Development and validation of mathematical nomogram for predicting the risk of poor sleep quality among medical students

• DOI: 10.3389/fnins.2022.930617
• Publication Date: 2022-09-23T09:15:40Z
• Authors: Jiahao Ding; Xin Guo; Mengqi Zhang; Mingxia Hao; Shuang Zhang; Rongshen Tian; Liting Long; Xiao Chen; Jihui Dong; Haiying Song; Jie Yuan
• Keywords: Nomogram; Univariate; Univariate analysis

Background Despite the increasing prevalence of poor sleep quality among medical students, only few studies have identified factors associated with it sing methods from epidemiological surveys. Predicting is critical for ensuring Students’ good physical and mental health. The aim this study was to develop a comprehensive visual predictive nomogram predicting risk in students. Methods We investigated association at JiTang College North China University Science Technology through cross-sectional study. A total 5,140 students were randomized into training cohort (75%) validation (25%). Univariate multivariate logistic regression models used explore quality. constructed predict individual studied. Results 31.9% reported performed analysis obtained final model, which confirmed protective ( p < 0.05). Protective included absence discomfort (OR = 0.638, 95% CI: 0.546–0.745). Risk current drinking 0.546∼0.745), heavy stress 2.753, 1.456∼5.631), very 3.182, 1.606∼6.760), depressive symptoms 4.305, 3.581∼5.180), anxiety 1.808, 1.497∼2.183). area under ROC curve set 0.776 0.770, indicates that our model has stability prediction accuracy. Decision calibration curves demonstrate clinical usefulness nomograms. Conclusion Our helps includes five drinking, stress, recent discomfort, symptoms, symptoms. performance can be further research on management