FC048: New Tool to Predict the Clinical Course and Renal Failure in Patients with Immunoglobulin a Nephropathy. (3rd May 2022)

• Record Type: Journal Article
• Authors: Paolo Schena, Francesco; Walter Anelli, Vto; Cornacchia, Giandomenico; DI Noia, Tommaso; Stangou, Maria; Papagianni, Aikaterina; Coppo, Rosanna
• Is Part Of: Nephrology dialysis transplantation. Volume 37(2022)Supplement 3
• Publication Date: 2022-05-03
• Subjects: Nephrology -- Periodicals; Hemodialysis -- Periodicals; Kidneys -- Transplantation -- Periodicals; Hemodialysis; Kidneys -- Transplantation; Nephrology; Periodicals; 616.61
• DOI: 10.1093/ndt/gfac105.004
• Languages: English
• ISSNs: 0931-0509

Abstract

Abstract: BACKGROUND AND AIMS: Idiopathic Immunoglobulin A nephropathy (IgAN) is the most common biopsy-proven glomerulonephritis in the world. Approximately 40% of IgAN patients reach renal failure (RF) 20 years after their kidney biopsy. The high prevalence of RF shows that IgAN has a significant economic impact in the countries because renal replacement therapy is costly. Moreover, the disease's onset in the second and third decades of life represents a social challenge because patients are typically very active and highly productive in the workplace. This challenge is one more reason to move on the prediction of the clinical course and RF in IgAN patients at the time of the kidney biopsy and during the follow-up. We developed an artificial neural network (ANN) tool (DialCheck 1.0) based on seven variables and the histological score of the kidney biopsy to predict RF in IgAN patients at the time of kidney biopsy [1 ]. METHOD: We have recently developed a new tool which consists of a set of ANN-powered models that combine temporally accurate observations for fine-grained features and leverage state-of-the-art deep neural network techniques to forecast the patient's clinical evolution. RESULTS: A cohort of 948 IgAN patients, of whom the clinical course of the disease was known, was used to develop the new tool that predicts the dynamics of age and disease laboratory parameters (serum creatinine, daily proteinuria), blood pressure, histological score of the kidney biopsy and the RF. The system was designed to help the physicians give a broader spectrum of information regarding the patient and the potential clinical development of IgAN and outcomes. In detail, the model computes a latent dynamic representation of the patient and predicts a prospective clinical picture of the patient and the probability of RF. The tool with an accuracy of >80% will be tested in an independent retrospective cohort of 454 IgAN patients and in a prospective multicenter randomized clinical study in which 426 IgAN patients will be enrolled in two different groups based on the type of kidney lesions (active and chronic renal lesions). CONCLUSION: We have a new tool (DialCheck 2.0), based on ANN, that may predict the outcome and the RF in IgAN patients. Moreover, it may help the physician to analyze the long-term response to therapy.