Development and validation of a nomogram to predict the depressive symptoms among older adults: A national survey in China

Depressive symptoms (DS) have become a global public health problem. However, a risk prediction model for DS in the elderly population has not been established. The purpose of this study was to develop and validate a predictive nomogram to screen for DS in the elderly population. A cross-sectional data of 3396 participants aged 60 and over were obtained from the China Health and Retirement Longitudinal Study 2018 (CHARLS). Participants were divided into the development and validation set. Predictive factors were selected through a single-factor analysis, and then a predictive model nomogram was established. The discrimination, calibration, and clinical validity were evaluated using the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow tests, and decision curve analyses (DCA). A total of 2379 and 1017 participants were included in the development and validation set, respectively. The analysis found that gender, residence, dyslipidemia, self-rated health, and ADL disability were risk factors for DS in older adults, and were included in the final model. This nomogram showed an acceptable predictive performance as evaluated by the area under the ROC curve with values of 0.684 (95 % confidence interval (CI): 0.663–0.706) and 0.687 (95 % CI: 0.655–0.719) in the development and validation set, respectively. The calibration curve indicated that the model was accurate, and DCA demonstrated a good clinical application value. Five factors were selected to establish a nomogram for predicting DS in older adults. The nomogram has a good evaluation performance and can be used as a reliable tool to predict DS among older adults.