Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness - Scorecard - MDSpire
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Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness
Clinical Scorecard: A Systematic Review of Federated Learning Applications in Cardiovascular Disease Prediction: Assessing Clinical Validation and Readiness for Implementation
At a Glance
Category
Detail
Condition
Key Mechanisms
Target Population
Patients with cardiovascular disease.
Care Setting
Key Highlights
Evidence base is predominantly retrospective with inconsistent validation.
Focus on privacy and security safeguards is critical for implementation.
Heterogeneity handling and operational feasibility are key factors for clinical translation.
Adoption priorities include external validation and subgroup fairness monitoring.
Guideline-Based Recommendations
Diagnosis
Utilize federated learning for early screening and clinical diagnosis of CVD.
Management
Implement communication-efficient personalization under drift.
Monitoring & Follow-up
Establish clinical-grade MLOps for monitoring and continual updating.
Risks
Address privacy and security threats associated with federated learning.
Patient & Prescribing Data
Federated learning models can enhance predictive capabilities.
Clinical Best Practices
Prioritize held-out-site and external validation.
Ensure robust calibration and subgroup performance reporting.
Maintain auditable privacy and security safeguards.
A prespecified exploratory analysis of the FIND-CKD clinical trial examined kidney function, albuminuria, and kidney failure outcomes in 903 patients with glomerular diseases.
