#6569 PREDICTION OF ALL-CAUSE MORTALITY FOR CHRONIC KIDNEY DISEASE PATIENTS USING FOUR MODELS OF MACHINE LEARNING

Abstract Background and Aims Prediction tools developed from general population data to predict all-cause mortality are not adapted to patients with chronic kidney disease (CKD), as this population has a higher risk of mortality. This study aimed to create a clinical prediction tool with good predictive performance to predict 2-year all-cause mortality in patients with stage 4 or 5 CKD using an innovative approach with machine learning models and a synthetic population. Method The national, observational, descriptive and prospective Photo-Graphe 3 study was used to create the learning database. Four models (i) logistic regression; (ii) deep learning; (iii) random forest and (iv) Bayesian network were used to create four prediction tools. The performance of each model, including the area under the receiver operating characteristic curve (AUC-ROC) value, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), was evaluated and compared using 10-fold cross-validation. The prediction tool with the best performance was selected and optimized using a synthetic population and the explanatory variables most related to mortality. The synthetic population was created by the Bayesian imputation method. The variables most associated with 2-year all-cause mortality were determined by compromising the number of variables selected and the AUC-ROC value when successively adding the variable according to its percentage variance value. The performance of the optimized prediction tool in predicting 2-year mortality was then evaluated by 10-fold cross validation. Results All prediction tools except the one developed with the random forest model showed satisfactory discrimination (AUC-ROC ≥ 0.70). Overall, the prediction tools developed using the Bayesian network and logistic regression tended to have better performance. Although not significantly different from logistic regression, the prediction tool developed using the Bayesian network was chosen for further development because of its advantages. From the 534 patients in the study population, a synthetic population of 2000 patients (survivor:death ratio = 1:1) was created. The seven most informative variables ranked in descending order were: age, ESA, CV history, smoking status, 25-OH vitamin D level, PTH level, and ferritin level (Figure 1). The optimized clinical prediction tool had satisfactory internal performance. The mean accuracy was 73.8% (SD = 3.6), the mean AUC-ROC was 0.81 (SD = 0.03), the mean sensitivity was 71.0% (SD = 5.4), the mean specificity was 76.5% (SD = 3.0), the mean PPV was 75.1% (SD = 3.2), and the mean NPV was 72.6% (SD = 4.1). Conclusion Bayesian network model was used to create a seven-variable prediction tool to predict the 2-year all-cause mortality in patients with stage 4–5 CKD. This prediction tool has a satisfactory performance. Prior to external validation, the proposed prediction tool can be used at: https://bit.ly/3JPhrkh for research purpose. This is the first time that a synthetic population has been applied to create predictive models. In this study, the synthetic population showed its advantage in dealing with sample size issues in developing predictive models and in improving the performance of the prediction tool in terms of sensitivity and PPV.