Hospices Civils de Lyon, Service de Néphrologie, Hôpital Edouard Herriot, Université Claude Bernard Lyon 1, CEDEX, F-69437 Lyon, France.
Pharmacie, Hospices Civils de Lyon, EMR3738 Ciblage Thérapeutique en Oncologie, Université Claude Bernard Lyon 1, CEDEX, F-69437 Lyon, France.
Nutrients. 2022 Jun 10;14(12):2419. doi: 10.3390/nu14122419.
There is a need for a reliable and validated method to estimate dietary potassium intake in chronic kidney disease (CKD) patients to improve prevention of cardiovascular complications. This study aimed to develop a clinical tool to estimate potassium intake using 24-h urinary potassium excretion as a surrogate of dietary potassium intake in this high-risk population. Data of 375 adult CKD-patients routinely collecting their 24-h urine were included to develop a prediction tool to estimate potassium diet. The prediction tool was built from a random sample of 80% of patients and validated on the remaining 20%. The accuracy of the prediction tool to classify potassium diet in the three classes of potassium excretion was 74%. Surprisingly, the variables related to potassium consumption were more related to clinical characteristics and renal pathology than to the potassium content of the ingested food. Artificial intelligence allowed to develop an easy-to-use tool for estimating patients' diets in clinical practice. After external validation, this tool could be extended to all CKD-patients for a better clinical and therapeutic management for the prevention of cardiovascular complications.
需要一种可靠且经过验证的方法来估计慢性肾脏病 (CKD) 患者的钾摄入量，以改善心血管并发症的预防。本研究旨在开发一种临床工具，使用 24 小时尿钾排泄来估计高危人群的饮食钾摄入量。纳入了 375 名常规收集 24 小时尿液的成年 CKD 患者的数据，以开发一种预测工具来估计钾饮食。该预测工具是从 80%的患者的随机样本中构建的，并在剩余的 20%的患者中进行了验证。该预测工具对三种尿钾排泄水平的钾饮食分类的准确性为 74%。令人惊讶的是，与钾摄入量相关的变量与临床特征和肾脏病理的关系比与摄入食物的钾含量的关系更密切。人工智能允许开发一种用于在临床实践中估计患者饮食的简单易用的工具。经过外部验证后，该工具可扩展至所有 CKD 患者，以更好地进行临床和治疗管理，预防心血管并发症。
