Francesco Pesce1, Mattea Diciolla2, Giulio Binetti2, David Naso2, Vito Claudio Ostuni2, Tommaso Di Noia2, Ann Merethe Vågane3, Rune Bjørneklett3, Hitoshi Suzuki4, Yasuhiko Tomino4, Eugenio Di Sciascio2, Francesco Paolo Schena5. 1. Cardiovascular Genetics and Genomics, National Heart and Lung Institute, Royal Brompton Hospital, Imperial College London, London, UK Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy. 2. Department of Electrical and Information Engineering, Politecnico of Bari, Bari, Italy. 3. Department of Clinical Medicine, Renal Research Group, University of Bergen, Bergen, Norway Department of Medicine, Haukeland University Hospital, Bergen, Norway. 4. Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Juntendo University, Tokyo, Japan. 5. C.A.R.S.O. Consortium, University of Bari, Bari, Italy Schena Foundation, European Research Centre of Kidney Diseases, Bari, Italy.
Abstract
BACKGROUND: The progression of IgA nephropathy (IgAN) to end-stage kidney disease (ESKD) depends on several factors that are not quite clear and tangle the risk assessment. We aimed at developing a clinical decision support system (CDSS) for a quantitative risk assessment of ESKD and its timing using available clinical data at the time of renal biopsy. METHODS: We included a total of 1040 biopsy-proven IgAN patients with long-term follow-up from Italy (N = 546), Norway (N = 441) and Japan (N = 53). Of these, 241 patients reached ESKD: 104 Italian [median time to ESKD = 5 (3-9) years], 134 Norwegian [median time to ESKD = 6 (2-11) years] and 3 Japanese [median time to ESKD = 3 (2-12) years]. We independently trained and validated two cooperating artificial neural networks (ANNs) for predicting first the ESKD status and then the time to ESKD (defined as three categories: ≤ 3 years, between > 3 and 8 years and over 8 years). As inputs we used gender, age, histological grading, serum creatinine, 24-h proteinuria and hypertension at the time of renal biopsy. RESULTS: The ANNs demonstrated high performance for both the prediction of ESKD (with an AUC of 89.9, 93.3 and 100% in the Italian, Norwegian and Japanese IgAN population, respectively) and its timing (f-measure of 90.7% in the cohort from Italy and 70.8% in the one from Norway). We embedded the two ANNs in a CDSS available online (www.igan.net). Entering the clinical parameters at the time of renal biopsy, the CDSS returns as output the estimated risk and timing of ESKD for the patient. CONCLUSIONS: This CDSS provides useful additional information for identifying 'high-risk' IgAN patients and may help stratify them in the context of a personalized medicine approach.
BACKGROUND: The progression of IgA nephropathy (IgAN) to end-stage kidney disease (ESKD) depends on several factors that are not quite clear and tangle the risk assessment. We aimed at developing a clinical decision support system (CDSS) for a quantitative risk assessment of ESKD and its timing using available clinical data at the time of renal biopsy. METHODS: We included a total of 1040 biopsy-proven IgANpatients with long-term follow-up from Italy (N = 546), Norway (N = 441) and Japan (N = 53). Of these, 241 patients reached ESKD: 104 Italian [median time to ESKD = 5 (3-9) years], 134 Norwegian [median time to ESKD = 6 (2-11) years] and 3 Japanese [median time to ESKD = 3 (2-12) years]. We independently trained and validated two cooperating artificial neural networks (ANNs) for predicting first the ESKD status and then the time to ESKD (defined as three categories: ≤ 3 years, between > 3 and 8 years and over 8 years). As inputs we used gender, age, histological grading, serum creatinine, 24-h proteinuria and hypertension at the time of renal biopsy. RESULTS: The ANNs demonstrated high performance for both the prediction of ESKD (with an AUC of 89.9, 93.3 and 100% in the Italian, Norwegian and Japanese IgAN population, respectively) and its timing (f-measure of 90.7% in the cohort from Italy and 70.8% in the one from Norway). We embedded the two ANNs in a CDSS available online (www.igan.net). Entering the clinical parameters at the time of renal biopsy, the CDSS returns as output the estimated risk and timing of ESKD for the patient. CONCLUSIONS: This CDSS provides useful additional information for identifying 'high-risk' IgANpatients and may help stratify them in the context of a personalized medicine approach.
Authors: Sean J Barbour; Rosanna Coppo; Hong Zhang; Zhi-Hong Liu; Yusuke Suzuki; Keiichi Matsuzaki; Ritsuko Katafuchi; Lee Er; Gabriela Espino-Hernandez; S Joseph Kim; Heather N Reich; John Feehally; Daniel C Cattran Journal: JAMA Intern Med Date: 2019-07-01 Impact factor: 21.873
Authors: Daniel P Gale; Karen Molyneux; David Wimbury; Patricia Higgins; Adam P Levine; Ben Caplin; Anna Ferlin; Peiran Yin; Christopher P Nelson; Horia Stanescu; Nilesh J Samani; Robert Kleta; Xueqing Yu; Jonathan Barratt Journal: J Am Soc Nephrol Date: 2017-02-16 Impact factor: 10.121