1. AI predicts out-of-hospital cardiac arrest risk using ECG and EHR data
Out-of-hospital cardiac arrest is hard to predict, particularly in people without known cardiovascular disease. A 2026 study in JACC Advances explored whether AI applied to routine 12-lead ECGs, combined with electronic health record data, could change that.
The combined ECG and EHR model outperformed either data source alone. In a real-world cohort, it flagged around two-thirds of people who later experienced out-of-hospital cardiac arrest within two years as high risk, using data that was already being collected as part of routine care.
What this means for research teams: Combining clinical data sources is powerful, but only when the underlying data is structured, validated and consistent. Studies like this depend on robust database design, reliable endpoint definitions and screening workflows that can handle information from multiple sources.
Source: JACC Advances, 2026
2. Wearables are becoming serious in rhythm disorders and eHealth
Wearables are no longer just consumer devices. They are being studied as part of structured care pathways, particularly in rhythm disorders. Smartwatch-based screening has shown real potential for detecting atrial fibrillation in higher-risk patients.
But the device is not the hard part. The clinical process around it is.
A wearable signal only becomes useful when it triggers ECG confirmation, clinical review and timely follow-up. Without that structure, the data sits unused.
What this means for research teams: Wearable-based studies generate large volumes of patient data. Making that data usable requires clear workflows for collection, review, validation and follow-up. Those workflows need to be built into the study design from the start, not added afterwards.
Source: EQUAL trial, ACC Journal Scan, 2026
3. Chronic heart failure is shifting towards home-based digital care
Heart failure management depends on follow-up, medication optimisation and catching deterioration early. In 2026, research into remote digital support is showing how much of that can happen outside the hospital.
Digital interventions are helping clinicians monitor patients between appointments and support more consistent delivery of guideline-directed therapy. The opportunity is not just to find new treatments. It is to help patients receive existing treatments more effectively.
What this means for research teams: Home-based heart failure research needs digital tools that fit the study design and the patient journey. Data must be accessible and reliable for investigators across sites, not just practical in theory.
Source: VITAL-HF, The Lancet Regional Health, 2026
4. FFR-guided PCI in TAVI: function over appearance
Patients undergoing TAVI frequently have coronary artery disease alongside their valve condition. The question of when, and whether, to treat coronary lesions has never been straightforward.
EuroPCR 2026 highlighted data on FFR-guided PCI in TAVI patients, pointing towards a more selective approach. Rather than treating a narrowing because it looks significant on imaging, FFR-guided decision-making asks whether it actually limits blood flow enough to matter.
Not every visible narrowing needs an intervention. The evidence is increasingly clear on that.
What this means for research teams: More personalised interventional decision-making means more complex trial data. Precise endpoint definitions, consistent procedural data capture and well-structured databases across international sites become even more important when the clinical decisions themselves are more nuanced.
Source: EuroPCR 2026 / ARTICA IPD meta-analysis, TouchCARDIO
5. LDL targets in secondary prevention keep moving lower
The Ez-PAVE trial compared an LDL-cholesterol target of below 55 mg/dL with below 70 mg/dL in patients with established atherosclerotic cardiovascular disease. The results support what international guidelines have been moving towards for several years: in patients who already have cardiovascular disease, lower is better.
For secondary prevention research, this matters. Long-term follow-up, consistent risk profiling and high-quality patient data are what make prevention studies work. And "low enough" keeps shifting.
What this means for research teams: Prevention studies require careful monitoring of treatment targets, outcomes and patient status over extended periods. Data workflows need to support that from day one, not be retrofitted when the study is already running.
Source: Ez-PAVE, ACC 2026
