基于人工智能的心血管疾病风险预测工具的范围综述

摘要(中文译文)

心血管疾病（CVD）是全球死亡的主要原因，因此早期风险预测对于改善预后至关重要。尽管人工智能（AI）模型有望改善预测，但在可解释性、风险因素的可靠性以及交叉验证的必要性方面仍存在问题。本范围界定综述考察了使用基于人工智能的预后模型预测未确诊CVD患者CVD风险的研究的范围、类型和报告质量。对PubMed、IEEE Xplore、Web of Science、Scopus和Google Scholar的系统搜索确定了6710条记录。筛选和数据提取遵循PRISMA ScR框架和JBI范围界定审查指南。根据TRIPOD-AI报告清单对纳入的研究进行了评估。审查方案已在开放科学框架（OSF：https://osf.io/8nq6s/).30项研究符合纳入标准；都是2017年以后出版的。大多数研究利用了大型数据集上的现有模型，主要利用单峰临床数据和已建立的机器学习算法，如随机森林和支持向量机。30项研究中有24项使用了单峰方法，6项多模态研究表现出了一贯的强劲表现，但它们都基于一组小而异质的研究。12项研究与Framingham风险评分等传统风险评分进行了直接比较，显示出可比或适度改善的辨别力，尽管方法论的异质性限制了这些结论的强度。只有7项研究报告了外部验证，30项纳入的研究中都没有报告校准（预测和观察到的事件率之间的一致性）。敏感性决定了模型识别真正高风险个体的能力，是报告最少的指标，仅出现在四项研究中，没有研究报告了决策曲线分析。基于人工智能的CVD风险预测工具显示出希望，但在验证、校准报告和临床效用评估方面存在关键差距，目前阻碍了临床部署。未来的研究应优先考虑不同人群的外部验证，强制报告校准和敏感性以及歧视指标，以及采用报告标准和评估工具，如个人预后或诊断的多变量预测模型的透明报告 + 人工智能（TRIPOD + AI）或预测模型偏差风险评估工具 + 人工智能（PROAST + AI）以提高透明度和可重复性。

Abstract

Cardiovascular disease (CVD) is a leading cause of death worldwide, making early risk prediction essential for improving outcomes. Although artificial intelligence (AI) models promise to improve predictions, questions remain about interpretability, the reliability of risk factors, and the need for cross-validation. This scoping review examined the extent, types, and reporting quality of studies that used AI-based prognostic models to predict CVD risk in individuals without established CVD. A systematic search of PubMed, IEEE Xplore, Web of Science, Scopus, and Google Scholar identified 6,710 records. Screening and data extraction followed the PRISMA-ScR framework and JBI Guidelines for Scoping Reviews. Included studies were evaluated against the TRIPOD-AI reporting checklist. The review protocol was registered with the Open Science Framework (OSF: https://osf.io/8nq6s/). Thirty studies met the inclusion criteria; all published after 2017. Most studies utilized existing models on large datasets, predominantly leveraging unimodal clinical data and established machine learning algorithms such as Random Forests and Support Vector Machines. Twenty-four of the 30 studies used unimodal approaches, and the six multimodal studies demonstrated consistently strong performance, but they rest on a small, heterogeneous set of studies. Twelve studies conducted direct comparisons with traditional risk scores such as the Framingham Risk Score, showing comparable or modestly improved discrimination, although methodological heterogeneity limits the strength of these conclusions. External validation was reported in only seven studies, and calibration (agreement between the predicted and observed event rates) was not reported in any of the 30 included studies. Sensitivity, which determines a model's ability to identify truly high-risk individuals, was the least reported metric, appearing in only four studies, and no study reported a decision curve analysis. AI-based CVD risk prediction tools show promise but have critical gaps in validation, calibration reporting, and clinical utility assessment, which currently preclude clinical deployment. Future research should prioritize external validation across diverse populations, mandatory reporting of calibration and sensitivity alongside discrimination metrics, as well as the adoption of reporting standards and appraisal tools such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis + Artificial Intelligence (TRIPOD + AI) or Prediction Model Risk of Bias Assessment Tool + Artificial Intelligence (PROBAST + AI) to improve transparency and reproducibility.

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