Developing and validating a prognostic prediction model for patients with chronic kidney disease stages 3-5 based on disease conditions and intervention methods: a retrospective cohort study in China

Abstract

Objectives: To develop and validate a nomogram model to predict chronic kidney disease (CKD) stages 3-5 prognosis.

Design: A retrospective cohort study. We used univariate and multivariate Cox regression analysis to select the relevant predictors. To select the best model, we evaluated the prediction models' accuracy by concordance index (C-index), calibration curve, net reclassification index (NRI) and integrated discrimination improvement (IDI). We evaluated the clinical utility by decision curve analysis.

Setting: Chronic Disease Management (CDM) Clinic in the Nephrology Department at the Guangdong Provincial Hospital of Chinese Medicine.

Participants: Patients with CKD stages 3-5 in the derivation and validation cohorts were 459 and 326, respectively.

Primary outcome measure: Renal replacement therapy (haemodialysis, peritoneal dialysis, renal transplantation) or death.

Results: We built four models. Age, estimated glomerular filtration rate and urine protein constituted the most basic model A. Haemoglobin, serum uric acid, cardiovascular disease, primary disease, CDM adherence and predictors in model A constituted model B. Oral medications and predictors in model A constituted model C. All the predictors constituted model D. Model B performed well in both discrimination and calibration (C-index: derivation cohort: 0.881, validation cohort: 0.886). Compared with model A, model B showed significant improvement in the net reclassification and integrated discrimination (model A vs model B: NRI: 1 year: 0.339 (-0.011 to 0.672) and 2 years: 0.314 (0.079 to 0.574); IDI: 1 year: 0.066 (0.010 to 0.127), p<0.001 and 2 years: 0.063 (0.008 to 0.106), p<0.001). There was no significant improvement between NRI and IDI among models B, C and D. Therefore, we selected model B as the optimal model.

Conclusions: We constructed a prediction model to predict the prognosis of patients with CKD stages 3-5 in the first and second year. Applying this model to clinical practice may guide clinical decision-making. Also, this model needs to be externally validated in the future.

Trial registration number: ChiCTR1900024633 (http://www.chictr.org.cn).

Keywords: chronic renal failure; end stage renal failure; nephrology.