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.

Background

There are major gaps in the implementation of guideline-concordant care for persons with chronic kidney disease (CKD). The CKD Cascade of Care (C³) initiative seeks to improve CKD care by improving detection and treatment of CKD in primary care.

Methods

C³ is a multi-modal initiative deployed in three major academic medical centers within the Department of Veterans Affairs (VA) Health Care System: San Francisco VA, San Diego VA, and Houston VA. The main objective of the first phase of C³ described in this protocol is to establish the infrastructure for universal CKD detection among primary care patients at high-risk for CKD with a triple-marker screen comprising cystatin C, creatinine, and albuminuria. Across the three sites, a comprehensive educational intervention and the integration of primary care-based clinical champions will be employed with the goal of improving CKD detection and treatment. The San Francisco VA will also implement a practice-facilitation intervention leveraging telehealth and health informatics tools and capabilities for enhanced CKD detection. Parallel formative evaluation across the three sites will assess the feasibility and acceptability of integrating cystatin C as part of routine CKD detection in primary care practice. The effectiveness of the interventions will be assessed using a pre-post observational design for change in the proportion of patients tested annually for CKD. Secondary outcomes will assess change in the initiation of cardio-kidney protective therapies and in nephrology referrals of high-risk patients.

Discussion

The first phase of C³ is a multi-facility multi-modal initiative that aims to improve CKD care by implementing a triple-marker screen for enhanced CKD detection in primary care.