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논문분류	춘계학술대회 초록집
제목	Machine learning approach to predict dialysis initiation in CKD stage 3-5 patients: a multi-center study
저자	Chia-Te Liao
출판정보	2024; 2024(1):
키워드
초록	Objectives: Optimal timing for initiating maintenance dialysis in stage 3-5 chronic kidney disease (CKD) patients is challenging. This study aimed to develop and validate a machine-learning (ML) model for early personalized prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among stage 3-5 CKD patients. Methods: Retrospective electronic health record data from the Taipei Medical University clinical research database were utilized. Newly diagnosed advanced CKD patients between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stage 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 6,123 and 5,279 patients were included for 1-year and 3-year of the model development. The artificial neural network (ANN) demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively (Table 1). Important features such as baseline estimated glomerular filtration rate (eGFR) and albuminuria significantly contributed to the predictive model (Figure 1). Conclusions: This study demonstrates the efficacy of a machine learning approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with advanced CKD. These findings have important implications for personalized treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.
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