Introduction
AI identifies heart failure by using machine learning techniques which have identified five distinct subtypes of heart failure (HF).
The study, which was conducted on large UK datasets, aimed to predict the prognosis and guide treatment plans for patients with CHF.
The research is one of the largest analyses of electronic health records using several machine learning and validation methods.
AI identifies heart failure: Machine learning for prediction
Researchers analyzed data from three datasets: the Clinical Practice Research Datalink (CPRD), the Health Improvement Network (THIN), and the UK Biobank.
Researchers evaluated factors such as demographic information, medical history, examinations, laboratory values, and medications.
They used four unsupervised machine learning methods (K-means, hierarchical, K-medoids, and mixture model clustering) to identify subspecies.
Five distinct CI subtypes were classified as early-onset, late-onset, and related to atrial fibrillation, metabolism, and cardiac metabolism.
Validation and results:
Subtypes showed consistency between datasets in terms of external validity.
Prognostic validity analysis revealed differences in 1-year mortality, non-fatal cardiovascular disease (CVD) risk, and all-cause hospitalizations between subtypes.
Genetic validity analysis showed associations with polygenic risk scores (PRS) for CI-related traits and single-nucleotide polymorphisms (SNPs).
Age and sex differed between subspecies, with the late-onset subtype containing the oldest participants and the early-onset subtype having the youngest participants.
The cardiomyopathy subtype had the highest prevalence of cardiovascular factors and diseases in the THIN dataset.
Implications for practice:
Specific subtypes of CI have implications for future research, clinical trials, and observational studies.
Subtypes may aid in the management and prognosis of CI in clinical practice.
Researchers have developed a prototype application for routine clinical use to assess efficacy.
Healthcare professionals should consider asking CI patients about common risk factors to understand the subtype.
AI studies like this can contribute to a better understanding of disease processes and drug discovery.
Conclusion:
The use of artificial intelligence models and machine learning techniques has enabled the identification of five distinct subtypes of heart failure. This breakthrough has the potential to improve prognostic accuracy, guide treatment decisions, and improve patient outcomes in the management of heart failure. Future research and clinical applications of these subtypes hold promise for advancing our understanding of the disease and personalized healthcare approaches.