Improving treatment decisions using personalized risk assessment from the International IgA Nephropathy Prediction Tool

Barbour, Sean J.; Canney, Mark; Coppo, Rosanna; Zhang, Hong; Liu, Zhihong; Suzuki, Yusuke; Matsuzaki, Keiichi; Katafuchi, Ritsuko; Induruwage, Dilshani; Er, Lee; Reich, Heather N.; Feehally, John; Barratt, Jonathan; Cattran, Daniel C.; Russo, María Luisa; Troyanov, Stéphan; Cook, H. Terence; Roberts, Ian S.; Tesař, Vladimı́r; Maixnerová, Dita; Lundberg, Sigrid; Gesualdo, Loreto; Emma, Francesco; Fuiano, Laura; Beltrame, Giulietta; Rollino, Cristiana; Amore, Alfonso; Camilla, Roberta; Peruzzi, Licia; Praga, Manuel; Feriozzi, Sandro; Polci, Rosaria; Segoloni, Giuseppe; Colla, Loredana; Pani, Antonello; Piras, Doloretta; Angioi, Andrea; Cancarini, Giovanni; Ravera, Sara; Durlik, M.; Moggia, Elisabetta; Ballarín, J.; Giulio, Salvatore Di; Pugliese, F.; Serriello, Ilaria; Çalışkan, Yaşar; Sever, Mehmet Şükrü; Kiliçaslan, İşın; Locatelli, Francesco; Vecchio, Lucia Del; Wetzels, Jack F.M.; Peters, Hilde P.E.; Berg, Ulla; Carvalho, Fernanda; Ferreira, Alves; Maggio, Milena; Wiȩcek, Andrzej; Ots-Rosenberg, Mai; Magistroni, Riccardo; Topaloğlu, Rezan; Bilginer, Yelda; D’Amico, Marco; Stangou, Μaria; Giacchino, F; Goumenos, Dimitris; Kalliakmani, Pantelitsa; Gerolymos, Miltiadis; Gales̃ić, Kres̆imir; Geddes, Colin C.; Siamopoulos, Kostas C.; Balafa, Olga; Galliani, Marco; Stratta, Piero; Quaglia, Marco; Bergia, R; Cravero, Raffaella; Salvadori, Maurizio; Cirami, Lino; Fellström, Bengt; Smerud, Hilde Kloster; Ferrario, Franco; Stellato, Tiziana; Egido, Jesús; Martín, Carina Aguilar; Floege, Jürgen; Eitner, Frank; Lupo, Antonio; Bernich, Patrizia; Menè, Paolo; Morosetti, Massimo; Kooten, Cees van; Rabelink, Ton J.; Reinders, Marlies E.J.; Grinyó, J.M.; Cusinato, Stefano; Benozzi, Luisa; Savoldi, Silvana; Licata, C.; Mizerska-Wasiak, Małgorzata; Martina, G; Messuerotti, Alessandra; Canton, Antonio Dal; Esposito, Ciro; Migotto, C.; Triolo, G; Mariano, Filippo; Pozzi, Claudio; Boero, R; Bellur, Shubha S.; Mazzucco, Gianna; Giannakakis, C.; Honsová, E; Sundelin, Bo; Palma, Anna Maria Di; Gutiérrez, Eduardo; Asunis, A.M.; Tardanico, Regina; Perkowska‐Ptasińska, Agnieszka; Terroba, J. Arce; Fortunato, M.; Pantzaki, Afroditi; Özlük, Yasemin; Steenbergen, Eric J.; Söderberg, Magnus; Riispere, Živile; Furci, Luciana; Orhan, Dıclehan; Kipgen, David; Casartelli, Donatella; Ljubanović, Danica Galešić; Gakiopoulou, Hariklia; Bertoni, E.; Ortiz, Pablo Cannata; Karkoszka, Henryk; Stoppacciaro, Antonella; Bajema, Ingeborg M.; Bruijn, Jan A.; Fulladosa, Xavier; Małdyk, Jadwiga; Ioachim, Elli; Bavbek, Nüket; Cook, Terry; Alpers, Charles E.; Berthoux, F; Bonsib, Stephen M.; D’Agati, Vivette D.; D’Amico, Giuseppe; Emancipator, Steven N.; Emmal, F.; Fervenza, Fernando C.; Florquin, Sandrine; Fogo, Agnes B.; Groene, Hermann Josef; Haas, Mark; Hill, Prue; Hogg, Ronald J.; Hsu, Stephen I‐Hong; Hunley, Tracy E.; Hladunewich, Michelle A.; Jennette, Caroline E.; Joh, Kensuke; Julian, Bruce A.; Kawamura, Tetsuya; Lai, Fernand Mac‐Moune; Leung, Chi Bon; Li, L.; Li, P.; Liu, Z.; Massat, Alfonso Eirin; Mackinnon, Bruce; Mezzano, Sergio; Schena, Francesco Paolo; Tomino, Yasuhiko; Walker, Patrick D.; Wang, H.; Weening, Jj; Zeng, Caihong; Shi, Sufang; Nogi, Chieko; Suzuki, Hodaka; Koike, Kentaro; Hirano, Keita; Yokoo, Takashi; Hanai, M.; Fukami, Kei; Takahashi, K.; Yuzawa, Yukio; Niwa, Misao; Yasuda, Yoshinari; Maruyama, Shoichi; Ichikawa, Daisuke; Suzuki, Takahide; Shirai, Sayuri; Fukuda, Akira; Fujimoto, Shota; Trimarchi, Hernán

doi:10.1016/j.kint.2020.04.042

Cited by 44 publications

(25 citation statements)

References 30 publications

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“…In this way, the "simple, robust" models validated in our cohort could be applied to improve risk prediction, which was approved to both increase treatment allocation to patients at high risk of disease progression and avoid treatment in patients with nonprogressive disease. 34 Moreover, for the full model without race, the calibration was acceptable. However, for the full model with race, it seemed to overestimate the prediction risk over 3 years in our cohort.…”

Section: Clinical Researchmentioning

confidence: 97%

External Validation of International Risk-Prediction Models of IgA Nephropathy in an Asian-Caucasian Cohort

Zhang

Guo

Wang

et al. 2020

Kidney International Reports

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Introduction: Two prediction models for IgA nephropathy (IgAN) using clinical variables and the Oxford MEST scores were developed and validated in 2 multiethnic cohorts. Additional external validation is required. Methods: Biopsy-proven Chinese and Argentinian patients with IgAN were included. The primary outcome was defined as a 50% decline in estimated glomerular filtration rate (eGFR) or end-stage renal disease. C-statistics and stratified analyses were used for model discrimination, coefficient of determination (R 2 D) for model fit, and calibration plots for model calibration. Baseline survival function was also evaluated. Results: A total of 1275 patients were enrolled, with a mean age of 34 (interquartile range: 27-42) years, 50% of whom (638 of 1275) were men. Use of renin-angiotensin system blockers was higher than in previously reported cohorts, whereas other variables were comparable. The C-statistic of the models was 0.81, and R 2 D was higher than reported. Survival curves in the subgroups (<16th, w16th to <50th, w50th to <84th, and $84th percentiles of linear predictor) were well separated. Most of the predictor variables, including hazard ratio, predicted 5-year risk, and eGFR decline slope, were worse with risk increasing. The baseline survival function was comparable in our cohort and the reported cohorts. The calibration was acceptable for the full model without race. However, the risk probability over 3 years was overestimated in the full model with race included. Conclusion: The prediction models showed good performance on personalized risk assessment, which may be used as drug-specific, precision-medicine approaches to treatment decisionmaking.

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Section: Clinical Researchmentioning

confidence: 97%

External Validation of International Risk-Prediction Models of IgA Nephropathy in an Asian-Caucasian Cohort

Zhang

Guo

Wang

et al. 2020

Kidney International Reports

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“…Due to the potential of these emerging technologies, we expect a substantial increase in studies assessing their potential in the coming years. There have been some notable advances predicting disease progression [106,112,113], but there is still room for improvement.…”

Section: Discussionmentioning

confidence: 99%

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

2021

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IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.

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“…The strength of our study is that it used a population of patients with long-term follow-up, far more than the original and other validation cohorts ( 7 , 23 ). This gives us the advantage of capturing those patients with silent and gradual but ominous disease progression well beyond after diagnosis which is an IgAN characteristic ( 24 ).…”

Section: Discussionmentioning

confidence: 99%

“…Attempts in the past to establish a prediction model have not met widespread acceptance ( 5 , 6 ). Although the Oxford MEST [mesangial (M) and endocapillary (E) hypercellularity, segmental sclerosis (S), and interstitial fibrosis/tubular atrophy (T)] histologic score in IgAN has been validated in international patient cohorts and is independently associated with a higher or lower risk of kidney function deterioration, it has only been recently incorporated into a risk prediction tool ( 7 ). This tool was developed by the International IgA Nephropathy Network, and it validated two versions: the full model without and with race.…”

Section: Introductionmentioning

confidence: 99%

Validation of the International IgA Nephropathy Prediction Tool in the Greek Registry of IgA Nephropathy

et al. 2022

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BackgroundImmunoglobulin A nephropathy (IgAN) is among the commonest glomerulonephritides in Greece and an important cause of end-stage kidney disease (ESKD) with an insidious chronic course. Thus, the recently published International IgAN prediction tool could potentially provide valuable risk stratification and guide the appropriate treatment module. This study aimed to externally validate this prediction tool using a patient cohort from the IgAN registry of the Greek Society of Nephrology.MethodsWe validated the predictive performance of the two full models (with or without race) derived from the International IgAN Prediction Tool study in the Greek Society of Nephrology registry of patients with IgAN using external validation of survival prediction models (Royston and Altman). The discrimination and calibration of the models were tested using the C-statistics and stratified analysis, coefficient of determination (RD2) for model fit, and the regression coefficient of the linear predictor (βPI), respectively.ResultsThe study included 264 patients with a median age of 39 (30–51) years where 65.2% are men. All patients were of Caucasian origin. The 5-year risk of the primary outcome (50% reduction in estimated glomerular filtration rate or ESKD) was 8%. The RD2 for the full models with and without race when applied to our cohort was 39 and 35%, respectively, and both were higher than the reported RD2 for the models applied to the original validation cohorts (26.3, 25.3, and 35.3%, respectively). Harrel's C statistic for the full model with race was 0.71, and for the model without race was 0.70. Renal survival curves in the subgroups (<16th, ~16 to <50th, ~50 to <84th, and >84th percentiles of linear predictor) showed adequate separation. However, the calibration proved not to be acceptable for both the models, and the risk probability was overestimated by the model.ConclusionsThe two full models with or without race were shown to accurately distinguish the highest and higher risk patients from patients with low and intermediate risk for disease progression in the Greek registry of IgAN.

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Improving treatment decisions using personalized risk assessment from the International IgA Nephropathy Prediction Tool

Cited by 44 publications

References 30 publications

External Validation of International Risk-Prediction Models of IgA Nephropathy in an Asian-Caucasian Cohort

External Validation of International Risk-Prediction Models of IgA Nephropathy in an Asian-Caucasian Cohort

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

Validation of the International IgA Nephropathy Prediction Tool in the Greek Registry of IgA Nephropathy

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