Risk prediction of the progression of chronic kidney disease stage 1 based on peripheral blood samples: construction and internal validation of a nomogram

Abstract

Patients with chronic kidney disease (CKD) have high morbidity and mortality, and the disease progression has a significant impact on their survival and living standards. This research aims to analyze risk factors for CKD stage 1 and provide a reference for clinical decision making. The clinical data and peripheral blood samples of 300 patients with CKD stage 1 were collected retrospectively. Patients were randomly assigned into a training set ( n = 210) and a validation set ( n = 90). Patients’ baseline characteristic levels were subjected to statistical tests for difference. Univariate and multivariate Cox regression analyses were utilized to identify risk factors influencing disease progression. Subsequently, a prediction model for disease progression was developed using a nomogram, and the model’s accuracy was assessed using the C-index and calibration curve. The results revealed that hypertension, diabetes, and urinary albumin were essential factors in the progression of CKD stage 1. The nomogram was constructed and then the C-index was calculated. The calibration curve was utilized to assess the risk model. The C-index of the training set was 0.75, and the C-index of the validation set was 0.73, suggesting a good predictive ability of the model. The risk model accurately predicted the progression of CKD stage 1, which is of great significance to developing personalized treatment for patients in clinical practice.