BackgroundThe 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. MethodsWe 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. ResultsThe 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. ConclusionsThis CDSS provides useful additional information for identifying 'high-risk' IgAN patients and may help stratify them in the context of a personalized medicine approach.

Clinical decision support system for end-stage kidney disease risk estimation in IgA nephropathy patients

Pesce F.;Binetti G.;Naso D.;Di Sciascio E.;Schena F. P.
2016-01-01

Abstract

BackgroundThe 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. MethodsWe 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. ResultsThe 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. ConclusionsThis CDSS provides useful additional information for identifying 'high-risk' IgAN patients and may help stratify them in the context of a personalized medicine approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/312975
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