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Amyloidosis secondary to familial Mediterranean fever: machine learning-based prediction models
Berkay Aktas1, Enes Azman1, Yusuf Ecren Oner1
1Division of Rheumatology, Internal Medicine Department, Cerrahpasa Faculty of Medicine, Istanbul-University Cerrahpasa, Istanbul, Türkiye.
Objectives:
FMF is a monogenic autoinflammatory disease caused by MEFV mutations, with amyloidosis as its most severe complication. This study aimed to compare logistic regression, random forest, and gradient-boosting models to predict amyloidosis in FMF patients.
Methods:
Patients with FMF diagnosed between 1990 and 2022 at Cerrahpaşa Faculty of Medicine were retrospectively screened. Eligible patients with available clinical records were included. Clinical, genetic, and laboratory variables were extracted. Univariate analyses and pooled multivariate logistic regression were conducted. Logistic regression, random forest, and gradient-boosting machine-learning models were developed. Models were optimized by hyperparameter tuning and evaluated on a held-out test set.
Results:
Of the 615 FMF patients included, 58 (9.4%) had amyloidosis. Patients with amyloidosis had earlier symptom onset, longer diagnostic delay and disease duration, were more often male, and more frequently carried M694V homozygosity. Comorbidities, parental consanguinity, frequent infections, erysipelas-like erythema, myalgia, arthritis, and higher median CRP levels were more prevalent in the amyloidosis group. In multivariate analysis, disease duration, M694V homozygosity, comorbidity, parental consanguinity, frequent infections, and erysipelas-like erythema were independently associated with amyloidosis. Random forest achieved the best performance on the test set (area under the receiver operating characteristic = 0.784), followed by logistic regression (0.766) and gradient boosting (0.749). SHAP analysis identified M694V homozygosity, myalgia, erysipelas-like erythema, disease duration, and arthritis as the strongest contributors to amyloidosis prediction.
Conclusion:
Amyloidosis, the most severe FMF complication, is driven by key clinical and genetic factors, with ensemble machine-learning models outperforming conventional regression for early risk prediction.
Insights
Machine learning models can predict amyloidosis, a severe complication of Familial Mediterranean Fever (FMF). Ensemble methods like random forest show promise for early risk identification in FMF patients.
Area of Science:
- Genetics and Precision Medicine
- Autoinflammatory Diseases
- Machine Learning in Healthcare
Background:
- Familial Mediterranean Fever (FMF) is a genetic autoinflammatory disorder.
- Amyloidosis is the most severe complication of FMF, leading to significant morbidity.
- Predicting amyloidosis risk is crucial for timely intervention in FMF patients.
Purpose of the Study:
- To compare the predictive performance of logistic regression, random forest, and gradient boosting models for amyloidosis in FMF patients.
- To identify key clinical and genetic factors associated with amyloidosis development in FMF.
Main Methods:
- Retrospective analysis of 615 FMF patients diagnosed between 1990 and 2022.
- Extraction of clinical, genetic, and laboratory data.
- Development and evaluation of logistic regression, random forest, and gradient boosting models using hyperparameter tuning and a held-out test set.
Main Results:
- Amyloidosis was present in 9.4% of patients, associated with earlier onset, male sex, M694V homozygosity, comorbidities, and specific clinical features.
- Multivariate analysis identified disease duration, M694V homozygosity, comorbidity, parental consanguinity, frequent infections, and erysipelas-like erythema as independent predictors.
- Random forest model demonstrated the highest predictive performance (ROC-AUC = 0.784), outperforming logistic regression and gradient boosting.
Conclusions:
- Amyloidosis in FMF is influenced by a combination of clinical and genetic factors.
- Ensemble machine learning models, particularly random forest, offer superior early risk prediction for amyloidosis compared to traditional logistic regression.
- Identifying high-risk FMF patients enables proactive management to prevent severe complications like amyloidosis.
