Faculty Bibliography

BACKGROUND/UNASSIGNED:Whether prolonged and excessive alcohol consumption contributes to dilated cardiomyopathy (DCM) remains uncertain. This study aimed to describe the prevalence of alcohol use in patients with DCM and their first-degree relatives (FDRs) and determine if cumulative alcohol exposure associates with DCM/partial DCM or modifies the association of DCM with DCM-relevant rare variants. METHODS/UNASSIGNED:All probands had DCM; FDRs were classified as with or without DCM or partial DCM. Alcohol exposure was measured with the Alcohol Use Disorder Identification Test-Consumption questionnaire and years of drinking. Rare variants in 36 DCM genes were classified as pathogenic, likely pathogenic, or variants of uncertain significance (pathogenic, likely pathogenic, variant of uncertain significance). Generalized linear mixed models were used to assess the association of DCM/partial DCM with alcohol use among FDRs. RESULTS/UNASSIGNED:=0.55). CONCLUSIONS/UNASSIGNED:Alcohol use was frequent among probands and FDRs. This study did not provide evidence supporting an association of cumulative alcohol exposure with DCM/partial DCM or a modifying effect of alcohol use on the association of DCM with DCM-relevant rare variants. REGISTRATION/UNASSIGNED:URL: https://www.clinicaltrials.gov; Unique identifier: NCT03037632.

BACKGROUND:In clinical settings, counseling patients on physical activity starts by assessing patients' current physical activity levels. Self-report measures of PA are generally easy to administer; however, they may be too long to be convenient and are known to correlate poorly with objective measures of physical activity. OBJECTIVE:To assess the concurrent validity of a self-report three-question physical activity vital sign with objective Fitbit step counts and the distance walked during a 6-min walk test. METHODS:This pilot study tested a best practice advisory embedded in the Epic electronic health record, which was designed to prompt providers in a preventive cardiology clinic to counsel patients reporting low levels of physical activity . Patients were invited to participate in the remote patient monitoring phase to assess the change in their physical activity by wearing a Fitbit for 12 weeks and completing a 6-min walk test at baseline and 12 weeks. This analysis used the cross-sectional data collected in this phase. Pearson correlations were conducted between self-reported physical activity, Fitbit step counts, and the distance walked during the 6-min walk-a measure associated with current physical activity levels. Kappa coefficients were calculated to assess agreement between the self-reported physical activity and step counts. RESULTS:Participants who enrolled in the Fitbit monitoring were approximately 50% female, with the majority identified as White non-Hispanic adults. Their most common cardiovascular risk factor was hypertension. The self-reported physical activity vital signs were significantly associated with step counts at baseline and 12 weeks but were not associated with the distance during the 6-min walk test. However, the distance walked was significantly associated with step counts at baseline and 12 weeks. The Kappa results demonstrate a poor level of agreement between two categories (meeting or not meeting current physical activity guidelines) of self-report physical activity vitals and the objective Fitbit step counts. DISCUSSION/CONCLUSIONS:There were moderate correlations between the self-reported physical activity vital signs and the Fitbit step counts, but there was lack of agreement when they were categorized. Further validation of this physical activity vital sign is warranted.

IMPORTANCE/UNASSIGNED:Classification of persons with long COVID (LC) or post-COVID-19 condition must encompass the complexity and heterogeneity of the condition. Iterative refinement of the classification index for research is needed to incorporate newly available data as the field rapidly evolves. OBJECTIVE/UNASSIGNED:To update the 2023 research index for adults with LC using additional participant data from the Researching COVID to Enhance Recovery (RECOVER-Adult) study and an expanded symptom list based on input from patient communities. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Prospective, observational cohort study including adults 18 years or older with or without known prior SARS-CoV-2 infection who were enrolled at 83 sites in the US and Puerto Rico. Included participants had at least 1 study visit taking place 4.5 months after first SARS-CoV-2 infection or later, and not within 30 days of a reinfection. The study visits took place between October 2021 and March 2024. EXPOSURE/UNASSIGNED:SARS-CoV-2 infection. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Presence of LC and participant-reported symptoms. RESULTS/UNASSIGNED:A total of 13 647 participants (11 743 with known SARS-CoV-2 infection and 1904 without known prior SARS-CoV-2 infection; median age, 45 years [IQR, 34-69 years]; and 73% were female) were included. Using the least absolute shrinkage and selection operator analysis regression approach from the 2023 model, symptoms contributing to the updated 2024 index included postexertional malaise, fatigue, brain fog, dizziness, palpitations, change in smell or taste, thirst, chronic cough, chest pain, shortness of breath, and sleep apnea. For the 2024 LC research index, the optimal threshold to identify participants with highly symptomatic LC was a score of 11 or greater. The 2024 index classified 20% of participants with known prior SARS-CoV-2 infection and 4% of those without known prior SARS-CoV-2 infection as having likely LC (vs 21% and 5%, respectively, using the 2023 index) and 39% of participants with known prior SARS-CoV-2 infection as having possible LC, which is a new category for the 2024 model. Cluster analysis identified 5 LC subtypes that tracked quality-of-life measures. CONCLUSIONS AND RELEVANCE/UNASSIGNED:The 2024 LC research index for adults builds on the 2023 index with additional data and symptoms to help researchers classify symptomatic LC and its symptom subtypes. Continued future refinement of the index will be needed as the understanding of LC evolves.

Electronic health record (EHR)-embedded tools are known to improve prescribing of guideline-directed medical therapy (GDMT) for patients with heart failure. However, physicians may perceive EHR tools to be unhelpful, and may be therefore hesitant to implement these in their practice. We surveyed cardiologists about two effective EHR-tools to improve heart failure care, and they perceived the EHR tools to be easy to use, helpful, and improve the overall management of their patients with heart failure.

OBJECTIVE:To estimate the prevalence of post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) after infection with SARS-CoV-2 during pregnancy and to characterize associated risk factors. METHODS:In a multicenter cohort study (NIH RECOVER [Researching COVID to Enhance Recovery]-Pregnancy Cohort), individuals who were pregnant during their first SARS-CoV-2 infection were enrolled across the United States from December 2021 to September 2023, either within 30 days of their infection or at differential time points thereafter. The primary outcome was PASC , defined as score of 12 or higher based on symptoms and severity as previously published by the NIH RECOVER-Adult Cohort, at the first study visit at least 6 months after the participant's first SARS-CoV-2 infection. Risk factors for PASC were evaluated, including sociodemographic characteristics, clinical characteristics before SARS-CoV-2 infection (baseline comorbidities, trimester of infection, vaccination status), and acute infection severity (classified by need for oxygen therapy). Multivariable logistic regression models were fitted to estimate associations between these characteristics and presence of PASC. RESULTS:Of the 1,502 participants, 61.1% had their first SARS-CoV-2 infection on or after December 1, 2021 (ie, during Omicron variant dominance); 51.4% were fully vaccinated before infection; and 182 (12.1%) were enrolled within 30 days of their acute infection. The prevalence of PASC was 9.3% (95% CI, 7.9-10.9%) measured at a median of 10.3 months (interquartile range 6.1-21.5) after first infection. The most common symptoms among individuals with PASC were postexertional malaise (77.7%), fatigue (76.3%), and gastrointestinal symptoms (61.2%). In a multivariable model, the proportion PASC positive with vs without history of obesity (14.9% vs 7.5%, adjusted odds ratio [aOR] 1.65, 95% CI, 1.12-2.43), depression or anxiety disorder (14.4% vs 6.1%, aOR 2.64, 95% CI, 1.79-3.88) before first infection, economic hardship (self-reported difficulty covering expenses) (12.5% vs 6.9%, aOR 1.57, 95% CI, 1.05-2.34), and treatment with oxygen during acute SARS-CoV-2 infection (18.1% vs 8.7%, aOR 1.86, 95% CI, 1.00-3.44) were associated with increased prevalence of PASC. CONCLUSION/CONCLUSIONS:The prevalence of PASC at a median time of 10.3 months after SARS-CoV-2 infection during pregnancy was 9.3% in the NIH RECOVER-Pregnancy Cohort. The predominant symptoms were postexertional malaise, fatigue, and gastrointestinal symptoms. Several socioeconomic and clinical characteristics were associated with PASC after infection during pregnancy. CLINICAL TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov , NCT05172024.

BACKGROUND/UNASSIGNED:There are currently no validated clinical biomarkers of postacute sequelae of SARS-CoV-2 infection (PASC). OBJECTIVE/UNASSIGNED:To investigate clinical laboratory markers of SARS-CoV-2 and PASC. DESIGN/UNASSIGNED:Propensity score-weighted linear regression models were fitted to evaluate differences in mean laboratory measures by prior infection and PASC index (≥12 vs. 0). (ClinicalTrials.gov: NCT05172024). SETTING/UNASSIGNED:83 enrolling sites. PARTICIPANTS/UNASSIGNED:RECOVER-Adult cohort participants with or without SARS-CoV-2 infection with a study visit and laboratory measures 6 months after the index date (or at enrollment if >6 months after the index date). Participants were excluded if the 6-month visit occurred within 30 days of reinfection. MEASUREMENTS/UNASSIGNED:Participants completed questionnaires and standard clinical laboratory tests. RESULTS/UNASSIGNED:levels was attenuated after participants with preexisting diabetes were excluded. Among participants with prior infection, no meaningful differences in mean laboratory values were found between those with a PASC index of 12 or higher and those with a PASC index of zero. LIMITATION/UNASSIGNED:Whether differences in laboratory markers represent consequences of or risk factors for SARS-CoV-2 infection could not be determined. CONCLUSION/UNASSIGNED:Overall, no evidence was found that any of the 25 routine clinical laboratory values assessed in this study could serve as a clinically useful biomarker of PASC. PRIMARY FUNDING SOURCE/UNASSIGNED:National Institutes of Health.

IMPORTANCE/UNASSIGNED:Most research to understand postacute sequelae of SARS-CoV-2 infection (PASC), or long COVID, has focused on adults, with less known about this complex condition in children. Research is needed to characterize pediatric PASC to enable studies of underlying mechanisms that will guide future treatment. OBJECTIVE/UNASSIGNED:To identify the most common prolonged symptoms experienced by children (aged 6 to 17 years) after SARS-CoV-2 infection, how these symptoms differ by age (school-age [6-11 years] vs adolescents [12-17 years]), how they cluster into distinct phenotypes, and what symptoms in combination could be used as an empirically derived index to assist researchers to study the likely presence of PASC. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Multicenter longitudinal observational cohort study with participants recruited from more than 60 US health care and community settings between March 2022 and December 2023, including school-age children and adolescents with and without SARS-CoV-2 infection history. EXPOSURE/UNASSIGNED:SARS-CoV-2 infection. MAIN OUTCOMES AND MEASURES/UNASSIGNED:PASC and 89 prolonged symptoms across 9 symptom domains. RESULTS/UNASSIGNED:A total of 898 school-age children (751 with previous SARS-CoV-2 infection [referred to as infected] and 147 without [referred to as uninfected]; mean age, 8.6 years; 49% female; 11% were Black or African American, 34% were Hispanic, Latino, or Spanish, and 60% were White) and 4469 adolescents (3109 infected and 1360 uninfected; mean age, 14.8 years; 48% female; 13% were Black or African American, 21% were Hispanic, Latino, or Spanish, and 73% were White) were included. Median time between first infection and symptom survey was 506 days for school-age children and 556 days for adolescents. In models adjusted for sex and race and ethnicity, 14 symptoms in both school-age children and adolescents were more common in those with SARS-CoV-2 infection history compared with those without infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. These symptoms affected almost every organ system. Combinations of symptoms most associated with infection history were identified to form a PASC research index for each age group; these indices correlated with poorer overall health and quality of life. The index emphasizes neurocognitive, pain, and gastrointestinal symptoms in school-age children but change or loss in smell or taste, pain, and fatigue/malaise-related symptoms in adolescents. Clustering analyses identified 4 PASC symptom phenotypes in school-age children and 3 in adolescents. CONCLUSIONS AND RELEVANCE/UNASSIGNED:This study developed research indices for characterizing PASC in children and adolescents. Symptom patterns were similar but distinguishable between the 2 groups, highlighting the importance of characterizing PASC separately for these age ranges.

BACKGROUND:Regular physical activity (PA) is a component of cardiovascular health and is associated with a lower risk of cardiovascular disease (CVD). However, only about half of US adults achieved the current PA recommendations. OBJECTIVE:The study purpose was to implement PA counseling using a clinical decision support tool in a preventive cardiology clinic and to assess changes in CVD risk factors in a sample of patients enrolled over 12 weeks of PA monitoring. METHODS:This intervention, piloted for 1 year, had 3 components embedded in the electronic health record: assessment of patients' PA, an electronic prompt for providers to counsel patients reporting low PA, and patient monitoring using a Fitbit. Cardiovascular disease risk factors included PA (self-report and Fitbit), body mass index, blood pressure, lipids, and cardiorespiratory fitness assessed with the 6-minute walk test. Depression and quality of life were also assessed. Paired t tests assessed changes in CVD risk. RESULTS:The sample who enrolled in the remote patient monitoring (n = 59) were primarily female (51%), White adults (76%) with a mean age of 61.13 ± 11.6 years. Self-reported PA significantly improved over 12 weeks ( P = .005), but not Fitbit steps ( P = .07). There was a significant improvement in cardiorespiratory fitness (469 ± 108 vs 494 ± 132 m, P = .0034), and 23 participants (42%) improved at least 25 m, signifying a clinically meaningful improvement. Only 4 participants were lost to follow-up over 12 weeks of monitoring. CONCLUSIONS:Patients may need more frequent reminders to be active after an initial counseling session, perhaps getting automated messages based on their step counts syncing to their electronic health record.

BACKGROUND:Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the US with the morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock allows prompt implementation of treatment measures. Our objective is to develop a new dynamic risk score, called CShock, to improve early detection of cardiogenic shock in cardiac intensive care unit (ICU). METHODS:We developed and externally validated a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock. We prepared a cardiac ICU dataset using MIMIC-III database by annotating with physician adjudicated outcomes. This dataset that consisted of 1500 patients with 204 having cardiogenic/mixed shock was then used to train CShock. The features used to train the model for CShock included patient demographics, cardiac ICU admission diagnoses, routinely measured laboratory values and vital signs, and relevant features manually extracted from echocardiogram and left heart catheterization reports. We externally validated the risk model on the New York University (NYU) Langone Health cardiac ICU database that was also annotated with physician adjudicated outcomes. The external validation cohort consisted of 131 patients with 25 patients experiencing cardiogenic/mixed shock. RESULTS:CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.821 (95% CI 0.792-0.850). CShock was externally validated in the more contemporary NYU cohort and achieved an AUROC of 0.800 (95% CI 0.717-0.884), demonstrating its generalizability in other cardiac ICUs. Having an elevated heart rate is most predictive of cardiogenic shock development based on Shapley values. The other top ten predictors are having an admission diagnosis of myocardial infarction with ST-segment elevation, having an admission diagnosis of acute decompensated heart failure, Braden Scale, Glasgow Coma Scale, Blood urea nitrogen, Systolic blood pressure, Serum chloride, Serum sodium, and Arterial blood pH. CONCLUSIONS:The novel CShock score has the potential to provide automated detection and early warning for cardiogenic shock and improve the outcomes for the millions of patients who suffer from myocardial infarction and heart failure.