Revolutionizing Heart Failure Management

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By uniting EHR-based and cardiac-imaging-based predictive models, Kardia.ai establishes a new paradigm in heart failure management — one where machine learning augments clinician expertise to deliver smarter, faster, and more personalized care.

Heart failure (HF) affects more than 64 million people worldwide and remains one of the most costly and deadly chronic conditions. Despite major advances in pharmacologic and device therapies, clinicians still struggle to accurately predict which patients are at imminent risk of decompensation, hospitalization, or death.

Current clinical management relies heavily on intermittent data — office visits, subjective symptoms, and clinician gestalt — rather than continuous, data-driven insight. As a result:

• Nearly 25% of HF patients are  readmitted within 30 days of discharge.
• Over 50% die within five years of initial diagnosis.
• Hospitals and health systems face billions in avoidable costs tied to readmissions, while patients experience preventable deterioration and poor quality of life.

Kardia.ai addresses one of the most persistent and high-impact challenges in cardiovascular care — the inaccurate prediction of near-term outcomes in patients with heart failure.

Our Software-as-a-Medical-Device (SaMD) platform provides clinicians with a validated, AI-driven prediction of 1-year mortality or severe decompensation risk using a two-step, progressively scalable deep learning approach derived from more than one billion electronic health record (EHR) data points and multimodal cardiac imaging.

Kardia’s ensemble deep learning algorithm leverages EHR data from over 127,000 heart failure patients (>1 billion variables) to generate real-time, patient-specific risk predictions (AUC = 0.91 internal; AUC = 0.79 external validation using the VA VCHAMPs dataset).

Embedded directly within the clinical workflow, this step provides immediate decision support to guide timing for high-risk interventions such as durable LVAD implantation or cardiac transplantation.

For patients where EHR-based model confidence is low or indeterminate, Kardia.ai employs a proprietary cardiac MRI model using regional left-ventricular multiparametric strain values (AUC = 0.94).

This MRI-based model independently predicts severe decompensation in dilated cardiomyopathy and, when ensembled with the EHR model, creates a composite predictive engine that unites structural and clinical data to improve prognostic accuracy.

Electronic Health Record-Based Deep Learning Prediction of Death or Severe Decompensation in Heart Failure Patients. McGilvray, M, Heaton, J, Guo, A. et al. Electronic Health Record-Based Deep Learning Prediction of Death or Severe Decompensation in Heart Failure Patients. J Am Coll Cardiol HF. 2022 Sep, 10 (9) 637–647.

https://doi.org/10.1016/j.jchf.2022.05.010