Study Population
Patients entered in National Trauma Data Bank (NTDB) between January 1st, 2009 and December 31st, 2010 were analyzed. NTBD is the largest and the most complete trauma database compiled by American College of Surgeons in the United States. This database contains uniformly collected clinical, demographic, and external cause and outcome information on over three million cases from over 900 registered U.S. trauma centers [6].
All the patients who were admitted to hospitals with traumatic brain injury or head and neck trauma were identified using the international classification of diseases, ninth revision, clinical modification (ICD-9-CM) diagnosis codes for traumatic brain injury (800.0–801.99, 803.0–804.99 or 850.0–854.19), head and neck injury (802, 802.0–802.9, 802.20–802.39), and cervical spine fracture (805.0, 805.00–805.08, 805.1, 805.10–805.18, 806.1, 806.0, 806.00 and 806.01–806.19) similar to the previously published studies [6–8].
Study Variables
Variables including patient’s demographics, admission injury severity score (ISS), Glasgow Coma Scale (GCS) score, in-hospital complications such as stroke (defined as an embolic, thrombotic, or hemorrhagic vascular accident or stroke with motor sensory or cognitive dysfunction that persists for 24 or more hours), myocardial infarction, pneumonia and deep vein thrombosis, and outcome measurements including total hospital stay, in-hospital mortality, and intensive care unit (ICU) days were extracted for each cases. VAD was identified by using ICD-9 diagnostic code of 443.24. The presence of surgical and endovascular repairs in patients with VAD were identified by using ICD-9 procedure codes of (38.01, 38.02, 38.12, 38.31, 38.32, 38.41, 38.42, 38.62, 38.82, 39.56–39.59) and (00.55, 00.61, 00.62, 39.50, 39.72, 39.74, and 39.90) for surgical and endovascular procedures, respectively. Hospital outcome was quantified by identifying discharge to home, discharge to nursing facility, and in-hospital mortality.
Statistical Analysis
We compared the demographic and clinical characteristics, and the rate of in-hospital complications, ICU days, hospital length of stay, ventilator days, in-hospital mortality, and discharge destination between patients with VAD and those without VAD. Chi-square test and t test were used for categorical data and for continuous data, respectively with a p value <0.05 considered statistically significant.
Multivariate linear regression was performed to determine the effect of VAD on ICU days, hospital length of stay, and ventilator days after adjusting for age, gender, admission GCS score, and ISS. In the same fashion, multivariate logistic regression analysis was performed to identify the impact of VAD on in-hospital mortality and discharge destination after adjusting for the confounding factors. We used the SAS 9.3 software (SAS Institute, Cary, NC) for statistical analysis.