Abstract
Introduction: The diagnosis of thoracic bone fractures in patients with chest trauma is a critical area of study, particularly in emergency medicine. Clinical findings and examinations play a significant role in accurately diagnosing these injuries, which can lead to improved patient outcomes.
Objectives: This investigation focuses on the predictive power of various clinical indicators and examination techniques used in diagnosing thorax bone fractures among patients referred to Taleghani hospital in Kermanshah, Iran, from April 2022 to November 2024.
Patients and Methods: This retrospective cross-sectional study was conducted to assess the predictive power of clinical findings and examinations in diagnosing thorax bone fractures among patients with chest trauma at Taleghani hospital from April 2022 to November 2024. Data were collected from clinical documents, including demographic information, clinical findings, and imaging studies (X-rays, ultrasounds, and CT scans), focusing on patients aged 18 and older. Logistic regression was conducted to evaluate the correlations between clinical indicators and bone fracture diagnoses.
Results: The study included 997 chest trauma patients with a mean age of 43.02 ± 16.7 years, of whom 114 (11.4%) were found to have thoracic bone fractures. Age was identified as an independent predictor for thorax bone fracture, with an OR of 1.02, and hemothorax and pneumothorax demonstrated a strong association, with an OR of 6.25 and 5.40. Similarly, patients with a pain score ≥ 5 were found to be 5.18 times more likely to experience bone fractures compared to less than five. Additionally, pulmonary sound loss (OR = 3.86), pulmonary contusion (OR = 5.28), and subcutaneous crepitation (OR = 3.49) were notable predictors of fractures.
Conclusion: In conclusion, age, hemothorax, pneumothorax, a pain score of 5 or higher, along with pulmonary sound loss, pulmonary contusion, and subcutaneous crepitation are significant predictors of thorax bone fractures and enhance the predictive model for identifying at-risk patients in chest trauma cases.