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Chronic kidney disease currently affects over 840 million people globally. Peritoneal dialysis serves as a critical home-based kidney replacement therapy for many of these individuals. While long-term survival is often equivalent between dialysis modalities, individual patient outcomes can vary. Consequently, peritoneal dialysis predictive models are becoming essential for tailoring personalized treatment plans. However, many clinicians still rely on personal experience rather than evidence-based tools. Therefore, a new scoping review protocol has been established to map and evaluate available research in this field.
Specifically, the review will include patients who are receiving or considering the initiation of peritoneal dialysis. Researchers aim to evaluate the development and validation of models that utilize multiple clinical predictors. This scope encompasses various healthcare settings without geographic or language restrictions. Furthermore, the analysis will use the Standardised Outcomes in Nephrology-Peritoneal Dialysis framework to categorize findings. Additionally, the team will assess the clinical accessibility of these tools to identify gaps in current population data.
Moreover, the reviewers will utilize standardized data extraction forms including TRIPOD+AI and PROBAST. This rigorous methodology ensures a high level of accuracy when evaluating the predictive performance of each model. For instance, the review will highlight validated tools that are ready for clinical implementation. By identifying these resources, the study aims to move nephrology toward a more data-driven approach. Notably, this evidence-based strategy could significantly improve long-term prognosis for patients undergoing home-based dialysis.
Predictive models allow for personalized outcome assessments. Consequently, this helps doctors tailor treatment plans to an individual's specific risk profile rather than relying solely on generalized clinical experience.
Commonly tracked outcomes include technique failure, patient survival, and patient-reported quality of life. The review specifically categorizes these using standardized nephrology frameworks.
Many current guidelines do not fully reflect available research. This review bridges the gap by identifying validated tools and guiding their practical use in any healthcare setting.
Disclaimer: This content is for informational and educational purposes only. It does not constitute medical advice or a substitute for professional healthcare consultation. Refer to the latest local and national guidelines for clinical practice.
References
Ruszkowski J et al. Predictive models for patients treated with or evaluated for peritoneal dialysis: a scoping review protocol. JBI Evid Synth. 2026 May 07. doi: 10.11124/JBIES-25-00478. PMID: 42100845.
Alfano G et al. Artificial intelligence and machine learning in peritoneal dialysis: a systematic review of clinical outcomes and predictive modeling. Life (Basel). 2022;12(12):1992.
Perl J et al. The Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS): Unifying efforts to inform practice and improve global outcomes in peritoneal dialysis. Perit Dial Int. 2016;36(3):297-307.
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This scoping review protocol aims to map and evaluate predictive models for peritoneal dialysis to improve personalized outcome prediction and clinical care...
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