Rosas, Steven

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.

The following describes a case of isolated visceral angioedema related to an angiotensin II receptor blocker (ARB) medication. Additionally, we discuss the pathophysiology of drug-induced angioedema, various presentations that can be encountered, and the leading theorized mechanisms of how renin-angiotensin-aldosterone system (RAAS) blocking medications lead to angioedema. The goal of sharing this case is to help increase awareness of the possibility of ARB-induced angioedema and to recommend keeping visceral angioedema as part of the differential diagnosis when presented with a patient who is taking an angiotensin converting enzyme inhibitor (ACEI) or ARB medication that is experiencing gastrointestinal symptoms of unclear etiology.

Introduction: Primary care clinicians are presented with hundreds of new clinical recommendations and guidelines. To consider practice change clinicians must identify relevant information and develop a contextual framework. Too much attention to information irrelevant to one’s practice results in wasted resources. Too little results in care gaps. A small group of primary care clinicians in a large health system sought to address the problem of vetting new information and providing peer reviewed context. This was done by engaging colleagues across the system though a primary care learning collaborative. Methods: The collaborative was a grass roots initiative between community and academic-based clinicians. They invited all the system’s primary care clinicians to participate. They selected new recommendations or guidelines and used surveys as the principal communication instrument. Surveys shared practice experience and also invited members to give narrative feedback regarding their acceptance of variation in care relate to the topic. A description of the collaborative along with its development, processes, and evolution are discussed. Process changes to address needs during the COVID-19 pandemic including expanded information sharing was necessary. Results: Collaborative membership reached across 5 states and included family medicine, internal medicine, and pediatrics. Members found involvement with the collaborative useful. Less variation in care was thought important for public health crises: the COVID pandemic and opioid epidemic. Greater practice variation was thought acceptable for adherence to multispecialty guidelines, such as diabetes, lipid management, and adult ADHD care. Process changes during the pandemic resulted in more communications between members to avoid practice gaps. Conclusion: An internet-based learning collaborative in a health system had good engagement from its members. Using novel methods, it was able to provide members with feedback related to the importance of new practice recommendations as perceived by their peers. Greater standardization was thought necessary when adopting measures to address public health crisis, and less necessary when addressing multispecialty guidelines. By employing a learning collaborative, this group was able to keep members interested and engaged. During the first year of the COVID pandemic the collaborative also served as a vehicle to share timely information.

In 2019, there were multiple outbreaks of measles in the United States. In the context of the public awareness of these outbreaks, we performed an intervention with the intent to improve the rate of measles immunization in our pediatric population. Pediatric patients that were lacking adequate measles immunization were identified by electronic medical record (EMR) survey. Charts were reviewed and updated if records were found to be incomplete. Parents of the remaining children were sent a letter, personally signed by the child’s primary care provider, encouraging measles immunization. A measles fact sheet, produced by the United States Center for Disease Control, was also included with the letter. There were 44 patients in the study group whose parents received a letter and measles fact sheet. As a result, 5 of these children were brought in for a measles, mumps, and rubella (MMR) immunization. The 44 patients whose parents received a letter included 20 patients whose parents had previously expressed intent to not vaccinate their children as documented in the EMR. None of these children received an MMR immunization. Although small in scope, this project provides a glimpse into the importance of personal provider guidance to parents who are inclined to immunize their children. Unfortunately, it also demonstrated that provider advice did not change the opinions of parents who had already taken a stance against vaccination, even in the context of an urgent public health situation that had garnered widespread coverage in the lay press and social media.

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.

The objective was to determine if a greater proportion of physician full-time equivalent (FTE%) relative to nurse practitioners/physician assistants (NPs/PAs) on care teams was associated with improved individual clinician diabetes quality outcomes. The authors conducted a retrospective cross-sectional study of 420 family medicine clinicians in 110 care teams in a Midwest health system, using administrative data from January 1, 2017 to December 31, 2017. Poisson regression was used to examine the relationship between physician FTE% and the number of patients meeting 5 criteria included in a composite metric for diabetes management (D5). Covariates included panel size, clinician type, sex, years in practice, region, patient satisfaction, care team size, rural location, and panel complexity. Of the 420 clinicians, 167 (40%) were NP/PA staff and 253 (60%) were physicians. D5 criteria were achieved in 37.9% of NP/PA panels compared with 44.5% of physician panels ( P < .001). In adjusted analysis, rate of patients achieving D5 was unrelated to physician FTE% on the care team ( P = .78). Physicians had a 1.082 (95% confidence interval 1.007–1.164) times greater rate of patients with diabetes achieving D5 than NPs/PAs. Clinicians at rural locations had a .904 (.852-.959) times lower rate of achieving D5 than those at urban locations. Physicians had a greater rate of patients achieving D5 compared with NPs/PAs, but physician FTE% on the care team was unrelated to D5 outcomes. This suggests that clinician team composition matters less than team roles and the dynamics of collaborative care between members.

Gynecomastia is benign enlargement of glandular tissue in the male breast. It occurs due to an imbalance of estrogen and testosterone. It may be unilateral or bilateral. Physiologic gynecomastia commonly occurs in infants and during puberty and is self-limited. Gynecomastia may affect up to 50% of adult men over age 50 years old and can be related to underlying medical illness or caused by certain medications. Known causative agents include anti-androgenic and estrogenic drugs. Probable agents include alcohol and anti-ulcer, psychoactive, and antiretroviral medications. Non-steroidal anti-inflammatory drugs (NSAIDs) are not commonly associated with the development of gynecomastia. This case presents an instance in which the NSAID, meloxicam, was the only identified variable in a patient who developed unilateral gynecomastia. His breast tenderness and abnormal exam resolved spontaneously within 4 weeks of cessation of meloxicam therapy.