Clinical-Community Partnerships to Identify Patients With Food Insecurity and Address Food Needs.

INTRODUCTION: More than 42 million people in the United States are food insecure. Although some health care entities are addressing food insecurity among patients because of associations with disease risk and management, little is known about the components of these initiatives. METHODS: The Systematic Screening and Assessment Method was used to conduct a landscape assessment of US health care entity-based programs that screen patients for food insecurity and connect them with food resources. A network of food insecurity researchers, experts, and practitioners identified 57 programs, 22 of which met the inclusion criteria of being health care entities that 1) screen patients for food insecurity, 2) link patients to food resources, and 3) target patients including adults aged 50 years or older (a focus of this assessment). Data on key features of each program were abstracted from documentation and telephone interviews. RESULTS: Most programs (n = 13) focus on patients with chronic disease, and most (n = 12) partner with food banks. Common interventions include referrals to or a list of food resources (n = 19), case managers who navigate patients to resources (n = 15), assistance with federal benefit applications (n = 14), patient education and skill building (n = 13), and distribution of fruit and vegetable vouchers redeemable at farmers markets (n = 8). Most programs (n = 14) routinely screen all patients. CONCLUSION: The programs reviewed use various strategies to screen patients, including older adults, for food insecurity and to connect them to food resources. Research is needed on program effectiveness in improving patient outcomes. Such evidence can be used to inform the investments of potential stakeholders, including health care entities, community organizations, and insurers.

Measurement and Application of Incidentally Detected Coronary Calcium: JACC Review Topic of the Week.

Coronary artery calcium (CAC) scoring is a powerful tool for atherosclerotic cardiovascular disease risk stratification. The nongated, noncontrast chest computed tomography scan (NCCT) has emerged as a source of CAC characterization with tremendous potential due to the high volume of NCCT scans. Application of incidental CAC characterization from NCCT has raised questions around score accuracy, standardization of methodology including the possibility of deep learning to automate the process, and the risk stratification potential of an NCCT-derived score. In this review, the authors aim to summarize the role of NCCT-derived CAC in preventive cardiovascular health today as well as explore future avenues for eventual clinical applicability in specific patient populations and broader health systems.

Accuracy of incidental visual coronary artery calcium assessment compared with dedicated coronary artery calcium scoring.

INTRODUCTION: CAC can be detected on routine chest computed tomography (CT) scans and may contribute to CVD risk estimation, but the accuracy of visual CAC scoring may be affected by the specialty of the interpreting radiologist and/or the use of contrast. METHODS: The accuracy of visual CAC estimation on non-gated CT scans was evaluated at UT Southwestern Medical Center (UTSW) and Parkland Health and Hospital System (PHHS). All adults who underwent CAC scanning and a non-gated CT scan within 6 months were identified and the scores from the two CTs were compared overall and stratified by type of reader and whether contrast was used. Visual CAC categories of none, small, moderate, and large were compared to CAC â€‹= â€‹0, 1-99, 100-399, and ≥400, respectively. RESULTS: From 2016 to 2021, 934 patients (mean age 60 â€‹± â€‹12 â€‹y, 43% male, 61% White, 34% Black, 24% Hispanic, 54% from PHHS) had both CT scans. Of these, 441 (47%) had no CAC, 278 (30%) small, 147 (16%) moderate, and 66 (7%) large CAC on non-gated CT. Visual CAC estimates were highly correlated with CAC scores (Kendalls tau-b â€‹= â€‹0.76, p â€‹< â€‹0.0001). Among those with no visual CAC, 76% had CAC â€‹= â€‹0 (72% of contrast-enhanced vs 85% of non-contrast scans, 88% of scans interpreted by CT radiologist vs 78% of those interpreted by other radiologist). In those with moderate-to-large visual CAC, 99% had CAC >0 and 88% had CAC ≥100, including 89% of those with contrast, 90% of those without contrast, 80% of those read by a CT radiologist, and 88% of those read by a non-CT radiologist. DISCUSSION: Visual CAC estimates on non-gated CT scans are concordant with Agatston score categories from cardiac CT scans. A lack of visual CAC on non-gated CT scans may not be sufficient to "de-risk" patients, particularly for contrast-enhanced scans and those read by non-CT radiologists. However, the presence of moderate-to-large CAC, including on contrasted scans and regardless of radiologist type, is highly predictive of CAC and may be used to identify high-risk patients for prevention interventions.

Systolic ejection click versus split first heart sound: Are our ears deceiving us?

OBJECTIVE: Bicuspid aortic valve (BAV) disease is associated with potential lifetime complications, but auscultation of a BAV click is commonly missed or mistaken for a benign split first heart sound. Our objective was to determine whether pediatric cardiologists could reliably distinguish between BAV clicks and benign split first heart sounds. DESIGN: Quality evaluation project using de-identified recordings from an outpatient pediatric cardiology clinic. OUTCOME MEASURES: Twenty-one cardiologists listened to five de-identified recordings of pediatric heart sounds (three with BAV clicks, two with mitral components of benign split first heart sounds) and indicated whether they believed each recording was a BAV or split first heart sound. The accuracy of diagnoses was determined using percent agreement and calculated kappa coefficients for the cohort and subgroups based on those with less than 10 years of experience versus those with ≥10 years. To assess precision, a kappa extension was used for multiple raters to assess interrater agreement. RESULTS: Among participants, diagnostic accuracy of BAV click was 38%, while accuracy of split first heart sound was 41%. No participant correctly diagnosed all sounds. No difference in agreement was observed when stratifying by experience. Kappa was -0.11 (CI 95% -0.31 to 0.08) for all raters, -0.03 (CI 95% -0.39 to 0.33) for those with less than 10 years' experience, and -0.15 (CI 95% -0.38 to 0.08) for those with ≥10 years' experience. The kappa statistic among the 21 raters was 0.01 (95% CI -0.03 to 0.04), indicating poor precision among the raters. CONCLUSIONS: In this sample of pediatric cardiologists, the diagnostic accuracy of BAV clicks versus split first heart sounds was worse than chance. There was no association between years of experience and diagnostic accuracy. While further study is needed, these data suggest that an echocardiogram may be valuable when either a systolic ejection click or split first heart sound is heard.