对慢性肾病 3-5 期患者开始维持性透析的个性化预测:使用机器学习方法的多中心研究

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
Anh Trung Hoang, Phung-Anh Nguyen, Thanh Phuc Phan, Gia Tuyen Do, Huu Dung Nguyen, I-Jen Chiu, Chu-Lin Chou, Yu-Chen Ko, Tzu-Hao Chang, Chih-Wei Huang, Usman Iqbal, Yung-Ho Hsu, Mai-Szu Wu, Chia-Te Liao
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引用次数: 0

摘要

背景 慢性肾脏病(CKD)3-5 期患者开始维持性透析的最佳时机具有挑战性。本研究旨在开发并验证一种机器学习(ML)模型,用于早期个性化预测 CKD 3-5 期患者在 1 年和 3 年内开始维持性透析的时间。方法 使用台北医学大学临床研究数据库中的回顾性电子健康记录数据。研究对象为 2008 年至 2017 年间新确诊的 CKD 3-5 期患者。观察期从确诊为 CKD 3-5 期开始,直至开始维持性透析或最长随访 3 年。利用患者人口统计学特征、合并症、实验室数据和药物建立了预测模型。数据集分为训练集和测试集,以确保模型性能稳定。模型评估指标包括曲线下面积(AUC)、灵敏度、特异性、阳性预测值、阴性预测值和 F1 分数。结果 在模型开发的 1 年和 3 年中,分别纳入了 6123 名和 5279 名患者。人工神经网络在预测 1 年和 3 年内开始维持性透析方面表现更佳,AUC 值分别为 0.96 和 0.92。基线估计肾小球滤过率和白蛋白尿等重要特征对预测模型有显著贡献。结论 本研究证明了多变量方法在开发高度预测模型方面的有效性,该模型可用于估计 CKD 3-5 期患者开始维持性透析的时间。这些发现对个性化治疗策略具有重要意义,有助于改善临床决策,并有可能提高患者的治疗效果。数据可能来自第三方,不对外公开。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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. Data may be obtained from a third party and are not publicly available.
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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