Chuemor Panuwat, Rerkasem Kittipan, Tantraworasin Apichat, Khorana Jiraporn, Thammathiwat Theerachai, Pichitsiri Watchara
Department of Surgery, Faculty of Medicine, Naresuan University, Phitsanulok 65000, Thailand.
Department of Surgery and Clinical Surgical Research Centre, Faculty of Medicine, Chiangmai University, Chiangmai 50200, Thailand.
J Clin Med. 2025 Mar 14;14(6):1981. doi: 10.3390/jcm14061981.
: Planned kidney replacement therapy (KRT) proactively selects and prepares appropriate dialysis modalities and ensures timely vascular access-be it arteriovenous or peritoneal-before dialysis is needed. This approach leads to better patient outcomes and fewer complications. We aimed to develop a predictive model using past estimated glomerular filtration rate (eGFR) measurements prior to KRT counseling to estimate individual patients' likelihood of initiating dialysis. In this prognostic prediction study, we retrospectively analyzed data from chronic kidney disease patients who received KRT counseling at Naresuan University Hospital in Thailand. A logistic regression model was developed incorporating the historical eGFR decline over the preceding twelve months (eGFRr) at the time of counseling. The model's performance was compared to the predictive accuracy of using a single eGFR measurement, as commonly practiced in clinical settings. This study included 103 patients who received their first KRT counseling between 1 January 2016 and 31 December 2022. Within one year, 62% initiated their first dialysis session. The eGFRr was a significant predictor of dialysis initiation. Logistic regression identified six key predictors: past eGFRr, age, systolic blood pressure, primary cause of chronic kidney disease, body mass index categories, and serum calcium levels. The predictive model showed good discriminative ability, with an area under the receiver operating characteristic curve of 0.836 (95% CI 0.754-0.918). Our predictive model estimates the likelihood of dialysis initiation, offering valuable decision support insights. Clinical implementation could enhance timely referral and preparation for patients requiring KRT. Prospective validation is needed to confirm its accuracy before clinical use.
计划性肾脏替代治疗(KRT)会主动选择并准备合适的透析方式,并在需要透析之前确保及时建立血管通路——无论是动静脉通路还是腹膜通路。这种方法能带来更好的患者预后并减少并发症。我们旨在利用KRT咨询前过去的估计肾小球滤过率(eGFR)测量值开发一个预测模型,以估计个体患者开始透析的可能性。在这项预后预测研究中,我们回顾性分析了在泰国那黎宣大学医院接受KRT咨询的慢性肾脏病患者的数据。开发了一个逻辑回归模型,纳入咨询时前十二个月的历史eGFR下降情况(eGFRr)。将该模型的性能与临床实践中常用的使用单次eGFR测量的预测准确性进行了比较。本研究纳入了2016年1月1日至2022年12月31日期间接受首次KRT咨询的103例患者。在一年内,62%的患者开始了首次透析。eGFRr是透析开始的一个重要预测因素。逻辑回归确定了六个关键预测因素:过去的eGFRr、年龄、收缩压、慢性肾脏病的主要病因、体重指数类别和血清钙水平。该预测模型显示出良好的判别能力,受试者操作特征曲线下面积为0.836(95%CI 0.754 - 0.918)。我们的预测模型估计了透析开始的可能性,提供了有价值的决策支持见解。临床应用可以加强对需要KRT的患者的及时转诊和准备。在临床使用之前,需要进行前瞻性验证以确认其准确性。