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Clinical risk scores based on traditional risk factors of atherosclerosis correlate imprecisely to an individuals complex pathophysiological predisposition to atherosclerosis and provide limited accuracy for predicting major adverse cardiovascular events (MACE). Over the past two decades, computed tomography scanners and techniques for coronary computed tomography angiography (CCTA) analysis have substantially improved, enabling more precise atherosclerotic plaque quantification and characterization. The accuracy of CCTA for quantifying stenosis and atherosclerosis has been validated in numerous multicentre studies and has shown consistent incremental prognostic value for MACE over the clinical risk spectrum in different populations. Serial CCTA studies have advanced our understanding of vascular biology and atherosclerotic disease progression. The direct disease visualization of CCTA has the potential to be used synergistically with indirect markers of risk to significantly improve prevention of MACE, pending large-scale randomized evaluation.

BACKGROUND: Patient-reported outcomes (PROs) may improve care for patients with heart failure. The Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12) is a patient survey that captures symptom frequency, symptom burden, physical limitations, social limitations, and quality of life. Despite the utility of PROs and the KCCQ-12, the implementation and routine use of these measures can be difficult. We conducted an evaluation of clinician perceptions of the KCCQ-12 to identify barriers and facilitators to implementation into clinical practice. METHODS: We conducted interviews with cardiologists from 4 institutions across the United States and Canada (n=16) and observed clinic visits at 1 institution in Northern California (n=5). Qualitative analysis was conducted in 2 rounds: (1) rapid analysis constructed around major themes related to the aims of the study and (2) content analysis with codes derived from the rapid analysis and implementation science. RESULTS: Most heart failure physicians and advanced practice clinicians reported that the KCCQ-12 was acceptable, appropriate, and useful in clinical care. Clinician engagement efforts, trialability, and the straightforward design of the KCCQ-12 facilitated its use in clinical care. Further opportunities identified to facilitate implementation include more streamlined integration into the electronic health record and comprehensive staff education on PROs. Participants highlighted that the KCCQ-12 was useful in clinic visits to improve the consistency of patient history taking, focus patient-clinician conversations, collect a more accurate account of patient quality of life, track trends in patient well-being over time, and refine clinical decision-making. CONCLUSIONS: In this qualitative study, clinicians reported that the KCCQ-12 enhanced several aspects of heart failure patient care. Use of the KCCQ-12 was facilitated by a robust clinician engagement campaign and the design of the KCCQ-12 itself. Future implementation

Survival analysis, or time-to-event analysis, is an important and widespread problem in healthcare research. Medical research has traditionally relied on Cox models for survival analysis, due to their simplicity and interpretability. Cox models assume a log-linear hazard function as well as proportional hazards over time, and can perform poorly when these assumptions fail. Newer survival models based on machine learning avoid these assumptions and offer improved accuracy, yet sometimes at the expense of model interpretability, which is vital for clinical use. We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models. Specifically, we use an improved version of survival stacking to transform a survival analysis problem to a classification problem, ControlBurn to perform feature selection, and Explainable Boosting Machines to generate interpretable predictions. To evaluate our pipeline, we predict risk of heart failure using a large-scale EHR database. Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.

The cost of heart failure care is high owing to the cost of hospitalization and chronic treatments. Heart failure treatments vary in their benefit and cost. The cost effectiveness of therapies can be determined by comparing the cost of treatment required to obtain a certain benefit, often defined as an increase in 1 year of life. This review was sponsored by the Heart Failure Society of America and describes the growing economic burden of heart failure for patients and the health care system in the United States. It also provides a summary of the cost effectiveness of drugs, devices, diagnostic tests, hospital care, and transitions of care for patients with heart failure. Many medications that are no longer under patent are inexpensive and highly cost-effective. These include angiotensin-converting enzyme inhibitors, beta-blockers and mineralocorticoid receptor antagonists. In contrast, more recently developed medications and devices, vary in cost effectiveness, and often have high out-of-pocket costs for patients.

Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = -2.86; Cohen's Kappa = 0.89, P < 0.0001) with current conventional manual scoring on a retrospective dataset of 79 patients and was found to perform the task faster (average time for automated CAC scoring using a graphics processing unit (GPU) was 3.5 ± 2.1 s vs. 261 s for manual scoring) in a prospective trial of 55 patients with little difference in scores compared to three technologists (mean difference in scores = 3.24, 5.12, and 5.48, respectively). Then using CAC scores from paired gated coronary CT as a reference standard, we trained a deep learning model on our internal data and a cohort from the Multi-Ethnic Study of Atherosclerosis (MESA) study (total training n = 341, Stanford test n = 42, MESA test n = 46) to perform CAC scoring on routine non-gated chest CT e

Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = -2.86; Cohen's Kappa = 0.89, P < 0.0001) with current conventional manual scoring on a retrospective dataset of 79 patients and was found to perform the task faster (average time for automated CAC scoring using a graphics processing unit (GPU) was 3.5 ± 2.1 s vs. 261 s for manual scoring) in a prospective trial of 55 patients with little difference in scores compared to three technologists (mean difference in scores = 3.24, 5.12, and 5.48, respectively). Then using CAC scores from paired gated coronary CT as a reference standard, we trained a deep learning model on our internal data and a cohort from the Multi-Ethnic Study of Atherosclerosis (MESA) study (total training n = 341, Stanford test n = 42, MESA test n = 46) to perform CAC scoring on routine non-gated chest CT e

IntroductionEvaluation of health status benefits, cost-effectiveness, and value of new heart failure therapies is critical for supporting their use. The Kansas City Cardiomyopathy Questionnaire (KCCQ) measures patients' heart failure-specific health status but does not provide utilities needed for cost-effectiveness analyses. We mapped the KCCQ scores to EQ-5D scores so that estimates of societal-based utilities can be generated to support economic analyses.MethodsUsing data from two US cohort studies, we developed models for predicting EQ-5D utilities (3L and 5L versions) from the KCCQ (23- and 12-item versions). In addition to predicting scores directly, we considered predicting the five EQ-5D health state items and deriving utilities from the predicted responses, allowing different countries' health state valuations to be used. Model validation was performed internally via bootstrap and externally using data from two clinical trials. Model performance was assessed using R2, mean prediction error, mean absolute prediction error, and calibration of observed vs. predicted values.ResultsThe EQ-5D-3L models were developed from 1000 health status assessments in 547 patients with heart failure and reduced ejection fraction (HFrEF), while the EQ-5D-5L model was developed from 3925 patients with HFrEF. For both versions, models predicting individual EQ-5D items performed as well as those predicting utilities directly. The selected models for the 3L had internally validated R2 of 48.4-50.5% and 33.7-45.6% on external validation. The 5L version had validated R2 of 57.7%.ConclusionMappings from the KCCQ to the EQ-5D can yield the estimates of societal-based utilities to support cost-effectiveness analyses when EQ-5D data are not available.