Predicting kidney transplant survival using tree-based modeling.
ABSTRACT Predicting the outcome of kidney transplantation is clinically important and computationally challenging. The goal of this project was to develop the models predicting probability of kidney allograft survival at 1, 3, 5, 7, and 10 years. Kidney transplant data from the United States Renal Data System (January 1, 1990, to December 31, 1999, with the follow-up through December 31, 2000) were used (n = 92,844). Independent variables included recipient demographic and anthropometric data, end-stage renal disease course, comorbidity information, donor data, and transplant procedure variables. Tree-based models predicting the probability of the allograft survival were generated using roughly two-thirds of the data (training set), with the remaining one-third left aside to be used for models validation (testing set). The prediction of the probability of graft survival in the independent testing dataset achieved a good correlation with the observed survival (r = 0.94, r = 0.98, r = 0.99, r = 0.93, and r = 0.98) and relatively high areas under the receiving operator characteristic curve (0.63, 0.64, 0.71, 0.82, and 0.90) for 1-, 3-, 5-, 7-, and 10-year survival prediction, respectively. The models predicting the probability of 1-, 3-, 5-, 7-, and 10-year allograft survival have been validated on the independent dataset and demonstrated performance that may suggest implementation in clinical decision support system.
SourceAvailable from: Viktor Y Dombrovskiy[Show abstract] [Hide abstract]
ABSTRACT: Background-The Kidney Transplant Morbidity Index (KTMI) is a novel prognostic morbidity index to help determine the impact that pretransplant comorbid conditions have on transplant outcome.Objective-To use national data to validate the KTMI.Design-Retrospective analysis of the Organ Procurement and Transplant Network/United Network for Organ Sharing database.Setting and Participants-The study sample consisted of 100 261 adult patients who received a kidney transplant between 2000 and 2008.Main Outcome Measure-Kaplan-Meier survival curves were used to demonstrate 3-year graft and patient survival for each KTMI score. Cox proportional hazards regression models were created to determine hazards for 3-year graft failure and patient mortality for each KTMI score.Results-A sequential decrease in graft survival (0 = 91.2%, 1 = 88.2%, 2 = 85.4%, 3 = 81.7%, 4 = 77.8%, 5 = 74.0%, 6 = 69.8%, and ≥7 = 68.7) and patient survival (0 = 98.2%, 1 = 96.6%, 2 = 93.7%, 3 = 89.7%, 4 = 84.8%, 5 = 80.8%, 6 = 76.0%, and ≥7 = 74.7%) is seen as KTMI scores increase. The differences in graft and patient survival between KTMI scores are all significant (P< .001) except between 6 and ≥7. Multivariate regression analysis reveals that KTMI is an independent predictor of higher graft failure and patient mortality rates and that risk increases as KTMI scores increase.Conclusion-The KTMI strongly predicts graft and patient survival by using pretransplant comorbid conditions; therefore, this easy-to-use tool can aid in determining outcome risk and transplant candidacy before listing, particularly in candidates with multiple comorbid conditions.Progress in transplantation (Aliso Viejo, Calif.) 03/2015; 25(1):70-6. DOI:10.7182/pit2015462 · 0.69 Impact Factor
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ABSTRACT: We consider a mathematical model for HIV/AIDS that incorporates staged progression and amelioration. Amelioration as a result of HAART treatment is allowed to occur across any number of stages. The global dynamics are completely determined by the basic reproduction number R(0). If R(0) <= 1, then the disease-free equilibrium (DFE) is globally asymptotically stable and the disease always dies out. If R(0) > 1, DFE is unstable and a unique endemic equilibrium (EE) is globally asymptotically stable, and the disease persists at the endemic equilibrium. The proof of global stability utilizes a global Lyapunov function. Crown CopyrightNonlinear Analysis Real World Applications 10/2011; DOI:10.1016/j.nonrwa.2011.02.021 · 2.34 Impact Factor
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ABSTRACT: Renal transplantation has dramatically improved the survival rate of hemodialysis patients. However, with a growing proportion of marginal organs and improved immunosuppression, it is necessary to verify that the established allocation system, mostly based on human leukocyte antigen matching, still meets today's needs. The authors turn to machine-learning techniques to predict, from donor-recipient data, the estimated glomerular filtration rate (eGFR) of the recipient 1 year after transplantation. The patient's eGFR was predicted using donor-recipient characteristics available at the time of transplantation. Donors' data were obtained from Eurotransplant's database, while recipients' details were retrieved from Charité Campus Virchow-Klinikum's database. A total of 707 renal transplantations from cadaveric donors were included. Two separate datasets were created, taking features with <10% missing values for one and <50% missing values for the other. Four established regressors were run on both datasets, with and without feature selection. The authors obtained a Pearson correlation coefficient between predicted and real eGFR (COR) of 0.48. The best model for the dataset was a Gaussian support vector machine with recursive feature elimination on the more inclusive dataset. All results are available at http://transplant.molgen.mpg.de/. For now, missing values in the data must be predicted and filled in. The performance is not as high as hoped, but the dataset seems to be the main cause. Predicting the outcome is possible with the dataset at hand (COR=0.48). Valuable features include age and creatinine levels of the donor, as well as sex and weight of the recipient.Journal of the American Medical Informatics Association 08/2011; 19(2):255-62. DOI:10.1136/amiajnl-2010-000004 · 3.93 Impact Factor