Development and validation using NHANES data of a predictive model for depression risk in myocardial infarction survivors
Di Wang, Siqi Jia, Shaoyi Yan, Yongping Jia
Abstract
BACKGROUND: Depression after myocardial infarction (MI) is associated with poor prognosis. This study aimed to develop and validate a nomogram to predict the risk of depression in patients with MI. METHODS: This retrospective study included 1615 survivors of MI aged >20 years who were selected from the 2005-2018 National Health and Nutrition Examination Survey database. The 899 subjects from the 2005-2012 survey comprised the development group, and the remaining 716 subjects comprised the validation group. Univariate and multivariate analyses identified variables significantly associated with depression. The least absolute shrinkage and selection operator (LASSO) binomial regression model was used to select the best predictive variables. RESULTS: A full predictive model and a simplified model were developed using multivariate analysis and LASSO binomial regression results, respectively, and validated using data from the validation group. The receiver operator characteristic curve and Hosmer-Lemeshow goodness of fit test were used to assess the nomogram's performance. The full nomogram model included 8 items: age, BMI, smoking, drinking, diabetes, exercise, insomnia, and PIR. The area under the curve for the development group was 0.799 and for the validation group was 0.731, indicating that our model has good stability and predictive accuracy. The goodness of fit test showed a good model calibration for both groups. The simplified model includes age, smoking, PIR, and insomnia. The AUC of the simplified model was 0.772 and 0.711 in the development and validation groups, respectively, indicating that the simplified model still possessed good predictive accuracy. CONCLUSION: Our nomogram helped assess the individual probability of depression after MI and can be used as a complement to existing depression screening scales to help physicians make better treatment decisions.