Development and validation of a nomogram to predict kidney survival at baseline in patients with C3 glomerulopathy

Abstract

Background: C3 glomerulopathy is a rare and heterogeneous complement-driven disease. It is often challenging to accurately predict in clinical practice the individual kidney prognosis at baseline. We herein sought to develop and validate a prognostic nomogram to predict long-term kidney survival.

Methods: We conducted a retrospective, multicenter observational cohort study in 35 nephrology departments belonging to the Spanish Group for the Study of Glomerular Diseases. The dataset was randomly divided into a training group (n = 87) and a validation group (n = 28). The least absolute shrinkage and selection operator (LASSO) regression was used to screen the main predictors of kidney outcome and to build the nomogram. The accuracy of the nomogram was assessed by discrimination and risk calibration in the training and validation sets.

Results: The study group comprised 115 patients, of whom 46 (40%) reached kidney failure in a median follow-up of 49 months (range 24-112). No significant differences were observed in baseline estimated glomerular filtration rate (eGFR), proteinuria or total chronicity score of kidney biopsies, between patients in the training versus those in the validation set. The selected variables by LASSO were eGFR, proteinuria and total chronicity score. Based on a Cox model, a nomogram was developed for the prediction of kidney survival at 1, 2, 5 and 10 years from diagnosis. The C-index of the nomogram was 0.860 (95% confidence interval 0.834-0.887) and calibration plots showed optimal agreement between predicted and observed outcomes.

Conclusions: We constructed and validated a practical nomogram with good discrimination and calibration to predict the risk of kidney failure in C3 glomerulopathy patients at 1, 2, 5 and 10 years.

Keywords: C3 glomerulopathy; calibration; discrimination; kidney failure; nomogram.

Graphical Abstract

FIGURE 1:

(A) LASSO coefficient profiles of the 10 variables included in the model against the log lambda. This analysis resulted in the selection of three factors: eGFR, proteinuria and total chronicity score. (B) Relationship between the log lambda and the mean-squared error in the LASSO regression. Dotted vertical lines were drawn at the optimal values by using the minimum criteria and the one standard error of the minimum criteria.

FIGURE 2:

Nomogram for the prediction of kidney failure at 1, 2, 5 and 10 years. Locate the patient's variable and draw a line up to the ‘points’ axis to find the value for each variable. Calculate the total point value by summing the scores of each variable. Then locate the total point value on the ‘total points’ axis and draw a line down to the 1-year kidney survival axis, the 2-year kidney survival axis, the 5-year kidney survival axis or the 1-year kidney survival axis to obtain the likelihood of kidney failure at 1, 2, 5 and 10 years. Please note that eGFR was measured as mL/min/1.73 m² and proteinuria as g/day. Example: A patient with a baseline eGFR of 65 mL/min/1.73 m², proteinuria of 2 g/day and total chronicity score of 3 would obtain a total score of 87 (45 + 12 + 30). Thus the corresponding kidney survival probability for this patient would be 94%, 90%, 77% and 72% at 1, 2, 5 and 10 years, respectively.

FIGURE 3:

(A) ROC curves of the training group, with their corresponding AUC at the different time points (1, 2, 5 and 10 years). (B) Calibration curves of predicted versus actual probabilities of kidney failure at different time points (1, 2, 5 and 10 years). The gray line represents an ideal agreement between actual and predicted probabilities. The red line represents our nomogram and the vertical bars represent 95% CIs. (C) Kaplan–Meier curve for kidney survival in the high-risk versus low-risk group (based on the total score of the predictive nomogram at the threshold of 98 points).

FIGURE 4:

(A) ROC curves of the validation group, with their corresponding AUC at the different time points (1, 2, 5 and 10 years). (B) Calibration curves of predicted versus actual probabilities of kidney failure at different time points in the validation group (1, 2, 5 and 10 years). The gray line represents an ideal agreement between actual and predicted probabilities, the red line represents our nomogram and the vertical bars represent 95% CIs. (C) Kaplan–Meier curve for kidney survival in the high-risk versus low-risk group of the validation group.