Association between historical neighborhood redlining and cardiovascular outcomes among US veterans with atherosclerotic cardiovascular diseases

Importance: In the 1930s, the government-sponsored Home Owners\u27 Loan Corporation (HOLC) established maps of US neighborhoods that identified mortgage risk (grade A [green] characterizing lowest-risk neighborhoods in the US through mechanisms that transcend traditional risk factors to grade D [red] characterizing highest risk). This practice led to disinvestments and segregation in neighborhoods considered redlined. Very few studies have targeted whether there is an association between redlining and cardiovascular disease.Objective: To evaluate whether redlining is associated with adverse cardiovascular outcomes in US veterans.Design, setting, and participants: In this longitudinal cohort study, US veterans were followed up (January 1, 2016, to December 31, 2019) for a median of 4 years. Data, including self-reported race and ethnicity, were obtained from Veterans Affairs medical centers across the US on individuals receiving care for established atherosclerotic disease (coronary artery disease, peripheral vascular disease, or stroke). Data analysis was performed in June 2022.Exposure: Home Owners\u27 Loan Corporation grade of the census tracts of residence.Main outcomes and measures: The first occurrence of major adverse cardiovascular events (MACE), comprising myocardial infarction, stroke, major adverse extremity events, and all-cause mortality. The adjusted association between HOLC grade and adverse outcomes was measured using Cox proportional hazards regression. Competing risks were used to model individual nonfatal components of MACE.Results: Of 79 997 patients (mean [SD] age, 74.46 [10.16] years, female, 2.9%; White, 55.7%; Black, 37.3%; and Hispanic, 5.4%), a total of 7% of the individuals resided in HOLC grade A neighborhoods, 20% in B neighborhoods, 42% in C neighborhoods, and 31% in D neighborhoods. Compared with grade A neighborhoods, patients residing in HOLC grade D (redlined) neighborhoods were more likely to be Black or Hispanic with a higher prevalence of diabetes, heart failure, and chronic kidney disease. There were no associations between HOLC and MACE in unadjusted models. After adjustment for demographic factors, compared with grade A neighborhoods, those residing in redlined neighborhoods had an increased risk of MACE (hazard ratio [HR], 1.139; 95% CI, 1.083-1.198; P \u3c .001) and all-cause mortality (HR, 1.129; 95% CI, 1.072-1.190; P \u3c .001). Similarly, veterans residing in redlined neighborhoods had a higher risk of myocardial infarction (HR, 1.148; 95% CI, 1.011-1.303; P \u3c .001) but not stroke (HR, 0.889; 95% CI, 0.584-1.353; P = .58). Hazard ratios were smaller, but remained significant, after adjustment for risk factors and social vulnerability.Conclusions and relevance: In this cohort study of US veterans, the findings suggest that those with atherosclerotic cardiovascular disease who reside in historically redlined neighborhoods continue to have a higher prevalence of traditional cardiovascular risk factors and higher cardiovascular risk. Even close to a century after this practice was discontinued, redlining appears to still be adversely associated with adverse cardiovascular events

Neoatherosclerosis prediction using plaque markers in intravascular optical coherence tomography images

IntroductionIn-stent neoatherosclerosis has emerged as a crucial factor in post-stent complications including late in-stent restenosis and very late stent thrombosis. In this study, we investigated the ability of quantitative plaque characteristics from intravascular optical coherence tomography (IVOCT) images taken just prior to stent implantation to predict neoatherosclerosis after implantation.MethodsThis was a sub-study of the TRiple Assessment of Neointima Stent FOrmation to Reabsorbable polyMer with Optical Coherence Tomography (TRANSFORM-OCT) trial. Images were obtained before and 18 months after stent implantation. Final analysis included images of 180 lesions from 90 patients; each patient had images of two lesions in different coronary arteries. A total of 17 IVOCT plaque features, including lesion length, lumen (e.g., area and diameter); calcium (e.g., angle and thickness); and fibrous cap (FC) features (e.g., thickness, surface area, and burden), were automatically extracted from the baseline IVOCT images before stenting using dedicated software developed by our group (OCTOPUS). The predictive value of baseline IVOCT plaque features for neoatherosclerosis development after stent implantation was assessed using univariate/multivariate logistic regression and receiver operating characteristic (ROC) analyses.ResultsFollow-up IVOCT identified stents with (n = 19) and without (n = 161) neoatherosclerosis. Greater lesion length and maximum calcium angle and features related to FC were associated with a higher prevalence of neoatherosclerosis after stent implantation (p < 0.05). Hierarchical clustering identified six clusters with the best prediction p-values. In univariate logistic regression analysis, maximum calcium angle, minimum calcium thickness, maximum FC angle, maximum FC area, FC surface area, and FC burden were significant predictors of neoatherosclerosis. Lesion length and features related to the lumen were not significantly different between the two groups. In multivariate logistic regression analysis, only larger FC surface area was strongly associated with neoatherosclerosis (odds ratio 1.38, 95% confidence interval [CI] 1.05–1.80, p < 0.05). The area under the ROC curve was 0.901 (95% CI 0.859–0.946, p < 0.05) for FC surface area.ConclusionPost-stent neoatherosclerosis can be predicted by quantitative IVOCT imaging of plaque characteristics prior to stent implantation. Our findings highlight the additional clinical benefits of utilizing IVOCT imaging in the catheterization laboratory to inform treatment decision-making and improve outcomes

Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach

Abstract Disparities in premature cardiovascular mortality (PCVM) have been associated with socioeconomic, behavioral, and environmental risk factors. Understanding the “phenotypes”, or combinations of characteristics associated with the highest risk of PCVM, and the geographic distributions of these phenotypes is critical to targeting PCVM interventions. This study applied the classification and regression tree (CART) to identify county phenotypes of PCVM and geographic information systems to examine the distributions of identified phenotypes. Random forest analysis was applied to evaluate the relative importance of risk factors associated with PCVM. The CART analysis identified seven county phenotypes of PCVM, where high-risk phenotypes were characterized by having greater percentages of people with lower income, higher physical inactivity, and higher food insecurity. These high-risk phenotypes were mostly concentrated in the Black Belt of the American South and the Appalachian region. The random forest analysis identified additional important risk factors associated with PCVM, including broadband access, smoking, receipt of Supplemental Nutrition Assistance Program benefits, and educational attainment. Our study demonstrates the use of machine learning approaches in characterizing community-level phenotypes of PCVM. Interventions to reduce PCVM should be tailored according to these phenotypes in corresponding geographic areas

Impact of residential social deprivation on prediction of heart failure in patients with type 2 diabetes: External validation and recalibration of the WATCH-DM score using real world data

BACKGROUND: Patients with type 2 diabetes are at risk of heart failure hospitalization. As social determinants of health are rarely included in risk models, we validated and recalibrated the WATCH-DM score in a diverse patient-group using their social deprivation index (SDI). METHODS: We identified US Veterans with type 2 diabetes without heart failure that received outpatient care during 2010 at Veterans Affairs medical centers nationwide, linked them to their SDI using residential ZIP codes and grouped them as SDI <20%, 21% to 40%, 41% to 60%, 61% to 80%, and >80% (higher values represent increased deprivation). Accounting for all-cause mortality, we obtained the incidence for heart failure hospitalization at 5 years follow-up; overall and in each SDI group. We evaluated the WATCH-DM score using the C statistic, the Greenwood Nam D’Agostino test χ2 test and calibration plots and further recalibrated the WATCH-DM score for each SDI group using a statistical correction factor. RESULTS: In 1 065 691 studied patients (mean age 67 years, 25% Black and 6% Hispanic patients), the 5-year incidence of heart failure hospitalization was 5.39%. In SDI group 1 (least deprived) and 5 (most deprived), the 5-year heart failure hospitalization was 3.18% and 11%, respectively. The score C statistic was 0.62; WATCH-DM systematically overestimated heart failure risk in SDI groups 1 to 2 (expected/observed ratios, 1.38 and 1.36, respectively) and underestimated the heart failure risk in groups 4 to 5 (expected/observed ratios, 0.95 and 0.80, respectively). Graphical evaluation demonstrated that the recalibration of WATCH-DM using an SDI group-based correction factor improved predictive capabilities as supported by reduction in the χ2 test results (801–27 in SDI groups I; 623–23 in SDI group V). CONCLUSIONS: Including social determinants of health to recalibrate the WATCH-DM score improved risk prediction highlighting the importance of including social determinants in future clinical risk prediction models

Residing in a food desert and adverse cardiovascular events in US veterans with established cardiovascular disease

Residents living in a “food desert” are known to be at a higher risk for developing cardiovascular disease (CVD). However, national-level data regarding the influence of residing in a food desert in patients with established CVD is lacking. Data from veterans with established atherosclerotic CVD who received outpatient care in the Veterans Health Administration system between January 2016 and December 2021 were obtained, with follow-up information collected until May 2022 (median follow-up: 4.3 years). A food desert was defined using the United States Department of Agriculture criteria, and census tract data were used to identify Veterans in these areas. All-cause mortality and the occurrence of major adverse cardiovascular events (MACEs; a composite of myocardial infarction/stroke/heart failure/all-cause mortality) were evaluated as the co-primary end points. The relative risk for MACE in food desert areas was evaluated by fitting multivariable Cox models adjusted for age, gender, race, ethnicity, and median household income, with food desert status as the primary exposure. Of the 1,640,346 patients (mean age 72 years, women 2.7%, White 77.7%, Hispanic 3.4%), 25,7814 (15.7%) belonged to the food desert group. Patients residing in food deserts were younger; more likely to be Black (22% vs 13%)or Hispanic (4% vs 3.5%); and had a higher prevalence of diabetes mellitus (52.7% vs 49.8%), chronic kidney disease (31.8% vs 30.4%,) and heart failure (25.6% vs 23.8%). Adjusted for covariates, food desert patients had a higher risk of MACE (hazard ratio 1.040 [1.033 to 1.047]; p <0.001) and all-cause mortality (hazard ratio 1.032 [1.024 to 1.039]; p <0.001). In conclusion, we observed that a large proportion of US veterans with established atherosclerotic CVD reside in food desert census tracts. Adjusting for age, gender, race, and ethnicity, residing in food deserts was associated with a higher risk of adverse cardiac events and all-cause mortality

Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning

Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning

Supplemental Material - County-Level Socio-Environmental Factors Associated With Stroke Mortality in the United States: A Cross-Sectional Study

Supplemental Material for County-Level Socio-Environmental Factors Associated With Stroke Mortality in the United States: A Cross-Sectional Study by Pedro RVO Salerno, Issam Motairek, Weichuan Dong, Khurram Nasir, Neel Fotedar, Setareh S. Omran, Sarju Ganatra, Omar Hahad, Salil V. Deo, Sanjay Rajagopalan, and Sadeer G. Al-Kindi in Angiology</p