Risk prediction models for delirium after adult cardiac surgery: A systematic review and meta-analysis
- VernacularTitle:成人心脏术后谵妄风险预测模型的系统评价与Meta分析
- Author:
Min CHEN
1
;
Amin YANG
1
;
Lu SUN
2
Author Information
1. Ward 2, Department of Cardiovascular Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University (General Hospital of Eastern Theater Command), Nanjing, 210002, P. R. China
2. Ward 305, Department of Geriatrics, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, P. R. China
- Publication Type:Journal Article
- Keywords:
Cardiac surgery;
postoperative delirium;
risk prediction model;
predictive factors;
area under the receiver operating characteristic curve;
prediction model risk of bias assessment tool;
system review/meta-analysis
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2026;33(03):444-453
- CountryChina
- Language:Chinese
-
Abstract:
Objective To systematically evaluate the risk prediction models for postoperative delirium in adults with cardiac surgery. Methods The SinoMed, CNKI, Wanfang, VIP, PubMed, EMbase, Web of Science, and Cochrane Library databases were searched to collect studies on risk prediction models for postoperative delirium in cardiac surgery published up to January 29, 2025. Two researchers screened the literature according to inclusion and exclusion criteria, used the PROBAST bias tool to assess the quality of the literature, and conducted a meta-analysis of common predictors in the model using Stata 17.0 software. Results A total of 21 articles were included, establishing 45 models with 28733 patients. Age, cardiopulmonary bypass time, history of diabetes, history of cerebrovascular disease, and gender were the top five common predictors. The area under the curve (AUC) of the 45 models ranged from 0.544 to 0.98. Fourteen out of the 21 studies had good applicability, while the applicability of the remaining seven was unclear; 20 studies had a high risk of bias. Meta-analysis showed that the incidence of postoperative delirium in adults with cardiac surgery was 18.6% [95%CI (15.7%, 21.6%)], and age [OR=1.045 (1.036, 1.054), P<0.001], history of cerebrovascular disease [OR=1.758 (1.459, 2.057), P<0.001], gender [OR=1.732 (1.430, 2.034), P<0.001], mini-mental state examination score [OR=3.930 (1.859, 8.309), P<0.001], and length of ICU stay [OR=5.586 (4.289, 6.883), P<0.001] were independent influencing factors for postoperative delirium after cardiac surgery. Conclusion The risk prediction models for postoperative delirium after cardiac surgery have good predictive performance, but there is a high overall risk of bias. In the future, large-sample, multicenter, high-quality prospective clinical studies should be conducted to construct the optimal risk prediction model for postoperative delirium in adults with cardiac surgery, aiming to identify and prevent the occurrence of postoperative delirium as early as possible.
