指导假体周围关节感染治疗的临床预测模型:系统回顾和荟萃分析。

IF 3.9 3区 医学 Q1 INFECTIOUS DISEASES
Elise Naufal, Cade Shadbolt, Marjan Wouthuyzen-Bakker, Siddharth Rele, Srujana Sahebjada, Sharmala Thuraisingam, Sina Babazadeh, Peter F Choong, Michelle M Dowsey
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引用次数: 0

摘要

背景:一些临床预测模型的目的是指导决策处理假体周围关节感染(PJI)已经开发。虽然一些模型已被推荐用于临床,但其适用性仍不确定。方法:我们系统地回顾和批判性地评价了PJI治疗的所有多变量预测模型。我们检索了MEDLINE, EMBASE, Web of Science和谷歌Scholar,从成立到2024年3月1日,包括开发或验证预测PJI结果的模型的研究。我们使用PROBAST(预测模型偏倚风险评估工具)来评估偏倚风险和适用性。通过随机效应荟萃分析汇总模型性能估计。结果:确定了13个预测模型和7个外部验证。所有研究都确定了方法学问题。综合估计表明,KLIC(肾、肝、指数手术、骨水泥假体、c反应蛋白)评分具有公平的判别性能(综合c统计量0.62,95% CI 0.55 ~ 0.69)。τ2(0.02)和I2(33.4)估计值均表明研究间异质性极小。荟萃分析表明,Shohat等人的模型具有良好的判别性能(合并c统计量0.74,95% CI 0.57 ~ 0.85)。τ2(0.0)和I2(0.0)均表明研究间异质性极小。结论:临床医生应该意识到用于开发预测PJI预后的可用模型的方法的局限性。由于没有模型在外部验证研究中始终表现出足够的性能,因此尚不清楚是否有任何可用的模型可以提供可靠的信息,用于指导临床决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clinical prediction models to guide treatment of periprosthetic joint infections: A systematic review and meta-analysis.

Background: Several clinical prediction models that aim to guide decisions about the management of periprosthetic joint infections (PJI) have been developed. While some models have been recommended for use in clinical settings, their suitability remains uncertain.

Methods: We systematically reviewed and critically appraised all multivariable prediction models for the treatment of PJI. We searched MEDLINE, EMBASE, Web of Science, and Google Scholar from inception until March 1st, 2024 and included studies that developed or validated models that predict the outcome of PJI. We used PROBAST (Prediction model Risk Of Bias ASsessment Tool) to assess the risk of bias and applicability. Model performance estimates were pooled via random effect meta-analysis.

Results: Thirteen predictive models and seven external validations were identified. Methodological issues were identified in all studies. Pooled estimates indicated that the KLIC (Kidney, Liver, Index surgery, Cemented prosthesis, C-reactive protein) score had fair discriminative performance (pooled c-statistic 0.62, 95% CI 0.55 to 0.69). Both the τ2 (0.02) and I2 (33.4) estimates indicated that between study heterogeneity was minimal. Meta-analysis indicated Shohat et al's model had good discriminative performance (pooled c-statistic 0.74, 95% CI 0.57 to 0.85). Both the τ2 (0.0) and I2 (0.0) indicated that between study heterogeneity was minimal.

Conclusions: Clinicians should be aware of limitations in the methods used to develop available models to predict outcomes of PJI. As no models have consistently demonstrated adequate performance across external validation studies, it remains unclear if any available models would provide reliable information if used to guide clinical decision-making.

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来源期刊
Journal of Hospital Infection
Journal of Hospital Infection 医学-传染病学
CiteScore
12.70
自引率
5.80%
发文量
271
审稿时长
19 days
期刊介绍: The Journal of Hospital Infection is the editorially independent scientific publication of the Healthcare Infection Society. The aim of the Journal is to publish high quality research and information relating to infection prevention and control that is relevant to an international audience. The Journal welcomes submissions that relate to all aspects of infection prevention and control in healthcare settings. This includes submissions that: provide new insight into the epidemiology, surveillance, or prevention and control of healthcare-associated infections and antimicrobial resistance in healthcare settings; provide new insight into cleaning, disinfection and decontamination; provide new insight into the design of healthcare premises; describe novel aspects of outbreaks of infection; throw light on techniques for effective antimicrobial stewardship; describe novel techniques (laboratory-based or point of care) for the detection of infection or antimicrobial resistance in the healthcare setting, particularly if these can be used to facilitate infection prevention and control; improve understanding of the motivations of safe healthcare behaviour, or describe techniques for achieving behavioural and cultural change; improve understanding of the use of IT systems in infection surveillance and prevention and control.
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