Prediction of all-cause mortality in haemodialysis patients using a Bayesian network.

Abstract

All-cause mortality in haemodialysis (HD) is high, reaching 15.6% in the first year according to the European Renal Association. 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. 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. 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.