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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.

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Background Extreme temperatures are increasingly experienced as a result of climate change. Both high and low temperatures, impacted by climate change, have been linked with cardiovascular disease (CVD). Global estimates on non-optimal temperature-related CVD are not known. Objectives The authors investigated global trends of temperature-related CVD burden over the last 3 decades. Methods The authors utilized the 1990-2019 global burden of disease methodology to investigate non-optimal temperature, low temperature-, and high temperature-related CVD deaths and disability-adjusted life years (DALYs) globally. Non-optimal temperatures were defined as above (high temperature) or below (low temperature) the location-specific theoretical minimum-risk exposure level, or the temperature associated with the lowest mortality rates. Analyses were later stratified by sociodemographic index (SDI) and world regions. Results In 2019, non-optimal temperature contributed to 1,194,196 (95% uncertainty interval [UI]: 963,816 to 1,425,090) CVD deaths and 21,799,370 (95% UI: 17,395,761 to 25,947,499) DALYs. low temperature contributed to 1,104,200 (95% UI: 897,783 to 1,326,965) CVD deaths and 19,768,986 (95% UI: 16,039,594 to 23,925,945) DALYs. High temperature contributed to 93,095 (95% UI: 10,827 to 158,386) CVD deaths and 2,098,989 (95% UI: 146,158 to 3,625,564) DALYs. Between 1990 and 2019, CVD deaths related to non-optimal temperature increased by 45% (95% UI: 32% to 63%), low temperature by 36% (95% UI: 25% to 48%), and high temperature by 600% (95% UI: -1879% to 2027%). Non-optimal temperature and high temperature-related CVD deaths increased more in countries with low income than countries with high income. Conclusion Non-optimal temperatures are significantly associated with global CVD deaths and DALYs, underscoring the significant impact of temperature on public health.

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.

ImportanceIn the 1930s, the government-sponsored Home Owners’ 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.ObjectiveTo evaluate whether redlining is associated with adverse cardiovascular outcomes in US veterans.Design, Setting, and ParticipantsIn 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.ExposureHome Owners’ Loan Corporation grade of the census tracts of residence.Main Outcomes and MeasuresThe 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.ResultsOf 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 < .001) and all-cause mortality (HR, 1.129; 95% CI, 1.072-1.190; P < .001). Similarly, veterans residing in redlined neighborhoods had a higher risk of myocardial infarction (HR, 1.148; 95% CI, 1.011-1.303; P < .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 RelevanceIn 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.

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