Development and Validation of Nomogram Prediction Model for Postoperative Sleep Disturbance in Patients Undergoing Non-Cardiac Surgery: A Prospective Cohort Study

Abstract

Shuting Yang,1 Qian Zhang,1 Yifan Xu,1 Futeng Chen,1 Fangming Shen,1 Qin Zhang,1 He Liu,2 Yueying Zhang3 1Xuzhou Medical University, Xuzhou City, Jiangsu Province, People’s Republic of China; 2Department of Anesthesiology, The Affiliated Huzhou Hospital, Zhejiang University School of Medicine; Huzhou Central Hospital, Huzhou City, Zhejiang Province, People’s Republic of China; 3Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou City, Jiangsu Province, People’s Republic of ChinaCorrespondence: Yueying ZhangDepartment of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou City, Jiangsu Province, People’s Republic of ChinaTel [phone]Email [email protected]: To develop a risk prediction nomogram of postoperative sleep disturbance (PSD) in patients undergoing non-cardiac surgery.Patients and methods: Data on 881 consecutive patients who underwent non-cardiac surgery at the Affiliated Hospital of Xuzhou Medical University between June 2020 and April 2021 were prospectively collected. Of these, we randomly divided 881 non-cardiac patients into two groups, training cohort (n = 617) and validation cohort (n = 264) at the ratio of 7:3. Characteristic variables were selected based on the data of training cohort through least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was used to identify the independent risk factors associated with PSD that then were incorporated into the nomogram. The predictive performance of the nomogram was measured by concordance index (C index), receiver operating characteristic (ROC) curve, and calibration with 1000 bootstrap samples to decrease the over-fit bias.Results: PSD was found in 443 of 617 patients (71.8%) and 190 of 264 patients (72.0%) in the training and validation cohorts, respectively. The perioperative risk factors associated with PSD were female sex, anxiety, dissatisfaction of ward environment, absence of combined regional nerve block, postoperative nausea and vomiting (PONV), the longer duration stayed in post anesthesia care unit (PACU), the higher dose of midazolam and sufentanil, the higher postoperative numeric rating score for pain (NRS) score. Incorporating these 9 factors, the nomogram achieved good concordance indexes of 0.82 (95% confidence interval [CI], 0.78– 0.85) and 0.80 (95% CI, 0.74– 0.85) in predicting PSD in the training and validation cohorts, respectively, and obtained well-fitted calibration curves. The sensitivity and specificity (95% CIs) of the nomogram were calculated, resulting in sensitivity of 74.0% (70.0– 78.2%) and 75.3% (68.4– 81.7%) and specificity of 79.3% (72.5– 85.2%) and 70.3% (58.4– 80.7%) for the training and validation cohorts, respectively. Patients who had a nomogram score of less than 262 or 262 or greater were considered to have low or high risks of PSD presence, respectively.Conclusion: The proposed nomogram achieved an optimal prediction of PSD in patients undergoing non-cardiac surgery. The risks for an individual patient to harbor PSD can be determined by this model, which can lead to a reasonable preventive and treatment measures.Keywords: postoperative sleep disturbance, surgery, LASSO regression analysis, nomogram

Keywords

• postoperative sleep disturbance
• surgery
• lasso regression analysis
• nomogram