OBJECTIVES: To develop and validate algorithms to define statin intolerance (SI) in an administrative database using electronic medical records (EMRs) as the reference comparison. METHODS: One thousand adults with one or more qualifying changes in statin therapy and one or more previous diagnoses of hyperlipidemia, hypercholesterolemia, or mixed dyslipidemia were identified from the Henry Ford Health System administrative database. Data regarding statin utilization, comorbidities, and adverse effects were extracted from the administrative database and corresponding EMR. Patients were stratified by cardiovascular (CV) risk. SI was classified as absolute intolerance or titration intolerance on the basis of changes in statin utilization and/or the occurrence of adverse effects and laboratory testing for creatine kinase. Measures of concordance (Cohen's kappa [κ]) and accuracy (sensitivity, specificity, positive predictive value [PPV], and negative predictive value) were calculated for the administrative database algorithms. RESULTS: Half of the sample population was white, 52.9% were women, mean age was 60.6 years, and 35.7% were at high CV risk. SI was identified in 11.5% and 14.0%, absolute intolerance in 2.2% and 3.1%, and titration intolerance in 9.7% and 11.8% of the patients in the EMR and the administrative database, respectively. The algorithm identifying any SI had substantial concordance (κ = 0.66) and good sensitivity (78.1%), but modest PPV (64.0%). The titration intolerance algorithm performed better (κ = 0.74; sensitivity 85.4%; PPV 70.1%) than the absolute intolerance algorithm (κ = 0.40; sensitivity 50%; PPV 35.5%) and performed best in the high CV-risk group (n = 353), with robust concordance (κ = 0.73) and good sensitivity (80.9%) and PPV (75.3%). CONCLUSIONS: Conservative but comprehensive algorithms are available to identify SI in administrative databases for application in real-world research. These are the first validated algorithms for use in administrative databases available to decision makers.
OBJECTIVES: To develop and validate algorithms to define statin intolerance (SI) in an administrative database using electronic medical records (EMRs) as the reference comparison. METHODS: One thousand adults with one or more qualifying changes in statin therapy and one or more previous diagnoses of hyperlipidemia, hypercholesterolemia, or mixed dyslipidemia were identified from the Henry Ford Health System administrative database. Data regarding statin utilization, comorbidities, and adverse effects were extracted from the administrative database and corresponding EMR. Patients were stratified by cardiovascular (CV) risk. SI was classified as absolute intolerance or titration intolerance on the basis of changes in statin utilization and/or the occurrence of adverse effects and laboratory testing for creatine kinase. Measures of concordance (Cohen's kappa [κ]) and accuracy (sensitivity, specificity, positive predictive value [PPV], and negative predictive value) were calculated for the administrative database algorithms. RESULTS: Half of the sample population was white, 52.9% were women, mean age was 60.6 years, and 35.7% were at high CV risk. SI was identified in 11.5% and 14.0%, absolute intolerance in 2.2% and 3.1%, and titration intolerance in 9.7% and 11.8% of the patients in the EMR and the administrative database, respectively. The algorithm identifying any SI had substantial concordance (κ = 0.66) and good sensitivity (78.1%), but modest PPV (64.0%). The titration intolerance algorithm performed better (κ = 0.74; sensitivity 85.4%; PPV 70.1%) than the absolute intolerance algorithm (κ = 0.40; sensitivity 50%; PPV 35.5%) and performed best in the high CV-risk group (n = 353), with robust concordance (κ = 0.73) and good sensitivity (80.9%) and PPV (75.3%). CONCLUSIONS: Conservative but comprehensive algorithms are available to identify SI in administrative databases for application in real-world research. These are the first validated algorithms for use in administrative databases available to decision makers.
Authors: Robert S Rosenson; Kate Miller; Martha Bayliss; Robert J Sanchez; Marie T Baccara-Dinet; Daniela Chibedi-De-Roche; Beth Taylor; Irfan Khan; Garen Manvelian; Michelle White; Terry A Jacobson Journal: Cardiovasc Drugs Ther Date: 2017-04 Impact factor: 3.727
Authors: Taehwan Park; Maureen Bresnahan; Scott K Griggs; Jiajing Chen; Alex H Cho; Yolene Gousse; Mark Feinglos Journal: Explor Res Clin Soc Pharm Date: 2021-05-05