Primary Care-Focused QI Effort Didn’t Improve Secondary CV Prevention: QUEL

A data-driven quality improvement (QI) program intended to enhance secondary prevention of coronary heart disease (CHD) in the primary-care setting did not impact outcomes, results from the QUEL randomized trial show.

Julie Redfern, PhD (Bond University, Robina, Australia), who led the study published earlier this month in Circulation: Population Health Outcomes, suggested two reasons why the QI intervention failed to reduce unplanned CVD hospitalizations, encourage more management planning, help more patients reach guideline-recommended targets, or increase prescriptions for medical therapy.

“Perhaps this intervention [simply] didn’t work,” she told TCTMD. “That’s a very real possibility in this case.” Another possibility is that the COVID-19 pandemic, which overlapped with the study period, dampened the effects of the intervention by straining primary-care resources and leading to an increase in hospitalizations.

“With the background of all of that, my feeling is that there’s something [positive] in using data to drive quality. It may not ultimately reduce the hospitalizations, but I think there is something there,” even if the field has yet to capture solid evidence to support this strategy, Redfern commented.

In efforts to improve care delivery and outcomes in cardiovascular disease, “primary care is obviously a very important environment . . . if we want to keep helping these people after they have their initial admission,” said Redfern. With the collaborative, data-driven framework tested in QUEL, the idea is for “practices to use their data to unpack what they’re doing well, what they’re not doing well [then] share their experiences, learn from each other, and try to improve.”

Most prior studies in this area focused on softer endpoints, such as whether the doctors are active participants in the QI intervention and if they like the experience, she said, “whereas we really wanted to look at: does something like this actually stop people from getting readmitted to hospital?”

The QUEL Trial

QUEL tested a collaborative approach to QI, which first emerged three decades ago, the researchers point out. An example is the Breakthrough Series collaborative, wherein participating teams focus on shared learning from peers and experts as a means to improve processes and care over a short-term period of 6 to 15 months. This strategy has been tested in various disease states including asthma, chronic heart failure, and others, but thus far the evidence base has relied mainly on surrogate endpoints, they note.

In recent years, there have been advances in the routine collection and integration of electronic health records, with increasingly more data being readily available for QI efforts.

The investigators set out to see whether combining both—collaboration and data—would help drive positive changes in secondary prevention and subsequent outcomes in patients with CHD.

For the cluster randomized controlled trial, Redfern and colleagues recruited 51 Australian primary care practices from 2019 to 2022 that used compliant data extraction software and saw at least 200 CHD patients annually. Each practice was randomized to either a standard care (control) or an intervention consisting of a 12-month data-driven QI program that included benchmarking, monthly reporting, and improvement planning.

Altogether these practices managed 7,864 CHD patients (mean age 71.9; 68% men; 24% with prior MI). At baseline, 69% of participants had been prescribed a statin, 59% an ACE inhibitor/ARB, and 24% an antiplatelet. Around 4% had a plan in place for general practice management and 11% for team care management.

By 24 months, there were no differences between those treated at intervention versus control practices for unplanned CVD-related hospitalizations (RR 0.91; 95% CI 0.75-1.10], the study’s primary endpoint. A variety of secondary endpoints—including management planning; prescriptions for antiplatelets, statins, and ACE inhibitors/ARBs; and targets met for LDL cholesterol, systolic blood pressure, and smoking—were similar between the two groups.

Participation varied among the practices. Among those assigned to the intervention, 85% took part in at least three of the six learning workshops and 69% submitted at least one PDSA (plan-do-study-act) cycle. Nearly all (94%) rated the first workshop, held in person, as highly satisfactory, and a majority still did so for the sixth workshop, a virtual event. Nine out of 10 said they were well informed about the workshop objectives, and 85% said these learning opportunities provided usable information.

Despite its negative results, QUEL “was arguably one of the largest and most robust studies evaluating the effectiveness of this type of intervention in primary care or an acute hospital setting and does add to the growing body of studies showing a lack of evidence,” the investigators conclude.

Primary care is obviously a very important environment . . . if we want to keep helping these people after they have their initial admission. Julie Redfern

Daniel McClintick, MD, Susan Cheng, MD, and Joseph E. Ebinger, MD (all from Cedars-Sinai Medical Center, Los Angeles, CA), in an editorial, underline why such research is a worthy pursuit. Thanks to “incredible advances in understanding, diagnosis, and treatment,” they say, “there is no shortage of efficacious therapies that reduce cardiovascular risk. What is lacking, however, are effective mechanisms to bring these therapies to patients.”

The question, they note, is how to improve the situation: “Why do provider education and data-supported feedback alone repeatedly fail to improve clinical outcomes or even surrogate markers of interest?

“These interventions are based on the assumption that providers are operating with a lack of knowledge or engagement, which in certain scenarios may be true,” McClintick and colleagues continue. “However, when asked, primary care providers themselves say this is often not the case. They are often aware of the clinical evidence and want to provide high-quality care to all their patients. The problem more frequently stems from competing demands, misaligned incentives, patient preferences, and a system that is designed to optimize billing, not building healthier communities.”

Given this, placing additional administrative burdens on doctors is unlikely to make a dent, they stress.

Instead creative solutions, such as “policy changes, realignment of incentives, improving the patient experience, and reducing burnout in our primary care colleagues,” are needed, the editorialists say. Examples include better reimbursement for appropriate care and automated features within electronic health records, as well as ensuring that “clinicians have time to discuss risk factor modification in dedicated cardiovascular prevention visits without other competing tasks in a busy primary care clinic.”

Redfern told TCTMD that their research continues in different settings, such as cardiac rehabilitation programs.

Despite the challenges, “I think there will be a growing place for using data to drive high-quality care and make sure people are not slipping through the cracks,” she said. Whether that will in in fact save lives and keep patients out of the hospital, or mainly leads to more efficiency at a system level, is not yet known, said Redfern.