LBCT Session Presents Findings From BETTER CARE-HF, PCDS Statin, NUDGE-FLU, Causal AI Trials

Research presented in a Late-Breaking Clinical Trial (LBCT) session at ACC.23/WCC explored how automated tools, electronic messages and artificial intelligence (AI) can support clinicians and guide patients, leading to improved adherence with guideline-recommended therapies and clinical outcomes.

The BETTER CARE-HF trial, presented by Amrita Mukhopadhyay, MD, and simultaneously published in JACC, found that clinicians who received customized electronic health record (EHR) alerts for specific patients were 2.5-times as likely to prescribe mineralocorticoid receptor antagonists (MRAs) compared with clinicians who did not receive such alerts.

The three-arm, cluster-randomized study compared the effectiveness of two automated EHR-embedded tools to usual care in prescribing MRAs to eligible patients with heart failure with reduced ejection fraction (HFrEF). One tool provided an alert during individual patient encounters while the other provided a message about multiple patients between encounters.

Out of 2,211 patients (alert n=755, message n=812, usual care n=644), with an average age of 72.2 years, average EF of 33%, 71% male and 69% White, the incidence of new MRA prescribing occurred in 29.6% of patients in the alert group, 15.6% in the message group and 11.7% in the usual care group. The alert tool showed superior effectiveness compared to the other two groups, more than doubling MRA prescribing vs. control (relative risk [RR], 2.53; p<0.0001) and improved MRA prescribing over the message group as well (RR, 1.67; p=0.002).

"This shows the power of electronic tools and reminders to dramatically improve care across a wide population of patients," said Mukhopadhyay. "These tools have great potential to improve prescribing and improve care, especially where we know gaps exist."

Findings from the PCDS Statin trial, presented by Salim S. Virani, MD, FACC, and simultaneously published in Circulation, demonstrated that patients with atherosclerotic cardiovascular disease (ASCVD) were significantly more likely to be prescribed guideline-recommended high-intensity statin therapy if their clinician was sent an automated reminder with information about their cardiovascular disease history, prior statin use and history of statin-associated side effects.

Researchers used machine learning algorithms to parse clinician notes for evidence of statin-associated side effects and generate summaries of relevant patient history, and then randomized 14 clinics (117 clinicians treating 18,427 patients) to implement the reminders for a 15-month period and 13 clinics (128 clinicians treating 18,214 patients) to continue usual care.

The primary outcome, change in high-intensity statin use between intervention and usual care sites, increased by 3.8% across all clinics in the intervention arm compared with usual care (odds ratio, 1.06). This proportion increased to 10.1% among patients who were featured a reminder in the intervention arm. Researchers also looked at a secondary outcome comparing statin adherence between groups, noting a 2.8% increase from patients in the intervention group (odds ratio, 1.12).

Despite the benefits seen from the alerts, 31.6% of participating clinicians opted out throughout the study, indicating a challenge in developing alerts that can attract clinicians' attention without overwhelming them.

"We found that if you're able to send reminders that have information that is personally relevant to the patient, it works," said Virani. "Our data also show that it is important to be mindful of how reminders fit in with the clinical workflow to avoid creating alert fatigue."

According to research from the NUDGE-FLU trial, presented by Niklas Dyrby Johansen, MD, and simultaneously published in the Lancet, making a connection between the flu and risk of subsequent heart problems via an electronic letter significantly increased flu vaccination rates among older Danish adults.

The study randomized 964,870 Danish citizens aged 65 or older across 691,820 households to receive one of nine electronic letters, each featuring a specific message about the upcoming flu season and the need for vaccination.

The primary endpoint, receipt of influenza vaccine on or before Jan. 1, 2023, was significantly higher in the group receiving the letter highlighting potential cardiovascular benefits of vaccination in comparison to those who received no letter (81% vs. 80.12%; difference of 0.89 percentage points; p<0.0001). The group receiving repeated letters (one at the start of the study and one at day 14) with a general reminder to get vaccinated also saw a significant increase in vaccine uptake (80.85 vs. 80.12%; difference 0.73 percentage points; p=0.0006).

"As cardiologists, it's very interesting that just telling people that we can also prevent other downstream issues like cardiovascular outcomes was what worked the best of all the nudge strategies – even better than the reminder, which we expected would be positive," said the study's lead author Tor Biering-Sørensen, MD, MSc, MPH, PhD.

A study presented by Brian A. Ference, MD, MPhil, MSc, showed that a causal AI system can accurately quantify how much a person must reduce their blood pressure (BP) or LDL-C to overcome their inherited risk of coronary artery disease (CAD).

Researchers used randomized data from clinical trials and genetic studies involving 1.8 million participants to train a causal AI system to estimate the effect of BP and LDL-C on risk of major coronary events. They then calculated polygenic risk scores for over 445,000 participants in the UK Biobank dataset and used the AI model to estimate how much each participant with higher-than-average polygenic risk must lower their BP, LDL-C or both to reduce their CAD risk to the same level as those with average polygenic risk, BP and LDL-C.

Researchers validated the system's accuracy by comparing its estimates to the observed risk of major coronary events in people with different levels of polygenic risk who by Mendelian randomization had higher or lower LDL-C, BP or both.

Results showed the AI model was accurate, but researchers found that family history is often a much stronger predictor of major coronary events than polygenic risk, suggesting that combining the two could provide an even better picture of inherited risk.

"[AI] empowers people with specific, actionable information and goals," said Ference. "This can not only help guide physicians and patients, it can even help governments and health care systems set more rational policy about how to incorporate polygenic risk into clinical medicine and public health policies."

Clinical Topics: Dyslipidemia, Heart Failure and Cardiomyopathies, Invasive Cardiovascular Angiography and Intervention, Prevention, Stable Ischemic Heart Disease, Atherosclerotic Disease (CAD/PAD), Nonstatins, Novel Agents, Statins, Acute Heart Failure, Interventions and Coronary Artery Disease, Interventions and Imaging, Angiography, Nuclear Imaging, Chronic Angina

Keywords: ACC Annual Scientific Session, ACC23, Angiography, Angina, Stable, Atherosclerosis, Biochemical Phenomena, Cardiovascular System, Coronary Artery Disease, Decision Support Systems, Clinical, Heart Failure, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Mediastinum, Secondary Prevention, ACC.23/WCC Meeting Newspaper, ACC Scientific Session Newspaper

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