# Risk prediction for cardiovascular events and all-cause mortality in maintenance hemodialysis patients

## TL;DR

This study uses machine learning to predict cardiovascular events and death in patients on hemodialysis, showing better results than traditional methods.

## Contribution

The study introduces machine learning models that outperform Cox regression in predicting outcomes for hemodialysis patients.

## Key findings

- XGBoost was the most accurate model for predicting cardiovascular events with AUC values up to 0.755 at 4 years.
- Random Forest performed best for predicting all-cause mortality with AUC values up to 0.931 at 2 years.
- Machine learning models outperformed traditional Cox regression models in predicting outcomes for hemodialysis patients.

## Abstract

This study is designed to develop predictive models for cardiovascular events (CVE) and all-cause mortality in maintenance hemodialysis (MHD) patients using machine learning (ML) algorithms. Furthermore, we aim to compare the performance of these ML-based models with that of traditional Cox regression models.

We conducted a retrospective study that included 275 patients who underwent MHD treatment from January 1, 2020, to January 1, 2022. We collected comprehensive data on their demographic characteristics, comorbidities, medication history, and baseline laboratory values, and followed up with them throughout the study period. To develop predictive models for CVE and all-cause mortality, we employed several ML algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Naive Bayes Model (NBM). Finally, we compared the predictive accuracy of the ML models with that of Cox regression models by evaluating their respective AUC values.

During a median follow-up period of 50.0 months, 119 patients experienced CVE and 75 patients died. The XGBoost model emerged as the most accurate predictor of CVE. The AUC values for predicting CVE at 1, 2, 3, and 4 years were 0.650, 0.702, 0.742, and 0.755 respectively. The accuracy, F1 score, recall, and precision were 0.731, 0.694, 0.706, and 0.683. Key predictors identified included a history of cardiovascular disease, total iron-binding capacity, body mass index, red blood cell count, mean corpuscular hemoglobin, and serum magnesium levels. For predicting all-cause mortality, the RF model demonstrated the highest performance. The AUC values for predicting all-cause mortality at 1, 2, 3, and 4 years were 0.903, 0.931, 0.882, and 0.862 respectively; the accuracy, F1 score, recall, and precision were 0.796, 0.517, 0.400, and 0.732. Significant predictors included dialysis vintage, post-dialysis β2-microglobulin levels, B-Carboxy-Terminal Peptide of Type I Collagen, total bilirubin, lymphocyte count, lactate dehydrogenase, mean corpuscular hemoglobin concentration, and the use of roxadustat. Across all endpoints, the ML models demonstrated better discrimination than Cox regression models.

Overall, ML models provided a more reliable prognostic assessment than Cox regression models for predicting CVE and all-cause mortality in MHD patients over the observation period.