Prediction models for postoperative pulmonary complications: a systematic review and meta-analysis.

IF 9.2 1区 医学 Q1 ANESTHESIOLOGY
Zhiyu Huang, Yang Han, Huijia Zhuang, Jingyao Jiang, Cheng Zhou, Hai Yu
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引用次数: 0

Abstract

Background: Postoperative pulmonary complications (PPCs) increase mortality, hospital stays, and healthcare costs. Multivariable prediction models can guide patient care by identifying high-risk patients. The discriminative ability and potential for clinical impact of PPC prediction models remains unclear.

Methods: We systematically searched Cochrane, Embase, and PubMed (up to June 2024) for studies developing or validating prediction models for PPCs that reported c-statistic. The primary outcome was the c-statistic of prediction models for composite PPCs, and the secondary outcome was the c-statistic for individual PPCs, including pneumonia, respiratory failure, reintubation, and others. Data were extracted using the CHARMS checklist, and bias was assessed with PROBAST. For models with data from three or more cohorts, discrimination was synthesised by pooling c-statistic using Bayesian meta-analysis, with heterogeneity assessed through prediction intervals.

Results: A total of 123 studies were included, covering 116 prediction models for PPCs, with 14 models (1 004 029 patients) eligible for meta-analysis. The c-statistic of all models ranged from 0.614 to 0.996 (median 0.80), with 50% of models self-reporting good (c-statistic >0.8) discrimination. In meta-analysis, the ARISCAT PPC score (summary c-statistic 0.76, 95% CI 0.67-0.86), Xue's model (0.82, 0.75-0.89), and CARDOT score (0.73, 0.61-0.85) demonstrated moderate (c-statistic 0.7-0.8) to good discrimination for composite PPCs; the DAGDA score (0.81, 0.74-0.88), Wang's model (0.78, 0.70-0.86), and Jin's model (0.75, 0.68-0.82) for postoperative pneumonia; and Yoon's model (0.90, 0.84-0.96) and Nizamuddin's model (0.85, 0.78-0.92) for postoperative respiratory failure. The reliability of these models, however, is currently limited by the lack of external validation cohorts. Overall, 90.2% of models were assessed as having a high risk of bias.

Conclusions: Many prediction models postoperative pulmonary complications have been developed, but the clinical utility of the vast majority remains uncertain.

Clinical trial registration: PROSPERO database (CRD42024580216).

术后肺部并发症的预测模型:系统回顾和荟萃分析。
背景:术后肺部并发症(PPCs)增加死亡率、住院时间和医疗费用。多变量预测模型可以通过识别高危患者来指导患者护理。PPC预测模型的鉴别能力和潜在的临床影响尚不清楚。方法:我们系统地检索了Cochrane、Embase和PubMed(截止到2024年6月),以研究开发或验证报告c统计量的PPCs预测模型。主要结局是复合PPCs预测模型的c统计量,次要结局是单个PPCs的c统计量,包括肺炎、呼吸衰竭、再插管等。使用CHARMS检查表提取数据,并使用PROBAST评估偏倚。对于数据来自三个或更多队列的模型,使用贝叶斯荟萃分析通过汇集c统计量来综合判别,并通过预测区间评估异质性。结果:共纳入123项研究,涵盖116个PPCs预测模型,其中14个模型(1 004 029例患者)符合meta分析。所有模型的c统计量范围为0.614 ~ 0.996(中位数0.80),其中50%的模型自报判别良好(c-statistic >0.8)。在荟萃分析中,ARISCAT PPC评分(汇总c统计量0.76,95% CI 0.67-0.86)、Xue's模型(0.82,0.75-0.89)和CARDOT评分(0.73,0.61-0.85)对复合PPC具有中等(c统计量0.7-0.8)到良好的鉴别能力;术后肺炎的DAGDA评分(0.81,0.74-0.88)、Wang模型(0.78,0.70-0.86)和Jin模型(0.75,0.68-0.82);Yoon模型(0.90,0.84-0.96)和Nizamuddin模型(0.85,0.78-0.92)的术后呼吸衰竭。然而,这些模型的可靠性目前受到缺乏外部验证队列的限制。总体而言,90.2%的模型被评估为具有高偏倚风险。结论:已经建立了许多预测术后肺部并发症的模型,但绝大多数模型的临床应用仍不确定。临床试验注册:PROSPERO数据库(CRD42024580216)。
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来源期刊
CiteScore
13.50
自引率
7.10%
发文量
488
审稿时长
27 days
期刊介绍: The British Journal of Anaesthesia (BJA) is a prestigious publication that covers a wide range of topics in anaesthesia, critical care medicine, pain medicine, and perioperative medicine. It aims to disseminate high-impact original research, spanning fundamental, translational, and clinical sciences, as well as clinical practice, technology, education, and training. Additionally, the journal features review articles, notable case reports, correspondence, and special articles that appeal to a broader audience. The BJA is proudly associated with The Royal College of Anaesthetists, The College of Anaesthesiologists of Ireland, and The Hong Kong College of Anaesthesiologists. This partnership provides members of these esteemed institutions with access to not only the BJA but also its sister publication, BJA Education. It is essential to note that both journals maintain their editorial independence. Overall, the BJA offers a diverse and comprehensive platform for anaesthetists, critical care physicians, pain specialists, and perioperative medicine practitioners to contribute and stay updated with the latest advancements in their respective fields.
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