Development and Validation of the Pharmacological Statin‐Associated Muscle Symptoms Risk Stratification Score Using Electronic Health Record Data
Fuente:
Wiley Open Access
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Artículo Open Access |
| Pubblicazione: |
Wiley
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1867005688895504384 |
|---|---|
| author | Boguang Sun Pui Ying Yew Chih‐Lin Chi Meijia Song Matt Loth Yue Liang Rui Zhang Robert J. Straka |
| author_facet | Boguang Sun Pui Ying Yew Chih‐Lin Chi Meijia Song Matt Loth Yue Liang Rui Zhang Robert J. Straka Boguang Sun Pui Ying Yew Chih‐Lin Chi Meijia Song Matt Loth Yue Liang Rui Zhang Robert J. Straka |
| collection | Wiley Open Access |
| contents | Development and Validation of the Pharmacological Statin‐Associated Muscle Symptoms Risk Stratification Score Using Electronic Health Record Data Boguang Sun Pui Ying Yew Chih‐Lin Chi Meijia Song Matt Loth Yue Liang Rui Zhang Robert J. Straka Clinical Pharmacology & Therapeutics Statin‐associated muscle symptoms (SAMS) can lead to statin nonadherence. This paper aims to develop a pharmacological SAMS risk stratification (PSAMS‐RS) score using a previously developed PSAMS phenotyping algorithm that distinguishes objective vs. nocebo SAMS using electronic health record (EHR) data. Using our PSAMS phenotyping algorithm, SAMS cases and controls were identified from Minnesota Fairview EHR, with the statin user cohort divided into derivation (January 1, 2010, to December 31, 2018) and validation (January 1, 2019, to December 31, 2020) cohorts. A Least Absolute Shrinkage and Selection Operator regression model was applied to identify significant features for PSAMS. PSAMS‐RS scores were calculated and the clinical utility of stratifying PSAMS risk was assessed by comparing hazard ratios (HRs) between fourth vs. first score quartiles. PSAMS cases were identified in 1.9% (310/16,128) of the derivation and 1.5% (64/4,182) of the validation cohorts. Sixteen out of 38 clinical features were determined to be significant predictors for PSAMS risk. Patients within the fourth quartile of the PSAMS scores had an over sevenfold (HR: 7.1, 95% confidence interval (CI): 4.03–12.45, derivation cohort) or sixfold (HR: 6.1, 95% CI: 2.15–17.45, validation cohort) higher hazard of developing PSAMS vs. those in their respective first quartile. The PSAMS‐RS score is a simple tool to stratify patients' risk of developing PSAMS after statin initiation which could inform clinician‐guided pre‐emptive measures to prevent PSAMS‐related statin nonadherence. 10.1002/cpt.3208 http://creativecommons.org/licenses/by-nc/4.0/ |
| doi_str_mv | 10.1002/cpt.3208 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_cpt_3208 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc/4.0/ |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Development and Validation of the Pharmacological Statin‐Associated Muscle Symptoms Risk Stratification Score Using Electronic Health Record Data Boguang Sun Pui Ying Yew Chih‐Lin Chi Meijia Song Matt Loth Yue Liang Rui Zhang Robert J. Straka Clinical Pharmacology & Therapeutics Development and Validation of the Pharmacological Statin‐Associated Muscle Symptoms Risk Stratification Score Using Electronic Health Record Data Boguang Sun Pui Ying Yew Chih‐Lin Chi Meijia Song Matt Loth Yue Liang Rui Zhang Robert J. Straka Clinical Pharmacology & Therapeutics Statin‐associated muscle symptoms (SAMS) can lead to statin nonadherence. This paper aims to develop a pharmacological SAMS risk stratification (PSAMS‐RS) score using a previously developed PSAMS phenotyping algorithm that distinguishes objective vs. nocebo SAMS using electronic health record (EHR) data. Using our PSAMS phenotyping algorithm, SAMS cases and controls were identified from Minnesota Fairview EHR, with the statin user cohort divided into derivation (January 1, 2010, to December 31, 2018) and validation (January 1, 2019, to December 31, 2020) cohorts. A Least Absolute Shrinkage and Selection Operator regression model was applied to identify significant features for PSAMS. PSAMS‐RS scores were calculated and the clinical utility of stratifying PSAMS risk was assessed by comparing hazard ratios (HRs) between fourth vs. first score quartiles. PSAMS cases were identified in 1.9% (310/16,128) of the derivation and 1.5% (64/4,182) of the validation cohorts. Sixteen out of 38 clinical features were determined to be significant predictors for PSAMS risk. Patients within the fourth quartile of the PSAMS scores had an over sevenfold (HR: 7.1, 95% confidence interval (CI): 4.03–12.45, derivation cohort) or sixfold (HR: 6.1, 95% CI: 2.15–17.45, validation cohort) higher hazard of developing PSAMS vs. those in their respective first quartile. The PSAMS‐RS score is a simple tool to stratify patients' risk of developing PSAMS after statin initiation which could inform clinician‐guided pre‐emptive measures to prevent PSAMS‐related statin nonadherence. 10.1002/cpt.3208 http://creativecommons.org/licenses/by-nc/4.0/ |
| title | Development and Validation of the Pharmacological Statin‐Associated Muscle Symptoms Risk Stratification Score Using Electronic Health Record Data |
| topic | Clinical Pharmacology & Therapeutics |
| url | https://ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.3208 |