Tran Dung N T, Ducher Michel, Fouque Denis, Fauvel Jean-Pierre
Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS Lyon, Université Claude Bernard Lyon 1, 43 Boulevard du 11 Novembre 1918, 69100, Lyon, Villeurbanne, France.
Service de Néphrologie, Hospices Civils de Lyon, Hôpital Edouard Herriot, 69003, Lyon, France.
J Nephrol. 2024 Nov;37(8):2267-2274. doi: 10.1007/s40620-024-02011-9. Epub 2024 Jul 4.
Chronic kidney disease (CKD) is associated with increased mortality. Individual mortality prediction could be of interest to improve individual clinical outcomes. Using an independent regional dataset, the aim of the present study was to externally validate the recently published 2-year all-cause mortality prediction tool developed using machine learning.
A validation dataset of stage 4 or 5 CKD outpatients was used. External validation performance of the prediction tool at the optimal cutoff-point was assessed by the area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and specificity. A survival analysis was then performed using the Kaplan-Meier method.
Data of 527 outpatients with stage 4 or 5 CKD were analyzed. During the 2 years of follow-up, 91 patients died and 436 survived. Compared to the learning dataset, patients in the validation dataset were significantly younger, and the ratio of deceased patients in the validation dataset was significantly lower. The performance of the prediction tool at the optimal cutoff-point was: AUC-ROC = 0.72, accuracy = 63.6%, sensitivity = 72.5%, and specificity = 61.7%. The survival curves of the predicted survived and the predicted deceased groups were significantly different (p < 0.001).
The 2-year all-cause mortality prediction tool for patients with stage 4 or 5 CKD showed satisfactory discriminatory capacity with emphasis on sensitivity. The proposed prediction tool appears to be of clinical interest for further development.
慢性肾脏病（CKD）与死亡率增加相关。个体死亡率预测可能有助于改善个体临床结局。本研究旨在利用一个独立的区域数据集，对最近发表的使用机器学习开发的2年全因死亡率预测工具进行外部验证。
使用4期或5期CKD门诊患者的验证数据集。通过受试者操作特征曲线下面积（AUC-ROC）、准确性、敏感性和特异性评估预测工具在最佳临界点的外部验证性能。然后使用Kaplan-Meier方法进行生存分析。
分析了527例4期或5期CKD门诊患者的数据。在2年的随访期间，91例患者死亡，436例存活。与学习数据集相比，验证数据集中的患者明显更年轻，验证数据集中死亡患者的比例明显更低。预测工具在最佳临界点的性能为：AUC-ROC = 0.72，准确性 = 63.6%，敏感性 = 72.5%，特异性 = 61.7%。预测存活组和预测死亡组的生存曲线有显著差异（p < 0.001）。
4期或5期CKD患者的2年全因死亡率预测工具显示出令人满意的区分能力，尤其注重敏感性。所提出的预测工具似乎具有进一步开发的临床意义。
