Original Research
Chest In Time: Investigating the utility of a novel X-ray based blunt chest trauma clinical prediction model in a resource-limited setting in South Africa
Submitted: 22 October 2025 | Published: 06 March 2026
About the author(s)
Avika Kalideen, Discipline of Radiology, School of Medicine, University of KwaZulu-Natal, Pietermaritzburg, South AfricaTanusha Sewchuran, Discipline of Radiology, School of Medicine, University of KwaZulu-Natal, Pietermaritzburg, South Africa
Abstract
Background: Blunt chest trauma (BCT) is a frequent manifestation of traumatic injury, either as isolated thoracic injury or in the setting of polytrauma.
Objectives: Several chest trauma scores (CTSs) exist based on clinical, biochemical and imaging findings to assist in the risk stratification of patients who have sustained blunt chest trauma. These injuries are often not initially clinically evident and a risk stratification tool serves to identify patients at risk for pulmonary compromise and to establish early diagnostic and therapeutic strategies to improve morbidity and mortality.
Method: Patient data were obtained from the Greys Hospital Emergency Department’s triage books. The images for patients within the study sample were scored under the categories of age, number of rib fractures, bilaterality of rib fractures, presence and significance of pulmonary contusions, and pleural-based injury. Patients were classified as either critical or non-critical using final disposition as a surrogate. The seminal CTS was initially calculated, following which a novel South African chest trauma score (SA-CTS) was hypothesised and computed based on the chest X-ray alone for ease of applicability and reproducibility in resource-constrained settings.
Results: A conventional CTS ≥ 5 was clinically significant for a critical outcome. However, with the SA-CTS, a score ≥ 4 was found to be statistically significant. Age was not found to be a significant contributing factor. Pleural-based injuries were found to be contributory factors to a critical outcome.
Conclusion: Clinical prediction models serve in the risk stratification and early identification and pre-emptive management of patients at high risk for clinical decompensation. Based on the results, the incorporation of the SA-CTS into daily practice in the management of BCT patients is proposed.
Contribution: This will prove especially useful in triggering early up-referral for both advanced imaging and tertiary level care in a resource limited setting.
Keywords
Sustainable Development Goal
Metrics
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