Early Predictors of Health-Related Quality of Life Outcomes in Polytrauma Patients with Spine Injuries: A Level 1 Trauma Center Study

Study Design Retrospective review on clinical-quality trauma registry prospective data. Objective To identify early predictors of suboptimal health status in polytrauma patients with spine injuries. Methods A retrospective review on a prospective cohort was performed on spine-injured polytrauma patients with successful discharge from May 2009 to January 2011. The Short Form 12-Questionnaire Health Survey (SF-12) was used in the health status assessment of these patients. Univariate and multivariate logistic regression models were applied to investigate the effects of the Injury Severity Score, age, blood sugar level, vital signs, brain trauma severity, comorbidities, coagulation profile, spine trauma-related neurologic status, and spine injury characteristics of the patients. Results The SF-12 had a 52.3% completion rate from 915 patients. The patients who completed the SF-12 were younger, and there were fewer patients with severe spinal cord injuries (American Spinal Injury Association classifications A, B, and C). Other comparison parameters were satisfactorily matched. Multivariate logistic regression revealed five early predictive factors with statistical significance (p ≤ 0.05). They were (1) tachycardia (odds ratio [OR] = 1.88; confidence interval [CI] = 1.11 to 3.19), (2) hyperglycemia (OR = 2.65; CI = 1.51 to 4.65), (3) multiple chronic comorbidities (OR = 2.98; CI = 1.68 to 5.26), and (4) thoracic spine injuries (OR = 1.54; CI = 1.01 to 2.37). There were no independent early predictive factors identified for suboptimal mental health-related qualify of life outcomes. Conclusion Early independent risk factors predictive of suboptimal physical health status identified in a level 1 trauma center in polytrauma patients with spine injuries were tachycardia, hyperglycemia, multiple chronic medical comorbidities, and thoracic spine injuries. Early spine trauma risk factors were shown not to predict suboptimal mental health status outcomes.