ArticleIranian journal of public health2016
Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests.
Article in Iranian journal of public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 49 citations in OpenAlex.
- Dynamic survival analysis via a landmarking-gradient boosting approach and its application to kidney transplant data.BMC medical informatics and decision making · 2025Article
- Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients.Renal failure · 2024Article
- Risk factors for the time to development of retinopathy of prematurity in premature infants in Iran: a machine learning approach.BMC ophthalmology · 2024Article
- Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality.Scientific reports · 2024Article
- Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation.JAMIA open · 2024Article
- Impact of Environment on Pain among the Working Poor: Making Use of Random Forest-Based Stratification Tool to Study the Socioecology of Pain Interference.International journal of environmental research and public health · 2024Article
- Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study.European radiology · 2023Article
- Debiased lasso for stratified Cox models with application to the national kidney transplant data.The annals of applied statistics · 2023Article
- Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression.Biomedicines · 2023Article
- Survival Analysis of Oncological Patients Using Machine Learning Method.Healthcare (Basel, Switzerland) · 2022Article
- Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system.BMC bioinformatics · 2022Article
- Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma.Computational and structural biotechnology journal · 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
- Can the Random Forests Model Improve the Power to Predict the Intention of the Elderly in a Community to Participate in a Cognitive Health Promotion Program?Iranian journal of public health · 2021Article
- Pro-inflammatory cytokine polymorphisms and interactions with dietary alcohol and estrogen, risk factors for invasive breast cancer using a post genome-wide analysis for gene-gene and gene-lifestyle interaction.Scientific reports · 2021Article
- Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System.Frontiers in medicine · 2021Article
- Pro-inflammatory cytokine polymorphisms in ONECUT2 and HNF4A and primary colorectal carcinoma: a post genome-wide gene-lifestyle interaction study.American journal of cancer research · 2020Article
- Post Genome-Wide Gene-Environment Interaction Study Using Random Survival Forest: Insulin Resistance, Lifestyle Factors, and Colorectal Cancer Risk.Cancer prevention research (Philadelphia, Pa.) · 2019Article
- Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method.BMC medical informatics and decision making · 2019Article
- The effects of genetic variants related to insulin metabolism pathways and the interactions with lifestyles on colorectal cancer risk.Menopause (New York, N.Y.) · 2019Observational
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKidney transplantation is the best alternative treatment for end-stage renal disease. Several studies have been devoted to investigate predisposing factors of graft rejection. However, there is inconsistency between the results. The objective of the present study was to utilize an intuitive and robust approach for variable selection, random survival forests (RSF), and to identify important risk factors in kidney transplantation patients.
methodsThe data set included 378 patients with kidney transplantation obtained through a historical cohort study in Hamadan, western Iran, from 1994 to 2011. The event of interest was chronic nonreversible graft rejection and the duration between kidney transplantation and rejection was considered as the survival time. RSF method was used to identify important risk factors for survival of the patients among the potential predictors of graft rejection.
resultsThe mean survival time was 7.35±4.62 yr. Thirty-seven episodes of rejection were occurred. The most important predictors of survival were cold ischemic time, recipient's age, creatinine level at discharge, donors' age and duration of hospitalization. RSF method predicted survival better than the conventional Cox-proportional hazards model (out-of-bag C-index of 0.965 for RSF vs. 0.766 for Cox model and integrated Brier score of 0.081 for RSF vs. 0.088 for Cox model).
conclusionA RSF model in the kidney transplantation patients outperformed traditional Cox-proportional hazard model. RSF is a promising method that may serve as a more intuitive approach to identify important risk factors for graft rejection.
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27057518PMC4822390W2270370899What OpenQuestion holds
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.