Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.

IF 1.4 4区医学 Q4 ENGINEERING, BIOMEDICAL

Technology and Health Care Pub Date : 2024-01-01 DOI:10.3233/THC-240087

Hongru Zhang, Chen Wang, Ning Yang

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引用次数: 0

Abstract

Background: Early identification of sepsis has been shown to significantly improve patient prognosis.

Objective: Therefore, the aim of this meta-analysis is to systematically evaluate the diagnostic efficacy of machine-learning algorithms for sepsis prediction.

Methods: Systematic searches were conducted in PubMed, Embase and Cochrane databases, covering literature up to December 2023. The keywords included machine learning, sepsis and prediction. After screening, data were extracted and analysed from studies meeting the inclusion criteria. Key evaluation metrics included sensitivity, specificity and the area under the curve (AUC) for diagnostic accuracy.

Results: The meta-analysis included a total of 21 studies with a data sample size of 4,158,941. Overall, the pooled sensitivity was 0.82 (95% confidence interval [CI] = 0.70-0.90; P< 0.001; I2= 99.7%), the specificity was 0.91 (95% CI = 0.86-0.94; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.91-0.96). The subgroup analysis revealed that in the emergency department setting (6 studies), the pooled sensitivity was 0.79 (95% CI = 0.68-0.87; P< 0.001; I2= 99.6%), the specificity was 0.94 (95% CI 0.90-0.97; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.92-0.96). In the Intensive Care Unit setting (11 studies), the sensitivity was 0.91 (95% CI = 0.75-0.97; P< 0.001; I2= 98.3%), the specificity was 0.85 (95% CI = 0.75-0.92; P< 0.001; I2= 99.9%), and the AUC was 0.93 (95% CI = 0.91-0.95). Due to the limited number of studies in the in-hospital and mixed settings (n< 3), no pooled analysis was performed.

Conclusion: Machine-learning algorithms have demonstrated excellent diagnostic accuracy in predicting the occurrence of sepsis, showing potential for clinical application.

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用于败血症预测的机器学习算法的诊断性能：最新荟萃分析。

背景：早期识别败血症可显著改善患者预后：脓毒症的早期识别已被证明能显著改善患者的预后：因此，本荟萃分析旨在系统评估脓毒症预测机器学习算法的诊断效果：在 PubMed、Embase 和 Cochrane 数据库中进行了系统检索，涵盖截至 2023 年 12 月的文献。关键词包括机器学习、败血症和预测。经过筛选，从符合纳入标准的研究中提取数据并进行分析。主要评价指标包括灵敏度、特异性和诊断准确性曲线下面积（AUC）：荟萃分析共纳入 21 项研究，数据样本量为 4,158,941 个。总体而言，汇总灵敏度为 0.82（95% 置信区间 [CI] = 0.70-0.90；P< 0.001；I2=99.7%），特异度为 0.91（95% CI = 0.86-0.94；P< 0.001；I2=99.9%），AUC 为 0.94（95% CI = 0.91-0.96）。亚组分析显示，在急诊科环境中（6 项研究），汇总灵敏度为 0.79（95% CI = 0.68-0.87；P< 0.001；I2= 99.6%），特异性为 0.94（95% CI 0.90-0.97；P< 0.001；I2= 99.9%），AUC 为 0.94（95% CI = 0.92-0.96）。在重症监护室环境中（11 项研究），灵敏度为 0.91（95% CI = 0.75-0.97；P< 0.001；I2= 98.3%），特异性为 0.85（95% CI = 0.75-0.92；P< 0.001；I2= 99.9%），AUC 为 0.93（95% CI = 0.91-0.95）。由于院内和混合环境中的研究数量有限（n< 3），因此没有进行汇总分析：机器学习算法在预测败血症的发生方面表现出了极高的诊断准确性，显示出了临床应用的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Technology and Health Care HEALTH CARE SCIENCES & SERVICES-ENGINEERING, BIOMEDICAL

CiteScore

2.10

自引率

6.20%

发文量

282

审稿时长

>12 weeks

期刊介绍： Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured. The main focus of THC is related to the overlapping areas of engineering and medicine. The following types of contributions are considered: 1.Original articles: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine. In particular, the clinical benefit deriving from the application of engineering methods and devices in clinical medicine should be demonstrated. Typically, full length original contributions have a length of 4000 words, thereby taking duly into account figures and tables. 2.Technical Notes and Short Communications: Technical Notes relate to novel technical developments with relevance for clinical medicine. In Short Communications, clinical applications are shortly described. 3.Both Technical Notes and Short Communications typically have a length of 1500 words. Reviews and Tutorials (upon invitation only): Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented. The Editorial Board is responsible for the selection of topics. 4.Minisymposia (upon invitation only): Under the leadership of a Special Editor, controversial or important issues relating to health care are highlighted and discussed by various authors. 5.Letters to the Editors: Discussions or short statements (not indexed).