The Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers

Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record

The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.

Identifying quality of life outcome patterns to inform treatment choices in ischemic cardiomyopathy

BACKGROUND

The Surgical Treatment for Ischemic Heart Failure (STICH) trial found that routine use of coronary artery bypass surgery (CABG) improved mean quality of life (QoL) scores relative to guideline-directed medical therapy (GDMT) in patients with ischemic cardiomyopathy. However, mean differences in QoL scores do not provide what patients want to know when facing a high-risk/high-benefit treatment choice.

METHODS

We analyzed Kansas City Cardiomyopathy Questionnaire (KCCQ) Overall Summary scores in CABG and GDMT patients over 36 months using a combination of statistical methods to group QoL data into clinically relevant outcome patterns (phenotype trajectories) and to then identify the main baseline predictors of each phenotype. QoL outcome phenotypes were developed using mixture models to define the dominant phenotype trajectories present in STICH QoL data. Logistic regression models were used to predict each patient's probability of achieving each outcome pattern with each treatment.

RESULTS

In STICH, 592 patients underwent CABG and 607 were managed with GDMT. Our analyses identified 3 phenotype trajectory patterns in both treatment groups. Two of the 3 trajectories showed improving patterns, and were classified as "good QoL trajectories," seen in 498 (84.1%) CABG and 449 (73.9%) GDMT patients. Defining a consequential CABG-GDMT treatment difference as a >10% higher absolute predicted probability of belonging to good QoL trajectories, 277 (23.5%) patients were more likely to have good outcome with CABG while 45 (3.8%) patients were more likely to have a good outcome with GDMT. For 644 (54.7%) patients, CABG and GDMT probabilities of a good outcome were within 5% of each other.

CONCLUSIONS

The pattern of QoL outcomes after CABG compared with GDMT in STICH followed 3 main phenotypic trajectories, which could be predicted based on individual baseline features. Patient-specific predictions about expected QoL outcomes with different treatment choices provide an intuitive framework for personalizing patient decision making.

The need for increased pragmatism in cardiovascular clinical trials

The majority of cardiovascular randomized controlled trials (RCTs) test interventions in selected patient populations under explicitly protocol-defined settings. Although these ‘explanatory’ trial designs optimize conditions to test the efficacy and safety of an intervention, they limit the generalizability of trial findings in broader clinical settings. The concept of ‘pragmatism’ in RCTs addresses this concern by providing counterbalance to the more idealized situation underpinning explanatory RCTs and optimizing effectiveness over efficacy. The central tenets of pragmatism in RCTs are to test interventions in routine clinical settings, with patients who are representative of broad clinical practice, and to reduce the burden on investigators and participants by minimizing the number of trial visits and the intensity of trial-based testing. Pragmatic evaluation of interventions is particularly important in cardiovascular diseases, where the risk of death among patients has remained fairly stable over the past few decades despite the development of new therapeutic interventions. Pragmatic RCTs can help to reveal the ‘real-world’ effectiveness of therapeutic interventions and elucidate barriers to their implementation. In this Review, we discuss the attributes of pragmatism in RCT design, conduct and interpretation as well as the general need for increased pragmatism in cardiovascular RCTs. We also summarize current challenges and potential solutions to the implementation of pragmatism in RCTs and highlight selected ongoing and completed cardiovascular RCTs with pragmatic trial designs.

In this Review, Khan and colleagues discuss the benefits and challenges of including pragmatism in the design, conduct and interpretation of randomized controlled trials (RCTs) for cardiovascular disease and highlight selected ongoing and completed cardiovascular RCTs that incorporate a pragmatic design.

Most cardiovascular randomized controlled trials (RCTs) conducted to date have been ‘explanatory’, that is, designed to study the intervention in optimized conditions with selected patient populations and frequent protocolized assessments.

Although explanatory RCT designs increase validity, they limit the generalizability of trial findings, whereas a ‘pragmatic’ approach to RCTs yields findings more relevant to real-world practice.

In pragmatic RCTs, interventions are tested in patients who are broadly representative of the condition being studied, and the study is aligned with routine clinical care to reduce costs and organizational burden.

Although pragmatic RCTs tend to attenuate estimates of treatment effects, they do provide a more realistic understanding of population-level effectiveness and costs than explanatory trials.

Pragmatic trials can highlight barriers to the implementation of therapies and are better suited than explanatory RCTs to assessing the effects of implementation strategies and health-care policies at the population level.

Widespread implementation of pragmatic trials would require the development of technological infrastructure to collect and share data as well as regulatory guidelines amenable to findings derived from routinely collected data.

12th Korea Healthcare Congress 2021; 김치국부터 마시지 말라; The Time for Digital Health is Almost Here

We are now on the cusp of massive adoption of digital health technologies. Medicine is becoming an information science intertwined with technology and data science. This talk aims to describe the current state of digital transformation in healthcare, to identify reasons for enthusiasm and caution, and to provide a framework for thinking about what is necessary for hospitals and health systems to be confident about incorporating these innovations into practice. I have three key recommendations. First, we should buy results, not claims. Those in positions that influence decisions about endorsing or purchasing digital products designed to improve care or outcomes ought to buy results, not claims or intermediate results. Moreover, although analytic validity and clinical validity are important, they sometimes do not reflect the impact of a product in its entirety. Ultimately, we need to know whether patients benefit. Second, we should insist on transparency. The performance of a product cannot be a secret. The basis on which developers make claims about their products should be open to all, including patients. Better yet, data on which experts reach a conclusion should be shared, just as many companies share research data on drugs and devices. Third, we should be aware of unintended adverse consequences. We should evaluate every intervention for unintended adverse consequences. Changes to systems, with all good intentions, can always go awry. In conclusion, insistence on good and evolving evidence is the best way to arrive at our destination: the use of innovations to improve outcomes.

Development and Validation of a Predictive Model to Identify Patients With an Ascending Thoracic Aortic Aneurysm

Background

Screening protocols do not exist for ascending thoracic aortic aneurysms (ATAAs). A risk prediction algorithm may aid targeted screening of patients with an undiagnosed ATAA to prevent aortic dissection. We aimed to develop and validate a risk model to identify those at increased risk of having an ATAA, based on readily available clinical information.

Methods and Results

This is a cross‐sectional study of computed tomography scans involving the chest at a tertiary care center on unique patients aged 50 to 85 years between 2013 and 2016. These criteria yielded 21 325 computed tomography scans. The double‐oblique technique was used to measure the ascending thoracic aorta, and an ATAA was defined as >40 mm in diameter. A logistic regression model was fitted for the risk of ATAA, with readily available demographics and comorbidity variables. Model performance was characterized by discrimination and calibration metrics via split‐sample testing. Among the 21 325 patients, there were 560 (2.6%) patients with an ATAA. The multivariable model demonstrated that older age, higher body surface area, history of arrhythmia, aortic valve disease, hypertension, and family history of aortic aneurysm were associated with increased risk of an ATAA, whereas female sex and diabetes were associated with a lower risk of an ATAA. The C statistic of the model was 0.723±0.016. The regression coefficients were transformed to scores that allow for point‐of‐care calculation of patients' risk.

Conclusions

We developed and internally validated a model to predict patients' risk of having an ATAA based on demographic and clinical characteristics. This algorithm may guide the targeted screening of an undiagnosed ATAA.