Joint Damage Prediction in Non-Severe Hemophilia A with Artificial Intelligence

Ana Marco-Rico,1 José Manuel Calvo-Villas,2 Francisco-Jose Lopez-Jaime,3 Mariana Canaro Hirnyk,4 María del Mar Nieto-Hernández,5 Sonia Herrero Martín,6 Laura Entrena-Ureña,7 Shally Marcellini-Antonio,8 Bolivar L Diaz-Jordan,9 Sergio Jurado-Herrera,10 Noelia F Pérez-González,10 Covadonga García-Díaz,11 Faustino García-Candel,12 Ihosvany Fernández-Bello,1 Jorge Mateo-Sotos,13,14 Pascual Marco-Vera1,15 1Department of Hematology and Hemotherapy, Dr. Balmis General University Hospital-ISABIAL, Alicante, Spain; 2Department of Hematology and Hemotherapy, Miguel Servet University Hospital, Zaragoza, Spain; 3Department of Hematology and Hemotherapy, Málaga Regional University Hospital-IBIMA, Málaga, Spain; 4Department of Hematology and Hemotherapy, Son Espases University Hospital, Palma de Mallorca, Spain; 5Department of Hematology and Hemotherapy, Jaén University Hospital, Jaén, Spain; 6Department of Hematology and Hemotherapy, Guadalajara University Hospital, Guadalajara, Spain; 7Department of Hematology and Hemotherapy, Virgen de Las Nieves University Hospital, Granada, Spain; 8Department of Hematology and Hemotherapy, Segovia General Hospital, Segovia, Spain; 9Department of Hematology and Hemotherapy, Valdepeñas General Hospital, Ciudad Real, Spain; 10Department of Hematology and Hemotherapy, Torrecárdenas University Hospital, Almería, Spain; 11Department of Hematology and Hemotherapy, Burgos University Hospital, Burgos, Spain; 12Department of Hematology and Hemotherapy, Virgen de la Arrixaca University Hospital, Murcia, Spain; 13Medical Analysis Expert Group, Castilla-La Mancha University, Cuenca, Spain; 14Health Research Institute in Castilla-La Mancha (IDISCAM), Toledo, Spain; 15Department of Clinical Medicine, Miguel Hernández University, Alicante, SpainCorrespondence: Ihosvany Fernández-Bello, Department of Hematology and Hemotherapy, Dr. Balmis General University Hospital-ISABIAL, Avenida Pintor Baeza 12, Alicante, 03010, Spain, Email ihosvanyf@yahoo.esPurpose: Patients with non-severe hemophilia A (PwnSHA) can develop joint damage (JD). The objective was to identify a machine learning model based on routinely collected variables to predict the presence of JD in PwnSHA.Patients and Methods: A nationwide, multicenter, cross-sectional study was conducted. Clinical and laboratory variables to assess joint health were included. Predictors were age, target joint history, thrombin generation capacity, baseline factor VIII (FVIII) measured by one-stage clotting (FVIII-CLOT) and chromogenic (FVIII-CHR) assays, and the FVIII-CLOT/FVIII-CHR ratio. The joint condition was described using the HEAD-US score. JD was defined as HEAD-US > 0. A Random Forest (RF) ensemble was trained with regression-based multiple imputation, z-scaling, and Synthetic Minority Oversampling within a stratified five-fold stratified cross-validation repeated 100 times. Support Vector Machine, Decision Tree, Gaussian Naïve Bayes and k-Nearest Neighbors were used as comparators. Model performance was assessed on held-out test folds, and 95% confidence intervals (CIs) were obtained by bootstrap resampling with 10,000 repetitions.Results: Eighty-four Spanish males ≥ 12 years old were enrolled. Forty-two percent (35/84) had JD. JD was present in 30% (3/10) of patients with moderate hemophilia and 43% (32/74) with mild hemophilia. The RF achieved an accuracy of 92.0% (95% CI: 90.72– 93.31), a recall of 92.1% (95% CI: 90.87– 93.41), a specificity of 91.9% (95% CI: 90.58– 93.27), and an AUC-ROC of 0.92 (95% CI: 0.907– 0.938), outperforming all alternative classifiers. Permutation-based feature importance identified age, target joint history, thrombin generation and the FVIII-CLOT/FVIII-CHR ratio as the most influential variables.Conclusion: The RF model identifies PwnSHA more likely to have prevalent, occult JD in a cross-sectional setting, enabling rapid triage for targeted HEAD-US evaluation. External and prospective validation in larger cohorts is now warranted to confirm generalizability and to facilitate integration into electronic health-record decision-support systems aimed at preserving long-term joint health in PwnSHA.Keywords: non-severe hemophilia a, joint damage, machine learning, random forest, thrombin generation