Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach.

BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5. METHODS: Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3-5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3-5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed. RESULTS: A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model. CONCLUSION: This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3-5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.

Contact Laxative Use and the Risk of Arteriovenous Fistula Maturation Failure in Patients Undergoing Hemodialysis: A Multi-Center Cohort Study.

Laxatives are commonly prescribed for constipation management; however, they are recognized as an independent factor associated with cardiovascular diseases. Arteriovenous fistula (AVF) is the closest to the ideal model of hemodialysis (HD) vascular access and part of the cardiovascular system. Our study aims to explore the association of contact laxative use with AVF maturation outcomes in patients undergoing HD. We conducted a multi-center cohort study of 480 contact laxative users and 472 non-users who had undergone initial AVF creation. All patients were followed until the outcomes of AVF maturation were confirmed. Multivariable logistic regression models were performed to evaluate the risk of AVF maturation failure imposed by laxatives. Here, we found that patients who used contact laxatives were significantly associated with an increased risk of AVF maturation failure compared to non-users (adjusted odds ratio, 1.64; p = 0.003). Notably, the risk of AVF maturation failure increased when increasing their average daily doses and cumulative treatment days. In conclusion, our study found a significant dose- and duration-dependent relationship between contact laxative use and an increased risk of AVF maturation failure. Thus, laxatives should be prescribed with caution in this population. Further studies are needed to validate these observations and investigate the potential mechanisms.