Zhong Yingyu, Wang He, Wang Yueyun
Shenzhen Maternity and Child Healthcare Hospital, China.
Heliyon. 2024 Nov 22;10(24):e39750. doi: 10.1016/j.heliyon.2024.e39750. eCollection 2024 Dec 30.
Sleep disturbances are common in pregnant and postpartum women, impacting their health. Predictive tools for timely intervention are scarce.
To develop and validate a nomogram predicting sleep disturbance risk in this demographic.
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.
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.
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.
睡眠障碍在孕妇和产后女性中很常见，会影响她们的健康。用于及时干预的预测工具很少。
开发并验证一种预测该人群睡眠障碍风险的列线图。
我们于2022年从深圳医院招募了单胎妊娠的初产妇，她们具有完整的数据并配合调查。收集的数据包括人口统计学信息、妊娠阶段、全身健康状况、睡眠状况和情绪状态。受试者被随机分为训练组（70%）和验证组（30%）。通过逻辑回归确定风险因素，并使用校准、ROC曲线和决策曲线分析对列线图进行评估。
该研究纳入了727名女性。确定的睡眠障碍风险因素包括教育程度、收入以及各种全身和情绪症状。列线图在两组中均显示出很强的预测准确性（AUC：0.93和0.91），校准和决策曲线分析证实了其可靠性。
该列线图能准确预测睡眠障碍风险，有助于早期发现并改善孕妇和产后女性的睡眠质量。其更广泛的适用性将在未来研究中得到证实。
