A nomogram for predicting postoperative delirium in pediatric patients following cardiopulmonary bypass: A prospective observational study

Nan Lin; Meng Lv; Shujun Li; Yujun Xiang; Jiahuan Li; Hongzhen Xu

doi:10.1016/j.iccn.2024.103717

A nomogram for predicting postoperative delirium in pediatric patients following cardiopulmonary bypass: A prospective observational study

Intensive Crit Care Nurs. 2024 Apr 30:83:103717. doi: 10.1016/j.iccn.2024.103717. Online ahead of print.

Authors

Nan Lin¹, Meng Lv¹, Shujun Li¹, Yujun Xiang¹, Jiahuan Li¹, Hongzhen Xu²

Affiliations

¹ Nursing Department, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China.
² Nursing Department, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China. Electronic address: 6184020@zju.edu.cn.

PMID: 38692080
DOI: 10.1016/j.iccn.2024.103717

Abstract

Objectives: To create a nomogram for early delirium detection in pediatric patients following cardiopulmonary bypass.

Research methodology/design: This prospective, observational study was conducted in the Cardiac Intensive Care Unit at a Children's Hospital, enrolling 501 pediatric patients from February 2022 to January 2023. Perioperative data were systematically collected through the hospital information system. Postoperative delirium was assessed using the Cornell Assessment of Pediatric Delirium (CAPD). For model development, Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify the most relevant predictors. These selected predictors were then incorporated into a multivariable logistic regression model to construct the predictive nomogram. The performance of the model was evaluated by Harrell's concordance index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. External validity of the model was confirmed through the C-index and calibration plots.

Results: Five independent predictors were identified: age, SpO₂ levels, lymphocyte count, diuretic use, and midazolam administration, integrated into a predictive nomogram. This nomogram demonstrated strong predictive capacity (AUC 0.816, concordance index 0.815) with good model fit (Hosmer-Lemeshow test p = 0.826) and high accuracy. Decision curve analysis showed a significant net benefit, and external validation confirmed the nomogram's reliability.

Conclusions: The study successfully developed a precise and effective nomogram for identifying pediatric patients at high risk of post-cardiopulmonary bypass delirium, incorporating age, SpO2 levels, lymphocyte counts, diuretic use, and midazolam medication.

Implications for clinical practice: This nomogram aids early delirium detection and prevention in critically ill children, improving clinical decisions and treatment optimization. It enables precise monitoring and tailored medication strategies, significantly contributes to reducing the incidence of delirium, thereby enhancing the overall quality of patient care.

Keywords: Cardiopulmonary Bypass; Delirium; Nomograms; Pediatrics; Risk.