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.).

Trans-Balance: Reducing demographic disparity for prediction models in the presence of class imbalance.

INTRODUCTION: Risk prediction, including early disease detection, prevention, and intervention, is essential to precision medicine. However, systematic bias in risk estimation caused by heterogeneity across different demographic groups can lead to inappropriate or misinformed treatment decisions. In addition, low incidence (class-imbalance) outcomes negatively impact the classification performance of many standard learning algorithms which further exacerbates the racial disparity issues. Therefore, it is crucial to improve the performance of statistical and machine learning models in underrepresented populations in the presence of heavy class imbalance. METHOD: To address demographic disparity in the presence of class imbalance, we develop a novel framework, Trans-Balance, by leveraging recent advances in imbalance learning, transfer learning, and federated learning. We consider a practical setting where data from multiple sites are stored locally under privacy constraints. RESULTS: We show that the proposed Trans-Balance framework improves upon existing approaches by explicitly accounting for heterogeneity across demographic subgroups and cohorts. We demonstrate the feasibility and validity of our methods through numerical experiments and a real application to a multi-cohort study with data from participants of four large, NIH-funded cohorts for stroke risk prediction. CONCLUSION: Our findings indicate that the Trans-Balance approach significantly improves predictive performance, especially in scenarios marked by severe class imbalance and demographic disparity. Given its versatility and effectiveness, Trans-Balance offers a valuable contribution to enhancing risk prediction in biomedical research and related fields.

Development and Validation of a Predictive Model for Maternal Cardiovascular Morbidity Events in Patients With Hypertensive Disorders of Pregnancy.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a major contributor to maternal morbidity, mortality, and accelerated cardiovascular (CV) disease. Comorbid conditions are likely important predictors of CV risk in pregnant people. Currently, there is no way to predict which people with HDP are at risk of acute CV complications. We developed and validated a predictive model for all CV events and for heart failure, renal failure, and cerebrovascular events specifically after HDP. METHODS: Models were created using the Premier Healthcare Database. The inclusion criteria for the model dataset were delivery with an HDP with discharge from October 1, 2015 to December 31, 2020. Machine learning methods were used to derive predictive models of CV events occurring during delivery hospitalization (Index Model) or during readmission (Readmission Model) using a training set (60%) to estimate model parameters, a validation set (20%) to tune model hyperparameters and select a final model, and a test set (20%) to evaluate final model performance. RESULTS: The total model cohort consisted of 553,658 deliveries with an HDP. A CV event occurred in 6501 (1.2%) of the delivery hospitalizations. Multilabel neural networks were selected for the Index Model and Readmission Model due to favorable performance compared to alternatives. This approach is designed for prediction of multiple events that share risk factors and may cooccur. The Index Model predicted all CV events with area under the receiver operating curve (AUROC) 0.878 and average precision (AP) 0.239 (cerebrovascular events: AUROC 0.941, heart failure: AUROC 0.898, and renal failure: AUROC 0.885). With a positivity threshold set to achieve ≥90% sensitivity, model specificity was 65.0%, 83.5%, 68.6%, and 65.6% for predicting all CV events, cerebrovascular events, heart failure, and renal failure, respectively. CV events within 1 year of delivery occurred in 3018 (0.6%) individuals. The Readmission Model predicted all CV events with AUROC 0.717 and AP 0.022 (renal failure: AUROC 0.748, heart failure: AUROC 0.734, and cerebrovascular events AUROC 0.698). Feature importance analysis indicated that the presence of chronic renal disease, cardiac disease, pulmonary hypertension, and preeclampsia with severe features had the greatest effect on the prediction of CV events. CONCLUSIONS: Among individuals with HDP, our multilabel neural network model predicted CV events at delivery admission with good classification and events within 1 year of delivery with fair classification.

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.

A Nationwide Network of Health AI Assurance Laboratories.

Importance: Given the importance of rigorous development and evaluation standards needed of artificial intelligence (AI) models used in health care, nationwide accepted procedures to provide assurance that the use of AI is fair, appropriate, valid, effective, and safe are urgently needed. Observations: While there are several efforts to develop standards and best practices to evaluate AI, there is a gap between having such guidance and the application of such guidance to both existing and new AI models being developed. As of now, there is no publicly available, nationwide mechanism that enables objective evaluation and ongoing assessment of the consequences of using health AI models in clinical care settings. Conclusion and Relevance: The need to create a public-private partnership to support a nationwide health AI assurance labs network is outlined here. In this network, community best practices could be applied for testing health AI models to produce reports on their performance that can be widely shared for managing the lifecycle of AI models over time and across populations and sites where these models are deployed.

The Influence of Hybrid Comprehensive Telerehabilitation on Anxiety in Heart Failure Patients: The TELEREH-HF Randomized Clinical Trial.

Telerehabilitation for heart failure (HF) patients is beneficial for physical functioning, prognosis, and psychological status. The study aimed at evaluating the influence of hybrid comprehensive telerehabilitation (HCTR) on the level of anxiety in comparison to usual care (UC). The TELEREH-HF study was a multicenter prospective RCT in 850 clinically stable HF participants. Patients underwent clinical examinations, including the assessment of anxiety, at entry and after the 9-week training program (HCTR) or observation (UC). The State-Trait Anxiety Inventory (STAI) was used. 20.3% HCTR and 20.1% UC patients reported high level of anxiety as a state at baseline, with higher STAI results in younger participants (< 63 y.o.) (p = .048 for HCTR; p = .026 for UC). At both stages of the study, patients with lower level of physical capacity (measured by a peak VO2) had shown significantly higher level of anxiety. There were no significant changes in anxiety levels during the 9-week observation for the entire study population, although there were different patterns of change in anxiety (both trait and state) in younger and older groups,with the decrease in younger patients, and the increase-in the older group.Trial registry number NCT02523560 (Clinical Trials.gov), date of registration: August 14, 2015.

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.

Predictors of proarrhythmic effect in heart failure patients after 9-week hybrid comprehensive telerehabilitation and their influence on cardiovascular mortality in long-term follow-up: Subanalysis of the TELEREH-HF randomized clinical trial.

BACKGROUND: Regular exercise training is beneficial in heart failure (HF) patients. However, its potential proarrhythmic effect is possible but has not been sufficiently investigated. OBJECTIVE: To identify patients at risk for proarrhythmic effect after the 9-week of hybrid comprehensive telerehabilitation (HCTR) program vs the 9-week of usual care (UC) and to investigate its predictors and impact on cardiovascular mortality based on data from the TELEREH-HF RCT. METHODS: Proarrhythmic effect, strictly defined on the basis of available standards was evaluated by comparing 24-h Holter ECG before and after 9-week of HCTR or UC of 773 HF patients (The New York Heart Association class I-III, left ventricular ejection fraction ≤40%). RESULTS: The proarrhytmic effect was found in 78 (20.4%) and in 61 (15.6%) patients in the HCTR and UC group respectively, and the difference between groups was not statistically significant (p = 0.081). However, univariate analysis identified several statistically significant predictors of proarrhythmia in HCTR only vs the UC group. After a multivariate analysis ischaemic aetiology of HF (OR = 2.27, p = 0.008), peak oxygen consumption at baseline <14 ml/kg/min (OR = 2.03, p = 0.012) and level of N-terminal-pro B-type natriuretic peptide (NT-proBNP) in the first and the second tercile (OR = 1.85, p = 0.043) were identified to be independent predictors of proarrhytmic effect of exercise training among the HF patients in HCTR group only. CONSLUSIONS: Patients who underwent a 9-week HCTR were not at a higher risk of proarrhythmic effect after its completion compared to UC. However, predictors of proarrhythmia such as ischemic aetiology of HF, poor physical capacity, lower NT-proBNP level were discovered in the HCTR group only, yet it does not cause a significant risk of cardiovascular mortality including sudden cardiac death in long-term follow-up.

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.