PDF(Pubmed)
Abstract：
BACKGROUND: Chronic kidney disease (CKD) requires accurate prediction of renal replacement therapy (RRT) initiation risk. This study developed deep learning algorithms (DLAs) to predict RRT risk in CKD patients by incorporating medical history and prescriptions in addition to biochemical investigations.
METHODS: A multi-centre retrospective cohort study was conducted in three major hospitals in Hong Kong. CKD patients with an eGFR < 30ml/min/1.73m2 were included. DLAs of various structures were created and trained using patient data. Using a test set, the DLAs\' predictive performance was compared to Kidney Failure Risk Equation (KFRE).
RESULTS: DLAs outperformed KFRE in predicting RRT initiation risk (CNN + LSTM + ANN layers ROC-AUC = 0.90; CNN ROC-AUC = 0.91; 4-variable KFRE: ROC-AUC = 0.84; 8-variable KFRE: ROC-AUC = 0.84). DLAs accurately predicted uncoded renal transplants and patients requiring dialysis after 5 years, demonstrating their ability to capture non-linear relationships.
CONCLUSIONS: DLAs provide accurate predictions of RRT risk in CKD patients, surpassing traditional methods like KFRE. Incorporating medical history and prescriptions improves prediction performance. While our findings suggest that DLAs hold promise for improving patient care and resource allocation in CKD management, further prospective observational studies and randomized controlled trials are necessary to fully understand their impact, particularly regarding DLA interpretability, bias minimization, and overfitting reduction. Overall, our research underscores the emerging role of DLAs as potentially valuable tools in advancing the management of CKD and predicting RRT initiation risk.
METHODS: A multi-centre retrospective cohort study was conducted in three major hospitals in Hong Kong. CKD patients with an eGFR < 30ml/min/1.73m2 were included. DLAs of various structures were created and trained using patient data. Using a test set, the DLAs\' predictive performance was compared to Kidney Failure Risk Equation (KFRE).
RESULTS: DLAs outperformed KFRE in predicting RRT initiation risk (CNN + LSTM + ANN layers ROC-AUC = 0.90; CNN ROC-AUC = 0.91; 4-variable KFRE: ROC-AUC = 0.84; 8-variable KFRE: ROC-AUC = 0.84). DLAs accurately predicted uncoded renal transplants and patients requiring dialysis after 5 years, demonstrating their ability to capture non-linear relationships.
CONCLUSIONS: DLAs provide accurate predictions of RRT risk in CKD patients, surpassing traditional methods like KFRE. Incorporating medical history and prescriptions improves prediction performance. While our findings suggest that DLAs hold promise for improving patient care and resource allocation in CKD management, further prospective observational studies and randomized controlled trials are necessary to fully understand their impact, particularly regarding DLA interpretability, bias minimization, and overfitting reduction. Overall, our research underscores the emerging role of DLAs as potentially valuable tools in advancing the management of CKD and predicting RRT initiation risk.
摘要：
背景：慢性肾脏病（CKD）需要准确预测肾脏替代疗法（RRT）的启动风险。这项研究开发了深度学习算法（DLA），通过结合病史和处方以及生化研究来预测CKD患者的RRT风险。
方法：一项多中心回顾性队列研究在香港三家主要医院进行。包括eGFR<30ml/min/1.73m2的CKD患者。使用患者数据创建和训练各种结构的DLA。使用测试集，将DLA预测性能与肾衰竭风险方程(KFRE)进行比较。
结果：DLA在预测RRT起始风险方面优于KFRE（CNN+LSTM+ANN层ROC-AUC=0.90；CNNROC-AUC=0.91；4变量KFRE：ROC-AUC=0.84；8变量KFRE：ROC-AUC=0.84）。DLA准确预测5年后未编码的肾移植和需要透析的患者，展示了他们捕捉非线性关系的能力。
结论：DLA可以准确预测CKD患者的RRT风险，超越KFRE等传统方法。合并病史和处方可提高预测性能。虽然我们的研究结果表明，DLA有望改善CKD管理中的患者护理和资源分配，进一步的前瞻性观察研究和随机对照试验是必要的，以充分了解其影响，特别是关于DLA的可解释性，偏置最小化，和过拟合减少。总的来说,我们的研究强调了DLA作为推进CKD管理和预测RRT起始风险的潜在有价值工具的新兴作用.
方法：一项多中心回顾性队列研究在香港三家主要医院进行。包括eGFR<30ml/min/1.73m2的CKD患者。使用患者数据创建和训练各种结构的DLA。使用测试集，将DLA预测性能与肾衰竭风险方程(KFRE)进行比较。
结果：DLA在预测RRT起始风险方面优于KFRE（CNN+LSTM+ANN层ROC-AUC=0.90；CNNROC-AUC=0.91；4变量KFRE：ROC-AUC=0.84；8变量KFRE：ROC-AUC=0.84）。DLA准确预测5年后未编码的肾移植和需要透析的患者，展示了他们捕捉非线性关系的能力。
结论：DLA可以准确预测CKD患者的RRT风险，超越KFRE等传统方法。合并病史和处方可提高预测性能。虽然我们的研究结果表明，DLA有望改善CKD管理中的患者护理和资源分配，进一步的前瞻性观察研究和随机对照试验是必要的，以充分了解其影响，特别是关于DLA的可解释性，偏置最小化，和过拟合减少。总的来说,我们的研究强调了DLA作为推进CKD管理和预测RRT起始风险的潜在有价值工具的新兴作用.
