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Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure
Szymon Urban1, Mikołaj Błaziak1, Maksym Jura2
1Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland.
Biomolecules
|November 11, 2022
Summary
Machine learning clustering identified three distinct subgroups within acute heart failure (AHF) patients. These groups showed significantly different rates of worsening renal function (WRF), offering new insights into AHF and WRF interactions.
Area of Science:
- Cardiology
- Nephrology
- Data Science
Background:
- Acute heart failure (AHF) is a severe condition with a poor prognosis.
- Worsening renal function (WRF) frequently complicates AHF, further deteriorating patient outcomes.
- The heterogeneity of AHF patients with WRF necessitates advanced analytical approaches.
Purpose of the Study:
- To apply machine learning clustering to identify distinct subgroups within the AHF population based on WRF occurrence.
- To analyze the clinical and prognostic differences between identified subgroups regarding WRF.
Main Methods:
- Utilized k-medoids clustering on data from 312 hospitalized AHF patients.
- Included 86 variables assessed at admission and creatinine levels measured four times during hospitalization.
- Assessed clustering quality using the Davies-Bouldin index.
Main Results:
- Successfully identified three distinct patient clusters within the AHF population.
- These clusters exhibited significantly different incidences of WRF (p = 0.004).
- The identified groups demonstrated varied renal prognoses.
Conclusions:
- Clustering analysis revealed clinically and prognostically relevant subgroups in AHF patients concerning WRF.
- These findings offer novel insights into the interplay between AHF and WRF.
- The results can inform future clinical trial design and personalized treatment strategies for AHF patients.
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