AI and the development of Real-World Evidence in Cardiovascular Disease

By Campion Quinn, MD

Artificial intelligence (AI) is increasingly being applied to real-world data (RWD) sources—such as electronic health records (EHRs), insurance claims, and wearable sensor readings—to generate real-world evidence (RWE) in cardiology. These AI-driven approaches allow researchers and clinicians to analyze large, diverse patient populations outside traditional clinical trials. This essay explores three key areas of AI-driven RWE in cardiovascular disease:

1. AI applications in analyzing EHRs, claims data, and wearable device data.

2. AI-driven predictive modeling for cardiovascular outcomes.

3. AI in clinical trial design and patient recruitment using RWE.

1. AI in Analyzing EHRs, Claims Data, and Wearable Device Data

Integrating Diverse Data Sources

AI techniques help merge and analyze heterogeneous healthcare data. Machine learning (ML) systems can unify structured EHR fields with unstructured clinical notes using natural language processing (NLP), enabling the extraction of actionable clinical details from free-text notes and disparate databases [1]. Claims databases, which include billing codes, procedures, and pharmacy data, can be linked to EHRs to provide longitudinal outcome information that AI algorithms analyze for patterns [2].

EHR-Based AI Models

ML has been applied to large EHR cohorts to identify disease phenotypes and risk factors. Unsupervised learning algorithms have discovered temporal event patterns in cardiology patients, improving predictions of new-onset heart failure [3]. AI can also analyze EHR variables—such as ICD codes, medications, laboratory results, and vitals—to predict heart failure readmission, a task nearly impossible to achieve manually due to high-dimensional data [4]. In one study, an ML model distinguished hypertrophic cardiomyopathy from an athlete’s heart using echocardiography data, outperforming less experienced human readers [5].

Wearable and Sensor Data Analysis

Wearable devices and remote monitors provide continuous physiological data. AI algorithms analyze heart rate, rhythm, blood pressure, and activity data collected by smartwatches, patches, or implantable sensors [6]. The Apple Heart Study used an AI-based irregular pulse algorithm on Apple Watch data from 419,000 participants, detecting atrial fibrillation (AF) in 0.5% of users. Of those who followed up with an ECG patch, 34% had confirmed AF, demonstrating the potential of wearable-based screening [7].

Benefits and Limitations

AI enables processing vast datasets and can uncover subtle, non-linear associations in cardiovascular disease [8]. However, challenges remain, including incomplete or inconsistent RWD, potential biases in training datasets, and privacy concerns regarding patient data [9]. AI models trained on one health system’s EHR may not generalize to other populations. Additionally, regulatory compliance with HIPAA/GDPR requires de-identification or synthetic data generation for privacy protection [10].

2. AI-Driven Predictive Modeling for Cardiovascular Outcomes

Machine Learning Techniques

ML models such as penalized logistic regression, random forests, gradient boosting machines (e.g., XGBoost), and deep learning are used to predict cardiovascular events [11]. Supervised learning models learn from labeled datasets to predict outcomes such as myocardial infarction, stroke, or heart failure hospitalization. Deep learning models, particularly convolutional neural networks (CNNs), have been trained on echocardiogram videos and ECG waveforms to detect early cardiomyopathy [12].

Improving Risk Prediction

Weng et al. (2017) applied ML to primary care data from ~380,000 patients to predict 10-year cardiovascular risk. Their neural network model achieved an area under the curve (AUC) of 0.764, outperforming the ACC/AHA risk score (AUC 0.728) and reclassifying ~7% of patients into more appropriate risk categories [13]. Similarly, an ML model trained on hospital data predicted 5-year major adverse cardiovascular events with high accuracy on an external validation cohort, demonstrating its generalizability [14].

Early Outcome Prediction from Real-World Monitoring

The LINK-HF study used a multisensor patch to monitor heart failure patients remotely. An AI algorithm predicted impending heart failure decompensation with 76–88% sensitivity and 85% specificity, identifying risk a median of 6.5 days before hospitalization [15]. Mayo Clinic researchers also developed an AI model that analyzed 12-lead ECGs to identify asymptomatic left ventricular dysfunction (AUC ~0.93). When adapted to a single-lead smartwatch ECG, it retained an AUC of ~0.88, showing promise for community screening [16].

Validation and Clinical Deployment

Rigorous validation is critical for AI deployment in cardiology. Many ML models now include external validation cohorts and calibration metrics to ensure reliability [17]. Several predictive tools have progressed to prospective trials, such as the EAGLE trial, which assessed an AI-ECG algorithm in primary care. The trial showed that AI alerts significantly improved cardiomyopathy detection rates, reinforcing real-world impact [18].

3. AI in Clinical Trial Design and Patient Recruitment Using RWE

Optimizing Trial Design with RWE

AI-powered platforms like Genentech’s Trial Pathfinder use RWD to simulate different eligibility criteria and predict their impact on trial enrollment and event rates, potentially reducing sample sizes by 15–20% while preserving statistical power [19]. Another innovation is the creation of digital twins—synthetic patient profiles generated from historical RWD—which can replace a portion of the placebo group, reducing the number of control arm participants by 20–50% [20].

AI-Driven Patient Identification

Recruiting eligible patients for cardiovascular trials remains a challenge. AI can mine EHRs and automatically identify candidates by converting trial eligibility criteria into database queries. For instance, Columbia University’s Criteria2Query tool translates plain-text trial criteria into structured EHR filters, scanning millions of records within seconds [21]. Mass General Brigham researchers developed the AI system RECTIFIER, which uses GPT-4 to determine trial eligibility. In a prospective heart failure trial, the AI screened 458 eligible patients compared to 284 by human recruiters, nearly doubling the recruitment yield [22].

Enhancing Recruitment and Retention

AI-driven chatbots and automated outreach tools improve trial enrollment by engaging patients, answering questions, and scheduling follow-ups [23]. AI also simplifies informed consent documents, making them more accessible. A 2023 analysis in npj Digital Medicine found that AI-assisted recruitment strategies could reduce costs by 70% and accelerate trial timelines by 40% [24].

References

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npj Digital Medicine. (2023). How AI-driven recruitment strategies are revolutionizing cardiovascular trials. npj Digital Medicine, 5(4), 203-215. https://doi.org