Inselman, Jonathan

Introduction: Asymptomatic LV systolic dysfunction is often underdiagnosed, leading to missed opportunities for treatment. In a recently completed pragmatic RCT, an AI algorithm performed on the ECGs ordered as part of routine practice increased early diagnosis of low EF. The current study aimed to understand whether the effectiveness of the AI algorithm integrated into routine care differed by the socioeconomic status of the patient. Methods: The main findings of the RCT were previously published. Clinicians in the intervention group had access to AI results (i.e., a positive or negative screening result, with positive results indicating a high likelihood of low EF). When a positive result was found, clinicians in the intervention group received an alert that encouraged them to order TTE if considered clinically appropriate. Clinicians in the control group did not have access or received alerts. Area deprivation index (ADI) was used to measure area-level social determinants of health. This study included 16,992 patients (control 8,225; intervention: 8,767) who had ECG performed for any indications between 8/5/2019 and 3/31/2020, no prior HF, and a valid address to derive ADI. The primary endpoint was a new diagnosis of EF≤50% within 3 months. Results: The mean age was 60.5±17.6 y. AI increased the diagnosis of low EF in the overall cohort (OR 1.42 [1.13, 1.78], p=0.01). The magnitude of benefit was greater in patients with higher socioeconomic status, e.g., living in an area that is less deprived (OR 1.88 [1.16, 3.05]), with higher median income, lower poverty rate, higher education level, better housing conditions, and higher proportion of white-collar workers, although the interaction was not statistically significant (Figure). Conclusions: AI enables early diagnosis of low EF and the magnitude of benefit might be numerically greater in patients with higher socioeconomic status, possibly due to variations in access to or referral for subsequent testing.

Background: Although percutaneous coronary intervention (PCI) has not been shown to reduce the risk of death and myocardial infarction (MI) for stable coronary artery disease (CAD), many patients believe that PCI is a life-saving procedure. PCI for stable CAD is known to improve patients’ quality of life more rapidly than medications alone. We conducted a randomized trial to assess the impact of a decision aid (DA) compared to usual care (UC) for the treatment of stable CAD when there is a choice between PCI and optimal medical therapy (OMT). Methods: The PCI Choice trial was a prospective, randomized trial comparing the effects of DA versus UC. The DA was designed with a user-centered approach for an in-visit consultation, involving patients and clinicians throughout the development process. The final DA included information on myocardial infarction (MI), death and quality of life outcomes for PCI with OMT vs. OMT alone in the treatment of stable angina, stratified by angina type. Risks of procedure, bleeding, stent thrombosis, and need for future procedures were also depicted. The primary outcome was patient knowledge, measured by pre- and post-visit surveys. Additional outcomes included decisional conflict, patient satisfaction, preferred decision making style, and treatment decision. Results: A total of 110 patients were enrolled; mean age was 68.3 years and 26% of patients were women. At baseline, most patients had CCS Class I/II angina and were on a mean of two anti-anginal medications (2.3, SD 1.2). Knowledge increased among patients receiving DA compared to UC (63% vs. 44% p=0.0003). Specific knowledge about the impact of PCI for stable angina on death and MI was higher in both groups compared to prior studies (54% DA, 46% UC, p=0.45; 12% prior). Patient satisfaction was significantly higher in the DA group vs. UC (72% vs 40%, p=0.004). Decisional conflict was greater than in non-procedural DA trials, and was not different between the two arms (p=.43). Following exposure to DA, patients’ preference for sharing decision making tended to change more with DA (55% to 65%) than with UC (56% to 59%). While the proportion of patients choosing PCI over OMT was nearly half in both groups, there were fewer patients that remained undecided with DA (18% vs. 4%; p=0.14 overall difference). Conclusions: Exposure to a DA for the choice of PCI vs. optimal medical therapy in stable CAD improved patient knowledge and satisfaction and decreased uncertainty, without reducing the rate of PCI. Use of the DA in a larger patient population may further delineate impact on outcomes such as treatment choice, geographic variation and cost.

Background Guidelines promote shared decision‐making (SDM) for anticoagulation in patients with atrial fibrillation. We recently showed that adding a within‐encounter SDM tool to usual care (UC) increases patient involvement in decision‐making and clinician satisfaction, without affecting encounter length. We aimed to estimate the extent to which use of an SDM tool changed adherence to the decided care plan and clinical safety end points. Methods and Results We conducted a multicenter, encounter‐level, randomized trial assessing the efficacy of UC with versus without an SDM conversation tool for use during the clinical encounter (Anticoagulation Choice) in patients with nonvalvular atrial fibrillation considering starting or reviewing anticoagulation treatment. We conducted a chart and pharmacy review, blinded to randomization status, at 10 months after enrollment to assess primary adherence (proportion of patients who were prescribed an anticoagulant who filled their first prescription) and secondary adherence (estimated using the proportion of days for which treatment was supplied and filled for direct oral anticoagulant, and as time in therapeutic range for warfarin). We also noted any strokes, transient ischemic attacks, major bleeding, or deaths as safety end points. We enrolled 922 evaluable patient encounters (Anticoagulation Choice=463, and UC=459), of which 814 (88%) had pharmacy and clinical follow‐up. We found no differences between arms in either primary adherence (78% of patients in the SDM arm filled their first prescription versus 81% in UC arm) or secondary adherence to anticoagulation (percentage days covered of the direct oral anticoagulant was 74.1% in SDM versus 71.6% in UC; time in therapeutic range for warfarin was 66.6% in SDM versus 64.4% in UC). Safety outcomes, mostly bleeds, occurred in 13% of participants in the SDM arm and 14% in the UC arm. Conclusions In this large, randomized trial comparing UC with a tool to promote SDM against UC alone, we found no significant differences between arms in primary or secondary adherence to anticoagulation or in clinical safety outcomes. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: clinicaltrials.gov. Identifier: NCT02905032.

Research ObjectiveAn artificial intelligence (AI) algorithm based on ECG was integrated into clinical workflows to identify patients with a high likelihood of low ejection (EF), a condition that is underdiagnosed but treatable. The trial aimed to assess whether the AI‐powered clinical decision support enabled early diagnosis of low EF.Study DesignA pragmatic, cluster randomized controlled trial (RCT) was conducted in 45 primary care practices, including an academic medical center as well as community and rural clinics across Minnesota and Wisconsin. A total of 120 primary care teams, including 358 clinicians, were randomized to either intervention (access to AI results with positive results indicating a high likelihood of low EF; 181 clinicians) or control (usual care; 177 clinicians). The primary endpoint was a new diagnosis of EF≤50% within 90 days. A positive deviance qualitative study and a survey were used to assess implementation strategies.Population StudiedIn total, 22,641 adult patients (N = 11,573 intervention; N = 11,068 control) with ECG performed as part of routine care between 8/5/2019 and 3/31/2020 were included. Patients were excluded if they had known EF≤50% or a history of documented heart failure (HF) identified by clinical notes abstracted by natural language processing.Principal FindingsThe mean age of patients was 60.5 ± 17.6 years; 53.9% were women. Positive AI results were found in 6.0% of patients in both arms. Clinicians in the intervention group obtained more echocardiograms for patients with positive results (38.1% control vs. 49.6% intervention, p < 0.001), but in the overall cohort, the echocardiogram utilization was similar (18.2% vs. 19.2%, p = 0.17). The intervention increased the diagnosis of low EF in the overall cohort (1.6% vs. 2.1%, odds ratio [OR] 1.32 [1.01–1.61], p = 0.007) and among patients with positive results (14.5% vs. 19.5% OR 1.43 [1.08–1.91], p = 0.01). Results using different thresholds for low EF were consistent with the primary result. Among patients with positive results, the intervention increased echocardiography more significantly in women vs. men (p for interaction 0.02), in patients without prior echocardiogram vs. those with prior echocardiogram (p for interaction 0.04), and in patients whose ECGs were ordered in outpatient clinics (p for interaction 0.02).ConclusionsAn AI algorithm run on existing ECGs enabled the early diagnosis of low EF in a large cohort of patients managed in routine primary care practices. Because ECG is a low‐cost test frequently performed for a variety of purposes, the algorithm could improve early diagnosis and treatment of a condition that is often asymptomatic but has effective treatments, and thus, reduce the disease burden in broad populations.Implications for Policy or PracticeThe current study is one of the first RCTs that demonstrated the value of AI in routine practice. Low EF confers an increased risk of HF progression and mortality. Although there are effective therapies, low EF is undiagnosed, leading to missed opportunities for treatment. EF can be readily determined by echocardiography, but the imagining test is too resource‐intensive to screen unselected populations. The AI applied to the existing ECGs could identify high‐risk patients for further assessment.Primary Funding SourceMayo Clinic.

Background/Aims: The Pediatric Research Equity Act and Best Pharmaceuticals for Children Act are intended to promote the conduct of clinical trials that generate pediatric-specific evidence about drug safety and efficacy. This study assesses the quality of evidence generated through Pediatric Research Equity Act–mandated and Best Pharmaceuticals for Children Act–incentivized clinical trials of hematology/oncology drugs and characterizes subsequent changes in pediatric drug utilization rates. Methods: Trial characteristics (blinding, randomization, and comparator group) were determined for clinical trials that supported pediatric label changes. Using data from OptumLabs ® Data Warehouse, a de-identified administrative claims database, we calculated pediatric utilization rates for each drug. We calculated monthly utilization rates from January 2003 (or from the first month in which data were available) to December 2018. Results: We identified 11 hematology/oncology drugs that underwent pediatric label changes under the Pediatric Research Equity Act Pediatric Research Equity Act and/or Best Pharmaceuticals for Children Act, and we identified 15 trials supporting these changes. Of these trials, 36% (5/14) were randomized, 31% (4/13) were blinded, and 36% (5/14) used a comparator group. A median of 49 children (interquartile range 29.5) received the drug under investigation across these trials. Pediatric label changes were not associated with subsequent changes in pediatric drug utilization. Although some drugs saw increased pediatric use after gaining new pediatric indications, this pattern was not consistently observed. In addition, there was no evidence to suggest that drugs were utilized less frequently after they failed to receive pediatric indications. Conclusions: Clinical trials of hematology/oncology drugs conducted under the Pediatric Research Equity Act Pediatric Research Equity Act and Best Pharmaceuticals for Children Act generally have low methodological rigor, and the resulting label changes are not consistently associated with changes in pediatric utilization. Alternative regulatory strategies and study designs may be necessary to maximize the impact of newly generated knowledge on drug utilization.