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Predictive models for patients treated with or evaluated for peritoneal dialysis: a scoping review protocol
Jakub Ruszkowski1,2, Sudha Ramakrishnan3, Julia Strzelec4
1Department of Nephrology, Transplantology and Internal Medicine, Faculty of Medicine, Medical University of Gdańsk, Gdańsk, Poland.
JBI Evidence Synthesis
|May 8, 2026
Summary
This scoping review maps research on predictive models for peritoneal dialysis outcomes. It aims to identify validated tools for personalized patient care in chronic kidney disease management.
Area of Science:
- Nephrology and Clinical Epidemiology
- Development and validation of predictive models for patient outcomes
Background:
- Chronic kidney disease (CKD) affects over 840 million globally; peritoneal dialysis (PD) is a key home-based therapy.
- Personalized prognosis is crucial as individual patient outcomes may favor specific dialysis modalities.
- Current clinical practice often relies on experience over evidence-based prediction models, necessitating an evaluation of available tools.
Purpose of the Study:
- To systematically map existing research on predictive models for clinical and patient-reported outcomes in peritoneal dialysis.
- To identify and evaluate the development, validation, and clinical applicability of these predictive models.
Main Methods:
- A comprehensive scoping review methodology will be employed, including extensive database searches (MEDLINE, Embase, Web of Science, Scopus) and gray literature.
- Two independent reviewers will screen titles/abstracts and assess full texts against predefined eligibility criteria.
- Data extraction and synthesis will utilize established frameworks (CHARMS, TRIPOD+AI, PROBAST, PROGRESS-Plus, SON-PD) for a descriptive quantitative and narrative analysis.
Main Results:
- The review will identify and categorize predictive models for PD-relevant outcomes.
- It will assess the validation status, clinical accessibility, and identify gaps in research and population representation.
- Outcome mapping and predictor database development will be key components of the synthesis.
Conclusions:
- This review will provide a comprehensive overview of the current landscape of predictive models in peritoneal dialysis.
- It aims to guide the clinical implementation of validated tools to enhance personalized patient care and decision-making in CKD management.
- Identifying research gaps will inform future development of robust prediction models for peritoneal dialysis patients.
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