day STEMI mortality with ~0.86 AUC.
Why the study?
A personalized nomogram model was needed to predict 30-day mortality in patients with ST-Segment Elevation Myocardial Infarction.
Does a personalized nomogram model accurately predict 30-day mortality in patients with STEMI?
Cohort (n=1,144)
No
Does a personalized nomogram model accurately predict 30-day mortality in patients with STEMI?
A personalized nomogram incorporating clinical and biomarker variables accurately predicts 30-day mortality in STEMI patients, aiding in short-term risk stratification.
May aid STEMI risk stratification; leaves open prospective validation before clinical adoption.
Objective: To develop and validate a personalized nomogram model to predict 30-day mortality in patients with ST-Segment Elevation Myocardial Infarction (STEMI).Methods: A total of 1144 STEMI patients admitted to Dongyang People’s Hospital from June 2013 to October 2019 were enrolled in the study and divided into two groups: Model group (n=801) and validation group (n=343). The biomarkers (maximum) in 24 hours after admission and the basic information of the patients were taken as variables, including creatinine, pro B-type natriuretic peptide (proBNP) and troponin T, as well as age, gender, etc. Logistic stepwise regression was employed to screen out independent risk factors that may led to death in patients with acute STEMI. A nomogram predication model was established with the screened independent risk factors as variables. Area underReceiver Operating Characteristic (ROC) curve and GiViTI calibration chart were adopted to estimate the classification accuracy and calibrate the predication model, respectively, and decline curve analysis (DCA) to assess the clinical efficiency of the model.Results: Logistic stepwise regression revealed that proBNP, creatine kinase-MB (CK-MB), age, creatinine, lactic acid and percutaneous coronary intervention (PCI) surgery or not were independent risk factors that decided whether a STEMI patient would die. After the establishment of the model, area under ROC curve was 0.897 and 0.859 in model group and validation group, respectively, suggesting that the new model was a good classifier. P value of GiViTI calibration test was 0.834 and 0.262, respectively. P>0.05 in GiViTI calibration indicated a high goodness of fit between the predication ratio of the model and the actual ratio, showing that the calibration of predications models in model group and validation group was complete. DCA curves in model group and validation group were both above two extreme curves, which suggested a good clinical significance.Conclusion: The personalized model for predicating 30-day mortality in patients with STEMI will help screen out those STEMI patients who may die in a short term.
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Wang et al. (2020) conducted a cohort in ST-Segment Elevation Myocardial Infarction (STEMI) (n=1,144). Nomogram model (age, proBNP, creatinine, CK-MB, lactic acid, PCI) was evaluated on 30-day mortality prediction (Area under the ROC curve). A personalized nomogram model incorporating age, proBNP, creatinine, CK-MB, lactic acid, and PCI status accurately predicted 30-day mortality in STEMI patients, with an AUC of 0.897 in the development cohort and 0.859 in the validation cohort.
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