机器学习(ML)越来越多地用于子类型定义和风险预测的研究,尤其是心血管疾病。没有现有的ML模型常规用于心血管疾病管理,他们的临床应用阶段是未知的,部分原因是缺乏明确的标准。我们评估了ML的亚型定义和心力衰竭(HF)的风险预测,急性冠状动脉综合征(ACS)和心房颤动(AF)。
对于亚型定义和风险预测的ML研究,我们对HF进行了系统的审查,ACS和AF,使用PubMed,MEDLINE和WebofScience从2000年1月到2019年12月。通过调整已发布的诊断和预后研究标准,我们开发了一个七域,ML特定的检查表。
在确定的5918项研究中,97包括在内。在亚型定义(n=40)和风险预测(n=57)的研究中,数据源有变化,人口规模(中位数606和中位数6769),临床设置(门诊,住院,不同部门),协变量数(中位数19和中位数48)和ML方法。所有研究都是单一疾病,大多数是北美(n=61/97),只有14项研究结合了定义和风险预测.亚型定义和风险预测研究在发展中分别存在局限性(例如,15.0%和78.9%的研究与患者受益相关;15.0%和15.8%的患者选择偏倚较低),验证(12.5%和5.3%外部验证)和影响(32.5%和91.2%改善了结果预测;没有有效性或成本效益评估).
HF中ML的研究,ACS和AF受包含协变量的数量和类型的限制,ML方法,人口规模,国家,临床设置和关注单一疾病,不重叠或多浊度。临床效用和实施依赖于开发的改进,验证和影响,由简单的清单促进。在心血管疾病和其他疾病领域的临床实践中安全实施机器学习之前,我们提供了明确的步骤。
Machine learning (ML) is increasingly used in research for
subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for
subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF).
For ML studies of
subtype definition and risk prediction, we conducted a systematic
review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist.
Of 5918 studies identified, 97 were included. Across studies for
subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction.
Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations).
Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.