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. 2025 Apr 25;7(7):101016.
doi: 10.1016/j.xkme.2025.101016. eCollection 2025 Jul.

Predicting Hospitalization and Related Outcomes in Advanced Chronic Kidney Disease: A Systematic Review, External Validation, and Development Study

Roemer J Janse  1 , Jet Milders  1 , Joris I Rotmans  2 , Fergus J Caskey  3 , Marie Evans  4 , Claudia Torino  5 , Maciej Szymczak  6 , Christiane Drechsler  7 , Christoph Wanner  8 , Maria Pippias  3   9 , Antonio Vilasi  5 , Vianda S Stel  10   11 , Nicholas C Chesnaye  10   11 , Kitty J Jager  10   11 , Friedo W Dekker  1 , Merel van Diepen  1 ; EQUAL study investigators
Collaborators, Affiliations

Predicting Hospitalization and Related Outcomes in Advanced Chronic Kidney Disease: A Systematic Review, External Validation, and Development Study

Roemer J Janse et al. Kidney Med. .

Abstract

Rationale & objective: Hospitalization is common in patients with advanced chronic kidney disease (CKD). Predicting hospitalization and related outcomes would be beneficial for hospitals and patients. Therefore, we aimed to (1) give an overview of current prediction models for hospitalization, length of stay, and readmission in patients with advanced CKD; (2) externally validate these models; and (3) develop a new model if no valid models were identified.

Study design: Systematic review, development, and external validation study.

Setting & participants: We were interested in prediction models of hospitalization, length of stay, or readmission for patients with advanced CKD. Our available development and validation data consisted of hemodialysis, peritoneal dialysis, and advanced CKD patients not receiving dialysis from a Dutch dialysis and European advanced CKD cohort.

Selection criteria for studies: We systematically searched PubMed. Studies had to intentionally develop, validate, or update a prediction model in adults with CKD.

Analytical approach: We used the PROBAST for risk of bias assessment. Identified models were externally validated on model discrimination (C-statistic) and calibration (calibration plot, slope, and calibration-in-the-large). We developed a Fine-Gray model for hospitalization within 1 year in patients initiating hemodialysis, accounting for the competing risk of death.

Results: We identified 45 models in 8 studies. The majority were of low quality with a high risk of bias. Due to underreporting and population-specific predictors, we could only validate 3 models. These were poorly calibrated and had poor discrimination. Using multiple modeling strategies, an adequate new model could not be developed.

Limitations: The outcome hospitalization might be too heterogeneous, and we did not have all relevant predictors available.

Conclusions: Hospitalizations are important but difficult to predict for patients with advanced CKD. An improved prediction model should be developed, for example, using a more specific outcome (eg, cardiovascular hospitalizations) and more predictors (eg, patient-reported outcome measures).

Keywords: Algorithm; chronic kidney disease; dialysis; hospital admission; hospitalization; length of stay; prediction; readmission; risk score.

Plain language summary

Hospitalizations often occur in patients with advanced chronic kidney disease. By predicting hospitalization and related outcomes, patients can better prepare for the future and cope with their disease. Therefore, we searched existing literature for existing methods to predict hospitalizations and related outcomes. Although many algorithms exist, they are often not available for use or are not reliable. We then developed our own algorithm to predict hospitalization in the coming year. However, it also did not predict reliably. In this study, we summarize what failed in existing prediction algorithms, what we learned from predicting hospitalization ourselves, and how to proceed to allow reliable predictions of hospitalizations.

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Figures

Figure 1
Figure 1
Flowchart showing inclusion of articles in the systematic review.
Figure 2
Figure 2
Calibration plots showing the predicted versus the observed probabilities of being hospitalized within 1 year. Plots are shown for the model in the development data (A), external validation data targeted at peritoneal dialysis patients (B), and external validation data targeted at patients with advanced chronic kidney disease who are not receiving dialysis (C).
See this image and copyright information in PMC

References

    1. Kovesdy C.P. Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl (2011) 2022;12(1):7–11. - PMC - PubMed
    1. Go A.S., Chertow G.M., Fan D., McCulloch C.E., Hsu C.Y. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351(13):1296–1305. - PubMed
    1. Daratha K.B., Short R.A., Corbett C.F., et al. Risks of subsequent hospitalization and death in patients with kidney disease. Clin J Am Soc Nephrol. 2012;7(3):409–416. - PubMed
    1. Khan S., Amedia C.A., Jr. Economic burden of chronic kidney disease. J Eval Clin Pract. 2008;14(3):422–434. - PubMed
    1. Doshi S., Wish J.B. Strategies to reduce rehospitalization in patients with CKD and kidney failure. Clin J Am Soc Nephrol. 2021;16(2):328–334. - PMC - PubMed

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