Faculty Bibliography

BACKGROUND:Coronary calcium is a marker of coronary atherosclerosis and established predictor of cardiovascular risk in general populations; however, there are limited studies examining its prognostic value among older adults (≥75 years) and even less regarding its utility in older males compared with females. Accordingly, we sought to examine the prognostic significance of both absolute and percentile coronary calcium scores among older adults. METHODS:The multicenter Coronary Artery Calcium Consortium consists of 66,636 asymptomatic patients without cardiovascular disease. Participants ages ≥75 were included in this study and stratified by sex. Multivariable Cox regression models were constructed to assess cardiovascular and all-cause mortality risk by Agatston coronary calcium scores and percentiles. RESULTS:Among 2,474 asymptomatic patients (mean age 79 years, 10.4-year follow-up), prevalence of coronary artery calcium was 92%. For both sexes, but in females more so than males, higher coronary calcium score and percentiles were associated with increased cardiovascular and all-cause mortality risk. Those at the lowest coronary calcium categories (0-9 and <25 percentile) had significantly lower risk of cardiovascular and all-cause mortality relative to the rest of the population. Multivariable analyses of traditional cardiovascular risk factors and coronary artery calcium variables revealed that age and coronary calcium were the strongest independent predictors for adverse outcomes. CONCLUSIONS:Both coronary artery calcium scores and percentiles are strongly predictive of cardiovascular and all-cause mortality among older adults, with greater risk-stratification among females than males. Both low coronary artery calcium scores 0-9 and <25th percentile define relatively low risk older adults.

Objectives/UNASSIGNED:The aim of this study was to test the mediating role of perceived discrimination and stress on associations between perceived neighborhood social environment (PNSE) and TV viewing. Methods/UNASSIGNED:Baseline data were used for 4716 participants (mean age = 55.1 y; 63.4% female) in the Jackson Heart Study (JHS), a large prospective cohort study of African Americans in Jackson, Mississippi. One binary TV viewing outcome was created: ≥4 h/day versus <4 h/day. PNSE variables included neighborhood violence, problems (higher value = more violence/problems), and social cohesion (higher value = more cohesion). Mediators included perceived lifetime discrimination, daily discrimination, and chronic stress (higher value = greater discrimination/stress). Multivariable regression was used with bootstrap-generated 95% bias-corrected confidence intervals (BC CIs) to test for mediation adjusting for demographics, health-related and psychosocial factors, and population density. Results/UNASSIGNED: = 1.01, 1.10, respectively). Daily discrimination was neither directly nor indirectly associated with TV viewing. Conclusions/UNASSIGNED:Each PNSE variable was indirectly associated with TV viewing via lifetime discrimination and perceived stress, but not with daily discrimination among JHS participants. Unexpected directionality of mediating effects of lifetime discrimination and chronic stress should be replicated in future studies. Further research is also needed to pinpoint effective community efforts and physical environmental policies (e.g., installing bright street lights, community policing) to reduce adverse neighborhood conditions and psychosocial factors, and decrease TV viewing and subsequent cardiovascular disease risk.

BACKGROUND & AIMS:Chronic liver disease (CLD) and cirrhosis are associated with immune dysregulation, leading to concerns that affected patients may be at risk of adverse outcomes following SARS-CoV-2 infection. We aimed to determine the impact of COVID-19 on patients with pre-existing liver disease, which currently remains ill-defined. METHODS:Between 25th March and 8th July 2020, data on 745 patients with CLD and SARS-CoV-2 (including 386 with and 359 without cirrhosis) were collected by 2 international registries and compared to data on non-CLD patients with SARS-CoV-2 from a UK hospital network. RESULTS:Mortality was 32% in patients with cirrhosis compared to 8% in those without (p <0.001). Mortality in patients with cirrhosis increased according to Child-Pugh class (A [19%], B [35%], C [51%]) and the main cause of death was from respiratory failure (71%). After adjusting for baseline characteristics, factors associated with death in the total CLD cohort were age (odds ratio [OR] 1.02; 1.01-1.04), Child-Pugh A (OR 1.90; 1.03-3.52), B (OR 4.14; 2.4-7.65), or C (OR 9.32; 4.80-18.08) cirrhosis and alcohol-related liver disease (OR 1.79; 1.03-3.13). Compared to patients without CLD (n = 620), propensity-score-matched analysis revealed significant increases in mortality in those with Child-Pugh B (+20.0% [8.8%-31.3%]) and C (+38.1% [27.1%-49.2%]) cirrhosis. Acute hepatic decompensation occurred in 46% of patients with cirrhosis, of whom 21% had no respiratory symptoms. Half of those with hepatic decompensation had acute-on-chronic liver failure. CONCLUSIONS:In the largest such cohort to date, we demonstrate that baseline liver disease stage and alcohol-related liver disease are independent risk factors for death from COVID-19. These data have important implications for the risk stratification of patients with CLD across the globe during the COVID-19 pandemic. LAY SUMMARY:This international registry study demonstrates that patients with cirrhosis are at increased risk of death from COVID-19. Mortality from COVID-19 was particularly high among patients with more advanced cirrhosis and those with alcohol-related liver disease.

BACKGROUND:Child maltreatment is a leading cause of pediatric morbidity and mortality. We previously reported on development and implementation of a child abuse clinical decision support system (CA-CDSS) in the Cerner electronic health record (EHR). Our objective was to develop a CA-CDSS in two different EHRs. METHODS:Using the CA-CDSS in Cerner as a template, CA-CDSSs were developed for use in four hospitals in the Northwell Health system who use Allscripts and two hospitals in the University of Wisconsin health system who use Epic. Each system had a combination of triggers, alerts and child abuse-specific order sets. Usability evaluation was done prior to launch of the CA-CDSS. RESULTS:Over an 18-month period, a CA-CDSS was embedded into Epic and Allscripts at two hospital systems. The CA-CDSSs vary significantly from each other in terms of the type of triggers which were able to be used, the type of alert, the ability of the alert to link directly to child abuse-specific order sets and the order sets themselves. CONCLUSIONS:Dissemination of CA-CDSS from one EHR into the EHR in other health care systems is possible but time-consuming and needs to be adapted to the strengths and limitations of the specific EHR. Site-specific usability evaluation, buy-in of multiple stakeholder groups and significant information technology support are needed. These barriers limit scalability and widespread dissemination of CA-CDSS.

OBJECTIVES/OBJECTIVE:The aim of this study was to evaluate whether machine learning (ML) of noncontrast computed tomographic (CT) and clinical variables improves the prediction of atherosclerotic cardiovascular disease (ASCVD) and coronary heart disease (CHD) deaths compared with coronary artery calcium (CAC) Agatston scoring and clinical data. BACKGROUND:The CAC score provides a measure of the global burden of coronary atherosclerosis, and its long-term prognostic utility has been consistently shown to have incremental value over clinical risk assessment. However, current approaches fail to integrate all available CT and clinical variables for comprehensive risk assessment. METHODS:The study included data from 66,636 asymptomatic subjects (mean age 54 ± 11 years, 67% men) without established ASCVD undergoing CAC scanning and followed for cardiovascular disease (CVD) and CHD deaths at 10 years. Clinical risk assessment incorporated the ASCVD risk score. For ML, an ensemble boosting approach was used to fit a predictive classifier for outcomes, followed by automated feature selection using information gain ratio. The model-building process incorporated all available clinical and CT data, including the CAC score; the number, volume, and density of CAC plaques; and extracoronary scores; comprising a total of 77 variables. The overall proposed model (ML all) was evaluated using a 10-fold cross-validation framework on the population data and area under the curve (AUC) as metrics. The prediction performance was also compared with 2 traditional scores (ASCVD risk and CAC score) and 2 additional models that were trained using all the clinical data (ML clinical) and CT variables (ML CT). RESULTS:The AUC by ML all (0.845) for predicting CVD death was superior compared with those obtained by ASCVD risk alone (0.821), CAC score alone (0.781), and ML CT alone (0.804) (p < 0.001 for all). Similarly, for predicting CHD death, AUC by ML all (0.860) was superior to the other analyses (0.835 for ASCVD risk, 0.816 for CAC, and 0.827 for ML CT; p < 0.001). CONCLUSIONS:The comprehensive ML model was superior to ASCVD risk, CAC score, and an ML model fitted using CT variables alone in the prediction of both CVD and CHD death.

BACKGROUND:There are specific issues regarding sexual orientation (SO) collection and analysis among transgender and nonbinary patients. A limitation to meaningful SO and gender identity (GI) data collection is their consideration as a fixed trait or demographic data point. METHODS:A de-identified patient database from a single electronic health record (EHR) that allows for searching any discrete data point in the EHR was used to query demographic data (sex assigned at birth and current GI) for transgender individuals from January 2011 to March 2020 at a large urban tertiary care academic health center. RESULTS: = 232). CONCLUSION:Current SO categories do not fully capture transgender individuals' identities and experiences, and limit the clinical and epidemiological utility of collecting this data in the current form. Anatomical assumptions based on SO should be seen as a potential shortcoming in over-reliance on SO as an indicator of screening needs and risk factors.