Development and Validation of the American Heart Association's PREVENT Equations.

BACKGROUND: Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the American Heart Association Predicting Risk of CVD EVENTs (PREVENT) equations among US adults 30 to 79 years of age without known CVD. METHODS: The derivation sample included individual-level participant data from 25 data sets (N=3 281 919) between 1992 and 2017. The primary outcome was CVD (atherosclerotic CVD and heart failure). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, antihypertensive or statin use, and diabetes) and estimated glomerular filtration rate. Models were sex-specific, race-free, developed on the age scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each data set and meta-analyzed. Discrimination was assessed using the Harrell C-statistic. Calibration was calculated as the slope of the observed versus predicted risk by decile. Additional equations to predict each CVD subtype (atherosclerotic CVD and heart failure) and include optional predictors (urine albumin-to-creatinine ratio and hemoglobin A1c), and social deprivation index were also developed. External validation was performed in 3 330 085 participants from 21 additional data sets. RESULTS: Among 6 612 004 adults included, mean±SD age was 53±12 years, and 56% were women. Over a mean±SD follow-up of 4.8±3.1 years, there were 211 515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763-0.809) in female and 0.757 (0.727-0.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81-1.16) and 0.94 (0.81-1.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD- and heart failure-specific models. The improvement in discrimination was small but statistically significant when urine albumin-to-creatinine ratio, hemoglobin A1c, and social deprivation index were added together to the base model to total CVD (ΔC-statistic [interquartile interval] 0.004 [0.004-0.005] and 0.005 [0.004-0.007] among female and male participants, respectively). Calibration improved significantly when the urine albumin-to-creatinine ratio was added to the base model among those with marked albuminuria (>300 mg/g; 1.05 [0.84-1.20] versus 1.39 [1.14-1.65]; P=0.01). CONCLUSIONS: PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults by using routinely available clinical variables.

Discordance Analysis of VLDL-C and ApoB in UK Biobank and Framingham Study: A Prospective Observational Study.

BACKGROUND: Recent observational and Mendelian randomization analyses have reported significant effects of VLDL-C (very-low density lipoprotein cholesterol) on risk that is independent of ApoB (apolipoprotein B). We aim to determine the independent association of VLDL-C and ApoB with the risk of new onset cardiovascular events in the UK Biobank and Framingham Heart Study cohorts. METHODS: We included 294 289 UK Biobank participants with a median age of 56 years, 42% men, and 2865 Framingham Heart Study participants (median age, 53 years; 47% men). The residual resulting from regressing VLDL-C on ApoB expresses the portion of VLDL-C not explained by ApoB, while the residual from regressing ApoB on VLDL-C expresses the portion of ApoB not explained by VLDL-C. Cox proportional hazards models for atherosclerotic cardiovascular disease incidence were created for residual VLDL-C and residual ApoB. Models were analyzed with and without high-density lipoprotein cholesterol (HDL-C). Furthermore, we investigated the independent effects of VLDL-C after accounting for ApoB and HDL-C and of HDL-C after accounting for ApoB and VLDL-C. RESULTS: In the UK Biobank, ApoB was highly correlated with VLDL-C (r=0.70; P<0.001) but weakly negatively correlated with HDL-C (r=-0.11; P<0.001). The ApoB residual and the VLDL-C residual were significantly associated with new-onset atherosclerotic cardiovascular disease (hazard ratio [HR], 1.08 and 1.05, respectively; P<0.001). After adjusting for HDL-C, the ApoB residual remained similar in magnitude (HR, 1.10; P<0.001), whereas the effect size of the VLDL-C residual was reduced (HR, 1.02; P=0.029). The independent effect of HDL-C (after accounting for ApoB and VLDL-C) remained robust (HR, 0.86; P<0.0001), while the independent effect of VLDL-C (after accounting for ApoB and HDL-C) was modest (HR, 1.02; P=0.029). All results were consistent in the Framingham cohort. CONCLUSIONS: When adjusted for HDL-C, the association of VLDL-C with cardiovascular risk was no longer clinically meaningful. Our residual discordance analysis suggests that adjustment for HDL-C cannot be ignored.

Discordance among apoB, non-high-density lipoprotein cholesterol, and triglycerides: implications for cardiovascular prevention.

BACKGROUND AND AIMS: Despite growing evidence that apolipoprotein B (apoB) is the most accurate marker of atherosclerotic cardiovascular disease (ASCVD) risk, its adoption in clinical practice has been low. This investigation sought to determine whether low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (HDL-C), and triglycerides are sufficient for routine cardiovascular care. METHODS: A sample of 293 876 UK Biobank adults (age: 40-73 years, 42% men), free of cardiovascular disease, with a median follow-up for new-onset ASCVD of 11 years was included. Distribution of apoB at pre-specified levels of LDL-C, non-HDL-C, and triglycerides was examined graphically, and 10-year ASCVD event rates were compared for high vs. low apoB. Residuals of apoB were constructed after regressing apoB on LDL-C, non-HDL-C, and log-transformed triglycerides and used as predictors in a proportional hazards regression model for new-onset ASCVD adjusted for standard risk factors, including HDL-C. RESULTS: ApoB was highly correlated with LDL-C and non-HDL-C (Pearson's r = .96, P < .001 for both) but less so with log triglycerides (r = .42, P < .001). However, apoB ranges necessary to capture 95% of all observations at pre-specified levels of LDL-C, non-HDL-C, or triglycerides were wide, spanning 85.8-108.8 md/dL when LDL-C 130 mg/dL, 88.3-112.4 mg/dL when non-HDL-C 160 mg/dL, and 67.8-147.4 md/dL when triglycerides 115 mg/dL. At these levels (±10 mg/dL), 10-year ASCVD rates for apoB above mean + 1 SD vs. below mean - 1 SD were 7.3 vs. 4.0 for LDL-C, 6.4 vs. 4.6 for non-HDL-C, and 7.0 vs. 4.6 for triglycerides (all P < .001). With 19 982 new-onset ASCVD events on follow-up, in the adjusted model, residual apoB remained statistically significant after accounting for LDL-C and HDL-C (hazard ratio 1.06, 95% confidence interval 1.0-1.07), after accounting for non-HDL-C and HDL-C (hazard ratio 1.04, 95% confidence interval 1.03-1.06), and after accounting for triglycerides and HDL-C (hazard ratio 1.13, 95% confidence interval 1.12-1.15). None of the residuals of LDL-C, non-HDL-C, or of log triglycerides remained significant when apoB was included in the model. CONCLUSIONS: High variability of apoB at individual levels of LDL-C, non-HDL-C, and triglycerides coupled with meaningful differences in 10-year ASCVD rates and significant residual information contained in apoB for prediction of new-onset ASCVD events demonstrate that LDL-C, non-HDL-C, and triglycerides are not adequate proxies for apoB in clinical care.

Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association.

Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.

A Synopsis of the Evidence for the Science and Clinical Management of Cardiovascular-Kidney-Metabolic (CKM) Syndrome: A Scientific Statement From the American Heart Association.

A growing appreciation of the pathophysiological interrelatedness of metabolic risk factors such as obesity and diabetes, chronic kidney disease, and cardiovascular disease has led to the conceptualization of cardiovascular-kidney-metabolic syndrome. The confluence of metabolic risk factors and chronic kidney disease within cardiovascular-kidney-metabolic syndrome is strongly linked to risk for adverse cardiovascular and kidney outcomes. In addition, there are unique management considerations for individuals with established cardiovascular disease and coexisting metabolic risk factors, chronic kidney disease, or both. An extensive body of literature supports our scientific understanding of, and approach to, prevention and management for individuals with cardiovascular-kidney-metabolic syndrome. However, there are critical gaps in knowledge related to cardiovascular-kidney-metabolic syndrome in terms of mechanisms of disease development, heterogeneity within clinical phenotypes, interplay between social determinants of health and biological risk factors, and accurate assessments of disease incidence in the context of competing risks. There are also key limitations in the data supporting the clinical care for cardiovascular-kidney-metabolic syndrome, particularly in terms of early-life prevention, screening for risk factors, interdisciplinary care models, optimal strategies for supporting lifestyle modification and weight loss, targeting of emerging cardioprotective and kidney-protective therapies, management of patients with both cardiovascular disease and chronic kidney disease, and the impact of systematically assessing and addressing social determinants of health. This scientific statement uses a crosswalk of major guidelines, in addition to a review of the scientific literature, to summarize the evidence and fundamental gaps related to the science, screening, prevention, and management of cardiovascular-kidney-metabolic syndrome.

Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association.

Cardiovascular-kidney-metabolic health reflects the interplay among metabolic risk factors, chronic kidney disease, and the cardiovascular system and has profound impacts on morbidity and mortality. There are multisystem consequences of poor cardiovascular-kidney-metabolic health, with the most significant clinical impact being the high associated incidence of cardiovascular disease events and cardiovascular mortality. There is a high prevalence of poor cardiovascular-kidney-metabolic health in the population, with a disproportionate burden seen among those with adverse social determinants of health. However, there is also a growing number of therapeutic options that favorably affect metabolic risk factors, kidney function, or both that also have cardioprotective effects. To improve cardiovascular-kidney-metabolic health and related outcomes in the population, there is a critical need for (1) more clarity on the definition of cardiovascular-kidney-metabolic syndrome; (2) an approach to cardiovascular-kidney-metabolic staging that promotes prevention across the life course; (3) prediction algorithms that include the exposures and outcomes most relevant to cardiovascular-kidney-metabolic health; and (4) strategies for the prevention and management of cardiovascular disease in relation to cardiovascular-kidney-metabolic health that reflect harmonization across major subspecialty guidelines and emerging scientific evidence. It is also critical to incorporate considerations of social determinants of health into care models for cardiovascular-kidney-metabolic syndrome and to reduce care fragmentation by facilitating approaches for patient-centered interdisciplinary care. This presidential advisory provides guidance on the definition, staging, prediction paradigms, and holistic approaches to care for patients with cardiovascular-kidney-metabolic syndrome and details a multicomponent vision for effectively and equitably enhancing cardiovascular-kidney-metabolic health in the population.

Comparative Effectiveness of Aspirin Dosing in Cardiovascular Disease.

BACKGROUND: The appropriate dose of aspirin to lower the risk of death, myocardial infarction, and stroke and to minimize major bleeding in patients with established atherosclerotic cardiovascular disease is a subject of controversy. METHODS: Using an open-label, pragmatic design, we randomly assigned patients with established atherosclerotic cardiovascular disease to a strategy of 81 mg or 325 mg of aspirin per day. The primary effectiveness outcome was a composite of death from any cause, hospitalization for myocardial infarction, or hospitalization for stroke, assessed in a time-to-event analysis. The primary safety outcome was hospitalization for major bleeding, also assessed in a time-to-event analysis. RESULTS: A total of 15,076 patients were followed for a median of 26.2 months (interquartile range [IQR], 19.0 to 34.9). Before randomization, 13,537 (96.0% of those with available information on previous aspirin use) were already taking aspirin, and 85.3% of these patients were previously taking 81 mg of daily aspirin. Death, hospitalization for myocardial infarction, or hospitalization for stroke occurred in 590 patients (estimated percentage, 7.28%) in the 81-mg group and 569 patients (estimated percentage, 7.51%) in the 325-mg group (hazard ratio, 1.02; 95% confidence interval [CI], 0.91 to 1.14). Hospitalization for major bleeding occurred in 53 patients (estimated percentage, 0.63%) in the 81-mg group and 44 patients (estimated percentage, 0.60%) in the 325-mg group (hazard ratio, 1.18; 95% CI, 0.79 to 1.77). Patients assigned to 325 mg had a higher incidence of dose switching than those assigned to 81 mg (41.6% vs. 7.1%) and fewer median days of exposure to the assigned dose (434 days [IQR, 139 to 737] vs. 650 days [IQR, 415 to 922]). CONCLUSIONS: In this pragmatic trial involving patients with established cardiovascular disease, there was substantial dose switching to 81 mg of daily aspirin and no significant differences in cardiovascular events or major bleeding between patients assigned to 81 mg and those assigned to 325 mg of aspirin daily. (Funded by the Patient-Centered Outcomes Research Institute; ADAPTABLE ClinicalTrials.gov number, NCT02697916.).

Empowering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare.

BACKGROUND: The concept of health equity by design encompasses a multifaceted approach that integrates actions aimed at eliminating biased, unjust, and correctable differences among groups of people as a fundamental element in the design of algorithms. As algorithmic tools are increasingly integrated into clinical practice at multiple levels, nurses are uniquely positioned to address challenges posed by the historical marginalization of minority groups and its intersections with the use of "big data" in healthcare settings; however, a coherent framework is needed to ensure that nurses receive appropriate training in these domains and are equipped to act effectively. PURPOSE: We introduce the Bias Elimination for Fair AI in Healthcare (BE FAIR) framework, a comprehensive strategic approach that incorporates principles of health equity by design, for nurses to employ when seeking to mitigate bias and prevent discriminatory practices arising from the use of clinical algorithms in healthcare. By using examples from a "real-world" AI governance framework, we aim to initiate a wider discourse on equipping nurses with the skills needed to champion the BE FAIR initiative. METHODS: Drawing on principles recently articulated by the Office of the National Coordinator for Health Information Technology, we conducted a critical examination of the concept of health equity by design. We also reviewed recent literature describing the risks of artificial intelligence (AI) technologies in healthcare as well as their potential for advancing health equity. Building on this context, we describe the BE FAIR framework, which has the potential to enable nurses to take a leadership role within health systems by implementing a governance structure to oversee the fairness and quality of clinical algorithms. We then examine leading frameworks for promoting health equity to inform the operationalization of BE FAIR within a local AI governance framework. RESULTS: The application of the BE FAIR framework within the context of a working governance system for clinical AI technologies demonstrates how nurses can leverage their expertise to support the development and deployment of clinical algorithms, mitigating risks such as bias and promoting ethical, high-quality care powered by big data and AI technologies. CONCLUSION AND RELEVANCE: As health systems learn how well-intentioned clinical algorithms can potentially perpetuate health disparities, we have an opportunity and an obligation to do better. New efforts empowering nurses to advocate for BE FAIR, involving them in AI governance, data collection methods, and the evaluation of tools intended to reduce bias, mark important steps in achieving equitable healthcare for all.

Predictive Utility of a Validated Polygenic Risk Score for Long-Term Risk of Coronary Heart Disease in Young and Middle-Aged Adults.

BACKGROUND: Understanding the predictive utility of previously derived polygenic risk scores (PRSs) for long-term risk of coronary heart disease (CHD) and its additive value beyond traditional risk factors can inform prevention strategies. METHODS: Data from adults 20 to 59 years of age who were free of CHD from the FOS (Framingham Offspring Study) and the ARIC (Atherosclerosis Risk in Communities) study were analyzed. Because the PRS was derived from samples of predominantly European ancestry, individuals who self-reported White race were included. The sample was stratified by age and cohort: young (FOS, 20-39 years [median, 30 years] of age), early midlife (FOS, 40-59 years [median, 43] years of age), and late midlife (ARIC, 45-59 years [median, 52 years] of age). Two previously derived and validated prediction tools were applied: (1) a 30-year traditional risk factor score and (2) a genome-wide PRS comprising >6 million genetic variants. Hazard ratios for the association between each risk estimate and incident CHD were calculated. Predicted and observed rates of CHD were compared to assess discrimination for each model individually and together with the optimism-corrected C index (95% CI). RESULTS: Among 9757 participants, both the traditional risk factor score (hazard ratio per 1 SD, 2.60 [95% CI, 2.08-3.27], 2.09 [95% CI, 1.83-2.40], and 2.11 [95% CI, 1.96-2.28]) and the PRS (hazard ratio, 1.98 [95% CI, 1.70-2.30], 1.64 [95% CI, 1.47-1.84], and 1.22 [95% CI, 1.15-1.30]) were significantly associated with incident CHD in young, early midlife, and late midlife, respectively. Discrimination was similar or better for the traditional risk factor score (C index, 0.74 [95% CI, 0.70-0.78], 0.70 [95% CI, 0.67-0.72], and 0.72 [95% CI, 0.70-0.73]) compared with an age- and sex-adjusted PRS (0.73 [95% CI, 0.69-0.78], 0.66 [95% CI, 0.62-0.69], and 0.66 [95% CI, 0.64-0.67]) in young, early-midlife, and late-midlife participants, respectively. The ΔC index when PRS was added to the traditional risk factor score was 0.03 (95% CI, 0.001-0.05), 0.02 (95% CI, -0.002 to 0.037), and 0.002 (95% CI, -0.002 to 0.006) in young, early-midlife, and late-midlife participants, respectively. CONCLUSIONS: Despite a statistically significant association between PRS and 30-year risk of CHD, the C statistic improved only marginally with the addition of PRS to the traditional risk factor model among young adults and did not improve among midlife adults. PRS, an immutable factor that cannot be directly intervened on, has minimal clinical utility for long-term CHD prediction when added to a traditional risk factor model.

Interplay of Atherogenic Particle Number and Particle Size and the Risk of Coronary Heart Disease.

BACKGROUND: We examined the interplay of apolipoprotein B (apoB) and LDL particle size, approximated by the LDL-cholesterol (LDL-C)/apoB ratio, on the risk of new-onset coronary heart disease (CHD). METHODS: Participants without cardiovascular disease from the UK Biobank (UKB; n = 308 182), the Women's Health Study (WHS; n = 26 204), and the Framingham Heart Study (FHS; n = 2839) were included. Multivariable Cox models were used to assess the relationship between apoB and LDL-C/apoB ratio and incidence of CHD (14 994 events). Our analyses were adjusted for age, sex (except WHS), HDL-cholesterol (HDL-C), systolic blood pressure, antihypertensive treatment, diabetes, and smoking. RESULTS: In all 3 studies, there was a strong positive correlation between apoB and LDL-C (correlation coefficients r = 0.80 or higher) and a weak inverse correlation of apoB with LDL-C/apoB ratio (-0.28 ≤ r ≤ -0.14). For all 3 cohorts, CHD risk was higher for higher levels of apoB. Upon multivariable adjustment, the association between apoB and new-onset CHD remained robust and statistically significant in all 3 cohorts with hazard ratios per 1 SD (95% CI): 1.24 (1.22-1.27), 1.33 (1.20-1.47), and 1.24 (1.09-1.42) for UKB, WHS, and FHS, respectively. However, the association between LDL-C/apoB and CHD was statistically significant only in the FHS cohort: 0.78 (0.64-0.94). CONCLUSIONS: Our analysis confirms that apoB is a strong risk factor for CHD. However, given the null association in 2 of the 3 studies, we cannot confirm that cholesterol-depleted LDL particles are substantially more atherogenic than cholesterol-replete particles. These results lend further support to routine measurement of apoB in clinical care.

Semi-supervised calibration of noisy event risk (SCANER) with electronic health records.

OBJECTIVE: Electronic health records (EHR), containing detailed longitudinal clinical information on a large number of patients and covering broad patient populations, open opportunities for comprehensive predictive modeling of disease progression and treatment response. However, since EHRs were originally constructed for administrative purposes not for research, in the EHR-linked studies, it is often not feasible to capture reliable information for analytical variables, especially in the survival setting, when both accurate event status and event times are needed for model building. For example, progression-free survival (PFS), a commonly used survival outcome for cancer patients, often involves complex information embedded in free-text clinical notes and cannot be extracted reliably. Proxies of PFS time such as time to the first mention of progression in the notes are at best good approximations to the true event time. This leads to difficulty in efficiently estimating event rates for an EHR patient cohort. Estimating survival rates based on error-prone outcome definitions can lead to biased results and hamper the power in the downstream analysis. On the other hand, extracting accurate event time information via manual annotation is time and resource intensive. The objective of this study is to develop a calibrated survival rate estimator using noisy outcomes from EHR data. MATERIALS AND METHODS: In this paper, we propose a two-stage semi-supervised calibration of noisy event rate (SCANER) estimator that can effectively overcome censoring induced dependency and attains more robust performance (i.e., not sensitive to misspecification of the imputation model) by fully utilizing both a small-labeled set of gold-standard survival outcomes annotated via manual chart review and a set of proxy features automatically captured via EHR in the unlabeled set. We validate the SCANER estimator by estimating the PFS rates for a virtual cohort of lung cancer patients from one large tertiary care center and the ICU-free survival rates for COVID patients from two large tertiary care centers. RESULTS: In terms of survival rate estimates, the SCANER had very similar point estimates compared to the complete-case Kaplan Meier estimator. On the other hand, other benchmark methods for comparison, which fail to account for the induced dependency between event time and the censoring time conditioning on surrogate outcomes, produced biased results across all three case studies. In terms of standard errors, the SCANER estimator was more efficient than the KM estimator, with up to 50% efficiency gain. CONCLUSION: The SCANER estimator achieves more efficient, robust, and accurate survival rate estimates compared to existing approaches. This promising new approach can also improve the resolution (i.e., granularity of event time) by using labels conditioning on multiple surrogates, particularly among less common or poorly coded conditions.

Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.

Importance: Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies. Objective: To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques. Design, Setting, and Participants: Retrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study for Atherosclerosis (MESA), and Reasons for Geographical and Racial Differences in Stroke (REGARDS) studies (1983-2019) conducted in the US. The 62 482 participants included at baseline were at least 45 years of age and free of stroke or transient ischemic attack. Exposures: Published stroke-specific algorithms from Framingham and REGARDS (based on self-reported risk factors) as well as pooled cohort equations for atherosclerotic cardiovascular disease plus 2 newly developed machine learning algorithms. Main Outcomes and Measures: Models were designed to estimate the 10-year risk of new-onset stroke (ischemic or hemorrhagic). Discrimination concordance index (C index) and calibration ratios of expected vs observed event rates were assessed at 10 years. Analyses were conducted by race, sex, and age groups. Results: The combined study sample included 62 482 participants (median age, 61 years, 54% women, and 29% Black individuals). Discrimination C indexes were not significantly different for the 2 stroke-specific models (Framingham stroke, 0.72; 95% CI, 0.72-073; REGARDS self-report, 0.73; 95% CI, 0.72-0.74) vs the pooled cohort equations (0.72; 95% CI, 0.71-0.73): differences 0.01 or less (P values >.05) in the combined sample. Significant differences in discrimination were observed by race: the C indexes were 0.76 for all 3 models in White vs 0.69 in Black women (all P values <.001) and between 0.71 and 0.72 in White men and between 0.64 and 0.66 in Black men (all P values ≤.001). When stratified by age, model discrimination was better for younger (<60 years) vs older (≥60 years) adults for both Black and White individuals. The ratios of observed to expected 10-year stroke rates were closest to 1 for the REGARDS self-report model (1.05; 95% CI, 1.00-1.09) and indicated risk overestimation for Framingham stroke (0.86; 95% CI, 0.82-0.89) and pooled cohort equations (0.74; 95% CI, 0.71-0.77). Performance did not significantly improve when novel machine learning algorithms were applied. Conclusions and Relevance: In this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.

The Expected 30-Year Benefits of Early Versus Delayed Primary Prevention of Cardiovascular Disease by Lipid Lowering.

BACKGROUND: Lipid-lowering recommendations for prevention of atherosclerotic cardiovascular disease rely principally on estimated 10-year risk. We sought to determine the optimal time for initiation of lipid lowering in younger adults as a function of expected 30-year benefit. METHODS: Data from 3148 National Health and Nutrition Examination Survey (2009-2016) participants, age 30 to 59 years, not eligible for lipid-lowering treatment recommendation under the most recent US guidelines, were analyzed. We estimated the absolute and relative impact of lipid lowering as a function of age, age at initiation, and non-high-density lipoprotein cholesterol (HDL-C) level on the expected rates of atherosclerotic cardiovascular disease over the succeeding 30 years. We modeled expected risk reductions based on shorter-term effects observed in statin trials (model A) and longer-term benefits based on Mendelian randomization studies (model B). RESULTS: In both models, potential reductions in predicted 30-year atherosclerotic cardiovascular disease risk were greater with older age and higher non-HDL-C level. Immediate initiation of lipid lowering (ie, treatment for 30 years) in 40- to 49-year-old patients with non-HDL-C ≥160 mg/dL would be expected to reduce their average predicted 30-year risk of 17.1% to 11.6% (model A; absolute risk reduction [ARR], 5.5%) or 6.5% (model B; ARR 10.6%). Delaying lipid lowering by 10 years (treatment for 20 years) would result in residual 30-year risk of 12.7% (A; ARR 4.4) or 9.9% (B; ARR 7.2%) and delaying by 20 years (treatment for 10 years) would lead to expected mean residual risk of 14.6% (A; ARR 2.6%) or 13.9% (B; ARR 3.2%). The slope of the achieved ARR as a function of delay in treatment was also higher with older age and higher non-HDL-C level. CONCLUSIONS: Substantial reduction in expected atherosclerotic cardiovascular disease risk in the next 30 years is achievable by intensive lipid lowering in individuals in their 40s and 50s with non-HDL-C ≥160 mg/dL. For many, the question of when to start lipid lowering might be more relevant than whether to start lipid lowering.

Impact of the COVID-19 pandemic on patterns of outpatient cardiovascular care.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic brought about abrupt changes in the way health care is delivered, and the impact of transitioning outpatient clinic visits to telehealth visits on processes of care and outcomes is unclear. METHODS: We evaluated ordering patterns during cardiovascular telehealth clinic visits in the Duke University Health System between March 15 and June 30, 2020 and 30-day outcomes compared with in-person visits in the same time frame in 2020 and in 2019. RESULTS: Within the Duke University Health System, there was a 33.1% decrease in the number of outpatient cardiovascular visits conducted in the first 15 weeks of the COVID-19 pandemic, compared with the same time period in 2019. As a proportion of total visits initially booked, 53% of visits were cancelled in 2020 compared to 35% in 2019. However, patients with cancelled visits had similar demographics and comorbidities in 2019 and 2020. Telehealth visits comprised 9.3% of total visits initially booked in 2020, with younger and healthier patients utilizing telehealth compared with those utilizing in-person visits. Compared with in-person visits in 2020, telehealth visits were associated with fewer new (31.6% for telehealth vs 44.6% for in person) or refill (12.9% vs 15.6%, respectively) medication prescriptions, electrocardiograms (4.3% vs 31.4%), laboratory orders (5.9% vs 21.8%), echocardiograms (7.3% vs 98%), and stress tests (4.4% vs 6.6%). When adjusted for age, race, and insurance status, those who had a telehealth visit or cancelled their visit were less likely to have an emergency department or hospital encounter within 30 days compared with those who had in-person visits (adjusted rate ratios (aRR) 0.76 [95% 0.65, 0.89] and aRR 0.71 [95% 0.65, 0.78], respectively). CONCLUSIONS: In response to the perceived risks of routine medical care affected by the COVID-19 pandemic, different phenotypes of patients chose different types of outpatient cardiology care. A better understanding of these differences could help define necessary and appropriate mode of care for cardiology patients.

Effects of hybrid comprehensive telerehabilitation on cardiopulmonary capacity in heart failure patients depending on diabetes mellitus: subanalysis of the TELEREH-HF randomized clinical trial.

BACKGROUND: Type 2 diabetes mellitus (DM) is one of the most common comorbidities among patients with heart failure (HF) with reduced ejection fraction (HFrEF). There are limited data regarding efficacy of hybrid comprehensive telerehabilitation (HCTR) on cardiopulmonary exercise capacity in patients with HFrEF with versus those without diabetes. AIM: The aim of the present study was to analyze effects of 9-week HCTR in comparison to usual care on parameters of cardiopulmonary exercise capacity in HF patients according to history of DM. METHODS: Clinically stable HF patients with left ventricular ejection fraction [LVEF] < 40% after a hospitalization due to worsening HF within past 6 months were enrolled in the TELEREH-HF (The TELEREHabilitation in Heart Failure Patients) trial and randomized to the HCTR or usual care (UC). Cardiopulmonary exercise tests (CPET) were performed on treadmill with an incremental workload according to the ramp protocol. RESULTS: CPET was performed in 385 patients assigned to HCTR group: 129 (33.5%) had DM (HCTR-DM group) and 256 patients (66.5%) did not have DM (HCTR-nonDM group). Among 397 patients assigned to UC group who had CPET: 137 (34.5%) had DM (UC-DM group) and 260 patients (65.5%) did not have DM (UC-nonDM group). Among DM patients, differences in cardiopulmonary parameters from baseline to 9 weeks remained similar among HCTR and UC patients. In contrast, among patients without DM, HCTR was associated with greater 9-week changes than UC in exercise time, which resulted in a statistically significant interaction between patients with and without DM: difference in changes in exercise time between HCTR versus UC was 12.0 s [95% CI - 15.1, 39.1 s] in DM and 43.1 s [95% CI 24.0, 63.0 s] in non-DM, interaction p-value = 0.016. Furthermore, statistically significant differences in the effect of HCTR versus UC between DM and non-DM were observed in ventilation at rest: - 0.34 l/min [95% CI - 1.60, 0.91 l/min] in DM and 0.83 l/min [95% CI - 0.06, 1.73 l/min] in non-DM, interaction p value = 0.0496 and in VE/VCO2 slope: 1.52 [95% CI - 1.55, 4.59] for DM vs. - 1.44 [95% CI - 3.64, 0.77] for non-DM, interaction p value = 0.044. CONCLUSIONS: The benefits of hybrid comprehensive telerehabilitation versus usual care on the improvement of physical performance, ventilatory profile and gas exchange parameters were more pronounced in patients with HFrEF without DM as compared to patients with DM. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02523560. Registered 3rd August 2015. https://clinicaltrials.gov/ct2/show/NCT02523560?term=NCT02523560&draw=2&rank=1 . Other Study ID Numbers: STRATEGME1/233547/13/NCBR/2015.

Assessment of ECG during hybrid comprehensive telerehabilitation in heart failure patients-Subanalysis of the Telerehabilitation in Heart Failure Patients (TELEREH-HF) randomized clinical trial.

BACKGROUND: Exercise training in heart failure (HF) patients should be monitored to ensure patients' safety. Electrocardiographic (ECG) telemonitoring was used to assess the safety of hybrid comprehensive telerehabilitation (HCTR). OBJECTIVE: Analysis of ECG recorded during HCTR in HF patients. METHODS: The TELEREH-HF multicenter, randomized, controlled trial enrolled 850 HF patients with New York Heart Association class I-III and left ventricular ejection fraction of ≤40%. This subanalysis focuses on 386 patients (aged 62 ± 11 years, LVEF 31 ± 7%) randomized to HCTR. HCTR was telemonitored with a device allowing to record 16-s fragments of ECG and to transmit the data via mobile phone network to the monitoring center. ResultsIn 386 patients, 16,622 HCTR sessions were recorded and 66,488 ECGs fragments were evaluated. Sinus rhythm was present in 320 (83%) and permanent atrial fibrillation (AF) in 66 (17%) patients, respectively. The most common arrhythmias were ventricular and atrial premature beats, recorded in 76.4% and 27.7% of the patients, respectively. Non-sustained ventricular tachycardia (21 episodes in 8 patients) and paroxysmal AF episodes (6 in 4 patients) were rare. None of the analyzed demographic and clinical characteristics was predictive for onset of the new arrhythmias on exercise. CONCLUSION: Telerehabilitation in HF patients was safe without the evidence for symptomatic arrhythmias requiring discontinuation of telerehabilitation. Only one mildly symptomatic paroxysmal AF episode led to the short-term suspension of the training program. The most common arrhythmias were atrial and ventricular premature beats. These arrhythmias did not result in any changes in rehabilitation and therapy regimens.

Quantifying Importance of Major Risk Factors for Coronary Heart Disease.

BACKGROUND: To optimize preventive strategies for coronary heart disease (CHD), it is essential to understand and appropriately quantify the contribution of its key risk factors. Our objective was to compare the associations of key modifiable CHD risk factors-specifically lipids, systolic blood pressure (SBP), diabetes mellitus, and smoking-with incident CHD events based on their prognostic performance, attributable risk fractions, and treatment benefits, overall and by age. METHODS: Pooled participant-level data from 4 observational cohort studies sponsored by the National Heart, Lung, and Blood Institute were used to create a cohort of 22 626 individuals aged 45 to 84 years who were initially free of cardiovascular disease. Individuals were followed for 10 years from baseline evaluation for incident CHD. Proportional hazards regression was used to estimate metrics of prognostic model performance (likelihood ratio, C index, net reclassification, discrimination slope), hazard ratios, and population attributable fractions for SBP, non-high-density lipoprotein cholesterol (non-HDL-C), diabetes mellitus, and smoking. Expected absolute risk reductions for antihypertensive and lipid-lowering treatment were assessed. RESULTS: Age, sex, and race capture 63% to 80% of the prognostic performance of cardiovascular risk models. In contrast, adding either SBP, non-HDL-C, diabetes mellitus, or smoking to a model with other risk factors increases the C index by only 0.004 to 0.013. However, primordial prevention could have a substantial effect as demonstrated by population attributable fractions of 28% for SBP≥130 mm Hg and 17% for non-HDL-C≥130 mg/dL. Similarly, lowering the SBP of all individuals to <130 mm Hg or lowering low-density lipoprotein cholesterol by 30% would be expected to lower a baseline 10-year CHD risk of 10.7% to 7.0 and 8.0, respectively (absolute risk reductions: 3.7% and 2.7%, respectively). Prognostic performance decreases with age (C indices for age groups 45-54, 55-64, 65-74, 75-84 are 0.75, 0.72, 0.66, and 0.62, respectively), whereas absolute risk reductions increase (SBP: 1.1%, 2.3%, 5.4%, 10.3%, respectively; non-HDL-C: 1.1%, 2.0%, 3.7%, 5.9%, respectively). CONCLUSIONS: Although individual modifiable CHD risk factors contribute only modestly to prognostic performance, our models indicate that eliminating or controlling these individual factors would lead to substantial reductions in total population CHD events. Metrics used to judge importance of risk factors should be tailored to the research objectives.

Adherence to adding inhaled corticosteroids to rescue therapy in a pragmatic trial with adults with asthma: A pilot study.

BACKGROUND: Underuse of guideline-recommended inhaled corticosteroids (ICS) controller therapy is a risk factor for greater asthma burden. ICS concomitantly used with rescue inhalers (Patient-Activated Reliever-Triggered ICS ['PARTICS']) reduced asthma exacerbations in efficacy trials, but whether PARTICS is effective in pragmatic trials is unknown. OBJECTIVE: We conducted this pilot to determine the feasibility of executing a large-scale pragmatic PARTICS trial and to improve study protocols. METHODS: Four sites recruited 33 Hispanic or black adults with persistent asthma, randomized them approximately 3:1 to intervention or usual care, and followed them for 12 weeks. All participants received asthma guideline-based educational videos; intervention participants received video-based instructions on implementing PARTICS plus usual medications. The study involved 1 randomization visit and monthly questionnaires. Timely questionnaire responses (±2 weeks) were monitored. Participants underwent qualitative phone interviews to assess self-reported adherence to PARTICS and understand barriers to completing study procedures. RESULTS: Timely questionnaire response rates were 61%, 64%, and 70% at 4, 8, and 12 weeks, respectively. Self-reported adherence to PARTICS was 76% (95% confidence interval [CI], 58%-94% [n = 21]), 88% (95%CI, 72%-100% [n = 16]), and 62% (95%CI, 36%-88% [n = 13]) at weeks 1, 6, and 12, respectively. Barriers to completing study procedures included difficulties with questionnaire access, remembering to use ICS and rescue inhalers together, and obtaining refills. Only 22% of participants recognized their short-acting bronchodilator as "reliever" or "rescue." CONCLUSION: Recruitment was feasible within the allocated period. Adherence to PARTICS was incomplete, questionnaire completion was suboptimal, and common rescue inhaler nomenclature usage was limited. We have modified the full study protocol to attempt to improve adherence to PARTICS and minimize barriers to study procedures. CLINICAL TRIALS REGISTRATION: pilot study for 'PeRson EmPowered Asthma Relief' (PREPARE, NCT02995733).

Application of net reclassification index to non-nested and point-based risk prediction models: a review.

Much of medical risk prediction involves externally derived prediction equations, nomograms, and point-based risk scores. These settings are vulnerable to misleading findings of incremental value based on versions of the net reclassification index (NRI) in common use. By applying non-nested models and point-based risk scores in the setting of stroke risk prediction in patients with atrial fibrillation (AF), we demonstrate current recommendations for presentation and interpretation of the NRI. We emphasize pitfalls that are likely to occur with point-based risk scores that are easy to neglect when statistical methodology is focused on continuous models. In order to make appropriate decisions about risk prediction and personalized medicine, physicians, researchers, and policy makers need to understand the strengths and limitations of the NRI.

Cardiovascular risk factor profiles in familial hypercholesterolemia patients with and without genetic mutation compared to a nationally representative sample of adults in a high-risk European country.

BACKGROUND: There is a paucity of data on the distribution of cardiovascular risk factors in patients with familial hypercholesterolemia (FH) as compared to the general population. The aim of the study was to compare cardiovascular risk factors in a cohort of FH patients to the representative sample of adults in Poland who represent a high-cardiovascular risk European region. METHODS: We compared the distribution of risk factors in 1,382 individuals with FH phenotype referred for genetic testing between 2006 and 2014 to the National Centre of Familial Hypercholesterolemia in Gdansk, Poland. The cohort was comprised of 637 positive FH(+) and 745 negative FH(-) patients who were compared to a nationally representative sample of 2,413 adults age 18-79, standardized by age and sex, from the NATPOL 2011 study (NATPOL). We analyzed patients' distribution of history of atherosclerotic cardiovascular disease (ASCVD) and standard risk factors including total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, triglycerides, systolic and diastolic blood pressure (SBP, DBP), body mass index, smoking, and diabetes. RESULTS: FH(+) patients (mean age 45.6 years) had the highest LDL-C of 241.7 mg/dL (95% CI 234.8-248.5) compared to 206.1 mg/dL (200.5-211.7) in FH(-) patients (mean age 48.2) and 126.2 mg/dL (124.8-127.6) in NATPOL. Mean SBP was the lowest in FH(+) patients at 128.7 mm Hg (126.7-130.7) compared to 133.4 mm Hg (132.6-134.3) in NATPOL and 134.4 mm Hg (132.3-136.5) in FH(-). No differences were found in the prevalence of diabetes and body mass index. Smoking was less common in FH(+) at 12.4% (9.4-15.4) compared to both FH(-) and NATPOL: 20.4% (16.6-24.1) and 28.4% (26.6-30.2), respectively. The prevalence of individuals with a history of ASCVD in both FH(+) and FH(-) was nearly 3-fold higher compared to NATPOL: 26% (21.8-30.1) and 26.6% (22.2-30.9) versus 9.5% (8.3-10.7), respectively. CONCLUSIONS: The FH(+) patients had significantly higher mean LDL-C, but the levels of nonlipid factors were lower or similar compared to the other groups. Both FH(+) and FH(-) were characterized by a heavy burden of ASCVD. This suggests that cholesterol, and no other risk factors, is a key contributor to cardiovascular risk in patients with FH, especially those with genetic mutation.