Division of Cardiovascular Medicine and the Cardiovascular Institute, Center for Academic Medicine, Stanford University School of Medicine, 453 Quarry Road, Stanford, CA, USA.
Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Sci Rep. 2022 Jan 7;12(1):23. doi: 10.1038/s41598-021-03796-6.
Statin therapy is the cornerstone of preventing atherosclerotic cardiovascular disease (ASCVD), primarily by reducing low density lipoprotein cholesterol (LDL-C) levels. Optimal statin therapy decisions rely on shared decision making and may be uncertain for a given patient. In areas of clinical uncertainty, personalized approaches based on real-world data may help inform treatment decisions. We sought to develop a personalized statin recommendation approach for primary ASCVD prevention based on historical real-world outcomes in similar patients. Our retrospective cohort included adults from a large Northern California electronic health record (EHR) aged 40-79 years with no prior cardiovascular disease or statin use. The cohort was split into training and test sets. Weighted-K-nearest-neighbor (wKNN) regression models were used to identify historical EHR patients similar to a candidate patient. We modeled four statin decisions for each patient: none, low-intensity, moderate-intensity, and high-intensity. For each candidate patient, the algorithm recommended the statin decision that was associated with the greatest percentage reduction in LDL-C after 1 year in similar patients. The overall cohort consisted of 50,576 patients (age 54.6 ± 9.8 years) with 55% female, 48% non-Hispanic White, 32% Asian, and 7.4% Hispanic patients. Among 8383 test-set patients, 52%, 44%, and 4% were recommended high-, moderate-, and low-intensity statins, respectively, for a maximum predicted average 1-yr LDL-C reduction of 16.9%, 20.4%, and 14.9%, in each group, respectively. Overall, using aggregate EHR data, a personalized statin recommendation approach identified the statin intensity associated with the greatest LDL-C reduction in historical patients similar to a candidate patient. Recommendations included low- or moderate-intensity statins for maximum LDL-C lowering in nearly half the test set, which is discordant with their expected guideline-based efficacy. A data-driven personalized statin recommendation approach may inform shared decision making in areas of uncertainty, and highlight unexpected efficacy-effectiveness gaps.
他汀类药物治疗是预防动脉粥样硬化性心血管疾病（ASCVD）的基石，主要通过降低低密度脂蛋白胆固醇（LDL-C）水平。最佳的他汀类药物治疗决策依赖于共同决策，对于特定患者可能存在不确定性。在临床不确定的情况下，基于真实世界数据的个性化方法可能有助于为治疗决策提供信息。我们旨在基于类似患者的历史真实世界结果，为 ASCVD 的一级预防开发一种个体化的他汀类药物推荐方法。我们的回顾性队列纳入了来自加利福尼亚州北部一个大型电子健康记录（EHR）的 40-79 岁、无既往心血管疾病或他汀类药物使用史的成年人。该队列分为训练集和测试集。加权-K 最近邻（wKNN）回归模型用于识别与候选患者相似的历史 EHR 患者。我们为每位患者建立了四种他汀类药物决策模型：不使用、低强度、中强度和高强度。对于每位候选患者，该算法推荐在类似患者中使用与 LDL-C 降低率最大相关的他汀类药物决策。总体队列包括 50576 名患者（年龄 54.6±9.8 岁），其中 55%为女性，48%为非西班牙裔白人，32%为亚洲人，7.4%为西班牙裔。在 8383 名测试集患者中，分别有 52%、44%和 4%被推荐使用高强度、中强度和低强度他汀类药物，预测最大平均 1 年 LDL-C 降低率分别为 16.9%、20.4%和 14.9%。总体而言，使用汇总 EHR 数据，一种个体化的他汀类药物推荐方法确定了与历史患者相似的候选患者 LDL-C 降低率最大的他汀类药物强度。推荐包括低强度或中强度他汀类药物，以最大限度地降低 LDL-C，在近一半的测试集中，这与基于指南的预期疗效不一致。数据驱动的个体化他汀类药物推荐方法可以为不确定领域的共同决策提供信息，并突出显示意想不到的疗效-有效性差距。
