Development and internal validation of a nomogram for sleep quality among Chinese medical student

Abstract

Abstract Objective Poor sleep quality is common among Chinese medical students. Therefore, identifying predictors is necessary to implement individualized interventions. This study aimed to develop and validate a nomogram to predict poor sleep quality among Chinese medical students. Methods A cross-sectional study was used to collect data among Chinese medical students at the Hubei University of Medicine. A total of 2038 medical students were randomly divided into training (70%) and validation (30%) groups. Multivariable logistic regression analysis was performed to examine factors associated with sleep quality. Thereafter, these factors were used to develop a nomogram for predicting sleep quality. The predictive performance was evaluated by receiver operating characteristics curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA). Results A total of 70.4% of medical students in the study reported poor sleep quality. The predictors of sleep quality included grade, gender, self-assessment of interpersonal relationships, and self-assessment of health status. The scores of the nomogram ranged from 28 to 176, and the corresponding risk ranged from 0.50 to 0.95. The calibration curve showed that the nomogram had good classification performance. The area under the curve (AUC) of the ROC for the training group is 0.676, and that for the validation group is 0.702. The DCA demonstrated that the model also had good net benefits. Conclusions The nomogram prediction model has sufficient accuracies, good predictive capabilities, and good net benefits. The model can also provide a reference for predicting the sleep quality of medical students.