Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models
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Published on: November 1, 2015
Fulvia Ceccarelli1, Marco Sciandrone2, Carlo Perricone1
1Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy.
Insights
Researchers used neural networks to predict chronic damage in Systemic Lupus Erythematosus (SLE) patients. The model identified individuals at risk, aiding in prevention strategies for this autoimmune disease.
Area of Science:
- Rheumatology
- Artificial Intelligence
- Medical Informatics
Background:
- Systemic Lupus Erythematosus (SLE) survival has increased, leading to a higher incidence of chronic damage in up to 50% of patients.
- Preventing chronic damage is a critical objective in managing SLE.
- Predictive models can aid in early intervention and improved patient outcomes.
Purpose of the Study:
- To develop and validate a predictive model for chronic damage in SLE patients using neural networks.
- To identify patients at high risk of developing chronic damage for targeted prevention strategies.
- To leverage longitudinal clinical data for accurate damage prediction.
Main Methods:
- Recurrent Neural Networks (RNNs) were employed as a machine-learning model.
- Longitudinal clinical data sequences from 413 SLE patients were utilized.
- The SLICC/ACR Damage Index (SDI) was used to assess chronic damage.
Main Results:
- The RNN model achieved an Area Under the Curve (AUC) of 0.77 for predicting damage development.
- A threshold of 0.35 demonstrated a sensitivity of 0.74 and specificity of 0.76 in identifying at-risk patients.
- 35.8% of patients presented with an SDI of 1 or higher at their initial visit.
Conclusions:
- Recurrent Neural Networks (RNNs) can effectively predict the risk of chronic damage in SLE patients.
- Utilizing longitudinal data from the Sapienza Lupus Cohort enabled the construction of a robust predictive model.
- This approach has the potential to identify patients requiring closer monitoring and early intervention to prevent damage progression.
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.
Conclusion:
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.
