Prediction of all-cause mortality in haemodialysis patients using a Bayesian network. Issue 8 (10th February 2020)

• Record Type: Journal Article
• Authors: Siga, Marleine Mefeugue; Ducher, Michel; Florens, Nans; Roth, Hubert; Mahloul, Nadir; Fouque, Denis; Fauvel, Jean-Pierre
• Is Part Of: Nephrology dialysis transplantation. Volume 35:Issue 8(2020)
• Page Start: 1420
• Page End: 1425
• Publication Date: 2020-02-10
• Subjects: Bayesian network -- epidemiology -- haemodialysis -- mortality -- risk prediction; Nephrology -- Periodicals; Hemodialysis -- Periodicals; Kidneys -- Transplantation -- Periodicals; Hemodialysis; Kidneys -- Transplantation; Nephrology; Periodicals; 616.61
• DOI: 10.1093/ndt/gfz295
• Languages: English
• ISSNs: 0931-0509

Abstract

Abstract: Background: All-cause mortality in haemodialysis (HD) is high, reaching 15.6% in the first year according to the European Renal Association. Methods: A new clinical tool to predict all-cause mortality in HD patients is proposed. It uses a post hoc analysis of data from the prospective cohort study Photo-Graph V3. A total of 35 variables related to patient characteristics, laboratory values and treatments were used as predictors of all-cause mortality. The first step was to compare the results obtained using a logistic regression to those obtained by a Bayesian network. The second step aimed to increase the performance of the best prediction model using synthetic data. Finally, a compromise between performance and ergonomics was proposed by reducing the number of variables to be entered in the prediction tool. Results: Among the 9010 HD patients included in the Photo-Graph V3 study, 4915 incident patients with known medical status at 2 years were analysed. All-cause mortality at 2 years was 34.1%. The Bayesian network provided the most reliable prediction. The final optimized models that used 14 variables had areas under the receiver operating characteristic curves of 0.78 ± 0.01, sensitivity of 72 ± 2%, specificity of 69 ± 2%, predictive positive value of 70 ± 1% and negative predictive value of 71 ± 2% for the prediction of all-cause mortality. Conclusions: Using artificial intelligence methods, a new clinical tool to predict all-cause mortality in incident HD patients is proposed. The latter can be used for research purposes before its external validation at: https://www.hed.cc/? a=twoyearsallcausemortalityhemod&n=2-years%20All-cause%20Mortality%20Hemodialysis.neta .