Zhang Xinyu, Zhang Lei
The First Affiliated Hospital of Jinzhou Medical University, 121001, Jinzhou, People's Republic of China.
Department of Nursing, Jinzhou Medical University, No.40, Section 3, Songpo Road, Linghe District, 121001, Jinzhou, People's Republic of China.
BMC Nurs. 2023 Aug 28;22(1):289. doi: 10.1186/s12912-023-01462-y.
Sleep disturbance occur among nurses at a high incidence.
To develop a Nomogram and a Artificial Neural Network (ANN) model to predict sleep disturbance in clinical nurses.
A total of 434 clinical nurses participated in the questionnaire, a cross-sectional study conducted from August 2021 to June 2022.They were randomly distributed in a 7:3 ratio between training and validation cohorts.Nomogram and ANN model were developed using predictors of sleep disturbance identified by univariate and multivariate analyses in the training cohort; The 1000 bootstrap resampling and receiver operating characteristic curve (ROC) were used to evaluate the predictive accuracy in the training and validation cohorts.
Sleep disturbance was found in 180 of 304 nurses(59.2%) in the training cohort and 80 of 130 nurses (61.5%) in the validation cohort.Age, chronic diseases, anxiety, depression, burnout, and fatigue were identified as risk factors for sleep disturbance. The calibration curves of the two models are well-fitted. The sensitivity and specificity (95% CI) of the models were calculated, resulting in sensitivity of 83.9%(77.5-88.8%)and 88.8% (79.2-94.4%) and specificity of83.1% (75.0-89.0%) and 74.0% (59.4-84.9%) for the training and validation cohorts, respectively.
The sleep disturbance risk prediction models constructed in this study have good consistency and prediction efficiency, and can effectively predict the occurrence of sleep disturbance in clinical nurses.
护士群体中睡眠障碍的发生率较高。
建立列线图和人工神经网络（ANN）模型以预测临床护士的睡眠障碍。
共有434名临床护士参与了问卷调查，这是一项于2021年8月至2022年6月进行的横断面研究。她们以7:3的比例随机分配到训练队列和验证队列中。使用训练队列中通过单因素和多因素分析确定的睡眠障碍预测因素建立列线图和ANN模型；采用1000次自助重采样和受试者工作特征曲线（ROC）来评估训练队列和验证队列中的预测准确性。
训练队列中304名护士中有180名（59.2%）存在睡眠障碍，验证队列中130名护士中有80名（61.5%）存在睡眠障碍。年龄、慢性病、焦虑、抑郁、职业倦怠和疲劳被确定为睡眠障碍的危险因素。两个模型的校准曲线拟合良好。计算了模型的敏感性和特异性（95%CI），训练队列和验证队列的敏感性分别为83.9%（77.5 - 88.8%）和88.8%（79.2 - 94.4%），特异性分别为83.1%（75.0 - 89.0%）和74.0%（59.4 - 84.9%）。
本研究构建的睡眠障碍风险预测模型具有良好的一致性和预测效率，能够有效预测临床护士睡眠障碍的发生。
