Prediction of chronic damage in systemic lupus erythematosus by using machine-learning modelsReportar como inadecuado

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The increased survival in Systemic Lupus Erythematosus SLE patients implies the development of chronic damage, occurring in up to 50% of cases. Its prevention is a major goal in the SLE management. We aimed at predicting chronic damage in a large monocentric SLE cohort by using neural networks.


We enrolled 413 SLE patients M-F 30-383; mean age ± SD 46.3±11.9 years; mean disease duration ± SD 174.6 ± 112.1 months. Chronic damage was assessed by the SLICC-ACR Damage Index SDI. We applied Recurrent Neural Networks RNNs as a machine-learning model to predict the risk of chronic damage. The clinical data sequences registered for each patient during the follow-up were used for building and testing the RNNs.


At the first visit in the Lupus Clinic, 35.8% of patients had an SDI≥1. For the RNN model, two groups of patients were analyzed: patients with SDI = 0 at the baseline, developing damage during the follow-up N = 38, and patients without damage SDI = 0. We created a mathematical model with an AUC value of 0.77, able to predict damage development. A threshold value of 0.35 sensitivity 0.74, specificity 0.76 seemed able to identify patients at risk to develop damage.


We applied RNNs to identify a prediction model for SLE chronic damage. The use of the longitudinal data from the Sapienza Lupus Cohort, including laboratory and clinical items, resulted able to construct a mathematical model, potentially identifying patients at risk to develop damage.

Autor: Fulvia Ceccarelli , Marco Sciandrone , Carlo Perricone, Giulio Galvan, Francesco Morelli, Luis Nunes Vicente, Ilaria Leccese, Lau



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