Granal Maelys, Brokhes-Le Calvez Sophie, Dimitrov Yves, Chantrel François, Borni-Duval Claire, Muller Clotilde, Délia May, Krummel Thierry, Hannedouche Thierry, Ducher Micher, Fauvel Jean-Pierre
Department of Nephrology, Hospices Civils de Lyon, Hôpital Edouard Herriot, UMR 5558 CNRS Lyon, Université Lyon 1, Lyon, France.
Renal Research Division, AURAL Strasbourg, Strasbourg, France.
Clin Kidney J. 2024 Apr 12;17(6):sfae095. doi: 10.1093/ckj/sfae095. eCollection 2024 Jun.
In recent years, a number of predictive models have appeared to predict the risk of medium-term mortality in hemodialysis patients, but only one, limited to patients aged over 70 years, has undergone sufficiently powerful external validation. Recently, using a national learning database and an innovative approach based on Bayesian networks and 14 carefully selected predictors, we have developed a clinical prediction tool to predict all-cause mortality at 2 years in all incident hemodialysis patients. In order to generalize the results of this tool and propose its use in routine clinical practice, we carried out an external validation using an independent external validation database.
A regional, multicenter, observational, retrospective cohort study was conducted to externally validate the tool for predicting 2-year all-cause mortality in incident and prevalent hemodialysis patients. This study recruited a total of 142 incident and 697 prevalent adult hemodialysis patients followed up in one of the eight Association pour l'Utilisation du Rein Artificiel dans la région Lyonnaise (AURAL) Alsace dialysis centers.
In incident patients, the 2-year all-cause mortality prediction tool had an area under the receiver curve (AUC-ROC) of 0.73, an accuracy of 65%, a sensitivity of 71% and a specificity of 63%. In prevalent patients, the performance for the external validation were similar in terms of AUC-ROC, accuracy and specificity, but was lower in term of sensitivity.
The tool for predicting all-cause mortality at 2 years, developed using a Bayesian network and 14 routinely available explanatory variables, obtained satisfactory external validation in incident patients, but sensitivity was insufficient in prevalent patients.
近年来，出现了一些预测模型来预测血液透析患者的中期死亡风险，但只有一个仅限于70岁以上患者的模型经过了足够强大的外部验证。最近，我们利用一个国家学习数据库以及基于贝叶斯网络和14个精心挑选的预测因子的创新方法，开发了一种临床预测工具，用于预测所有新发血液透析患者2年的全因死亡率。为了推广该工具的结果并提议在常规临床实践中使用，我们使用一个独立的外部验证数据库进行了外部验证。
进行了一项区域性、多中心、观察性、回顾性队列研究，以外部验证用于预测新发和现存血液透析患者2年全因死亡率的工具。本研究共招募了142例新发和697例现存成年血液透析患者，这些患者在里昂地区人工肾使用协会（AURAL）阿尔萨斯的八个透析中心之一接受随访。
在新发患者中，2年全因死亡率预测工具的受试者工作特征曲线下面积（AUC-ROC）为0.73，准确率为65%，灵敏度为71%，特异度为63%。在现存患者中，外部验证在AUC-ROC、准确率和特异度方面表现相似，但在灵敏度方面较低。
使用贝叶斯网络和14个常规可用的解释变量开发的2年全因死亡率预测工具，在新发患者中获得了令人满意的外部验证，但在现存患者中灵敏度不足。
