Artificial neural network - an effective tool for predicting the lupus nephritis outcome

• Published in: BMC nephrology Vol. 23; no. 1; pp. 1 - 381
• Main Authors: Stojanowski, Jakub, Konieczny, Andrzej, Rydzyńska, Klaudia, Kasenberg, Izabela, Mikołajczak, Aleksandra, Gołębiowski, Tomasz, Krajewska, Magdalena, Kusztal, Mariusz
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 28-11-2022; BioMed Central; BMC
• Subjects: Accuracy; Analysis; Artificial intelligence; Biopsy; Care and treatment; Complications and side effects; Creatinine; Diagnosis; End-stage renal disease; Health aspects; Kidney diseases; Kidneys; Lupus; Lupus nephritis; Machine learning; Methods; Nephritis; Nephrology; Nephropathy; Neural networks; Neurons; Patients; Prediction models; Proteinuria; Remission; Remission (Medicine); Risk factors; Systemic lupus erythematosus; Poland
• ISSN: 1471-2369; 1471-2369
• DOI: 10.1186/s12882-022-02978-2

Background Lupus nephropathy (LN) occurs in approximately 50% of patients with systemic lupus erythematosus (SLE), and 20% of them will eventually progress into end-stage renal disease (ESRD). A clinical tool predicting remission of proteinuria might be of utmost importance. In our work, we focused on predicting the chance of complete remission achievement in LN patients, using artificial intelligence models, especially an artificial neural network, called the multi-layer perceptron. Methods It was a single centre retrospective study, including 58 individuals, with diagnosed systemic lupus erythematous and biopsy proven lupus nephritis. Patients were assigned into the study cohort, between 1st January 2010 and 31st December 2020, and eventually randomly allocated either to the training set (N = 46) or testing set (N = 12). The end point was remission achievement. We have selected an array of variables, subsequently reduced to the optimal minimum set, providing the best performance. Results We have obtained satisfactory results creating predictive models allowing to assess, with accuracy of 91.67%, a chance of achieving a complete remission, with a high discriminant ability (AUROC 0.9375). Conclusion Our solution allows an accurate assessment of complete remission achievement and monitoring of patients from the group with a lower probability of complete remission. The obtained models are scalable and can be improved by introducing new patient records. Keywords: Artificial intelligence, Machine learning, Proteinuria, Systemic lupus erythematosus, Lupus nephritis, End-stage renal disease