DEVELOPMENT OF A PREDICTIVE COMPOSITE INDEX FOR EARLY DIAGNOSIS OF PSORIATIC ARTHRITIS

Objective. Psoriatic arthritis (PsA) is a progressive inflammatory disease with diagnostic challenges in early stages. This study aimed to develop a mathematical model for early PsA diagnosis, integrating clinical manifestations, inflammatory biomarkers, imaging findings, and immunological alterations to distinguish early PsA from cutaneous psoriasis (PsO) without musculoskeletal involvement.

Materials and Methods. A retrospective case-control study was conducted from 2014 to 2022 at IMSP Republican Clinical Hospital Timofei MoÈ™neaga. The study included 200 patients: early PsA (n=100) and PsO without musculoskeletal involvement (n=100). Clinical assessments included tender joint count (TJC)and swollen joint counts (SJC), morning stiffness, enthesitis, and dactylitis. Inflammatory markers and imaging evaluations were analyzed. A predictive model was developed using multiple regression analysis, incorporating significant diagnostic variables. Sensitivity and specificity were evaluated via ROC curve analysis and validated through bootstrapping.

Results. Early PsA patients had significantly higher TJC (7.5  0.5 vs. 2.2  0.5, p = 0.0032), SJC (4.5  0.3 vs. 2.9  0.7, p = 0.0057), and morning stiffness (37.7  5.5 min vs. 10.2  4.5 min, p = 0.00018). Enthesitis prevalence was higher in early PsA (78%) vs. PsO (31%, p = 0.00023). The predictive model demonstrated 89% sensitivity and 84% specificity in identifying early PsA.

Conclusion. The model effectively differentiates early PsA from PsO, integrating key clinical and laboratory parameters. Its high sensitivity and specificity support clinical utility for early diagnosis and intervention. Further validation in multicenter cohorts is needed.