Lead/Presenter: Salim Virani, COIN - Houston
All Authors: Virani SS (Michael E. DeBakey VA Medical Center Health Services Research & Development Center for Innovations, Baylor College of Medicine), Akeroyd JA (Michael E. DeBakey VA Medical Center Health Services Research & Development Center for Innovations, Baylor College of Medicine), Ahmed ST (Michael E. DeBakey VA Medical Center Health Services Research & Development Center for Innovations, Baylor College of Medicine) Krittanawong C (Icahn School of Medicine at Mount Sinai St. Luke's and Mount Sinai West) Martin LA (Michael E. DeBakey VA Medical Center Health Services Research & Development Center for Innovations, Baylor College of Medicine) Slagle J (Tennessee Valley Healthcare System, Department of Veterans Affairs, Vanderbilt University School of Medicine) Gobbel GT (Tennessee Valley Healthcare System, Department of Veterans Affairs, Vanderbilt University School of Medicine) Matheny M (Tennessee Valley Healthcare System, Department of Veterans Affairs, Vanderbilt University School of Medicine) Ballantyne CM (Baylor College of Medicine) Petersen LA (Michael E. DeBakey VA Medical Center Health Services Research & Development Center for Innovations, Baylor College of Medicine)
Objectives:
Accurate identification of patients with statin-associated side effects (SASEs) is critical for health care systems to institute strategies to improve guideline-concordant statin use. We aimed to assess whether SASEs could be ascertained in patients with clinical ASCVD using these structured adverse drug reaction (ADR) files with data collected over a 15-year period in the VA health care system, the positive predictive value of this approach when compared to manual chart review, and to understand statin treatment patterns in such patients.
Methods:
We identified 1,248,214 atherosclerotic cardiovascular disease (ASCVD) patients seeking care in the Department of Veterans Affairs from October 1, 2014 to September 30, 2015. Using a central ADR data repository, we identified SASEs in 15 major symptom categories. We compared statin therapy and intensity, LDL-C, and non-HDL-C levels between patients with and without SASEs. Positive predictive value (PPV) was assessed using a random chart review of 200 ASCVD patients with identified SASEs.
Results:
We identified 171,189 ASCVD patients (13.71%) with documented SASEs over a 15-year period (9.9%, 2.7%, and 1.1% to 1, 2, or > 2 statins, respectively). Statin use, high-intensity statin use, LDL-C, and non-HDL-C levels were 72%, 28.1%, 99mg/dl and 129mg/dl among those with SASEs versus 81%, 31.1%, 84mg/dl, and 111mg/dl among those without SASEs. Among patients with ASCVD and documentation of SASEs to 2 or more statins, only 52% of the patients were currently on a statin (12.5% on high-intensity statin therapy) with mean LDL-C and non-HDL-C levels of 117 and 148 mg/dl, respectively. The great majority of SASEs documented by the providers were related to muscle symptoms (58.1% myalgia and 6.7% myopathy (6.7%) with gastrointestinal side effects constituting the next major category. Two-thirds of SASEs were related to muscle symptoms. PPV compared to manual chart review was 99%.
Implications:
A strategy of using adverse drug reaction entry in the EMR is feasible and reliable in identifying ASCVD patients with SASEs.
Impacts:
This automated strategy to identify SASEs can serve as an effective tool in large health care systems to operationalize efforts directed towards improving guideline-concordant lipid lowering therapy use in patients with clinical ASCVD and SASEs.