Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models
Open accessAbstract
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.
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Many AI applications in lupus primarily focus on SLE diagnosis, severity classification, or risk prediction of complications such as lupus nephritis.
“Many AI applications in lupus primarily focus on SLE diagnosis, severity classification, or risk prediction of complications such as lupus nephritis (Ceccarelli et al, 2017; Li et al, 2022; Ma et al, 2022; Yaung et al, 2023).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Ceccarelli et al, 2017
The Introduction and Discussion (page numbers not provided) describe prior machine-learning applications predicting lupus nephritis and classifying its histology, supporting the complication-prediction part of the claim. They do not establish that many lupus AI applications primarily focus on SLE diagnosis or severity classification; this paper's own model predicts chronic damage. (LLM, conf=0.82)
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- Authors
- Ceccarelli, F., Sciandrone, M., Perricone, C., Galvan, G., Morelli, F., Vicente, L. N., Leccese, I., Massaro, L., Cipriano, E., Spinelli, F. R., Alessandri, C., Valesini, G., & Conti, F.
- Affiliations
- Sapienza University of Rome (IT)
- University of Florence (IT)
- University of Coimbra (PT)
- Corresponding author
- Alessandri, Cristiano
- ORCID
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-03-22
- Volume
- 12
- Issue
- 3
- Pages
- e0174200
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5362169
- OpenAlex
- W2598710582
- Cited by
- 53
- References
- 25
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 6 · 2023: 6 · 2022: 14 · 2021: 8 · 2020: 6 · 2019: 4 · 2018: 1 · 2017: 1 · 2016: 1
- FWCI
- 2.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Florence Research (University of Florence) (submittedVersion, other-oa)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- Topics
- Health Sciences › Medicine › Rheumatology › Systemic Lupus Erythematosus Research
- Health Sciences › Medicine › Pathology and Forensic Medicine › Systemic Sclerosis and Related Diseases
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- systemic lupus erythematosus, damage prediction, recurrent neural networks, SLICC/ACR damage index, longitudinal clinical data, risk stratification
- MeSH terms
- Machine Learning, Adult, Female, Humans, Longitudinal Studies, Lupus Erythematosus, Systemic, Male, Sensitivity and Specificity, Severity of Illness Index, Disease Progression
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