Cardiovascular Disease Is Entering Its Precision Era. Trial Design Needs to Catch Up.
The shift toward precision cardiology starts with better trial design.
Cardiology has long operated in a broad therapeutic category: large patient populations, well-established standards of care, and treatments designed to work across a wide range of diseases. But this approach is starting to change, and the cardiovascular pipeline reflects it. There are now more than 10 therapies in active development specifically targeting Heart Failure with preserved ejection fraction (HFpEF), a condition that for years was lumped together with other forms of heart failure despite HFpEF’s meaningful physiological differences. Similar focus is emerging across other indications like ASCVD, cardiac amyloidosis (ATTR-CM), atrial fibrillation (A-fib), and more. Not to mention the myriad comorbidities associated with these patient populations, such as Chronic Kidney Disease, MASH, and Obesity, which only adds complexity.
In some ways, Cardiology is starting to mirror Oncology’s precision medicine approach, which became possible once genomic sequencing gave clinicians and researchers the ability to substratify tumors with enough granularity to design treatments around specific mutations.
In cardiology, the catalyst for precision medicine is different. It’s less about genomics and more about physiology, revealed through the combination of richer data sources and AI-enabled analytics. This convergence now allows researchers to tap into diagnostic, imaging, and qualitative data to characterise patients at a level of depth that wasn't previously possible.
Now, clinical development teams have the opportunity to run faster, less costly, more targeted trials…and reach cardiology patients who have historically been underserved.
Why Historical Cardio Trial Design Has Fallen Short
Cardiovascular diseases like HFpEF, ASCVD, and ATTR-CM share a common challenge: they are clinically heterogeneous, often underdiagnosed, and difficult to identify early. HFpEF affects roughly 1% of the U.S. population but is frequently diagnosed late, if at all. ATTR-CM patients spend an average of six years moving through the healthcare system before receiving a confirmed diagnosis. Patients with subclinical ASCVD often don't surface in standard diagnostics until they've had an event, delaying treatment.
Part of what's made diagnosis and research difficult is the nature of available data. Electronic health records were built primarily to support billing, not clinical research. Which is why, despite heart failure having multiple distinct subtypes with vastly different risk profiles, a single billing code covers them all. Claims data captures diagnoses and procedures, but not the physiological detail that defines patient phenotype: echocardiographic measurements, ECG signatures, imaging-derived biomarkers. That information exists in clinical settings, but is buried in clinical information systems not easily accessible for trial teams or even hospitals themselves. Yet it's often exactly what's needed to phenotype patients at an indication-specific level.
Historically, broad enrollment made sense for blockbuster cardiovascular drugs treating a wide disease spectrum. But for therapies targeting more narrow indications, non-specific patient populations dilute signal and increase the risk of failure. Some of these therapies may go the opposite direction: shuttering trials for potentially exciting new therapies because they can’t find the right patients for a trial.
These challenges are compounded by the fact that many enrolled patients are already on existing cardiovascular treatments, raising the bar for any new therapy to demonstrate incremental benefit.
The Time is Now for Precision Cardio Trial Design
Despite these historical challenges, big shifts are unlocking the level of phenotyping needed to enable precision trial design.
For one, technology can now bridge the clinical and healthcare data silos that have historically kept patient data fragmented. Provider systems, imaging repositories, and lab systems were each built for their own teams and purposes, a byproduct of how healthcare systems evolved rather than a deliberate choice to keep them separated. What's changed is the ability to unify those sources into a single, cohesive view of a patient population. For the first time, clinical development teams have access to rich, multimodal data that precision trial design requires.
Bringing that data together is only half the equation. AI and machine learning now make it possible to extract insights that were never previously accessible. Cardiology has always generated some of the richest clinical data in medicine. Until recently, the limitation hasn't been data volume; it's been extractability. When a cardiologist reviews an echocardiogram, they capture exactly what they need to for that patient encounter. But a wealth of additional insights stays buried in that same image, never making it into the record. Now, AI algorithms can systematically review raw imaging and electrical data to surface measurements and biomarkers that aren’t readily visible without this deep analysis.
“Precision” is no longer a theoretical goal in clinical development. It’s something teams can start to apply in concrete trial design decisions today.
Precision Cardio Trial Design in Practice
In cardiovascular diseases specifically, three indications illustrate what precision looks like in practice:
HFpEF: The condition is both common and heterogeneous. Patients share a preserved ejection fraction but may have quite different comorbidity profiles, cardiac physiologies, and disease trajectories. Now, teams can precisely characterize HFpEF patients using echo-derived physiologic measurements and longitudinal biomarker data, supporting tighter cohort definitions and earlier patient identification.
ASCVD: Standard of care has improved substantially for ASCVD, so any ASCVD trial needs to identify patients with meaningful residual risk despite background treatments. The data that defines that residual risk (coronary artery calcium scores, plaque burden and progression, PAD severity) is often captured in imaging but not systematically extracted. With advancements in AI and data analysis, it’s possible to enrich patient cohorts around these exact signals.
ATTR-CM: ATTR-CM is substantially underdiagnosed. Patients average six years between symptom onset and confirmed diagnosis, and many physicians have limited familiarity with how this condition presents. AI algorithms applied to ECGs and echocardiograms can detect signatures consistent with ATTR-CM earlier than clinical diagnosis typically occurs.
Across these indications, the pattern is consistent: better phenotyping supports tighter cohorts, more meaningful endpoints, and earlier signal… without defaulting to larger, longer trials. What's been missing is the data infrastructure and analytical capability to make this a reality. That's where Dandelion comes in.
How Dandelion is Enabling More Precise Trial Design
Dandelion is a clinical data and AI platform built around this exact opportunity: unifying fragmented healthcare data sources and extracting intelligence to design faster, more targeted trials. With Dandelion, teams can phenotype patients with precision, surface optimal biomarkers and trial endpoints, and identify care gaps.
At the foundation is Dandelion's data. Dandelion partners with large community health systems comprising millions of patients, harmonizing data across the full breadth of clinical and diagnostic sources into a single, longitudinal view. This view includes structured sources that traditional RWD cover, like diagnoses, procedures, labs, and medications, as well as the raw imaging and waveform data (echo DICOMs, ECG waveforms, CT scans, MRIs), which carry the most meaningful biological signals.
Teams can leverage Dandelion’s data platform directly, or go deeper by layering AI algorithms to surface new clinical measurements or biomarkers that aren’t accessible with traditional analysis; for example, converting raw imaging data into quantifiable clinical scores.
With Dandelion, teams can:
Map complex inclusion and exclusion criteria directly to real-world patient data, including criteria that would typically require manual chart review
Run trial simulations to model how adjusting a criterion affects population size, event rate, and trial timeline
Identify patient cohorts defined by specific phenotypic characteristics, rather than relying on broad ICD codes or historical trial data
In one analysis, Dandelion emulated a landmark cardiovascular outcomes trial that ran for three years before showing signal. Applying an AI-derived ECG biomarker alongside Dandelion's data, researchers uncovered the same signal in half the time and identified a primary prevention population approximately 12 to 15 times larger than the enrolled cohort.
In another example, we analysed a past trial in atrial fibrillation and found extremely promising results:
Shortened trial timelines by 40% or more
Decreased trial size by up to 36 percent
Reduced amendment risk by 34 to 68 percent
Saved $49 to $84 million in total trial costs
Implications Beyond the Trial
Dandelion's value extends beyond trial design. The same capabilities that support precise patient phenotyping also shed light on gaps in current care delivery. After all, the patients who are hardest to identify for a clinical trial are often the same patients receiving suboptimal care, either because they haven't been diagnosed correctly or because their treatment doesn't match their underlying disease. Building a deeper, data-driven understanding of these patients in the real world is a meaningful step toward closing those gaps and delivering higher-quality care beyond trial settings.
What’s Next
Better trial design in cardiology isn't only about enrolling faster or spending less. It's about designing trials that genuinely serve the patients they're meant to help, and that give promising therapies the best possible chance to show their impact. If your team is working on designing more precise cardio trials, we’d love to talk.
