Development and Application of Pharmacological Statin-Associated Muscle Symptoms Phenotyping Algorithms Using Structured and Unstructured Electronic Health Records Data

Abstract

Background: Statins are widely prescribed cholesterol-lowering medications in the US, but their clinical benefits can be diminished by statin-associated muscle symptoms (SAMS), leading to discontinuation. In this study, we aimed to develop and validate a pharmacological SAMS clinical phenotyping algorithm using electronic health records (EHRs) data from Minnesota Fairview. Methods: We retrieved structured and unstructured EHR data of statin users and manually ascertained a gold standard set of SAMS cases and controls using the SAMS-CI tool from clinical notes in 200 patients. We developed machine learning algorithms and rule-based algorithms that incorporated various criteria, including ICD codes, statin allergy, creatine kinase elevation, and keyword mentions in clinical notes. We applied the best performing algorithm to the statin cohort to identify SAMS. Results: We identified 16,889 patients who started statins in the Fairview EHR system from 2010-2020. The combined rule-based (CRB) algorithm, which utilized both clinical notes and structured data criteria, achieved similar performance compared to machine learning algorithms with a precision of 0.85, recall of 0.71, and F1 score of 0.77 against the gold standard set. Applying the CRB algorithm to the statin cohort, we identified the pharmacological SAMS prevalence to be 1.9% and selective risk factors which included female gender, coronary artery disease, hypothyroidism, use of immunosuppressants or fibrates. Conclusion: Our study developed and validated a simple pharmacological SAMS phenotyping algorithm that can be used to create SAMS case/control cohort for further analysis such as developing SAMS risk prediction model.

Development and validation of the pharmacological statin-associated muscle symptoms risk stratification (PSAMS-RS) score using real-world electronic health record data

“…The cohort identification flow diagram from our previous PSAMS phenotyping study provides a detailed overview of the patient selection process. 8 Among the final included patients, 1.9% (310/16128) in the derivation and 1.5% (64/4182) in the validation cohort were identified as PSAMS cases. BMI is body mass index, IQR is interquartile range, ACE is angiotensin-converting enzymes.…”

“…3 Previously, our research team accomplished the development of a PSAMS phenotyping algorithm based on the University of Minnesota (UMN) Fairview EHR system. 8 Our PSAMS algorithm, developed based on an annotated gold-standard set using the SAMS-Clinical Index (SAMS-CI) tool 9 , is capable of specifically phenotyping non-nocebo PSAMS. 8 This approach allowed for a more precise identification of objective muscle symptoms pharmacologically related to statin use.…”

“…8 Our PSAMS algorithm, developed based on an annotated gold-standard set using the SAMS-Clinical Index (SAMS-CI) tool 9 , is capable of specifically phenotyping non-nocebo PSAMS. 8 This approach allowed for a more precise identification of objective muscle symptoms pharmacologically related to statin use.…”

“…The precise identification of PSAMS cases and controls was accomplished by our PSAMS phenotyping algorithm. 8 The development of the PSAMS-RS score can enable healthcare providers to proactively identify individuals who may be at higher risk of PSAMS and thereby tailor their treatment approaches accordingly to improve statin adherence.…”