Clinical Kidney Journal, 2022, vol. 15, no. 9, 1737–1746 https:/doi.org/10.1093/ckj/sfac108 Advance Access Publication Date: 28 April 2022 Original Article O R I G I NA L A RT I C L E Development and validation of a nomogram to predict kidney survival at baseline in patients with C3 Downloaded from https://academic.oup.com/ckj/article/15/9/1737/6575575 by guest on 12 July 2023 glomerulopathy Fernando Caravaca-Fontán 1,2 , Marta Rivero3 , Teresa Cavero3 , Montserrat Díaz-Encarnación4 , Virginia Cabello5 , Gema Ariceta6 , Luis F. Quintana 7 , Helena Marco8,∗ , Xoana Barros9 , Natalia Ramos10 , Nuria Rodríguez-Mendiola11 , Sonia Cruz12 , Gema Fernández-Juárez13 , Adela Rodríguez14 , Ana Pérez de José15 , Cristina Rabasco16 , Raquel Rodado17 , Loreto Fernández18 , Vanessa Pérez-Gómez 19 , Ana Ávila20 , Luis Bravo21 , Natalia Espinosa22 , Natalia Allende 23 , Maria Dolores Sanchez de la Nieta24 , Eva Rodríguez25 , Teresa Olea26 , Marta Melgosa27 , Ana Huerta 28 , Rosa Miquel29 , Carmen Mon30 , Gloria Fraga31 , Alberto de Lorenzo32 , Juliana Draibe33 , Fayna González34 , Amir Shabaka 35 , Maria Esperanza López-Rubio36 , María Ángeles Fenollosa37 , Luis Martín-Penagos38 , Iara Da Silva4,† , Juana Alonso Titos39 , Santiago Rodríguez de Córdoba40 , Elena Goicoechea de Jorge1,41 and Manuel Praga1,2,3 ; on behalf of the Spanish Group for the Study of Glomerular Diseases 1 Instituto de Investigación Hospital 12 de Octubre, Madrid, Spain, 2 Department of Medicine, Universidad Complutense de Madrid, Madrid, Spain, 3 Department of Nephrology, Hospital Universitario 12 de Octubre, Madrid, Spain, 4 Department of Nephrology, Fundación Puigvert, Barcelona, Spain, 5 Department of Nephrology, Hospital Universitario Virgen del Rocío, Sevilla, Spain, 6 Department of Pediatric Nephrology, Hospital Universitario Vall d’Hebron, Universidad Autónoma de Barcelona, Barcelona, Spain, 7 Department of Nephrology and Renal Transplantation, Hospital Clinic de Barcelona and Department of Medicine, University of Barcelona, IDIBAPS, Barcelona, Spain, 8 Department of Nephrology, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain, 9 Department of Nephrology, Hospital Universitario Doctor Josep Trueta, Gerona, Spain, 10 Department of Nephrology, Hospital Universitario Vall d’Hebron, Barcelona, Spain, 11 Department of Nephrology, Hospital Universitario Ramón y Cajal, Madrid, Spain, 12 Department of Nephrology, Hospital Universitario Juan Ramón Jiménez, Huelva, Spain, 13 Department of Nephrology, Hospital Received: 1.2.2022; Editorial decision: 18.3.2022 © The Author(s) 2022. Published by Oxford University Press on behalf of the ERA. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 1737 1738 F. Caravaca-Fontán et al. Universitario Fundación Alcorcón, Alcorcón, Madrid, Spain, 14 Department of Pediatric Nephrology, Hospital Universitario Virgen del Rocío, Sevilla, Spain, 15 Department of Nephrology, Hospital Universitario Gregorio Marañón, Madrid, Spain, 16 Department of Nephrology, Hospital Universitario Reina Sofía, Córdoba, Spain, 17 Department of Nephrology, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain, 18 Department of Nephrology, Complejo Hospitalario de Navarra, Navarra, Spain, 19 Department of Nephrology, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain, 20 Department of Nephrology, Hospital Universitario Doctor Peset, Valencia, Spain, 21 Department of Nephrology, Hospital Universitario A Coruña, La Coruña, Spain, 22 Pediatric Nephrology Unit, Hospital Universitario Son Espases, Balearic Islands Health Research Institute (IdISBa). Palma de Mallorca, Spain, 23 Department of Nephrology, Hospital Universitario Son Espases, Palma de Mallorca, Spain, 24 Department of Nephrology, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain, 25 Department of Nephrology, Hospital del Mar, Barcelona, Spain, 26 Department of Nephrology, Hospital Universitario La Paz, Madrid, Spain, 27 Department of Pediatric Nephrology, Hospital Universitario La Paz, Downloaded from https://academic.oup.com/ckj/article/15/9/1737/6575575 by guest on 12 July 2023 Madrid, Spain, 28 Department of Nephrology, Hospital Universitario Puerta de Hierro, Madrid, Spain, 29 Department of Nephrology, Hospital Universitario Canarias, Tenerife, Spain, 30 Department of Nephrology, Hospital Universitario Severo Ochoa, Leganés, Madrid, Spain, 31 Department of Pediatric Nephrology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain, 32 Department of Nephrology, Hospital Universitario de Getafe, Madrid, Spain, 33 Department of Nephrology, Hospital Universitario de Bellvitge, Barcelona, Spain, 34 Department of Nephrology, Hospital Doctor Negrín, Gran Canaria, Spain, 35 Department of Nephrology, Hospital Universitario Clínico San Carlos, Madrid, Spain, 36 Department of Nephrology, Complejo Hospitalario Universitario de Albacete, Albacete, Spain, 37 Department of Nephrology, Hospital General Universitario de Castellón, Castellón, Spain, 38 Department of Nephrology, Hospital Universitario Marqués de Valdecilla, Santander, Spain, 39 Department of Nephrology, Hospital Regional Universitario Carlos Haya, Málaga, Spain, 40 Centro de Investigaciones Biológicas, Consejo Superior de Investigaciones Científicas, Madrid and Centro de Investigación Biomédica en Red en Enfermedades Raras, Madrid, Spain and 41 Department of Immunology, Universidad Complutense de Madrid, Madrid, Spain ∗ Current address: Department of Nephrology, Fundación Puigvert, Barcelona, Spain † Current address: Department of Nephrology, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain Correspondence to: Manuel Praga; E-mail: mpragat@senefro.org 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. Prognostic nomogram for C3 glomerulopathy 1739 GRAPHICAL ABSTRACT Downloaded from https://academic.oup.com/ckj/article/15/9/1737/6575575 by guest on 12 July 2023 Keywords: C3 glomerulopathy, calibration, discrimination, kidney failure, nomogram INTRODUCTION is often challenging to accurately predict in clinical practice the individual kidney prognosis at baseline. Thus the development C3 glomerulopathy (C3G) represents a heterogeneous group of of a nomogram (i.e. a multivariable visualization prediction tool) glomerular diseases characterized by an overactivation of the could provide clinicians an intuitive tool for prediction of kidney alternative complement pathway in plasma and glomerular mi- outcomes at baseline, and this information could also be rele- croenvironment, resulting in the deposition of the C3 molecule vant for research purposes in C3G. and its degradation fragments that are detectable in a kidney Nomograms have been widely used in oncology to predict biopsy [1, 2]. outcomes of patients with malignant tumors [12]. However, to In recent years, several important efforts have led to a bet- the best of our knowledge, no study has proposed a nomogram ter understanding of the pathogenesis and natural history of to predict the prognosis of C3G patients based on the clinical and C3G [1, 3]. However, the often slowly progressive and pauci- histologic features at the time of diagnosis. symptomatic course of the disease at its early stages may lead to Hence, in this study, we aimed to construct and validate a a delayed diagnosis of the disease in many cases, with the con- prognostic nomogram to predict the long-term kidney survival sequent development of irreversible chronic histologic lesions at the time of diagnosis in a multicentric cohort of patients with [3, 4]. In fact, the extent of disease chronicity in kidney biopsy C3G. has been recognized as a major predictor of kidney outcomes in C3G [4, 5]. The results from the largest cohort studies on C3G have re- vealed that among the most important predictors of kidney sur- MATERIALS AND METHODS vival are the age at diagnosis, the estimated glomerular filtra- Study patients tion rate (eGFR) and the degree of proteinuria at the time of clinical presentation, together with the disease chronicity on Patients diagnosed with C3 glomerulopathy between January kidney biopsy [5–10]. In addition, our group recently found a 1995 and June 2020 in 35 nephrology departments belonging strong association between the longitudinal change in protein- to the Spanish Group for the Study of Glomerular Diseases uria and the risk of kidney failure, which could provide clinicians (GLOSEN) were enrolled. Patients with other underlying autoim- a dynamic prediction of kidney outcomes during follow-up [11]. mune diseases, infected with hepatitis B or C or those with miss- However, considering the high heterogeneity of C3G patients, it ing data were excluded. 1740 F. Caravaca-Fontán et al. The diagnosis of C3G was based on the 2013 Consensus The accuracy of the nomogram was assessed by discrimina- Guidelines criteria, which required C3 staining on immunoflu- tion [using C-index and time-dependent receiver operating char- orescence at least two orders of magnitude greater than any acteristics (ROC) curves, with the corresponding area under the immunoglobulin staining [13]. Patients were considered to have curve (AUC)] and risk calibration, in the training and validation dense deposit disease (DDD) when highly electron-dense in- groups. To further analyze the discrimination of the nomogram, tramembranous deposits were observed on electron microscopy, a total score was calculated for each patient and then stratified and C3 glomerulonephritis (C3GN) when deposits did not fulfill into two groups based on the median scores. Distributions of this criterion. Baseline and follow-up data were compiled from time to kidney failure were depicted by survival curves using the the medical records of all participating centers, as described Kaplan–Meier method and the survival curves were compared elsewhere [4, 7]. using the logrank test. Kidney biopsy specimens were examined in the pathology A P-value <.05 was considered to be significant. Analyses departments of the participating hospitals. The degree of dis- were performed using R software version 3.6.3 (R Foundation ease activity and chronicity was analyzed according to the pre- for Statistical Computing, Vienna, Austria), and the R pack- viously published C3G histologic index [4, 5], using a semiquan- ages ‘caret’, ‘glmnet’, ‘rms’, ‘nomogramEx’, ‘survival’, ‘ggplot2’, titative scale of 0–3, except for arteriosclerosis, which was scored ‘Hmisc’, ‘survival’, ‘survivalROC’ and ‘survminer’. Downloaded from https://academic.oup.com/ckj/article/15/9/1737/6575575 by guest on 12 July 2023 as 0–1. Total activity and chronicity scores were then calculated as the sum of each individual item, with a total possible score of 21 for activity lesions and a total possible score of 10 for chronic RESULTS lesions. Baseline characteristics All patients gave written informed consent. The study was approved by the institutional review board of Hospital Universi- The study group comprised 115 patients with a median age of tario 12 de Octubre and was conducted in accordance with the 30 years (IQR 19–50), 44% of whom were female (Table 1). The Declaration of Helsinki. median baseline eGFR was 54 mL/min/1.73 m2 (IQR 23–106) and the median baseline proteinuria was 3 g/day (IQR 1.6–5.7). An inverse correlation was observed between eGFR and proteinuria, Definitions and outcome as well as eGFR and total chronicity score (Supplementary data, Figure S1). Baseline was defined as the time at which the kidney biopsy was Eighteen patients (16%) received nonimmunosuppressive performed and follow-up period as the interval of time elapsed therapies, whereas 15 patients received corticosteroids alone between the kidney biopsy and the last outpatient visit or kidney (13%), 46 corticosteroids plus mycophenolate mofetil (40%), 7 rit- failure. uximab (6%), 10 eculizumab (9%) and 19 received other immuno- Nephrotic syndrome was defined as a proteinuria of suppressive regimens (Supplementary data, Tables S1 and S2). >3.5 g/day along with serum albumin <3 g/dL. Nephritic Overall, 46 patients (40%) reached kidney failure in a me- syndrome was defined as the combination of hematuria, dian follow-up of 49 months (IQR 24–112). The kidney survival nonnephrotic proteinuria, hypertension and kidney function rate was 80% at 1 year, 75% at 2 years, 61% at 5 years and 24% impairment. Asymptomatic urinary abnormalities were defined at 10 years. No significant changes were observed in the kid- by the presence of nonnephrotic proteinuria and/or persistent ney outcomes of patients according to the year of diagnosis microscopic hematuria >5 erythrocytes per high power field. (Supplementary data, Figure S2). The main outcome was kidney failure, defined as an eGFR After randomly splitting the sample, 87 patients were as- <15 mL/min/1.73 m2 , the need for dialysis or preemptive kidney signed to the training group and 28 to the validation group. transplantation. The median eGFR and proteinuria in the training group were 53 mL/min/1.73 m2 (IQR 20–106) and 3 g/day (IQR 1.6–5.7), respectively. In contrast, the median eGFR and proteinuria in Statistical analyses the validation group were 55 mL/min/1.73 m2 (IQR 27–110) and We conducted a retrospective, multicenter observational cohort 3.1 g/day (IQR 2–6.6), respectively. No significant differences were study. Descriptive statistics are presented as mean ± standard observed in baseline eGFR, proteinuria or total chronicity scores deviation or median and interquartile range (IQR) for continu- between patients in the training versus those in the validation ous variables and absolute values and percentages for categor- group (Table 1). Thirty-five (40%) patients in the training group ical variables. Parametric and nonparametric tests were chosen reached kidney failure versus 11 (39%) in the validation group. as appropriate for descriptive comparisons of continuous vari- ables and the chi-squared test for categorical variables. For the Development of the nomogram comparisons in smaller groups we performed a Fisher’s exact test. A total of 10 variables were included in the LASSO model: age, The dataset was randomly divided into a training group (n = gender, presence of complement pathogenic variants and anti- 87) and a validation group (n = 28), with a ratio of 3:1. The training bodies against complement components, baseline eGFR, serum group was used for the development and internal validation of albumin, serum C3 levels, proteinuria, total activity and chronic- the nomogram whereas the validation group was used to verify ity scores, of which 3 variables were left with nonzero coef- the model. ficients. The selected variables included baseline eGFR, pro- The least absolute shrinkage and selection operator (LASSO) teinuria and total chronicity score. The respective coefficients regression was used to screen the main predictors of kidney out- of these factors were calculated when log lambda was 2.36 come in order to avoid collinearity of covariates. The predictors (Fig. 1A and B). The three variables obtained by LASSO regres- selected by LASSO regression were incorporated into a Cox pro- sion were included in the Cox proportional hazards regression portional hazards regression model to build the nomogram, fol- model (Table 2), and emerged as independent predictors of kid- lowing the methodology described elsewhere [12, 14, 15]. Kidney ney failure. Based on the Cox model, a nomogram was developed survival at 1, 2, 5 and 10 years was estimated. for the prediction of kidney survival at 1, 2, 5 and 10 years (Fig. 2). Prognostic nomogram for C3 glomerulopathy 1741 Table 1. Clinical characteristics of study patients Total Training cohort Validation cohort P- Characteristics (n = 115) (n = 87) (n = 28) value Baseline Age (years), median (IQR) 30 (19–50) 30 (19–48) 30 (15–54) .91 Sex, female (%) 51 (44) 41 (47) 10 (36) .29 Hypertension, n (%) 75 (65) 56 (64) 19 (68) .74 Antecedent infection, n (%) 29 (25) 21 (24) 8 (29) .64 C3GN/DDD, n (%) 95 (83) / 20 73 (84)/14 (16) 22 (79)/6 (21) .52 (17) Clinical presentation, n (%) .75 Nephrotic syndrome 46 (40) 36 (42) 11 (39) Nephritic syndrome 34 (30) 26 (30) 8 (29) Isolated nonnephrotic proteinuria 15 (13) 9 (10) 5 (18) Asymptomatic urinary abnormalities 20 (17) 16 (18) 4 (14) Downloaded from https://academic.oup.com/ckj/article/15/9/1737/6575575 by guest on 12 July 2023 Creatinine at diagnosis (mg/dL), median (IQR) 1.4 (0.8–3) 1.4 (0.8–3) 1.5 (0.8–2.2) .73 eGFR (mL/min/1.73 m2 ), median (IQR) .81 >90 37 (32) 29 (33) 8 (29) 60–90 11 (10) 8 (9) 3 (11) 30–60 26 (23) 18 (21) 8 (29) 15–30 19 (16) 13 (15) 6 (21) <15 22 (19) 19 (22) 3 (11) Serum albumin (g/dL), median (IQR) 3.1 (2.5–3.8) 3.1 (2.5–3.8) 3.1 (2.4–3.9) .58 Serum C3 (mg/dL), median (IQR) 65 (27–90) 65 (35–90) 65 (20–98) .75 Serum C4 (mg/dL), median (IQR) 24 (17–31) 25 (18–31) 22 (16–28) .26 Proteinuria (g/24 h), n (%) .93 <1 15 (13) 12 (14) 3 (11) ≥1–<3 39 (34) 31 (35) 9 (32) ≥3–<5 26 (23) 18 (21) 7 (25) ≥5 35 (30) 26 (30) 9 (32) Follow-up (months), median (IQR) 49 (24–112) 46 (22–96) 52 (24–112) .25 Alternative complement pathway studiesa , n (%) Complement pathogenic variants 23 (20) 15 (17) 8 (29) .19 Variants of unknown significance 41 (36) 31 (36) 10 (36) .99 Antibodies against complement components 33 (29) 24 (28) 9 (32) .64 Kidney biopsy Immunofluorescence, n (%) .21 C3 alone 54 (47) 38 (44) 16 (57) C3 dominant 61 (53) 49 (56) 12 (43) C3G histologic index—activity score, median (IQR) Mesangial hypercellularity (0–3) 3 (2–3) 3 (2–3) 3 (2–3) .31 Endocapillary proliferation (0–3) 1 (0–2) 1 (0–2) 1 (0–2) .93 Membranoproliferative morphology (0–3) 3 (0–3) 3 (0–3) 3 (1–3) .54 Leukocyte infiltration (0–3) 1 (0–2) 1 (0–2) 1 (0–2) .19 Crescent formation (0–3) 0 (0–1) 0 (0–1) 0 (0) .26 Fibrinoid necrosis (0–3) 0 (0) 0 (0) 0 (0) .72 Interstitial inflammation (0–3) 0 (0–1) 0 (0–1) 1 (0–1) .90 Total activity score (0–21) 8 (6–10) 7 (6–10) 8 (5–11) .52 C3G histologic index—chronicity score, median (IQR) Global/segmental glomerulosclerosis (0–3) 1 (0–2) 1 (0–2) 1 (0–1) .61 Tubular atrophy (0–3) 1 (0–2) 1 (0–1) 1 (0–2) .52 Interstitial fibrosis (0–3) 1 (0–2) 1 (0–2) 1 (0–2) .41 Arterio- and arteriolosclerosis (0–1) 0 (0–1) 0 (0–1) 0 (0–1) .65 Total chronicity score (0–10) 3 (1–6) 3 (1–5) 3 (0–6) .92 a A complete description of pathogenic variants and antibodies against complement components are described in Supplementary data, Tables S3 and S4. According to the nomogram, each variable is assigned a score As an example of nomogram usage, a patient with an eGFR of according to the category (‘points’) and the total score is com- 45 mL/min/1.73 m2 with a proteinuria of 5.2 g/day at the time of puted by summing individual scores (‘total points’). The corre- clinical diagnosis and a total chronicity score of 6 would obtain sponding kidney survival probability suggested by the nomo- a total score of 147.5 (52.5 + 35 + 60). Thus the corresponding gram at each time point (1, 2, 5 and 10 years) can be obtained kidney survival probability of this patient would be 74%, 60%, from the total points obtained. 25% and 17% at 1, 2, 5 and 10 years, respectively. 1742 F. Caravaca-Fontán et al. A B Downloaded from https://academic.oup.com/ckj/article/15/9/1737/6575575 by guest on 12 July 2023 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. Table 2. Multivariable Cox regression analysis Variable Hazard ratio Lower 95% CI Upper 95% CI P-value 2 eGFR (mL/min/1.73 m ) .01 >90 1.00 (reference) 1.00 (reference) 1.00 (reference) 60–90 2.11 0.69 9.19 30–60 2.63 1.03 4.16 15–30 3.47 1.62 5.53 <15 6.78 2.49 9.98 Proteinuria (g/day) .002 <1 1.00 (reference) 1.00 (reference) 1.00 (reference) ≥1–<3 2.12 1.17 5.84 ≥3–<5 2.11 1.22 5.78 ≥5 4.59 2.43 7.67 Total chronicity score 1.36 1.19 1.56 <.001 Abbreviations: CI, confidence interval; eGFR, estimated glomerular filtration rate. Internal validation External validation The validation of the model was based on discrimination The C-index of the nomogram for the prediction of kidney and calibration. The C-index of the nomogram was 0.860 survival in the validation set was 0.865 (95% CI 0.815–0.914). [95% confidence interval (CI) 0.834–0.887]. Figure 3A depicts Figure 4A depicts time-dependent ROC curves of the validation time-dependent ROC curves of the training group. The AUCs group. The AUCs were 0.92, 0.92, 0.86 and 0.93 at 1 , 2, 5 and were 0.87, 0.91, 0.90 and 0.91 at 1, 2, 5 and 10 years, respec- 10 years, respectively. Likewise, the calibration plot depicted in tively. Figure 3B depicts calibration plots at the same time points, Figure 4B showed overall optimal agreement between predicted showing an optimal agreement between predicted and observed and observed values. Finally, Kaplan–Meier curves according to outcomes. median total points of the nomogram showed a significant dif- The median total points obtained from the nomogram in ference in kidney survival between patients categorized as high the overall cohort was 98 (IQR 43–131). Based on these median risk and those categorized as low risk. values, the training group was divided into high risk and low risk, depending on whether the values were above or below the median. The median survival time in the high-risk and low- DISCUSSION risk groups was 20 and 258 months, respectively. A Kaplan– In this study we constructed and validated a nomogram for Meier curve (Fig. 3C) illustrated that this model has good per- predicting kidney failure in patients with C3G based on a large formance in identifying the population with different risk levels multicentric cohort of patients with this disease. There are (P < .0001). several major findings in this study. First, through LASSO Prognostic nomogram for C3 glomerulopathy 1743 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 Downloaded from https://academic.oup.com/ckj/article/15/9/1737/6575575 by guest on 12 July 2023 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. regression we screened for the main risk factors of kidney total disease activity and chronicity were significantly associ- failure, among which we found baseline eGFR, proteinuria and ated with the primary outcome. total chronicity score from Columbia University’s C3G Histologic In an attempt to identify patients at higher risk of kid- Index [5]. These variables were analyzed by Cox regression to ney failure, in our study we further stratified patients into two calculate the kidney survival of patients and were finally used to groups based on the median score obtained from the predictive construct the nomogram. Second, our results showed that this nomogram. Despite sample size constraints—particularly in the nomogram performs well in distinguishing between patients at validation group—we managed to identify and confirm in the high and low risk of developing kidney failure. Third, the kidney validation group a numerical threshold for the classification of prognosis of C3G patients was also accurately predicted based patients as high or low risk. on these parameters at diagnosis, suggesting that this model Finally, we assessed the accuracy of the proposed nomogram could be implemented in routine clinical practice. through discrimination, which is the ability to accurately rank Nomograms represent a visualization of a complex model individuals’ risk, and calibration, which represents the agree- equation in which the behavior of predictors is represented in ment between observed and predicted risks [17], and found over- scales [15]. Nomograms have become an attractive and useful all good performance of the model. clinical tool, as they provide estimates of the probability of an Taken together, the results of this study combined with those event, tailored to the profile of individual patients [12]. They pro- reported in another subanalysis from our group [11] suggest vide clinicians with an estimate of prognosis that, in the case of that the nomogram would be an appropriate clinical tool be C3G, is of great interest and can help in titrating the intensity of used in patients with C3G for the prediction of prognosis at treatment at baseline. baseline, whereas the longitudinal evaluation of proteinuria at In our study, the main predictors of kidney failure were eGFR, each visit would be more suitable to dynamically predict out- proteinuria and the total disease chronicity in the kidney biopsy. comes. In contrast, although we only included C3G without un- We used the LASSO method for variable selection to avoid over- derlying monoclonal gammopathy [18–20], the similarities in fitting of the model [16]. However, unlike in previous studies [4, several clinical and pathologic features with C3G patients with 7], the use of the different treatment strategies was not included monoclonal gammopathy could justify the use of this nomo- in the regression model since the main objective was to develop gram for the prediction of prognosis of these patients at base- a predictive model at baseline in treatment-naïve patients. line. However, further studies are warranted to validate this Our results are in line with those reported in other case hypothesis. series. For instance, in the study by Servais et al. [10], baseline Several limitations should be acknowledged. First, due to the eGFR correlated well with eGFR at the last follow-up and kidney observational and retrospective nature of the study, no causal re- survival was found to be worse in patients with baseline eGFR lationships can be established. Second, only variables known to <60 mL/min/1.73 m2 . In the study by Caliskan et al. [6], the main be determinants of prognosis were included in the model and predictors of kidney failure were age at diagnosis, eGFR, protein- thus we cannot rule out that other unmeasured confounders uria and percentage of crescentic/sclerotic glomeruli, together could have a prognostic influence. Third, although in this study with the degree of interstitial fibrosis. Likewise, Ravindran we were able to identify C3G patients with poor short- and et al. [8] found age at diagnosis, serum creatinine, proteinuria medium-term kidney prognosis, longer follow-up may be neces- >3 g/day, glomerulosclerosis and degree of interstitial/tubular sary to fully validate these results. Despite these limitations, this atrophy as the main predictors of kidney failure or doubling of study collected a large series of patients with C3G that allowed serum creatinine. In contrast, in the study by Medjeral-Thomas us to construct a predictive nomogram and further contributes et al. [9], kidney impairment at presentation only predicted to the understanding of the natural history and prognosis of the worse outcomes in patients with DDD. In contrast, in the study disease. by Bomback et al. [5], baseline eGFR emerged as the only clinical In conclusion, in this study we found that eGFR, proteinuria variable associated with kidney failure and, unlike ours, both and total chronicity score at baseline were the main predictors of 1744 F. Caravaca-Fontán et al. Downloaded from https://academic.oup.com/ckj/article/15/9/1737/6575575 by guest on 12 July 2023 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). Prognostic nomogram for C3 glomerulopathy 1745 Downloaded from https://academic.oup.com/ckj/article/15/9/1737/6575575 by guest on 12 July 2023 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. 1746 F. Caravaca-Fontán et al. kidney failure in C3G patients. Furthermore, a simple and easy- 5. Bomback AS, Santoriello D, Avasare RS et al. C3 glomeru- to-use nomogram with good discrimination and calibration was lonephritis and dense deposit disease share a similar constructed and validated in this study to predict the risk of kid- disease course in a large United States cohort of pa- ney failure in C3G patients at 1, 2, 5 and 10 years. tients with C3 glomerulopathy. Kidney Int 2018; 93: 977– 985 6. Caliskan Y, Torun ES, Tiryaki TO et al. Immunosuppressive DATA AVAILABILITY STATEMENT treatment in C3 glomerulopathy: is it really effective? Am J The data underlying this article will be shared upon reasonable Nephrol 2017; 46: 96–107 request to the corresponding author. 7. Caravaca-Fontán F, Díaz-Encarnación MM, Lucientes L et al. Mycophenolate mofetil in C3 glomerulopathy and pathogenic drivers of the disease. Clin J Am Soc Nephrol 2020; SUPPLEMENTARY DATA 15: 1287–1298 8. Ravindran A, Fervenza FC, Smith RJH et al. C3 glomerulopa- Supplementary data are available at ckj online. thy: ten years’ experience at Mayo Clinic. Mayo Clin Proc 2018; 93: 991–1008 Downloaded from https://academic.oup.com/ckj/article/15/9/1737/6575575 by guest on 12 July 2023 9. Medjeral-Thomas NR, O’Shaughnessy MM, O’Regan JA et al. ACKNOWLEDGEMENTS C3 glomerulopathy: clinicopathologic features and pre- Work on this study was supported by the Instituto de Salud dictors of outcome. Clin J Am Soc Nephrol 2014; 9: 46– Carlos III / Fondo Europeo de Desarrollo Regional (ISCIII/FEDER; 53 grants PI16/01685 and PI19/1624) and Red de Investigación Renal 10. Servais A, Noël LH, Roumenina LT et al. Acquired and genetic (RD12/0021/0029; to M.P.) and the Autonomous Region of Madrid complement abnormalities play a critical role in dense de- (S2017/BMD-3673; to M.P.). S.R.d.C. is supported by the Minis- posit disease and other C3 glomerulopathies. Kidney Int 2012; terio de Economia y Competitividad (grant PID2019-104912RB- 82: 454–464 I00) and the Autonomous Region of Madrid (grant S2017/BMD- 11. Caravaca-Fontán F, Díaz-Encarnación M, Cabello V et al. 3673). None of the funders had any role in the study design, Longitudinal change in proteinuria and kidney outcomes data collection, analyses, reporting or decision to submit for in C3 glomerulopathy. Nephrol Dialysis Transplant 2021; doi: publication. 10.1093/ndt/gfab075 12. Iasonos A, Schrag D, Raj GV et al. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 2008; 26: 1364– CONFLICT OF INTEREST STATEMENT 1370 13. Hou J, Markowitz GS, Bomback AS et al. Toward a working F.C.-F. reports personal fees from Novartis outside the submit- definition of C3 glomerulopathy by immunofluorescence. ted work. M.D.-E. reports personal fees from Novartis outside Kidney Int 2014; 85: 450–456 the submitted work. G.A. reports personal fees and nonfinan- 14. Zhou Z-R, Wang W-W, Li Y et al. In-depth mining of clinical cial support from Alexion Pharmaceuticals, personal fees and data: the construction of clinical prediction model with R. nonfinancial support from Recordati Rare Diseases, Advincenne Ann Transl Med 2019; 7: 796–796 and Kyowa Kirim and personal fees from Chiesi, Dicerna and 15. Zhang Z, Kattan MW. Drawing nomograms with R: applica- Alnylam outside the submitted work. L.Q. reports personal fees tions to categorical outcome and survival data. Ann Transl from Otsuka, Alexion and GlaxoSmithKline outside the submit- Med 2017; 5: 211 ted work. A.H. reports personal fees from Alexion outside the 16. Tibshirani R. The LASSO method for variable selection in the submitted work. M.P. reports personal fees from Otsuka, Alexion, Cox model. Stat Med 1997; 16: 385–395 Retrophin and Novartis outside the submitted work. The rest of 17. Caravaca-Fontán F, Trujillo H, Caravaca F et al. Contribution authors declare no conflicts of interest. of a histologic index to the prognostic information of C3 glomerulopathy. Nephrol Dialysis Transplant 2021; 36: 2148– 2150 REFERENCES 18. Chauvet S, Frémeaux-Bacchi V, Petitprez F et al. Treat- 1. Smith RJH, Appel GB, Blom AM et al. C3 glomerulopathy — ment of B-cell disorder improves renal outcome of patients understanding a rare complement-driven renal disease. Nat with monoclonal gammopathy-associated C3 glomerulopa- Rev Nephrol 2019; 15: 129–143 thy. Blood 2017; 129: 1437–1447 2. Caravaca-Fontán F, Lucientes L, Cavero T, Praga M. Update 19. Ravindran A, Fervenza FC, Smith RJH et al. C3 glomerulopa- on C3 glomerulopathy: a complement-mediated disease. thy associated with monoclonal Ig is a distinct subtype. Nephron 2020; 144: 272–280 Kidney Int 2018; 94: 178–186 3. Ahmad SB, Bomback AS. C3 glomerulopathy: pathogenesis 20. Caravaca-Fontán F, Lucientes L, Serra N et al. C3 glomeru- and treatment. Adv Chronic Kidney Dis 2020; 27: 104–110 lopathy associated with monoclonal gammopathy: impact 4. Caravaca-Fontán F, Trujillo H, Alonso M et al. Validation of a of chronic histologic lesions and beneficial effects of clone- histologic scoring index for C3 glomerulopathy. Am J Kidney targeted therapies. Nephrol Dialysis Transplant 2021; doi: Dis 2021; 77: 684–695.e1 10.1093/ndt/gfab302 Our distribution partners and end users agree on several reasons why NephroCan presents a unique offering: 1. 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