Reuter Anna, Ali Mohammed K, Mohan Viswanathan, Chwastiak Lydia, Singh Kavita, Narayan K M Venkat, Prabhakaran Dorairaj, Tandon Nikhil, Sudharsanan Nikkil
German Federal Institute of Population Research, Wiesbaden, Germany.
Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany.
NPJ Digit Med. 2024 Dec 10;7(1):357. doi: 10.1038/s41746-024-01353-9.
A substantial share of patients at risk of developing cardiovascular disease (CVD) fail to achieve control of CVD risk factors, but clinicians lack a structured approach to identify these patients. We applied machine learning to longitudinal data from two completed randomized controlled trials among 1502 individuals with diabetes in urban India and Pakistan. Using commonly available clinical data, we predict each individual's risk of failing to achieve CVD risk factor control goals or meaningful improvements in risk factors at one year after baseline. When classifying those in the top quartile of predicted risk scores as at risk of failing to achieve goals or meaningful improvements, the precision for not achieving goals was 73% for HbA1c, 30% for SBP, and 24% for LDL, and for not achieving meaningful improvements 88% for HbA1c, 87% for SBP, and 85% for LDL. Such models could be integrated into routine care and enable efficient and targeted delivery of health resources in resource-constrained settings.
很大一部分有患心血管疾病（CVD）风险的患者未能实现对CVD风险因素的控制，但临床医生缺乏一种结构化方法来识别这些患者。我们将机器学习应用于印度城市和巴基斯坦1502名糖尿病患者的两项已完成的随机对照试验的纵向数据。利用常见的临床数据，我们预测了每位个体在基线后一年未能实现CVD风险因素控制目标或风险因素有意义改善的风险。当将预测风险评分处于前四分位数的个体分类为有未能实现目标或有意义改善的风险时，HbA1c未实现目标的精确率为73%，收缩压（SBP）为30%，低密度脂蛋白（LDL）为24%；对于未实现有意义改善，HbA1c为88%，SBP为87%，LDL为85%。此类模型可整合到常规护理中，并能在资源有限的环境中实现卫生资源的高效和有针对性的分配。
