Trial reportScientific reports2020
Prediction of the Mortality Risk in Peritoneal Dialysis Patients using Machine Learning Models: A Nation-wide Prospective Cohort in Korea.
Trial report in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Mortality Risk Prediction Models for People With Kidney Failure: A Systematic Review.JAMA network open · 2025Pooled it
- Artificial intelligence and machine learning in peritoneal dialysis: a systematic review of clinical outcomes and predictive modeling.International urology and nephrology · 2024Pooled it
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Prediction of Five-Year Mortality Risk of Chronic Kidney Disease Using Artificial Intelligence-Based Models: A Retrospective Study.Health science reports · 2026Article
- Peritoneal dialysis effluent biomarkers from a multi-omics and artificial intelligence perspective: advances and challenges.Clinical kidney journal · 2026Review
- Decoding Natural Language Processing and Large Language Models in Kidney Health.Clinical journal of the American Society of Nephrology : CJASN · 2026Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Biomarker and clinical data-based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal study.BMJ health & care informatics · 2025Article
- Optimizing CAPD Patient Monitoring Through Automated Vs Rule-Based Artificial Intelligence: A Systematic Comparative Review.International journal of nephrology and renovascular disease · 2025Review
- Assessing biomarker trajectories for mortality risk in peritoneal dialysis: A focus on multivariate joint modeling.PloS one · 2025Article
- Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.Scientific reports · 2024Article
- Machine learning for identification of short-term all-cause and cardiovascular deaths among patients undergoing peritoneal dialysis.Clinical kidney journal · 2024Article
- Machine learning algorithms for the prediction of adverse prognosis in patients undergoing peritoneal dialysis.BMC medical informatics and decision making · 2024Article
- Nephrology: a flourishing field with plentiful emerging topics.Frontiers in medicine · 2024Article
- Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients.Patterns (New York, N.Y.) · 2023Article
- Artificial intelligence in peritoneal dialysis: general overview.Renal failure · 2022Article
- Article
- Synchrony of biomarker variability indicates a critical transition: Application to mortality prediction in hemodialysis.iScience · 2022Article
- Article
- Treatment Strategy for Dialysis Patient with Urothelial Carcinoma.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Herein, we aim to assess mortality risk prediction in peritoneal dialysis patients using machine-learning algorithms for proper prognosis prediction. A total of 1,730 peritoneal dialysis patients in the CRC for ESRD prospective cohort from 2008 to 2014 were enrolled in this study. Classification algorithms were used for prediction of N-year mortality including neural network. The survival hazard ratio was presented by machine-learning algorithms using survival statistics and was compared to conventional algorithms. A survival-tree algorithm presented the most accurate prediction model and outperformed a conventional method such as Cox regression (concordance index 0.769 vs 0.745). Among various survival decision-tree models, the modified Charlson Comorbidity index (mCCI) was selected as the best predictor of mortality. If peritoneal dialysis patients with high mCCI (>4) were aged ≥70.5 years old, the survival hazard ratio was predicted as 4.61 compared to the overall study population. Among the various algorithm using longitudinal data, the AUC value of logistic regression was augmented at 0.804. In addition, the deep neural network significantly improved performance to 0.841. We propose machine learning-based final model, mCCI and age were interrelated as notable risk factors for mortality in Korean peritoneal dialysis patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.