Abstract
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.
Highlights
Statin therapy is the cornerstone of preventing atherosclerotic cardiovascular disease (ASCVD), primarily by reducing low density lipoprotein cholesterol (LDL-C) levels
Prior CVD was defined by the International Classification of Diseases, 9th and 10th revision (ICD-9-CM/ICD-10-CM) coding scheme as per the 2013 American College of Cardiology/American Heart Association Guideline on the Assessment of Cardiovascular Risk (CV conditions excluded from the derivation cohort of the Pooled Cohort Equations (PCE) and from PCE application; Supplementary Table 1)17
Using aggregate patient data from a cohort of similar patients, we developed a personalized statin recommendation approach for primary ASCVD prevention that estimated LDL-C outcomes across different statin treatment strategies and recommended the intensity associated with the highest LDL-C reduction
Summary
Statin therapy is the cornerstone of preventing atherosclerotic cardiovascular disease (ASCVD), primarily by reducing low density lipoprotein cholesterol (LDL-C) levels. 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. For a given patient, determining the statin recommendation associated with optimal real-world outcomes— and overcoming discrepancies between guidelines and real-world effectiveness—is crucial for decision-making To bridge this gap, aggregate historical real-world data may help understand prior treatment responses and guide personalized decision making by incorporating real-world outcomes in areas of therapeutic u ncertainty. Machine learning (ML) can leverage aggregate outcomes to understand prior responses to therapies, which may help guide patient-clinician discussions11–16 These approaches are likely most helpful in primary ASCVD prevention, when there may be more uncertainty and ambiguity about the best treatment options, in understudied racial/ethnic groups. We hypothesized that there would be differences in personalized statin recommendations across patients which are not fully explained by the expected LDL-C lowering of each statin intensity
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
