Blakoe Mitti, Olsen Dorte Baek, Noergaard Marianne Wetendorff
Department of Cardiology, Rigshospitalet, The Heart Center, Copenhagen University Hospital, Copenhagen, Denmark.
Department of Cardiothoracic Surgery, Rigshospitalet, The Heart Center, Copenhagen University Hospital, Copenhagen, Denmark.
Contemp Nurse. 2025 Mar 10:1-19. doi: 10.1080/10376178.2025.2473930.
Postoperative delirium is believed to be preventable in up to 40% of all cases. Researchers have proposed various preoperative risk prediction models for postoperative delirium in patients undergoing cardiac surgery, however, no consensus exists on which model is the most suitable.
To identify and map existing preoperative risk prediction models, detecting cardiac surgery patients at elevated risk of developing postoperative delirium.
This scoping review considered cohort and case-control studies eligible if they developed or validated preoperative prediction models for postoperative delirium, in adult patients admitted for cardiac surgery via sternotomy.
The primary search was conducted on May 6th, 2022, and a secondary search was conducted on September 18th, 2024. We searched MEDLINE, CINAHL, Embase, and PsycINFO where 2126 references were identified and 15 were included for full-text analysis.
This scoping review was conducted in line with the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (the PRISMA-ScR) guideline.
Twelve unique risk prediction models and three validation studies were included in this review, comprising between 77 and 45,744 participants. In total, 157 candidate prognostic variables were investigated of which 40 had a predictive value and thus, were included in the prediction models. The included models revealed an AUC from 0.68-0.93 in the derivation cohorts and 0.61-0.89 in the validation cohorts.
Twelve unique prediction models and 3 validation studies were identified and mapped. Collectively, the models demonstrated an AUC ranging from 0.61-0.93, indicating a fair to good discrimination performance.
A protocol is registered at Open Science Framework (OSF) https://osf.io/wr93y/?view_only=d129c3bb6be04357bac35c2c41ba2a40.
据信，高达40%的术后谵妄病例是可以预防的。研究人员已经提出了各种针对心脏手术患者术后谵妄的术前风险预测模型，然而，对于哪种模型最合适尚无共识。
识别并梳理现有的术前风险预测模型，检测有术后谵妄高风险的心脏手术患者。
本综述性研究纳入队列研究和病例对照研究，条件是这些研究为接受胸骨切开术的成年心脏手术患者开发或验证了术后谵妄的术前预测模型。
于2022年5月6日进行了一次主要检索，并于2024年9月18日进行了一次二次检索。我们检索了MEDLINE、CINAHL、Embase和PsycINFO，共识别出2126篇参考文献，其中15篇纳入全文分析。
本综述性研究按照《系统综述与Meta分析扩展版：综述性研究（PRISMA-ScR）指南》进行。
本综述纳入了12个独特的风险预测模型和3项验证研究，参与者人数在77至45744人之间。总共研究了157个候选预后变量其中40个具有预测价值，因此被纳入预测模型。纳入的模型在推导队列中的AUC为0.68至0.93，在验证队列中的AUC为0.61至0.89。
识别并梳理了12个独特的预测模型和3项验证研究。总体而言，这些模型的AUC范围为0.61至0.93，表明具有中等至良好的区分性能。
该方案已在开放科学框架（OSF）https://osf.io/wr93y/?view_only=d129c3bb6be04357bac35c2c41ba2a40上注册。
