ArticleJournal of preventive medicine and hygiene2020
Predictors of mortality among hemodialysis patients in Hamadan province using random survival forests.
Article in Journal of preventive medicine and hygiene, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mortality Risk Prediction Models for People With Kidney Failure: A Systematic Review.JAMA network open · 2025Pooled it
- Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis.BMC medical informatics and decision making · 2025Article
- Risk factors for the time to development of retinopathy of prematurity in premature infants in Iran: a machine learning approach.BMC ophthalmology · 2024Article
- Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis.Frontiers in pharmacology · 2024Article
- A novel nomogram for predicting mortality risk in young and middle-aged patients undergoing maintenance hemodialysis: a retrospective study.Frontiers in medicine · 2024Article
- Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study.European radiology · 2023Article
- Evaluation of laboratory values affecting mortality of end-stage renal disease patients: a competing risks approach.BMC nephrology · 2023Article
- 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
- Machine learning approaches for the mortality risk assessment of patients undergoing hemodialysis.Therapeutic advances in chronic disease · 2022Article
- Construction data mining methods in the prediction of death in hemodialysis patients using support vector machine, neural network, logistic regression and decision tree.Journal of preventive medicine and hygiene · 2021Article
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4 authors.
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Abstract
backgroundHemodialysis patients are at a high risk for morbidity and mortality. This study aimed to find the predictors of mortality and survival in hemodialysis patients in Hamadan province of Iran.
methodsA number of 785 patients during the entire 10 years were enrolled into this historical cohort study. Data were gathered by a checklist of hospital records. The survival time was the time between the start of hemodialysis treatment to patient's death as the end point. Random survival forests (RSF) method was used to identify the main predictors of survival among the patients.
resultsThe median survival time was 613 days. The number of 376 deaths was occurred. The three most important predictors of survival were hemoglobin, CRP and albumin. RSF method predicted survival better than the conventional Cox-proportional hazards model (out-of-bag C-index of 0.808 for RSF vs. 0.727 for Cox model).
conclusionsWe found that positivity of CRP, low serum albumin and low serum hemoglobin were the top three most important predictors of low survival for HD patients.
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