Year: 2023
Type: article
Study design: Observational Study
Abstract: Background: Head injury is a major contributor to global morbidity and mortality, particularly in low- and middle-income countries. Early and accurate identification of patients who require neurosurgical intervention is essential to reduce secondary brain injury and improve outcomes. Computed tomography (CT) remains the first-line imaging modality in acute head trauma, providing rapid assessment of intracranial pathology and guiding clinical decision-making. Objectives: To assess the predictive value of initial CT scan findings for determining the need for neurosurgical intervention in patients presenting with acute head injury. Methodology: This prospective study was conducted at Neurosurgery Bacha khan Medical College / Mardan Medical Complex, Mardan included 150 patients with acute head injury who underwent non-contrast CT brain imaging on admission. CT variables analyzed included type of intracranial hemorrhage, midline shift, mass effect, skull fractures, cerebral edema, and basal cistern status. Patients were followed during hospitalization and were classified according to whether neurosurgical intervention was required. Data were analyzed using SPSS version 24.0, with results expressed as mean ± standard deviation and frequencies. A p-value < 0.05 was considered statistically significant. Results: Among 150 patients, the mean age was 38.6 ± 14.2 years, with males comprising 74.7%. Neurosurgical intervention was required in 46 patients (30.7%). Significant CT predictors included acute subdural and epidural hematomas, contusions with mass effect, midline shift ≥5 mm, basal cistern compression, and cerebral edema (all p < 0.01). Conclusion: Specific CT scan findings are strong predictors of the need for neurosurgical intervention in patients with head injury. Early identification of these features can facilitate prompt referral, optimize management strategies, and improve clinical outcomes. Keywords: Head injury; CT scan; Neurosurgical intervention; Prediction
Language: English
Subfield: Clinical Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7122585014