ArticleClinical epidemiology2017
Prediction models for the mortality risk in chronic dialysis patients: a systematic review and independent external validation study.
Article in Clinical epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Evaluating and Updating the IMPACT Model to Predict Outcomes in Two Contemporary North American Traumatic Brain Injury Cohorts.Journal of neurotrauma · 2025Trial
- Development of a machine learning-based prediction model for hypothyroidism-associated delirium in elderly hypothyroid patients in the intensive care unit.BMC geriatrics · 2026Article
- Circulating Humanin Improves the Prognostic Accuracy of Cardiovascular Risk Models in Chronic Hemodialysis Patients.Cardiorenal medicine · 2026Article
- Discrete Time Neural Network Models to Address Time-Varying Predictor Importance: An Illustration in Predicting Mortality Over Different Time Horizons.IEEE journal of biomedical and health informatics · 2026Article
- External Validation of a Multivariable Diagnostic Prediction Model for Acute Invasive Fungal Rhinosinusitis in Tertiary Care Settings.International forum of allergy & rhinology · 2026Article
- A machine-learning method for predicting the 1-year risk of death in maintenance hemodialysis patients based on continuous compliance with dialysis quality indicators.BMC nephrology · 2025Article
- Can we predict mortality of older patients with advanced chronic kidney disease?Internal medicine journal · 2025Article
- Development and Validation of Survival Scores and the Assessment of Spatial Trends in End-Stage Kidney Disease Outcomes.Research square · 2025Article
- Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Machine learning and deep learning predictive models for prognosis in patients with atrial fibrillation: a systematic review and meta-analysis.BMJ digital health & AI · 2025Article
- Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study.Renal failure · 2024Article
- Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.Scientific reports · 2024Article
- Nephrology providers' perspective and use of mortality prognostic tools in dialysis patients.BMC nephrology · 2024Article
- Designing an Implementable Clinical Prediction Model for Near-Term Mortality and Long-Term Survival in Patients on Maintenance Hemodialysis.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2024Article
- Short-term and long-term survival in patients with prevalent haemodialysis-an integrated prognostic model: external validation.BMJ supportive & palliative care · 2024Article
- Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients.Scientific reports · 2023Observational
- Patient-focused outcomes after initiation of dialysis for ESRD: mortality, hospitalization and functional impairment.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2023Observational
- Predicting mortality risk in dialysis: Assessment of risk factors using traditional and advanced modeling techniques within the Monitoring Dialysis Outcomes initiative.Hemodialysis international. International Symposium on Home Hemodialysis · 2023Article
- Predicting mortality after start of long-term dialysis-International validation of one- and two-year prediction models.PloS one · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveIn medicine, many more prediction models have been developed than are implemented or used in clinical practice. These models cannot be recommended for clinical use before external validity is established. Though various models to predict mortality in dialysis patients have been published, very few have been validated and none are used in routine clinical practice. The aim of the current study was to identify existing models for predicting mortality in dialysis patients through a review and subsequently to externally validate these models in the same large independent patient cohort, in order to assess and compare their predictive capacities.
methodsA systematic review was performed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. To account for missing data, multiple imputation was performed. The original prediction formulae were extracted from selected studies. The probability of death per model was calculated for each individual within the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD). The predictive performance of the models was assessed based on their discrimination and calibration.
resultsIn total, 16 articles were included in the systematic review. External validation was performed in 1,943 dialysis patients from NECOSAD for a total of seven models. The models performed moderately to well in terms of discrimination, with
conclusionOverall, the performance of the models was poorer in the external validation than in the original population, affirming the importance of external validation. Floege et al's models showed the highest predictive performance. The present study is a step forward in the use of a prediction model as a useful tool for nephrologists, using evidence-based medicine that combines individual clinical expertise, patients' choices, and the best available external evidence.
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