Table 2_Machine learning approaches for risk prediction in aortic dissection: a systematic review and meta-analysis.docx

BackgroundAortic dissection (AD) is a life-threatening cardiovascular emergency with high morbidity and mortality. Accurate risk prediction is essential for timely intervention, yet traditional statistical models often fail to capture the complex, nonlinear interactions inherent in AD pathophysiology. In recent years, machine learning (ML) has emerged as a promising approach to improve prognostic accuracy. However, the overall performance, methodological quality, and clinical applicability of ML-based prediction models for AD have not been comprehensively evaluated.
ObjectiveThis systematic review and meta-analysis followed PRISMA, CHARMS, and TRIPOD guidelines and was registered with PROSPERO (CRD420251154262). Six major databases (PubMed, Web of Science, Cochrane Library, Embase, CNKI, Wanfang) were searched from inception to September 30, 2025. Studies developing or validating ML models for predicting adverse outcomes in AD were included. Data extraction adhered to CHARMS, and risk of bias was assessed using PROBAST. Meta-analysis synthesized C-statistics (AUC) using fixed- or random-effects models depending on heterogeneity. Subgroup, sensitivity, and publication bias analyses were performed.
ResultsForty studies were included, covering outcomes such as early mortality, long-term mortality, acute kidney injury (AKI), neurological complications, gastrointestinal bleeding, mesenteric malperfusion, and composite adverse events. ML algorithms included random forest, SVM, XGBoost, LightGBM, neural networks, and ensemble approaches. The pooled C-statistic demonstrated excellent discriminative performance for early mortality (0.891, 95% CI: 0.854–0.927) and long-term mortality (0.847, 95% CI: 0.794–0.900), and strong performance for AKI prediction (0.825, 95% CI: 0.756–0.894). Many complication-specific models achieved AUCs above 0.90. However, these estimates must be interpreted with extreme caution. Significant heterogeneity was observed across analyses (I2 = 61.3–78.8%), and the PROBAST assessment revealed that 100% (40/40) of studies were at high or unclear risk of bias, predominantly due to deficiencies in the analysis domain (e.g., inadequate events-per-variable, lack of external validation). Adherence to TRIPOD reporting standards was suboptimal (average 78.7%), with critical shortcomings in reporting predictor definitions (62.5% unreported), sample size justification (82.5% unreported), and full model specifications (72.5% unreported). Methodological limitations were common, including inadequate events-per-variable ratios, a near-absence of robust external validation (only 5 of 40 studies), inconsistent outcome definitions, and incomplete reporting of model specifications. Furthermore, over a quarter (27.5%) of models omitted calibration assessment, and decision-curve analysis was rarely performed, limiting insights into clinical utility.
ConclusionML-based prediction models demonstrate strong potential for risk stratification in AD across multiple clinically relevant outcomes. However, current evidence does not justify their routine clinical implementation. The high reported performance metrics are likely optimistic estimates derived from methodologically weak studies. Future research should emphasize rigorous analytic frameworks, standardized outcome definitions, transparent reporting, and, most critically, multicenter external validation before these tools can be considered for real-world clinical utility.
Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251154262, identifier CRD420251154262.

背景 主动脉夹层（Aortic dissection, AD）是一类危及生命的心血管急症，具有较高的发病率与死亡率。精准的风险预测对于及时干预至关重要，但传统统计模型往往无法捕捉主动脉夹层病理生理学中固有的复杂非线性相互作用。近年来，机器学习（Machine Learning, ML）已成为提升预后预测准确性的极具潜力的手段。然而，目前尚未有研究对主动脉夹层相关机器学习预测模型的整体性能、方法学质量及临床适用性进行全面评估。
目的 本系统评价与荟萃分析遵循PRISMA、CHARMS及TRIPOD指南，并已在PROSPERO平台注册（注册号：CRD420251154262）。检索了自建库至2025年9月30日的6个主流数据库，包括PubMed、Web of Science、Cochrane Library、Embase、CNKI及Wanfang。纳入所有开发或验证用于预测主动脉夹层不良结局的机器学习模型的研究。数据提取严格遵循CHARMS指南，并采用PROBAST工具评估偏倚风险。荟萃分析根据异质性情况，采用固定效应模型或随机效应模型合并C统计量（受试者工作特征曲线下面积，Area Under the Receiver Operating Characteristic Curve, AUC）。此外，还进行了亚组分析、敏感性分析及发表偏倚评估。
结果 本研究共纳入40项研究，覆盖的结局指标包括早期死亡率、长期死亡率、急性肾损伤（Acute kidney injury, AKI）、神经系统并发症、胃肠道出血、肠系膜灌注不良及复合不良事件。所涉及的机器学习算法包括随机森林、支持向量机（Support Vector Machine, SVM）、XGBoost、LightGBM、神经网络及集成学习方法。合并后的C统计量显示，模型对早期死亡率（合并C值=0.891，95%置信区间：0.854~0.927）与长期死亡率（合并C值=0.847，95%置信区间：0.794~0.900）具有极佳的区分性能，对急性肾损伤预测的性能亦较为优异（合并C值=0.825，95%置信区间：0.756~0.894）。多数针对特定并发症的模型AUC值均高于0.90。但对该结果的解读需格外谨慎：各项分析间存在显著异质性（I²=61.3%~78.8%），且PROBAST评估显示，100%（40/40）的研究存在高或不明偏倚风险，主要源于分析领域的缺陷，例如每变量事件数不足、缺乏外部验证。研究对TRIPOD报告标准的依从性欠佳（平均依从率78.7%），在预测因子定义（62.5%未报告）、样本量合理性说明（82.5%未报告）及完整模型规格（72.5%未报告）方面存在关键缺陷。方法学局限性较为普遍，包括每变量事件数比例不足、几乎未开展严格的外部验证（40项研究中仅5项）、结局定义不统一以及模型规格报告不完整。此外，超过四分之一（27.5%）的模型未进行校准评估，且极少开展决策曲线分析，这限制了对模型临床实用性的深入认识。
结论 基于机器学习的预测模型在多种临床相关结局的主动脉夹层风险分层中展现出良好的应用潜力。但目前的证据尚不足以支持其在临床常规应用。已报道的高性能指标很可能源于方法学存在缺陷的研究，属于偏乐观的估计。未来相关研究应注重构建严谨的分析框架、统一结局定义、确保报告透明性，而最为关键的是，在这些工具能够投入实际临床应用前，需开展多中心外部验证。
系统评价注册信息 注册链接：https://www.crd.york.ac.uk/PROSPERO/view/CRD420251154262，注册号：CRD420251154262。