{"title":"Practical prognostic tools to predict the risk of postoperative delirium in older patients undergoing cardiac surgery: visual and dynamic nomograms.","authors":"Chernor Sulaiman Bah, Bongani Mbambara, Xianhai Xie, Junlin Li, Asha Khatib Iddi, Chen Chen, Hui Jiang, Yue Feng, Yi Zhong, Xinlong Zhang, Huaming Xia, Libo Yan, Yanna Si, Juan Zhang, Jianjun Zou","doi":"10.1007/s10877-024-01219-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Postoperative Delirium (POD) has an incidence of up to 65% in older patients undergoing cardiac surgery. We aimed to develop two dynamic nomograms to predict the risk of POD in older patients undergoing cardiac surgery.</p><p><strong>Methods: </strong>This was a single-center retrospective cohort study, which included 531 older patients who underwent cardiac surgery from July 2021 to June 2022 at Nanjing First Hospital, China. Univariable and multivariable logistic regression were used to identify the significant predictors used when constructing the models. We evaluated the performances and accuracy, validated, and estimated the clinical utility and net benefit of the models using the receiver operating characteristic (ROC), the 10-fold cross-validation, and decision curve analysis (DCA).</p><p><strong>Results: </strong>A total of 30% of the patients developed POD, the significant predictors in the preoperative model were ASA ( p < 0.001 OR = 3.220), cerebrovascular disease (p < 0.001 OR = 2.326), Alb (p < 0.037 OR = 0.946), and URE (p < 0.001 OR = 1.137), while for the postoperative model they were ASA (p = 0.044, OR = 1.737), preoperative MMSE score (p = 0.005, OR = 0.782), URE (p = 0.017 OR = 1.092), CPB duration (p < 0.001 OR = 1.010) and APACHE II (p < 0.001, OR = 1.353). The preoperative and postoperative models achieved satisfactory predictive performances, with AUC values of 0.731 and 0.799, respectively. The web calculators can be accessed at https://xxh152.shinyapps.io/Pre-POD/ and https://xxh152.shinyapps.io/Post-POD/ .</p><p><strong>Conclusion: </strong>We established two nomogram models based on the preoperative and postoperative time points to predict POD risk and guide the flexible implementation of possible interventions at different time points.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Monitoring and Computing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10877-024-01219-1","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ANESTHESIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: Postoperative Delirium (POD) has an incidence of up to 65% in older patients undergoing cardiac surgery. We aimed to develop two dynamic nomograms to predict the risk of POD in older patients undergoing cardiac surgery.
Methods: This was a single-center retrospective cohort study, which included 531 older patients who underwent cardiac surgery from July 2021 to June 2022 at Nanjing First Hospital, China. Univariable and multivariable logistic regression were used to identify the significant predictors used when constructing the models. We evaluated the performances and accuracy, validated, and estimated the clinical utility and net benefit of the models using the receiver operating characteristic (ROC), the 10-fold cross-validation, and decision curve analysis (DCA).
Results: A total of 30% of the patients developed POD, the significant predictors in the preoperative model were ASA ( p < 0.001 OR = 3.220), cerebrovascular disease (p < 0.001 OR = 2.326), Alb (p < 0.037 OR = 0.946), and URE (p < 0.001 OR = 1.137), while for the postoperative model they were ASA (p = 0.044, OR = 1.737), preoperative MMSE score (p = 0.005, OR = 0.782), URE (p = 0.017 OR = 1.092), CPB duration (p < 0.001 OR = 1.010) and APACHE II (p < 0.001, OR = 1.353). The preoperative and postoperative models achieved satisfactory predictive performances, with AUC values of 0.731 and 0.799, respectively. The web calculators can be accessed at https://xxh152.shinyapps.io/Pre-POD/ and https://xxh152.shinyapps.io/Post-POD/ .
Conclusion: We established two nomogram models based on the preoperative and postoperative time points to predict POD risk and guide the flexible implementation of possible interventions at different time points.
期刊介绍：
The Journal of Clinical Monitoring and Computing is a clinical journal publishing papers related to technology in the fields of anaesthesia, intensive care medicine, emergency medicine, and peri-operative medicine.
The journal has links with numerous specialist societies, including editorial board representatives from the European Society for Computing and Technology in Anaesthesia and Intensive Care (ESCTAIC), the Society for Technology in Anesthesia (STA), the Society for Complex Acute Illness (SCAI) and the NAVAt (NAVigating towards your Anaestheisa Targets) group.
The journal publishes original papers, narrative and systematic reviews, technological notes, letters to the editor, editorial or commentary papers, and policy statements or guidelines from national or international societies. The journal encourages debate on published papers and technology, including letters commenting on previous publications or technological concerns. The journal occasionally publishes special issues with technological or clinical themes, or reports and abstracts from scientificmeetings. Special issues proposals should be sent to the Editor-in-Chief. Specific details of types of papers, and the clinical and technological content of papers considered within scope can be found in instructions for authors.
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