Abstract
BACKGROUND: Sleep disturbances are common in pregnant and postpartum women, impacting their health. Predictive tools for timely intervention are scarce. OBJECTIVE: To develop and validate a nomogram predicting sleep disturbance risk in this demographic. METHODS: We enrolled unipara with singleton pregnancies from Shenzhen hospitals in 2022, with complete data and survey cooperation. Data collected included demographics, pregnancy stage, systemic health, sleep status, and emotional state. Subjects were randomly assigned to training (70 %) and validation (30 %) groups. Risk factors were identified via logistic regression, and the nomogram was evaluated using calibration, ROC curves, and DCA. RESULTS: The study involved 727 women. Identified risk factors for sleep disturbance included education, income, and various systemic and emotional symptoms. The nomogram demonstrated strong predictive accuracy in both groups (AUC: 0.93 and 0.91), with calibration and DCA confirming its reliability. CONCLUSION: The nomogram accurately predicts sleep disturbance risk, aiding early detection and improving sleep quality in pregnant and postpartum women. Its broader applicability will be confirmed in future studies.