Department of Nephrology, Peking University Third Hospital, 49 North Garden Rd, Haidian District, Beijing, 100191, People's Republic of China.
Department of Otolaryngology-Head and Neck Surgery, Bill Wilkerson Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Sci Rep. 2022 May 19;12(1):8377. doi: 10.1038/s41598-022-12316-z.
The purpose of this study was to assess the feasibility of machine learning (ML) in predicting the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease (CKD). Data were obtained from a longitudinal CKD cohort. Predictor variables included patients' baseline characteristics and routine blood test results. The outcome of interest was the presence or absence of ESKD by the end of 5 years. Missing data were imputed using multiple imputation. Five ML algorithms, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors were trained and tested using fivefold cross-validation. The performance of each model was compared to that of the Kidney Failure Risk Equation (KFRE). The dataset contained 748 CKD patients recruited between April 2006 and March 2008, with the follow-up time of 6.3 ± 2.3 years. ESKD was observed in 70 patients (9.4%). Three ML models, including the logistic regression, naïve Bayes and random forest, showed equivalent predictability and greater sensitivity compared to the KFRE. The KFRE had the highest accuracy, specificity, and precision. This study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Three ML models with adequate performance and sensitivity scores suggest a potential use for patient screenings. Future studies include external validation and improving the models with additional predictor variables.
本研究旨在评估机器学习 (ML) 在预测慢性肾脏病 (CKD) 患者终末期肾病 (ESKD) 风险中的可行性。数据来自纵向 CKD 队列。预测变量包括患者的基线特征和常规血液检查结果。感兴趣的结局是在 5 年内是否存在 ESKD。使用多重插补法处理缺失数据。使用五重交叉验证训练和测试了包括逻辑回归、朴素贝叶斯、随机森林、决策树和 K-最近邻在内的五种 ML 算法。比较了每个模型的性能与肾衰竭风险方程 (KFRE) 的性能。数据集包含 2006 年 4 月至 2008 年 3 月间招募的 748 名 CKD 患者，随访时间为 6.3±2.3 年。70 名患者（9.4%）出现 ESKD。逻辑回归、朴素贝叶斯和随机森林这三种 ML 模型与 KFRE 相比具有同等的预测能力和更高的敏感性。KFRE 的准确性、特异性和精度最高。本研究表明，基于易于获取的特征，ML 在评估 CKD 预后方面具有可行性。三种 ML 模型具有足够的性能和敏感性评分，表明它们可能适用于患者筛查。未来的研究包括外部验证和使用其他预测变量来改进模型。
