Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models.
OBJECTIVE
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy.
Fulvia
F
Ceccarelli
Dipartimento di Ingegneria dell'Informazione, Università di Firenze, Florence, Italy.
Marco
M
Sciandrone
Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy.
Carlo
C
Perricone
Dipartimento di Ingegneria dell'Informazione, Università di Firenze, Florence, Italy.
Giulio
G
Galvan
Dipartimento di Ingegneria dell'Informazione, Università di Firenze, Florence, Italy.
Francesco
F
Morelli
Departemento de Matematica, Universidade de Coimbra, Coimbra, Portugal.
Luis Nunes
LN
Vicente
Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy.
Ilaria
I
Leccese
Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy.
Laura
L
Massaro
Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy.
Enrica
E
Cipriano
Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy.
Francesca Romana
FR
Spinelli
Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy.
Cristiano
C
Alessandri
Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy.
Guido
G
Valesini
Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy.
Fabrizio
F
Conti
D000328:Adult; D018450:Disease Progression; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008180:Lupus Erythematosus, Systemic / Q000473:pathology*; D000069550:Machine Learning; D008297:Male; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index