Abstract: FR-PO1055
Interrelationships Among Dialysis Quality Metrics
Session Information
- Hemodialysis: Clinical Challenges, Patient-Centered Outcomes, and Quality of Life
October 23, 2026 | Location: Exhibit Hall A, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Dialysis
- 801 Dialysis: Hemodialysis and Frequent Dialysis
Authors
- Blankenship, Derek M., Renal Research Institute, New York, New York, United States
- Torgbenu, Moses, Renal Research Institute, New York, New York, United States
- Stone, Shannon L., Fresenius Medical Care Holdings Inc, Waltham, Massachusetts, United States
- Chatoth, Dinesh K., Fresenius Medical Care (GMO), Waltham, Massachusetts, United States
Background
Dialysis quality is frequently assessed through composite metrics like the Clinical Quality Score (CQS) and Quality Incentive Program (QIP). To accurately interpret clinic performance, it is necessary to understand the correlation among these high-level domains and their sub-components. This study assesses the cross-sectional associations of these metrics to evaluate their interdependencies.
Methods
We conducted a cross-sectional descriptive and correlation analysis using August 2025 data from 2,639 US dialysis clinics. Metrics included: 1) Composite: CQS; 2) Regulatory: QIP, including Hospital Readmissions (CMS 5-Star excluded as monthly data was unavailable); 3) Clinical: Adequacy Rate; 4) Treatment Process: Missed Treatments, Fluid Action Group, and Shortened Treatments. Summary statistics and Pearson correlations were used to assess intra- and inter-category relationships. Generative AI (Google Gemini) was used to refine and edit this submission.
Results
Across up to 2,639 clinics, mean scores were: Overall CQS (50.10), QIP (37.7), and Clinical Measures (47.3). At a macro-level, Overall CQS correlated strongly and evenly with its primary sub-components: Treatment Process (r = 0.78), QIP (r = 0.77), and Clinical Measures (r = 0.73), validating the composite captures a balanced view of care. At a micro-level, missed treatments and fluid actions positively correlated with each other (0.36) and negatively with clinical adequacy (r = -0.26 and r = -0.29). Within QIP, Hospital Readmissions (Mean: 0.28) acted independently, demonstrating low correlation with the in-center metrics (r = -0.11 to 0.18).
Conclusion
Strong macro-level correlations confirm high-performing facilities execute successfully through the demonstration of consistent performance across operational, clinical, and regulatory categories. The association between patient adherence and adequacy hypothesizes an upstream effect where physiological outcomes are driven by operational performance. Conversely, low correlation between readmissions and dialytic parameters suggests readmissions are likely driven by non-dialysis factors, such as comorbidities, rather than the dialysis prescription itself, a finding that warrants further analysis.
Funding
- Commercial Support – Fresenius Medical Care