心脏手术患者术后谵妄的术前预测模型-范围综述。

Mitti Blakoe, Dorte Baek Olsen, Marianne Wetendorff Noergaard
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引用次数: 0

摘要

背景:术后谵妄被认为在所有病例中可预防的比例高达40%。研究者提出了各种心脏手术患者术后谵妄的术前风险预测模型,但哪种模型最合适,尚无共识。目的:识别和绘制现有的术前风险预测模型,发现心脏手术患者术后发生谵妄的高危人群。设计:本研究纳入队列研究和病例对照研究,如果这些研究开发或验证了胸骨切开行心脏手术的成年患者术后谵妄的术前预测模型,则符合条件。资料来源:首次搜索时间为2022年5月6日,二次搜索时间为2024年9月18日。检索MEDLINE、CINAHL、Embase和PsycINFO,共检索到2126篇文献,其中15篇纳入全文分析。方法:本范围评价按照范围评价的系统评价和元分析扩展(PRISMA-ScR)指南进行。结果:本综述纳入了12个独特的风险预测模型和3个验证研究,包括77至45,744名参与者。总共调查了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。纳入的模型显示,衍生队列的AUC为0.68-0.93,验证队列的AUC为0.61-0.89。结论:确定了12个独特的预测模型和3个验证研究。总体而言,模型的AUC范围为0.61-0.93,表明模型具有良好的识别性能。协议注册:协议在开放科学框架(OSF) https://osf.io/wr93y/?view_only=d129c3bb6be04357bac35c2c41ba2a40上注册。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preoperative prediction models for postoperative delirium in cardiac surgery patients - a scoping review.

Background: 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.

Aim: To identify and map existing preoperative risk prediction models, detecting cardiac surgery patients at elevated risk of developing postoperative delirium.

Design: 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.

Data sources: 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.

Method: This scoping review was conducted in line with the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (the PRISMA-ScR) guideline.

Results: 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.

Conclusions: 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.

Protocol registration: A protocol is registered at Open Science Framework (OSF) https://osf.io/wr93y/?view_only=d129c3bb6be04357bac35c2c41ba2a40.

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