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Practical prognostic tools to predict the risk of postoperative delirium in older patients undergoing cardiac surgery: visual and dynamic nomograms.
- Source :
-
Journal of clinical monitoring and computing [J Clin Monit Comput] 2024 Sep 21. Date of Electronic Publication: 2024 Sep 21. - Publication Year :
- 2024
- Publisher :
- Ahead of Print
-
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.<br />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).<br />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/ .<br />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.<br /> (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)
Details
- Language :
- English
- ISSN :
- 1573-2614
- Database :
- MEDLINE
- Journal :
- Journal of clinical monitoring and computing
- Publication Type :
- Academic Journal
- Accession number :
- 39305450
- Full Text :
- https://doi.org/10.1007/s10877-024-01219-1