Key result
A nomogram model based on six clinical predictors demonstrated good discrimination for predicting in-hospital mortality in non-diabetic patients with NSTEMI (AUC 0.89; 95% CI 0.84-0.94).
Why the study?
NSTEMI is a life-threatening clinical emergency with poor prognosis, but individualized nomogram models to identify high-risk patients who may experience death are lacking.
Does a nomogram model based on six clinical predictors accurately predict in-hospital mortality in non-diabetic patients with NSTEMI?
Observational (n=774)
Does a nomogram model based on six clinical predictors accurately predict in-hospital mortality in non-diabetic patients with NSTEMI?
Effect estimate: AUC 0.89 (95% CI 0.84-0.94)
A novel nomogram incorporating age, BUN, albumin, triglyceride, D-dimer, and aspirin use accurately predicts in-hospital mortality in non-diabetic patients with NSTEMI.
May aid risk stratification in non-diabetic NSTEMI; leaves open external validation before clinical adoption.
Non-ST-segment elevation acute myocardial infarction (NSTEMI) is a life-threatening clinical emergency with a poor prognosis. However, there are no individualized nomogram models to identify patients at high risk of NSTEMI who may undergo death. The aim of this study was to develop a nomogram for in-hospital mortality in patients with NSTEMI to facilitate rapid risk stratification of patients. A total of 774 non-diabetic patients with NSTEMI were included in this study. Least Absolute Shrinkage and Selection Operator regression was used to initially screen potential predictors. Univariate and multivariate logistic regression (backward stepwise selection) analyses were performed to identify the optimal predictors for the prediction model. The corresponding nomogram was constructed based on those predictors. The receiver operating characteristic curve, GiViTI calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. The nomogram model consisting of six predictors: age (OR = 1.10; 95% CI: 1.05-1.15), blood urea nitrogen (OR = 1.06; 95% CI: 1.00-1.12), albumin (OR = 0.93; 95% CI: 0.87-1.00), triglyceride (OR = 1.41; 95% CI: 1.09-2.00), D-dimer (OR = 1.39; 95% CI: 1.06-1.80), and aspirin (OR = 0.16; 95% CI: 0.06-0.42). The nomogram had good discrimination (area under the curve (AUC) = 0.89, 95% CI: 0.84-0.94), calibration, and clinical usefulness. In this study, we developed a nomogram model to predict in-hospital mortality in patients with NSTEMI based on common clinical indicators. The proposed nomogram has good performance, allowing rapid risk stratification of patients with NSTEMI.
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Li et al. (2024) conducted an observational in Non-ST-segment elevation acute myocardial infarction (NSTEMI) in non-diabetic patients (n=774). Nomogram model (age, blood urea nitrogen, albumin, triglyceride, D-dimer, and aspirin) was evaluated on In-hospital mortality prediction performance (AUC) (AUC 0.89, 95% CI 0.84-0.94). A nomogram model based on six clinical predictors demonstrated good discrimination for predicting in-hospital mortality in non-diabetic patients with NSTEMI (AUC 0.89; 95% CI 0.84-0.94).
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