Yang Jinxi, Zeng Siyao, Cui Shanpeng, Zheng Junbo, Wang Hongliang
The Second Clinical Medical College, Harbin Medical University, Heilongjiang Province, Harbin, China.
Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang Province, Harbin, China.
J Med Internet Res. 2025 May 13;27:e66615. doi: 10.2196/66615.
Acute respiratory distress syndrome (ARDS) is a critical condition commonly encountered in the intensive care unit (ICU), characterized by a high incidence and substantial mortality rate. Early detection and accurate prediction of ARDS can significantly improve patient outcomes. While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of consensus on the most effective model or methodology. This study is the first to systematically evaluate the performance of ARDS prediction models based on multiple quantitative data sources. We compare the effectiveness of ML models via a meta-analysis, revealing factors affecting performance and suggesting strategies to enhance generalization and prediction accuracy.
This study aims to evaluate the performance of existing ARDS prediction models through a systematic review and meta-analysis, using metrics such as area under the receiver operating characteristic curve, sensitivity, specificity, and other relevant indicators. The findings will provide evidence-based insights to support the development of more accurate and effective ARDS prediction tools.
We performed a search across 6 electronic databases for studies developing ML predictive models for ARDS, with a cutoff date of December 29, 2024. The risk of bias in these models was evaluated using the Prediction model Risk of Bias Assessment Tool. Meta-analyses and investigations into heterogeneity were carried out using Meta-DiSc software (version 1.4), developed by the Ramón y Cajal Hospital's Clinical Biostatistics team in Madrid, Spain. Furthermore, sensitivity, subgroup, and meta-regression analyses were used to explore the sources of heterogeneity more comprehensively.
ML models achieved a pooled area under the receiver operating characteristic curve of 0.7407 for ARDS. The additional metrics were as follows: sensitivity was 0.67 (95% CI 0.66-0.67; P<.001; I²=97.1%), specificity was 0.68 (95% CI 0.67-0.68; P<.001; I²=98.5%), the diagnostic odds ratio was 6.26 (95% CI 4.93-7.94; P<.001; I²=95.3%), the positive likelihood ratio was 2.80 (95% CI 2.46-3.19; P<.001; I²=97.3%), and the negative likelihood ratio was 0.51 (95% CI 0.46-0.57; P<.001; I²=93.6%).
This study evaluates prediction models constructed using various ML algorithms, with results showing that ML demonstrates high performance in ARDS prediction. However, many of the existing models still have limitations. During model development, it is essential to focus on model quality, including reducing bias risk, designing appropriate sample sizes, conducting external validation, and ensuring model interpretability. Additionally, challenges such as physician trust and the need for prospective validation must also be addressed. Future research should standardize model development, optimize model performance, and explore how to better integrate predictive models into clinical practice to improve ARDS diagnosis and risk stratification.
PROSPERO CRD42024529403; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024529403.
急性呼吸窘迫综合征（ARDS）是重症监护病房（ICU）中常见的危急病症，具有高发病率和高死亡率的特点。早期发现和准确预测ARDS可显著改善患者预后。虽然机器学习（ML）模型越来越多地用于ARDS预测，但对于最有效的模型或方法尚无共识。本研究首次基于多个定量数据源系统评估ARDS预测模型的性能。我们通过荟萃分析比较ML模型的有效性，揭示影响性能的因素，并提出提高泛化能力和预测准确性的策略。
本研究旨在通过系统评价和荟萃分析，使用受试者操作特征曲线下面积、敏感性、特异性等指标及其他相关指标，评估现有ARDS预测模型的性能。研究结果将提供基于证据的见解，以支持开发更准确有效的ARDS预测工具。
我们在6个电子数据库中进行检索，查找截至2024年12月29日开发用于ARDS的ML预测模型的研究。使用预测模型偏倚风险评估工具评估这些模型的偏倚风险。使用西班牙马德里拉蒙·伊·卡哈尔医院临床生物统计学团队开发的Meta-DiSc软件（版本1.4）进行荟萃分析和异质性调查。此外，使用敏感性分析分组分析和荟萃回归分析更全面地探索异质性来源。
ML模型对ARDS的受试者操作特征曲线下合并面积为0.7407。其他指标如下：敏感性为0.67（95%CI 0.66 - 0.67；P <.001；I² = 97.1%），特异性为0.68（95%CI 0.67 - 0.68；P <.001；I² = 98.5%），诊断比值比为6.26（95%CI 4.93 - 7.94；P <.001；I² = 95.3%），阳性似然比为2.80（95%CI 2.46 - 3.19；P <.001；I² = 97.3%），阴性似然比为~0.51（95%CI 0.46 - 0.57；P <.001；I² = 93.6%）。
本研究评估了使用各种ML算法构建的预测模型，结果表明ML在ARDS预测中表现出高性能。然而，许多现有模型仍有局限性。在模型开发过程中，必须注重模型质量，包括降低偏倚风险、设计合适的样本量、进行外部验证以及确保模型可解释性。此外，还必须解决诸如医生信任和前瞻性验证需求等挑战。未来研究应规范模型开发，优化模型性能，并探索如何更好地将预测模型整合到临床实践中，以改善ARDS诊断和风险分层。
PROSPERO CRD42024529403；https://www.crd.york.ac.uk/PROSPERO/view/CRD42024529403 。
