Gibertoni Dino, Rucci Paola, Mandreoli Marcora, Corradini Mattia, Martelli Davide, Russo Giorgia, Mancini Elena, Santoro Antonio
Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
Nephrology and Dialysis Unit, Ospedale S. Maria della Scaletta, Via Montericco, 4, 40026, Imola, Italy.
BMC Nephrol. 2019 May 17;20(1):177. doi: 10.1186/s12882-019-1345-7.
A classification tree model (CT-PIRP) was developed in 2013 to predict the annual renal function decline of patients with chronic kidney disease (CKD) participating in the PIRP (Progetto Insufficienza Renale Progressiva) project, which involves thirteen Nephrology Hospital Units in Emilia-Romagna (Italy). This model identified seven subgroups with specific combinations of baseline characteristics that were associated with a differential estimated glomerular filtration rate (eGFR) annual decline, but the model's ability to predict mortality and renal replacement therapy (RRT) has not been established yet.
Survival analysis was used to determine whether CT-PIRP subgroups identified in the derivation cohort (n = 2265) had different mortality and RRT risks. Temporal validation was performed in a matched cohort (n = 2051) of subsequently enrolled PIRP patients, in which discrimination and calibration were assessed using Kaplan-Meier survival curves, Cox regression and Fine & Gray competing risk modeling.
In both cohorts mortality risk was higher for subgroups 3 (proteinuric, low eGFR, high serum phosphate) and lower for subgroups 1 (proteinuric, high eGFR), 4 (non-proteinuric, younger, non-diabetic) and 5 (non-proteinuric, younger, diabetic). Risk of RRT was higher for subgroups 3 and 2 (proteinuric, low eGFR, low serum phosphate), while subgroups 1, 6 (non-proteinuric, old females) and 7 (non-proteinuric, old males) showed lower risk. Calibration was excellent for mortality in all subgroups while for RRT it was overall good except in subgroups 4 and 5.
The CT-PIRP model is a temporally validated prediction tool for mortality and RRT, based on variables routinely collected, that could assist decision-making regarding the treatment of incident CKD patients. External validation in other CKD populations is needed to determine its generalizability.
2013年开发了一种分类树模型(CT-PIRP),用于预测参与PIRP(进行性肾功能不全项目)的慢性肾脏病(CKD)患者的年度肾功能下降情况,该项目涉及意大利艾米利亚-罗马涅的13个肾脏病医院科室。该模型识别出了七个亚组,其基线特征的特定组合与不同的估计肾小球滤过率(eGFR)年度下降相关,但该模型预测死亡率和肾脏替代治疗(RRT)的能力尚未得到证实。
采用生存分析来确定在推导队列(n = 2265)中识别出的CT-PIRP亚组是否具有不同的死亡率和RRT风险。在随后入组的PIRP患者的匹配队列(n = 2051)中进行时间验证,其中使用Kaplan-Meier生存曲线、Cox回归和Fine & Gray竞争风险模型评估辨别力和校准情况。
在两个队列中,3组(蛋白尿、低eGFR、高血清磷酸盐)的死亡率风险较高,而1组(蛋白尿、高eGFR)、4组(非蛋白尿、年轻、非糖尿病)和5组(非蛋白尿、年轻、糖尿病)的死亡率风险较低。3组和2组(蛋白尿、低eGFR、低血清磷酸盐)的RRT风险较高,而1组、6组(非蛋白尿、老年女性)和7组(非蛋白尿、老年男性)的RRT风险较低。所有亚组的死亡率校准都非常好,而对于RRT来说,除了4组和5组外总体良好。
CT-PIRP模型是一种基于常规收集变量的、经过时间验证的死亡率和RRT预测工具,可协助对新发CKD患者的治疗进行决策。需要在其他CKD人群中进行外部验证以确定其普遍性。