Year: 2025
Type: article
Abstract: Background: Lateral mass screw fixation is a widely used technique for cervical spine stabilization, but its accuracy remains a critical concern due to the risk of neurovascular injury and malpositioned screws. Objective: To determine the accuracy of neuronavigation-guided lateral mass screw fixation in cervical spine disorders and compare it with conventional free-hand techniques. Methods: This Descriptive study was conducted at Department of Neurosurgery, Mayo Hospital, Lahore from 03 August 2024 till 03 February 2025. Data were collected through Non-probability, consecutive sampling. Our technique was designed to facilitate insertion point understanding and direction. The insertion point was initially determined using preoperative CT. As the lateral mass (LM) was quadrangular, the intersection of its diagonals was marked. Results: The mean age of the patients was 38.74 ± 9.29 years, with 62.81% males and 37.19% females. The most commonly affected cervical levels were C4 (24.79%) and C7 (23.97%). Neuronavigation demonstrated an accuracy rate of 91.4%, with only 8.6% of screws showing malposition. Accuracy was slightly higher in males (92.0%) compared to females (90.5%), but the difference was not statistically significant (p > 0.05). Cervical level-based accuracy showed the highest precision at C4 (94.0%) and the lowest at C5 (88.9%), with no significant differences (p > 0.05). Conclusion: Neuronavigation-assisted lateral mass screw fixation demonstrates high accuracy (91.4%) and safety in cervical spine stabilization. This technique significantly minimizes screw malposition and associated complications compared to the conventional free-hand approach. Its precision remains consistent across varying demographics and spinal levels, establishing it as a reliable modality in cervical spine surgery.
Authors: Ahtsham Ishaq, Ammar Bin Ahsan, Abrar Ullah Khan, Muhammad Rizwan, Syed Asim Altaf Andrabi, Ch.Muhammad Tajammal Saeed, Shahzad Shams
Institutions: Mayo Hospital, King Edward Medical University
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4408299480