Factors Affecting Length of Stay Following Elective Anterior and Posterior Cervical Spine Surgery.

UNLABELLED

Background Disease of the cervical spine is widely prevalent, most commonly secondary to degenerative disc changes and spondylosis. Objective The goal of the paper was to identify a possible discrepancy regarding the length of stay (LOS) between the anterior and posterior approaches to elective cervical spine surgery and identify contributing factors. Methods A retrospective study was performed on 587 patients (341 anterior, 246 posterior) that underwent elective cervical spinal surgery between October 2001 and March 2014. Pre- and intraoperative data were analyzed. Statistical analysis was performed using GraphPad Prism 5 (GraphPad Software, Inc., La Jolla, CA) and the Statistical Package for Social Sciences (SPSS) (IBM SPSS Statistics, Armonk, NY). Results Average LOS was 3.21 ± 0.32 days for patients that benefited from the anterior approach cervical spinal surgery and 5.28 ± 0.37 days for patients that benefited from the posterior approach surgery, P-value < 0.0001. Anterior patients had lower American Society of Anesthesiologists scores (2.43 ± 0.036 vs. 2.70 ± 0.044). Anterior patients also had fewer intervertebral levels operated upon (2.18 ± 0.056 vs. 4.11 ± 0.13), shorter incisions (5.49 ± 0.093 cm vs. 9.25 ± 0.16 cm), lower estimated blood loss (EBL) (183.8 ± 9.0 cc vs. 340.0 ± 8.7 cc), and shorter procedure times (4.12 ± 0.09 hours vs. 4.47 ± 0.10 hours). Chi-squared tests for hypertension, coronary artery disease, congestive heart failure, chronic obstructive pulmonary disease, and asthma showed no significant difference between groups.

CONCLUSIONS

Patients with anterior surgery performed experienced a length of stay that was 2.07 days shorter on average. Higher EBL, longer incisions, more intervertebral levels, and longer operating time were significantly associated with the posterior approach. Future studies should include multiple surgeons. The goal would be to create a model that could accurately predict the postoperative length of stay based on patient and operative factors.