Derivation and external validation of a simple risk score for predicting severe acute kidney injury after intravenous cisplatin: cohort study.

OBJECTIVE: To develop and externally validate a prediction model for severe cisplatin associated acute kidney injury (CP-AKI). DESIGN: Multicenter cohort study. SETTING: Six geographically diverse major academic cancer centers across the US. PARTICIPANTS: Adults (≥18 years) receiving their first dose of intravenous cisplatin, 2006-22. MAIN OUTCOME MEASURES: The primary outcome was CP-AKI, defined as a twofold or greater increase in serum creatinine or kidney replacement therapy within 14 days of a first dose of intravenous cisplatin. Independent predictors of CP-AKI were identified using a multivariable logistic regression model, which was developed in a derivation cohort and tested in an external validation cohort. For the primary model, continuous variables were examined using restricted cubic splines. A simple risk model was also generated by converting the odds ratios from the primary model into risk points. Finally, a multivariable Cox model was used to examine the association between severity of CP-AKI and 90 day survival. RESULTS: A total of 24 717 adults were included, with 11 766 in the derivation cohort (median age 59 (interquartile range (IQR) 50-67)) and 12 951 in the validation cohort (median age 60 (IQR 50-67)). The incidence of CP-AKI was 5.2% (608/11 766) in the derivation cohort and 3.3% (421/12 951) in the validation cohort. Each of the following factors were independently associated with CP-AKI in the derivation cohort: age, hypertension, diabetes mellitus, serum creatinine level, hemoglobin level, white blood cell count, platelet count, serum albumin level, serum magnesium level, and cisplatin dose. A simple risk score consisting of nine covariates was shown to predict a higher risk of CP-AKI in a monotonic fashion in both the derivation cohort and the validation cohort. Compared with patients in the lowest risk category, those in the highest risk category showed a 24.00-fold (95% confidence interval (CI) 13.49-fold to 42.78-fold) higher odds of CP-AKI in the derivation cohort and a 17.87-fold (10.56-fold to 29.60-fold) higher odds in the validation cohort. The primary model had a C statistic of 0.75 and showed better discrimination for CP-AKI than previously published models, the C statistics for which ranged from 0.60 to 0.68 (DeLong P<0.001 for each comparison). Greater severity of CP-AKI was monotonically associated with shorter 90 day survival (adjusted hazard ratio 4.63 (95% CI 3.56 to 6.02) for stage 3 CP-AKI versus no CP-AKI). CONCLUSION: This study found that a simple risk score based on readily available variables from patients receiving intravenous cisplatin could predict the risk of severe CP-AKI, the occurrence of which is strongly associated with death.

In Utero Exposure to Alcohol and Tobacco and Electroencephalogram Power During Childhood.

Importance: Prenatal alcohol exposure (PAE) and prenatal tobacco exposure (PTE) are risk factors associated with adverse neurobehavioral and cognitive outcomes. Objective: To quantify long-term associations of PAE and PTE with brain activity in early and middle childhood via electroencephalography (EEG). Design, Setting, and Participants: This cohort study included participants enrolled in the Safe Passage Study (August 2007 to January 2015), from which a subset of 649 participants were followed up in the Environmental Influences on Child Health Outcomes Program. From September 2018 through November 2022, EEG recordings were obtained at ages 4, 5, 7, 9, or 11 years. Data were analyzed from November 2022 to November 2023. Exposures: Maternal self-reported consumptions of alcohol and tobacco during pregnancy were captured at the recruitment interview and at up to 3 visits during pregnancy (20-24, 28-32, and ≥34 weeks' gestation). Classifications of PAE (continuous drinking, quit-early drinking, and nondrinking) and PTE (continuous smoking, quit-early smoking, and nonsmoking) were previously obtained. Main Outcomes and Measures: EEG band powers (theta, alpha, beta, gamma) were extracted from the EEG recordings. Linear regression models were used to estimate the associations of PAE and PTE with EEG estimates. Results: The final sample included 649 participants (333 [51.3%] female) aged 4, 5, 7, 9, or 11 years. Children whose mothers were in the quit-early drinking cluster had increased alpha power (0.116 [95% CI, 0.023 to 0.209] µV2; P = .02) compared with individuals without PAE. The magnitude of this increase was approximately double for children exposed to continuous drinking (0.211 [95% CI, 0.005 to 0.417] µV2; P = .04). Children whose mothers were in the continuous smoking cluster had decreased beta power (-0.031 [95% CI, -0.059 to -0.003] µV2; P = .03) and gamma power (-0.020 [95% CI, -0.039 to -0.000] µV2; P = .04) compared with the nonsmoking cluster. In exploratory sex-stratified models, male participants in the quit-early PAE cluster had greater EEG power in the alpha band (0.159 [95% CI, 0.003 to 0.315] µV2; P = .04) compared with those with no PAE, and the difference was approximately double for male participants with continuous PAE (0.354 [95% CI, 0.041 to 0.667] µV2; P = .03). Male participants in the continuous PTE cluster had decreased beta (-0.048 [95% CI, -0.090 to - 0.007] µV2; P = .02) and gamma (-0.032 [95% CI, -0.061 - 0.002] µV2; P = .04) power compared with those with no PTE. Conclusions and Relevance: These findings suggest that even low levels of PAE and PTE were associated with long-term alterations of brain activity.

Race as a Component of Cardiovascular Disease Risk Prediction Algorithms.

PURPOSE OF REVIEW: Several prediction algorithms include race as a component to account for race-associated variations in disease frequencies. This practice has been questioned recently because of the risk of perpetuating race as a biological construct and diverting attention away from the social determinants of health (SDoH) for which race might be a proxy. We evaluated the appropriateness of including race in cardiovascular disease (CVD) prediction algorithms, notably the pooled cohort equations (PCE). RECENT FINDINGS: In a recent investigation, we reported substantial and biologically implausible differences in absolute CVD risk estimates upon using PCE for predicting CVD risk in Black and White persons with identical risk factor profiles, which might result in differential treatment decisions based solely on their race. We recommend the development of raceless CVD risk prediction algorithms that obviate race-associated risk misestimation and racializing treatment practices, and instead incorporate measures of SDoH that mediate race-associated risk differences.

Lifetime healthcare expenses across demographic and cardiovascular risk groups: The application of a novel modeling strategy in a large multiethnic cohort study.

Objective: To understand the burden of healthcare expenses over the lifetime of individuals and evaluate differences among those with cardiovascular risk factors and among disadvantaged groups based on race/ethnicity and sex. Methods: We linked data from the longitudinal multiethnic Dallas Heart Study, which recruited participants between 2000 and 2002, with inpatient and outpatient claims from all hospitals in the Dallas-Fort Worth metroplex through December 2018, capturing encounter expenses. Race/ethnicity and sex, as well as five risk factors, hypertension, diabetes, hyperlipidemia, smoking, and overweight/obesity, were defined at cohort enrollment. For each individual, expenses were indexed to age and cumulated between 40 and 80 years of age. Lifetime expenses across exposures were evaluated as interactions in generalized additive models. Results: A total of 2184 individuals (mean age, 45±10 years; 61% women, 53% Black) were followed between 2000 and 2018. The mean modeled lifetime cumulative healthcare expenses were $442,629 (IQR, $423,850 to $461,408). In models that included 5 risk factors, Black individuals had $21,306 higher lifetime healthcare spending compared with non-Black individuals (P < .001), and men had modestly higher expenses than women ($5987, P < .001). Across demographic groups, the presence of risk factors was associated with progressively higher lifetime expenses, with significant independent association of diabetes ($28,075, P < .001), overweight/obesity ($8816, P < .001), smoking ($3980, P = .009), and hypertension ($528, P = .02) with excess spending. Conclusion: Our study suggests Black individuals have higher lifetime healthcare expenses, exaggerated by the substantially higher prevalence of risk factors, with differences emerging in older age.

Historical Redlining, Socioeconomic Distress, and Risk of Heart Failure Among Medicare Beneficiaries.

BACKGROUND: The association of historical redlining policies, a marker of structural racism, with contemporary heart failure (HF) risk among White and Black individuals is not well established. METHODS: We aimed to evaluate the association of redlining with the risk of HF among White and Black Medicare beneficiaries. Zip code-level redlining was determined by the proportion of historically redlined areas using the Mapping Inequality Project within each zip code. The association between higher zip code redlining proportion (quartile 4 versus quartiles 1-3) and HF risk were assessed separately among White and Black Medicare beneficiaries using generalized linear mixed models adjusted for potential confounders, including measures of the zip code-level Social Deprivation Index. RESULTS: A total of 2 388 955 Medicare beneficiaries (Black n=801 452; White n=1 587 503; mean age, 71 years; men, 44.6%) were included. Among Black beneficiaries, living in zip codes with higher redlining proportion (quartile 4 versus quartiles 1-3) was associated with increased risk of HF after adjusting for age, sex, and comorbidities (risk ratio, 1.08 [95% CI, 1.04-1.12]; P<0.001). This association remained significant after further adjustment for area-level Social Deprivation Index (risk ratio, 1.04 [95% CI, 1.002-1.08]; P=0.04). A significant interaction was observed between redlining proportion and Social Deprivation Index (Pinteraction<0.01) such that higher redlining proportion was significantly associated with HF risk only among socioeconomically distressed regions (above the median Social Deprivation Index). Among White beneficiaries, redlining was associated with a lower risk of HF after adjustment for age, sex, and comorbidities (risk ratio, 0.94 [95% CI, 0.89-0.99]; P=0.02). CONCLUSIONS: Historical redlining is associated with an increased risk of HF among Black patients. Contemporary zip code-level social determinants of health modify the relationship between redlining and HF risk, with the strongest relationship between redlining and HF observed in the most socioeconomically disadvantaged communities.

Prevalence of Statin Use for Primary Prevention of Atherosclerotic Cardiovascular Disease by Race, Ethnicity, and 10-Year Disease Risk in the US: National Health and Nutrition Examination Surveys, 2013 to March 2020.

Importance: The burden of atherosclerotic cardiovascular disease (ASCVD) in the US is higher among Black and Hispanic vs White adults. Inclusion of race in guidance for statin indication may lead to decreased disparities in statin use. Objective: To evaluate prevalence of primary prevention statin use by race and ethnicity according to 10-year ASCVD risk. Design, Setting, and Participants: This serial, cross-sectional analysis performed in May 2022 used data from the National Health and Nutrition Examination Survey, a nationally representative sample of health status in the US, from 2013 to March 2020 (limited cycle due to the COVID-19 pandemic), to evaluate statin use for primary prevention of ASCVD and to estimate 10-year ASCVD risk. Participants aged 40 to 75 years without ASCVD, diabetes, low-density lipoprotein cholesterol levels 190 mg/dL or greater, and with data on medication use were included. Exposures: Self-identified race and ethnicity (Asian, Black, Hispanic, and White) and 10-year ASCVD risk category (5%-<7.5%, 7.5%-<20%, ≥20%). Main Outcomes and Measures: Prevalence of statin use, defined as identification of statin use on pill bottle review. Results: A total of 3417 participants representing 39.4 million US adults after applying sampling weights (mean [SD] age, 61.8 [8.0] years; 1289 women [weighted percentage, 37.8%] and 2128 men [weighted percentage, 62.2%]; 329 Asian [weighted percentage, 4.2%], 1032 Black [weighted percentage, 12.7%], 786 Hispanic [weighted percentage, 10.1%], and 1270 White [weighted percentage, 73.0%]) were included. Compared with White participants, statin use was lower in Black and Hispanic participants and comparable among Asian participants in the overall cohort (Asian, 25.5%; Black, 20.0%; Hispanic, 15.4%; White, 27.9%) and within ASCVD risk strata. Within each race and ethnicity group, a graded increase in statin use was observed across increasing ASCVD risk strata. Statin use was low in the highest risk stratum overall with significantly lower rates of use among Black (23.8%; prevalence ratio [PR], 0.90; 95% CI, 0.82-0.98 vs White) and Hispanic participants (23.9%; PR, 0.90; 95% CI, 0.81-0.99 vs White). Among other factors, routine health care access and health insurance were significantly associated with higher statin use in Black, Hispanic, and White adults. Prevalence of statin use did not meaningfully change over time by race and ethnicity or by ASCVD risk stratum. Conclusions and Relevance: In this study, statin use for primary prevention of ASCVD was low among all race and ethnicity groups regardless of ASCVD risk, with the lowest use occurring among Black and Hispanic adults. Improvements in access to care may promote equitable use of primary prevention statins in Black and Hispanic adults.

Prenatal smoking and drinking are associated with altered newborn autonomic functions.

BACKGROUND: Prenatal smoking and drinking are associated with sudden infant death syndrome and neurodevelopmental disorders. Infants with these outcomes also have altered autonomic nervous system (ANS) regulation. We examined the effects of prenatal smoking and drinking on newborn ANS function. METHODS: Pregnant women were enrolled in Northern Plains, USA (NP) and Cape Town (CT), South Africa. Daily drinking and weekly smoking data were collected prenatally. Physiological measures were obtained during sleep 12-96 h post-delivery. RESULTS: In all, 2913 infants from NP and 4072 from CT were included. In active sleep, newborns of mothers who smoked throughout pregnancy, compared to non-smokers, had higher breathing rates (2.2 breaths/min; 95% CI: 0.95, 3.49). Quit-early smoking was associated with reductions in beat-to-beat heart rate variability (HRV) in active (-0.08 s) and quiet sleep (-0.11 s) in CT. In girls, moderate-high continuous smoking was associated with increased systolic (3.0 mmHg, CI: 0.70, 5.24) and diastolic blood pressure (2.9 mmHg, CI: 0.72, 5.02). In quiet sleep, low-continuous drinking was associated with slower heart rate (-4.5 beat/min). In boys, low-continuous drinking was associated with a reduced ratio of low-to-high frequency HRV (-0.11, CI: -0.21, -0.02). CONCLUSIONS: These findings highlight potential ANS pathways through which prenatal drinking and smoking may contribute to neurodevelopment outcomes. IMPACT: In this prospective cohort study of 6985 mother-infant dyads prenatal drinking and smoking were associated with multiple ANS parameters. Smoking was associated with increased neonatal breathing rates among all infants, and heart rate variability (HRV) and blood pressure (BP) among girls. Drinking was associated with reductions in HR and BP among all newborns, and reductions in the ratio of low to-high frequency HRV among boys. These findings suggest that prenatal smoking and drinking alter newborn ANS which may presage future neurodevelopmental disorders.

Association of US County-Level Eviction Rates and All-Cause Mortality.

BACKGROUND: Housing instability is a key social determinant of health and has been linked to adverse short- and long-term health. Eviction reflects a severe form of housing instability and disproportionately affects minority and women residents in the USA; however, its relationship with mortality has not previously been described. OBJECTIVE: To evaluate the independent association of county-level eviction rates with all-cause mortality in the USA after adjustment for county demographic, socioeconomic, and health-related characteristics. DESIGN: Cross-sectional. PARTICIPANTS: Six hundred eighty-six US counties with available 2016 county-level eviction and mortality data. EXPOSURE: 2016 US county-level eviction rate. OUTCOME: 2016 US county-level age-adjusted all-cause mortality. KEY RESULTS: Among 686 counties (66.1 million residents, 50.5% [49.7-51.2] women, 2% [0.5-11.1] Black race) with available eviction and mortality data in 2016, we observed a significant and graded relationship between county-level eviction rate and all-cause mortality. Counties in the highest eviction tertile demonstrated a greater proportion of residents of Black race and women and a higher prevalence of poverty and comorbid health conditions. After adjustment for county-level sociodemographic traits and prevalent comorbid health conditions, age-adjusted all-cause mortality was highest among counties in the highest eviction tertile (Tertile 3 vs 1 (per 100,000 people) 33.57: 95% CI: 10.5-56.6 p=.004). Consistent results were observed in continuous analysis of eviction, with all-cause mortality increasing by 9.32 deaths per 100,000 people (4.77, 13.89, p<.0001) for every 1% increase in eviction rates. Significant interaction in the relationship between eviction and all-cause mortality was observed by the proportion of Black and women residents. CONCLUSIONS: In this cross-sectional analysis, county-level eviction rates were significantly associated with all-cause mortality with the strongest effects observed among counties with the highest proportion of Black and women residents. State and federal protections from evictions may help to reduce the health consequences of housing instability and address disparities in health outcomes.

Race, Social Determinants of Health, and Length of Stay Among Hospitalized Patients With Heart Failure: An Analysis From the Get With The Guidelines-Heart Failure Registry.

BACKGROUND: Racial disparities in heart failure hospitalization and mortality are well established; however, the association between different social determinants of health (SDOH) and length of stay (LOS) and the extent to which this association may differ across racial groups is not well established. METHODS: We utilized data from the Get With The Guidelines-Heart Failure registry to evaluate the association between SDOH, as determined by patients' residential ZIP Code and LOS among patients hospitalized with heart failure. We also assessed the race-specific contribution of the ZIP Code-level SDOH to LOS in patients of Black and non-Black races. Finally, we evaluated SDOH predictors of racial differences in LOS at the hospital level. RESULTS: Among 301 500 patients (20.2% Black race), the median LOS was 4 days. In adjusted analysis accounting for patient-level and hospital-level factors, SDOH parameters of education, income, housing instability, and foreign-born were significantly associated with LOS after adjusting for clinical status and hospital-level factors. SDOH parameters accounted for 25.8% of the total attributable risk for prolonged LOS among Black patients compared with 10.1% in patients of non-Black race. Finally, hospitals with disproportionately longer LOS for Black versus non-Black patients were more likely to care for disadvantaged patients living in ZIP Codes with a higher percentage of foreign-born and non-English speaking areas. CONCLUSIONS: ZIP Code-level SDOH markers can identify patients at risk for prolonged LOS, and the effects of SDOH parameters are significantly greater among Black adults with heart failure as compared with non-Black adults.

Association of polypill therapy with cardiovascular outcomes, mortality, and adherence: A systematic review and meta-analysis of randomized controlled trials.

Prior studies have reported improvements in population-level risk factor burden and cardiovascular disease (CVD) outcomes using polypills for CVD risk reduction. However, a comprehensive assessment of the impact of polypills on CVD outcomes, mortality, adherence, and side effects across different settings has not previously been reported. We performed a systematic review and meta-analysis of randomized controlled trials examining the association between polypill therapy and CVD outcomes published before February 2021. The primary outcome of interest was the risk of major adverse CVD events (MACE). Risk ratios for dichotomous outcomes were converted to log RR and pooled using a generic inverse variance weighted random-effects model. Data for continuous outcomes were pooled using random-effects modeling and presented as mean differences with 95% CIs. Eight studies representing 25,584 patients were included for analysis. In the overall pooled analysis, the use of polypills was associated with a non-significant reduction in the risk of MACE (RR: 0.85; 95% CI: 0.70-1.02) and significant reductions in the risk of all-cause mortality (RR: 0.90; 95% CI: 0.81-1.00). The reductions in the risk of MACE with polypill use varied by baseline risk and nature of the study population (primary prevention vs. secondary prevention), with the most significant risk reduction among lower-risk cohorts, including within primary prevention populations [RR 0.70 (0.62, 0.79)]. Among measures of CVD risk factors, modest but significant reductions were observed for systolic and diastolic blood pressure [systolic: mean difference 1.99 mmHg (95% CI: -3.07 to -0.91); diastolic: mean difference 1.30 mmHg (95% CI: -2.42 to -0.19), but not for levels of total or low-density lipoprotein-cholesterol. Use of the polypill strategy significantly improved drug adherence (RR: 1.31; 95% CI: 1.11-1.55) with no association between polypill use and rates of adverse events or drug discontinuation. The use of polypill formulations is associated with significant reductions in CVD risk factors and the risk of all-cause mortality and MACE, particularly in the low-risk and primary prevention population.

Genetic European Ancestry and Incident Diabetes in Black Individuals: Insights From the SPRINT Trial.

BACKGROUND: Black individuals have high incident diabetes risk, despite having paradoxically lower triglyceride and higher HDL (high-density lipoprotein) cholesterol levels. The basis of this is poorly understood. We evaluated the participants of SPRINT (Systolic Blood Pressure Intervention Trial) to assess the association of estimated European genetic ancestry with the risk of incident diabetes in self-identified Black individuals. METHODS: Self-identified non-Hispanic Black SPRINT participants free of diabetes at baseline were included. Black participants were stratified into tertiles (T1-T3) of European ancestry proportions estimated using 106 biallelic ancestry informative genetic markers. The multivariable-adjusted association of European ancestry proportion with indices of baseline metabolic syndrome (ie, fasting plasma glucose, triglycerides, HDL cholesterol, body mass index, and blood pressure) was assessed. Multivariable-adjusted Cox regression determined the risk of incident diabetes (fasting plasma glucose ≥126 mg/dL or self-reported diabetes treatment) across tertiles of European ancestry proportion. RESULTS: Among 2466 Black SPRINT participants, a higher European ancestry proportion was independently associated with higher baseline triglyceride and lower HDL cholesterol levels (P<0.001 for both). European ancestry proportion was not associated with baseline fasting plasma glucose, body mass index, and blood pressure (P>0.05). Compared with the first tertile, those in the second (hazard ratio, 0.64 [95% CI, 0.45-0.90]) and third tertiles (hazard ratio, 0.61 [95% CI, 0.44-0.89]) of the European ancestry proportion had a lower risk of incident diabetes. A 5% point higher European ancestry was associated with a 29% lower risk of incident diabetes (hazard ratio, 0.71 [95% CI, 0.55-0.93]). There was no evidence of a differential association between the European ancestry proportion tertiles and incident diabetes between those randomized to intensive versus standard blood pressure treatment. CONCLUSIONS: The higher risk of incident diabetes in Black individuals may have genetic determinants in addition to adverse social factors. Further research may help understand the interplay between biological and social determinants of cardiometabolic health in Black individuals. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01206062.

Community-Level Economic Distress, Race, and Risk of Adverse Outcomes After Heart Failure Hospitalization Among Medicare Beneficiaries.

BACKGROUND: Socioeconomic disadvantage is a strong determinant of adverse outcomes in patients with heart failure. However, the contribution of community-level economic distress to adverse outcomes in heart failure may differ across races and ethnicities. METHODS: Patients of self-reported Black, White, and Hispanic race and ethnicity hospitalized with heart failure between 2014 and 2019 were identified from the Medicare MedPAR Part A 100% Files. We used patient-level residential ZIP code to quantify community-level economic distress on the basis of the Distressed Community Index (quintile 5: economically distressed versus quintiles 1-4: nondistressed). The association of continuous and categorical measures (distressed versus nondistressed) of Distressed Community Index with 30-day, 6-month, and 1-year risk-adjusted mortality, readmission burden, and home time were assessed separately by race and ethnicity groups. RESULTS: The study included 1 611 586 White (13.2% economically distressed), 205 840 Black (50.6% economically distressed), and 89 199 Hispanic (27.3% economically distressed) patients. Among White patients, living in economically distressed (versus nondistressed) communities was significantly associated with a higher risk of adverse outcomes at 30-day and 1-year follow-up. Among Black and Hispanic patients, the risk of adverse outcomes associated with living in distressed versus nondistressed communities was not meaningfully different at 30 days and became more prominent by 1-year follow-up. Similarly, in the restricted cubic spline analysis, a stronger and more graded association was observed between Distressed Community Index score and risk of adverse outcomes in White patients (versus Black and Hispanic patients). Furthermore, the association between community-level economic distress and risk of adverse outcomes for Black patients differed in rural versus urban areas. Living in economically distressed communities was significantly associated with a higher risk of mortality and lower home time at 1-year follow-up in rural areas but not urban areas. CONCLUSIONS: The association between community-level economic distress and risk of adverse outcomes differs across race and ethnic groups, with a stronger association noted in White patients at short- and long-term follow-up. Among Black patients, the association of community-level economic distress with a higher risk of adverse outcomes is less evident in the short term and is more robust and significant in the long-term follow-up and rural areas.

Longitudinal Trajectories and Factors Associated With US County-Level Cardiovascular Mortality, 1980 to 2014.

Importance: Cardiovascular (CV) mortality has declined for more than 3 decades in the US. However, differences in declines among residents at a US county level are not well characterized. Objective: To identify unique county-level trajectories of CV mortality in the US during a 35-year study period and explore county-level factors that are associated with CV mortality trajectories. Design, Setting, and Participants: This longitudinal cross-sectional analysis of CV mortality trends used data from 3133 US counties from 1980 to 2014. County-level demographic, socioeconomic, environmental, and health-related risk factors were compiled. Data were analyzed from December 2019 to September 2021. Exposures: County-level characteristics, collected from 5 county-level data sets. Main Outcomes and Measures: Cardiovascular mortality data were obtained for 3133 US counties from 1980 to 2014 using age-standardized county-level mortality rates from the Global Burden of Disease study. The longitudinal K-means approach was used to identify 3 distinct clusters based on underlying mortality trajectory. Multinomial logistic regression models were constructed to evaluate associations between county characteristics and cluster membership. Results: Among 3133 US counties (median, 49.5% [IQR, 48.9%-50.5%] men; 30.7% [IQR, 27.1%-34.4%] older than 55 years; 9.9% [IQR, 4.5%-22.7%] racial minority group [individuals self-identifying as Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian, Pacific Islander, other, or multiple races/ethnicities]), CV mortality declined by 45.5% overall and by 38.4% in high-mortality strata (694 counties), by 45.0% in intermediate-mortality strata (1382 counties), and by 48.3% in low-mortality strata (1057 counties). Counties with the highest mortality in 1980 continued to demonstrate the highest mortality in 2014. Trajectory groups were regionally distributed, with high-mortality trajectory counties focused in the South and in portions of Appalachia. Low- vs high-mortality groups varied significantly in demographic (racial minority group proportion, 7.6% [IQR, 4.1%-14.5%]) vs 23.9% [IQR, 6.5%-40.8%]) and socioeconomic characteristics such as high-school education (9.4% [IQR, 7.3%-12.6%] vs 20.1% [IQR, 16.1%-23.2%]), poverty rates (11.4% [IQR, 8.8%-14.6%] vs 20.6% [IQR, 17.1%-24.4%]), and violent crime rates (161.5 [IQR, 89.0-262.4] vs 272.8 [IQR, 155.3-431.3] per 100 000 population). In multinomial logistic regression, a model incorporating demographic, socioeconomic, environmental, and health characteristics accounted for 60% of the variance in the CV mortality trajectory (R2 = 0.60). Sociodemographic factors such as racial minority group proportion (odds ratio [OR], 1.70 [95% CI, 1.35-2.14]) and educational attainment (OR, 6.17 [95% CI, 4.55-8.36]) and health behaviors such as smoking (OR for high vs low, 2.04 [95% CI, 1.58-2.64]) and physical inactivity (OR, 3.74 [95% CI, 2.83-4.93]) were associated with the high-mortality trajectory. Conclusions and Relevance: Cardiovascular mortality declined in all subgroups during the 35-year study period; however, disparities remained unchanged during that time. Disparate trajectories were associated with social and behavioral risks. Health policy efforts across multiple domains, including structural and public health targets, may be needed to reduce existing county-level cardiovascular mortality disparities.

Corrigendum to U.S. Population at Increased Risk of Severe Illness from COVID-19.

[This corrects the article DOI: 10.1016/j.ajpc.2021.100156.].

Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis.

BACKGROUND: Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine learning (ML) may offer advantages over traditional modeling techniques to develop race-specific HF risk prediction models and to elucidate important contributors of HF development across races. METHODS: We performed a retrospective analysis of 4 large, community cohort studies (ARIC [Atherosclerosis Risk in Communities], DHS [Dallas Heart Study], JHS [Jackson Heart Study], and MESA [Multi-Ethnic Study of Atherosclerosis]) with adjudicated HF events. The study included participants who were >40 years of age and free of HF at baseline. Race-specific ML models for HF risk prediction were developed in the JHS cohort (for Black race-specific model) and White adults from ARIC (for White race-specific model). The models included 39 candidate variables across demographic, anthropometric, medical history, laboratory, and electrocardiographic domains. The ML models were externally validated and compared with prior established traditional and non-race-specific ML models in race-specific subgroups of the pooled MESA/DHS cohort and Black participants of ARIC. The Harrell C-index and Greenwood-Nam-D'Agostino χ2 tests were used to assess discrimination and calibration, respectively. RESULTS: The ML models had excellent discrimination in the derivation cohorts for Black (n=4141 in JHS, C-index=0.88) and White (n=7858 in ARIC, C-index=0.89) participants. In the external validation cohorts, the race-specific ML model demonstrated adequate calibration and superior discrimination (Black individuals, C-index=0.80-0.83; White individuals, C-index=0.82) compared with established HF risk models or with non-race-specific ML models derived with race included as a covariate. Among the risk factors, natriuretic peptide levels were the most important predictor of HF risk across both races, followed by troponin levels in Black and ECG-based Cornell voltage in White individuals. Other key predictors of HF risk among Black individuals were glycemic parameters and socioeconomic factors. In contrast, prevalent cardiovascular disease and traditional cardiovascular risk factors were stronger predictors of HF risk in White adults. CONCLUSIONS: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance compared with traditional HF risk and non-race-specific ML models. This approach identifies distinct race-specific contributors of HF.

U.S. population at increased risk of severe illness from COVID-19.

BACKGROUND: The U.S. Centers for Disease Control and Prevention (CDC) recognizes that older adults and individuals with certain medical conditions are at increased risk of severe COVID-19 infection. Understanding the proportion of the population at risk of severe infection, including among those with heart disease, could assist current vaccine strategy efforts. METHODS: Using data from the 2015-2018 National Health and Nutrition Examination Survey (NHANES), we estimated the weighted prevalence of any of eight of eleven increased-risk conditions (including age ≥65) in U.S. adults aged ≥18 (N = 10,581) and extrapolated these results to a population of 233.8 million U.S. adults ≥18, and subgroups from the overall population defined by race/ethnicity, education, income and history of heart disease. RESULTS: An estimated 176.1 million individuals representing 75.4% of U.S. adults had at least one increased-risk condition, 40.3% ≥2 and, 18.5% ≥3 conditions. Approximately 129 million adults aged <65 (69.2%) were also estimated to be at increased-risk. Compared to Whites, similar proportions of Blacks in the overall population (78.0 vs. 75.6%, p>0.05) and Hispanics in the younger population (70.8 vs 68.4%) were estimated to be at increased-risk. Conversely, a greater proportion of individuals with lower education and income levels were estimated to be at increased-risk both in the overall and younger population. In addition, an estimated 6.2 million individuals (14.5%) had heart disease. Among these, virtually all had at least one additional CDC risk factor (97.9%) and most had ≥2 or ≥3 risk factors (83.8% and 58.5%, respectively). CONCLUSIONS: As vaccination strategies are being explored, these results demonstrate that >75% of adults in the U.S. would be considered at increased-risk for severe COVID-19 infection by CDC criteria. Risk factor prevalence alone may not adequately capture the totality of risk, particularly among Black and Hispanic racial/ethnic groups and those with heart disease.

Association of Medicaid Expansion With Rates of Utilization of Cardiovascular Therapies Among Medicaid Beneficiaries Between 2011 and 2018.

BACKGROUND: The Affordable Care Act expanded Medicaid eligibility allowing low-income individuals greater access to health care. However, the uptake of state Medicaid expansion has been variable. It remains unclear how the Medicaid expansion was associated with the temporal trends in use of evidence-based cardiovascular drugs. METHODS: We used the publicly available Medicaid Drug Utilization and Current Population Survey to extract filled prescription rates per 1000 Medicaid beneficiaries of statins, antihypertensives, P2Y12 inhibitors, and direct oral anticoagulants. We defined expander states as those who expanded Medicaid on or before January 1, 2014, and nonexpander states as those who had not expanded by December 31, 2018. Difference-in-differences (DID) analyses were performed to compare the association of the Medicaid expansion with per-capita cardiovascular drug prescription rates in expander versus nonexpander states. RESULTS: Between 2011 and 2018, the total number of prescriptions among all Medicaid beneficiaries increased, with gains of 89.7% in statins (11.0 to 20.8 million), 76% in antihypertensives (35.3 to 62.2 million), and 37% in P2Y12 inhibitors (1.7 to 2.3 million). Medicaid expansion was associated with significantly greater increases in quarterly prescriptions (per 1000 Medicaid beneficiaries) of statins (DID estimate [95% CI]: 22.5 [16.5-28.6], P<0.001), antihypertensives (DID estimate [95% CI]: 63.2 [47.3-79.1], P<0.001), and P2Y12 inhibitors (DID estimate [95% CI]: 1.7 [1.2-2.2], P<0.001). Between 2013 and 2018, >75% of the expander states had increases in prescription rates of both statins and antihypertensives. In contrast, 44% of nonexpander states saw declines in statins and antihypertensives. The Medicaid expansion was not associated with higher direct oral anticoagulants prescription rates (DID estimate [95% CI] 0.9 [-0.3 to 2.1], P=0.142). CONCLUSIONS: The 2014 Medicaid expansion was associated with a significant increase in per-capita utilization of cardiovascular prescription drugs among Medicaid beneficiaries. These gains in utilization may contribute to long-term cardiovascular benefits to lower-income and previously underinsured populations.

Association of Genetic West African Ancestry, Blood Pressure Response to Therapy, and Cardiovascular Risk Among Self-Reported Black Individuals in the Systolic Blood Pressure Reduction Intervention Trial (SPRINT).

IMPORTANCE: Self-identified Black race is associated with higher hypertension prevalence and worse blood pressure (BP) control compared with other race/ethnic groups. The contribution of genetic West African ancestry to these racial disparities appears not to have been completely determined. OBJECTIVE: To determine the association between the proportion of West African ancestry with the response to antihypertensive medication, BP control, kidney function, and risk of adverse cardiovascular (CV) events among self-identified Black individuals in the Systolic Blood Pressure Intervention Trial (SPRINT). DESIGN, SETTING, AND PARTICIPANTS: This post hoc analysis of the SPRINT trial incorporated data from a multicenter study of self-identified Black participants with available West African ancestry proportion, estimated using 106 biallelic autosomal ancestry informative genetic markers. Recruitment started on October 20, 2010, and ended on August 20, 2015. Data were analyzed from May 2020 to September 2020. MAIN OUTCOMES AND MEASURES: Trajectories of BP and kidney function parameters on follow-up of the trial were assessed across tertiles of the proportion of West African ancestry using linear mixed-effect modeling after adjustment for potential confounders. Multivariable adjusted Cox models evaluated the association of West African ancestry with the risk of composite CV events (nonfatal myocardial infarction, CV death, and heart failure event). RESULTS: Among 2466 participants in the current analysis (1122 women [45.5%]; median West African ancestry, 81% [interquartile range, 73%-87%]), there were 120 composite CV events (4.9%) over a mean (SD) of 3.2 (0.9) years of follow-up. At baseline, mean (SD) high-density lipoprotein cholesterol levels were higher (tertile 3: 56.5 [15.0] mg/dL vs tertile 1: 54.2 [14.9] mg/dL; P = .006), smoking prevalence (never smoking: tertile 3: 367 [47.9%] vs tertile 1: 372 [42.2%]; P = .009) and mean (SD) Framingham Risk scores (tertile 3: 16.7 [9.7] vs tertile 1: 18.1 [10.2]; P = .01) were lower, and baseline BP was not different across increasing tertiles of West African ancestry. On follow-up, there was no evidence of differences in longitudinal trajectories of BP, kidney function parameters, or left ventricular mass (Cornell voltage by electrocardiogram) across tertiles of West African ancestry in either intensive or standard treatment arms. In adjusted Cox models, higher West African ancestry was associated with a lower risk of a composite CV event after adjustment for potential confounders (adjusted hazard ratio per 5% higher West African ancestry, 0.92 [95% CI, 0.85-0.99]). CONCLUSIONS AND RELEVANCE: Among self-reported Black individuals enrolled in SPRINT, the trajectories of BP, kidney function, and left ventricular mass over time were not different across tertiles of the proportion of West African ancestry. A higher proportion of West African ancestry was associated with a modestly lower risk for CV events. These findings suggest that extrinsic and structural societal factors, more than genetic ancestry, may be the major drivers of the well-established racial disparity in cardiovascular health associated with hypertension.

County-level phenomapping to identify disparities in cardiovascular outcomes: An unsupervised clustering analysis: Short title: Unsupervised clustering of counties and risk of cardiovascular mortality.

INTRODUCTION: Significant heterogeneity in cardiovascular disease (CVD) risk and healthcare resource allocation has been demonstrated in the United States, but optimal methods to capture heterogeneity in county-level characteristics that contribute to CVD mortality differences are unclear. We evaluated the feasibility of unsupervised machine learning (ML)-based phenomapping in identifying subgroups of county-level social and demographic risk factors with differential CVD outcomes. METHODS: We performed a cross-sectional study using county-level data from 2008 to 2018 from the Centers for Disease Control (CDC) WONDER platform and the 2020 Robert Wood Johnson County Health Rankings program. Unsupervised clustering was performed on 46 facets of population characteristics spanning the demographic, health behaviors, socioeconomic, and healthcare access domains. Spatial autocorrelation was assessed using the Moran's I test, and temporal trends in age-adjusted CVD outcomes were evaluated using linear mixed effect models and least square means. RESULTS: Among 2676 counties, 4 county-level phenogroups were identified (Moran's I p-value <0.001). Phenogroup 1 (N â€‹= â€‹924; 24.5%) counties were largely white, suburban households with high income and access to healthcare. Phenogroup 2 counties (N â€‹= â€‹451; 16.9%) included predominantly Hispanic residents and below-average prevalence of CVD risk factors. Phenogroup 3 (N â€‹= â€‹951; 35.5%) counties included rural, white residents with the lowest levels of access to healthcare. Phenogroup 4 (350; 13.1%) comprised counties with predominantly Black residents, substantial cardiovascular comorbidities, and physical and socioeconomic burdens. Least square means in age-adjusted cardiovascular mortality over time increased in a stepwise fashion from 223 in phenogroup 1 to 317 per 100,000 residents in phenogroup 4. CONCLUSIONS: Unsupervised ML-based clustering on county-level population characteristics can identify unique phenogroups with differential risk of CVD mortality. Phenogroup identification may aid in developing a uniform set of preventive initiatives for clustered counties to address regional differences in CVD mortality.