Indicating statin eligibility in health records may not change prescribing rates
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Alerting cardiologists with active or passive prompts within electronic health records regarding statin therapy in eligible patients with atherosclerotic CVD did not significantly change prescribing rates, researchers found.
The study published in JAMA Cardiology found that the active choice prompt slightly increased statin prescribing at optimal doses.
“Active choice prompts led to small increases in prescribing the right dose of statins for patients at highest risk — those who already had atherosclerotic heart disease,” Srinath Adusumalli, MD, assistant professor of cardiovascular medicine at University of Pennsylvania Perelman School of Medicine, said in a press release. “These are the types of patients who stand to benefit the most from statin therapy with regard to reduction in major adverse cardiovascular events like a heart attack and mortality.”
Cardiologists treating ASCVD
In this three-arm cluster randomized clinical trial, researchers analyzed data from 11,693 patients (mean age, 64 years; 58% men; mean 10-year ASCVD risk score, 15.5; 68.2% with ASCVD) and 82 cardiologists from 16 practices within the University of Pennsylvania Health System. The trial consisted of a 6-month preintervention period (March 24 to Sept. 23, 2018) and a 6-month intervention period (Sept. 24, 2018, to March 23, 2019).
Cardiologists were assigned usual care (n = 27), passive choice (n = 27) within the EHR or active choice (n = 28) within the EHR. The passive choice group used a non-interruptive alert for patients who met guideline recommendations but were not taking statin therapy, whereas the active choice group required the cardiologist to accept or decline statin therapy before proceeding further within a patient’s record.
The primary outcome for this study was change in percentage of patients prescribed statin therapy at guideline-recommended doses. The secondary outcome was the change in percentage of eligible patients prescribed a statin at any dose.
At baseline, the optimal dose of statin therapy was prescribed in 40.3% of cardiologists in the control group, 39.1% of those in the passive choice group and 41.2% of cardiologists in the active choice group.
Adjusted analyses determined that the change in optimal-dose statin prescribing rates did not significantly differ from control to the passive choice group (adjusted difference in percentage points, 0.2; 95% CI, 2.9 to 2.8, P = .86) or to the active choice group (adjusted difference in percentage points, 2.4; 95% CI, 0.6 to 5; P = .08). When adjusted subset analyses focused on patients diagnosed with ASCVD, prescribing the optimal dose of statin therapy significantly increased in the active choice group compared with the control group (adjusted difference in percentage points, 3.8; 95% CI, 1-6.4; P = .008). Significant differences were not observed in other subset analyses.
Adjusted analyses also assessed statin prescribing at any dose. Statin prescribing over time did not significantly differ when the control group was compared with the passive choice group (adjusted difference in percentage points, 0.5; 95% CI, 2.6 to 1.6; P = .6) or with the active choice group (adjusted difference in percentage points, –0.05; 95% CI, 2.6 to 2.2; P = .96).
“Further study is needed to evaluate the active choice intervention among patients with clinical ASCVD, and future interventions could focus on ways to improve the design of active choice and combine it with other approaches to further improve statin prescribing,” Adusumalli and colleagues wrote.
Lessons learned
In a related editorial, Thomas M. Maddox, MD, MSc, executive director of the Healthcare Innovation Lab, cardiologist and professor of medicine (cardiology) at Washington University School of Medicine in St. Louis, wrote: “Several lessons are clear. Clinical decision support interventions require codesign and iterative testing with their intended users. Speaking to clinicians’ needs to understand the clinical rationale behind any recommendations and tailoring them to individual patient characteristics are key. Appreciating the context in which clinicians work and incorporating clinical decision support to accommodate those realities is essential. Studying all of these aspects of clinical decision support in a rigorous fashion and designing its metrics to enhance learning, regardless of the effect of the clinical decision support on the clinical outcome, must occur.”
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