Objectives: This analysis sought to determine factors (including adiposity-related factors) most associated with HF-type symptoms (fatigue, shortness of breath, and edema) in adults with chronic kidney disease (CKD).
Background: Symptom burden impairs quality of life in CKD, especially symptoms that overlap with HF. These symptoms are common regardless of clinical HF diagnosis, and may be affected by subtle cardiac dysfunction, kidney dysfunction, and other factors. We used machine learning to investigate cross-sectional relationships of clinical variables with symptom scores in a CKD cohort.
Methods: Participants in the Chronic Renal Insufficiency Cohort (CRIC) with a baseline modified Kansas City Cardiomyopathy Questionnaire (KCCQ) score were included, regardless of prior HF diagnosis. The primary outcome was Overall Summary Score as a continuous measure. Predictors were 99 clinical variables representing demographic, cardiac, kidney and other health dimensions. A correlation filter was applied. Random forest regression models were fitted. Variable importance scores and adjusted predicted outcomes are presented.
Results: The cohort included 3426 individuals, 10.3% with prior HF diagnosis. BMI was the most important factor, with BMI 24.3 kg/m2 associated with the least symptoms. Symptoms worsened with higher or lower BMIs, with a potentially clinically relevant 5 point score decline at 35.7 kg/m2 and a 1-point decline at the threshold for low BMI, 18.5 kg/m2. The most important cardiac and kidney factors were heart rate and eGFR, the 4th and 5th most important variables, respectively. Results were similar for secondary analyses.
Conclusions: In a CKD cohort, BMI was the most important feature for explaining HF-type symptoms regardless of clinical HF diagnosis, identifying an important focus for symptom directed investigations.
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.