{"title":"预测心肺旁路术后儿科患者术后谵妄的提名图:前瞻性观察研究","authors":"Nan Lin, Meng Lv, Shujun Li, Yujun Xiang, Jiahuan Li, Hongzhen Xu","doi":"10.1016/j.iccn.2024.103717","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>To create a nomogram for early delirium detection in pediatric patients following cardiopulmonary bypass.</p></div><div><h3>Research Methodology/Design</h3><p>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.</p></div><div><h3>Results</h3><p>Five independent predictors were identified: age, SpO<sub>2</sub> 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.</p></div><div><h3>Conclusions</h3><p>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.</p></div><div><h3>Implications for Clinical Practice</h3><p>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.</p></div>","PeriodicalId":51322,"journal":{"name":"Intensive and Critical Care Nursing","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0964339724001022/pdfft?md5=b5ab5a6f9de9254954a57d8250e9dbaa&pid=1-s2.0-S0964339724001022-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A nomogram for predicting postoperative delirium in pediatric patients following cardiopulmonary bypass: A prospective observational study\",\"authors\":\"Nan Lin, Meng Lv, Shujun Li, Yujun Xiang, Jiahuan Li, Hongzhen Xu\",\"doi\":\"10.1016/j.iccn.2024.103717\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><p>To create a nomogram for early delirium detection in pediatric patients following cardiopulmonary bypass.</p></div><div><h3>Research Methodology/Design</h3><p>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.</p></div><div><h3>Results</h3><p>Five independent predictors were identified: age, SpO<sub>2</sub> 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.</p></div><div><h3>Conclusions</h3><p>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.</p></div><div><h3>Implications for Clinical Practice</h3><p>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.</p></div>\",\"PeriodicalId\":51322,\"journal\":{\"name\":\"Intensive and Critical Care Nursing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0964339724001022/pdfft?md5=b5ab5a6f9de9254954a57d8250e9dbaa&pid=1-s2.0-S0964339724001022-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Intensive and Critical Care Nursing\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0964339724001022\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intensive and Critical Care Nursing","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0964339724001022","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
A nomogram for predicting postoperative delirium in pediatric patients following cardiopulmonary bypass: A prospective observational study
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.
期刊介绍:
The aims of Intensive and Critical Care Nursing are to promote excellence of care of critically ill patients by specialist nurses and their professional colleagues; to provide an international and interdisciplinary forum for the publication, dissemination and exchange of research findings, experience and ideas; to develop and enhance the knowledge, skills, attitudes and creative thinking essential to good critical care nursing practice. The journal publishes reviews, updates and feature articles in addition to original papers and significant preliminary communications. Articles may deal with any part of practice including relevant clinical, research, educational, psychological and technological aspects.