Immunoglobulin A nephropathy (IgAN), first described by Berger in 1968, is the most common type of glomerulonephritis and an important cause of end-stage renal disease (ESRD) worldwide (1–3). Because of the heterogeneous prognostic nature of IgAN, it is important to identify high-risk patients at diagnosis not only for the selection for treatment strategies and clinical trials but also for patient health education (4–7).

In recent decades, dozens of clinical risk factors, including proteinuria, hypertension, and estimated glomerular filtration rate (eGFR), at the time of renal biopsy have been reported to be associated with worse renal prognosis in IgAN (8). In addition to clinical parameters at baseline, proteinuria or blood pressure during the first 2 years of follow-up after diagnosis also has clear correlations with the prognosis of IgAN (9). Among these risk factors, baseline eGFR was established as the most consistent indicator. An outstanding question in the field is whether combined pathological indicators, such as mesangial hypercellularity, endocapillary hypercellularity, segmental sclerosis, and interstitial fibrosis, can increase the accuracy of clinical indicators for prognosis prediction (9–11).

To date, several prediction models of IgAN progression have been established based on patients from different populations at different stages of renal function (9, 12–17). We previously established a clinical model (CLIN model) and a combined model containing both clinical and pathological variables (CLINPATH model), which had good performance in predicting the occurrence of ESRD at 10 years in the validation cohort (18). Later, a large-scale study of a combined multiethnic IgAN cohort performed by Barbour et al. established risk prediction models based on 3,927 IgAN patients (19). The clinical model in this study included proteinuria, blood pressure, and eGFR at renal biopsy. In addition to the clinical indicators, the limited model contained the MESTC histologic score, and the full model included age, medication, and racial/ethnic characteristics. The authors found that the limited model [area under the curve (AUC) = 0.80; 95% confidence interval (CI), 0.79–0.81] and full model (AUC = 0.82; 95% CI, 0.81–0.82) showed improved performance in predicting the composite outcome (defined as a 50% decline in eGFR or ESRD) compared to the clinical model (AUC = 0.78; 95% CI, 0.77–0.78). In addition, whether risk models of chronic kidney disease (CKD) can be used to predict the prognosis of IgAN is an interesting question. The study by Tangri developed and validated CKD risk models by including 8,391 Canadian CKD patients. Model 3 (C statistic, 0.91; 95% CI, 0.89–0.93), a clinical model, had good performance in predicting disease progression in patients with CKD stages 3 to 5 (20). Both studies by Barbour and Tangri studies have been further assessed and externally validated (21–23), but the prediction models would still benefit from additional external validation to improve confidence in using them in practice.

The objective of this study was to use our established multicenter Chinese IgAN cohort to conduct an independent external validation study of Barbour’s IgAN models and Tangri’s CKD model. We also compared the performance of pure clinical models (including clinical variables only) and combined models (including both clinical and pathological variables). We aimed to determine whether pathological parameters independently contribute to clinical models in predicting IgAN prognosis.

Clinical challenges of IgAN include accurately stratifying patients, helping clinicians to identify high-risk patients to enhance treatment, and avoiding unnecessary hormone and immunosuppressive therapies for low-risk patients. Recently, with the efforts of clinical nephrologists, multiple risk models have been established. Some models include baseline or follow-up clinical parameters, such as eGFR, proteinuria, and blood pressure, and some models add pathological parameters to the clinical models to establish combined models. These predictive models still benefit from other external validations, thereby increasing the confidence in their clinical use. In addition, whether pathological parameters, such as Oxford MEST predictors, can enhance the predictive value of clinical parameters for the prognosis of patients with IgA nephropathy remains controversial.

In this study, we assessed the performance of international IgAN prediction tools and another CKD model by external validation in a large, multicenter Chinese IgAN cohort. Relative to those in the derivation cohort of the international IgAN prediction tools, our follow-up time was shorter, and the incidence of a 50% decline in eGFR or development of ESRD was lower (12.5% versus 17.7%). Compared to the derivation cohort of the CKD model, our cohort had a lower proportion of patients with baseline eGFR < 30 ml/min/1.73 m², and fewer patients progressed to kidney failure/ESRD (9.3% versus 11%) during the follow-up period.

Indeed, we still found that the four models performed well at predicting the 5-year risk of renal outcomes. Compared with composite outcomes, better performance of those tools used for predicting ESRD at 5 years was observed. These models also performed relatively well at predicting short-term prognosis. In addition, we found that the clinical models based on baseline eGFR and various other clinical parameters had good performance, as did the combined model that included clinical and pathological indicators. For IgAN patients at low risk characterized by higher baseline eGFR, adding pathological variables could enhance the discriminatory ability of models that contain only clinical variables. Finally, after application to patients at low risk, the full model had the best performance in predicting ESRD among the four reported models.

Among patients with IgAN, there can be considerable heterogeneity in the risk for progression to kidney failure. Risk factors associated with IgAN progression have gained increasing attention over the last two decades (26–29). The emerging literature suggests improved patient outcomes with individualized risk prediction models (30–34). The availability of these risk prediction tools has led to better adherence to treatment guidelines and encouraged individual decision making (32–34). Despite these benefits, the lack of easily applicable and externally validated models has delayed the widespread integration of risk prediction in all fields of medicine (35, 36). We confirmed that the models rely on clinical data and histological markers of IgAN severity to predict the early risk of kidney failure at 5 years. Similar to Barbour (37, 38), we also confirmed that a lower estimated eGFR, more severe proteinuria, and male gender predict faster progression to kidney failure. In addition, a higher percentage of tubular injury and segmental sclerosis also predict a higher risk of kidney failure. These markers may enable a better estimate of the underlying processes of disease (39, 40).

Considering that those laboratory and pathological markers have been associated with the progression of IgAN, risk prediction models integrate them in different combinations. Based on our data, the performances of all models according to ROC analysis were compared. The clinical models and combined models showed similar performance in predicting ESRD after 5 years of follow-up. However, for patients at low risk characterized by higher baseline eGFR (≥60 ml/min/1.73 m²), the pathological variables could add predictive value to clinical variables, likely because the contribution of pathological indicators to ESRD prediction is diminished by the subsequent use of immunosuppressive therapy in high-risk patients characterized by lower eGFR.

Risk prediction models have important implications for clinical practice, research, and public health policy. Different risk thresholds could be used to triage patients for decision-making. For example, primary care physicians could manage lower-risk patients without additional testing or treatment of complications, whereas higher-risk patients could receive more intensive testing, intervention, and early nephrology care (41). Furthermore, the risk prediction model could be used to select higher-risk patients for enrollment into clinical trials and for the evaluation of risk-treatment interactions. In addition, the risk prediction model may be useful for identifying high-risk patients for public health interventions, thereby improving the cost-effectiveness of medical care.

The strength of our study is that we added a strict primary endpoint of ESRD, which is more relevant than other common endpoints based on declined eGFR or CKD stage. Specifically, for patients at low risk characterized by higher baseline eGFR (≥60 ml/min/1.73 m²), using the full model to predict ESRD at 5 years should be more precise. Additionally, both the CKD model and IgAN model exhibited similar performance in our IgAN cohort. We still need to improve the predictive ability of the models by adding IgAN-specific biomarkers, such as HAA-IgA1 levels. Alternatively, considering the genetic background of IgAN (1, 42–45), adding genetic risk factors (29, 46) involved in disease progression could also be useful for improving risk prediction of disease progression.

Our analysis also has limitations. We did not explicitly model the risk of all-cause mortality in our IgAN population because the number of deaths in our cohort might have been underestimated. In addition, disease duration and treatment information prior to renal biopsy were incomplete, thus we did not involve these data for the analysis. Moreover, although the CKD model from the study by Tangri was evaluated and performed well in our Chinese IgAN cohort, more CKD cohorts are still needed to validate it. Additionally, as this study was a multicenter cohort study, the heterogeneity of the study population is a limitation. The lack of detailed data on systematic therapies from all the centers is another limitation.

In summary, we validated recently reported highly accurate predictive models for the progression of IgAN to kidney failure. Especially for patients at low risk characterized by higher baseline eGFR (≥60 ml/min/1.73 m²), an improvement in model performance was observed after adding histological indicators to these clinical indicators.

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.

The studies involving human participants were reviewed and approved by the Ethics Research Committee from Ruijin Hospital, Medical School of Shanghai Jiaotong University. The patients/participants provided their written informed consent to participate in this study.

JXi designed and was responsible for the study. YO analyzed the data and drafted the paper. ZZ, GL, HL, FX, LS, ZC, SY, YJ, JXu, MS, HH, WD, ZF, XP, WM, and NC collected the data. JXi revised the paper. All authors approved the final version of the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by the Major International (Regional) Joint Research Program of National Natural Science Foundation of China (No:8211001014), the National Natural Science Foundation of China (Nos. 81870460, 81570598, 81370015, 81900656), “Excellent Academic Leader” by Shanghai Science and Technology Commission (No: 21XD1402000). Shanghai Jiao Tong University “Jiaotong Star” Plan Medical Engineering Cross Research Key Project (No: YG2019ZDA18, YG2019QNA37), Shanghai Shenkang Hospital Development Center “Three-year Action Plan for Promoting Clinical Skills and Clinical Innovation in Municipal Hospitals” (No : SHDC2020CR6017), Science and Technology Innovation Action Plan of Shanghai Science and Technology Committee (No. 17441902200), Shanghai Municipal Education Commission, Gaofeng Clinical Medicine Grant (No. 20152207), Shanghai Jiao Tong University School of Medicine, Multi-Center Clinical Research Project (No: DLY201510), Shanghai Health and Family Planning Committee Hundred Talents Program (No: 2018BR37), and Shanghai Municipal Key Clinical Specialty (shslczdzk02502). No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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