Abstract
BACKGROUND: Osteonecrosis of the femoral head (ONFH) is a progressive and debilitating condition characterized by the death of bone tissue due to inadequate blood supply. Despite advances in diagnostic imaging and treatment strategies, predicting the risk of femoral head collapse remains a significant clinical challenge. This study seeks to address this gap by developing a robust prognostic model that integrates clinical, imaging, and laboratory data to improve early diagnosis and guide therapeutic decision-making. METHODS: We conducted a qualitative systematic review and employed the Delphi method to select key prognostic factors from clinical data, imaging findings, and laboratory indicators. The study included ONFH patients treated from January 2014 to December 2021. We used univariate and multivariate Cox regression analyses to develop a nomogram for predicting the risk of femoral head collapse. The model's performance was evaluated using the concordance index (C-index), calibration plots, and decision curve analysis (DCA). RESULTS: The study included 297 patients (454 hips) with ONFH. Key prognostic factors identified included pain presence (p < 0.001, RR = 0.185, 95% CI: 0.11-0.31), JIC classification (C1: p < 0.001, RR = 0.096, 95% CI: 0.054-0.171; C2: p < 0.001, RR = 0.323, 95% CI: 0.215-0.487), necrotic area (3 < MNAI < 6: p < 0.001, RR = 0.107, 95% CI: 0.061-0.190; MNAI ≥ 6: p < 0.001, RR = 0.466, 95% CI: 0.314-0.692), weight-bearing reduction (p < 0.001, RR = 0.466, 95% CI: 0.323-0.672), preservation of the anterolateral pillar (p < 0.001, RR = 0.223, 95% CI: 0.223-0.473), and subchondral bone fracture on CT (p < 0.001, RR = 0.32, 95% CI: 0.217-0.472). The nomogram demonstrated a high C-index of 0.88, indicating excellent predictive accuracy. Calibration plots showed good agreement between predicted and observed outcomes, and DCA confirmed the model's clinical utility. CONCLUSIONS: The prognostic model developed in this study provides a reliable tool for predicting femoral head collapse in ONFH patients. It allows for early identification of high-risk patients, guiding personalized treatment strategies to improve patient outcomes and reduce the need for invasive surgical procedures.