We performed a two-centre retrospective cohort study in keeping with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.24 The University of British Columbia and hospital clinical research ethics boards approved the protocol and waived the requirement for written informed consent (H12-02502).
Study population and hospitals
We defined our cohort as all patients admitted to the intensive care units (ICU) at Vancouver General Hospital (31 beds) and Royal Columbian Hospital (16 beds) during April 2006 to May 2012 with a diagnosis of severe TBI, as defined by a post-resuscitation Glasgow Coma Score (GCS) of ≤ 8, and with ICP monitoring.
Affiliated with the University of British Columbia, both ICUs are closed mixed medical-surgical ICUs that run on an approximate 1:1.2 nurse-to-patient ratio. Both units are staffed by fellowship-trained subspecialty critical care medicine consultants with specialty residents and house staff in attendance.
Data collection
Data were recorded in an electronic database using Microsoft Access 2013® and subsequently exported to Microsoft Excel 2013® (Redmond, WA, USA). Demographic data consisted of age, sex, and injury details (date, time, and location). We also collected the following baseline data: pupillary abnormalities, pre-hospital hypoxia (SpO2 < 92%) or hypotension (systolic blood pressure < 90 mmHg), mechanism of injury, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and Rotterdam computed tomography (CT) head score.25 The following daily ICU management data were collected: medication use (desmopressin, vasopressors, neuromuscular blockers, barbiturates), volume (total millilitres) of intravenous fluids per day (crystalloids, colloids, red blood cells, and mannitol), 8 AM ICP, 8 AM GCS, 8 AM mean arterial pressure, presence of external ventricular drain, and use of therapeutic hypothermia. Data on surgical procedures (type, date) were also collected. We collected total volume and percentage of HTS administered each day. We also collected daily Na values. We obtained the following outcome data from the ICU database: intensive care and hospital days, duration (in days) of mechanical ventilation, and both intensive care and hospital mortality.
Traumatic brain injury management
Both hospitals use TBI management protocols based on the Brain Trauma Foundation Guidelines.7 The head-of-bed elevation is maintained at ≥ 30°, and all ICP monitoring is performed using external ventricular drains (EVDs) (Medtronic, Inc. Minneapolis, MN, USA). If the ICP increases to > 20 mmHg for more than five minutes, the EVD is opened to allow drainage of cerebrospinal fluid. Three percent HTS is administered as a continuous infusion at both sites, whereas administration of HTS as bolus therapy is not standard practice. Osmolar therapy, therapeutic hypothermia, neuromuscular blocking drugs, and barbiturates are used at the discretion of the attending physician.
Statistical analysis
All analyses, including plots, were done in R (R Project for Statistical Computing, http://www.r-project.org). All reported P values were two-tailed and a complete case analysis was performed. Descriptions of normally distributed data, non-normally distributed data, and categorical data were stratified by HTS use and performed using mean (standard deviation [SD]), median (interquartile range [IQR]), and proportion (percent), respectively. Normality of data was assessed using the Shapiro-Wilk test. Univariable comparisons of continuous variables were performed using Student’s t tests for normally distributed data and Mann-Whitney U test for non-normally distributed data where appropriate. The sample size was one of convenience and designed to ensure stability around our point estimates of a multivariable model. Assuming an expected mortality of 20-25%26 and approximately seven to eight events per covariate27 with a final model of approximately eight covariates, a sample size of approximately 250 patients would be required. The degree of missing data is presented when applicable.
Generation of time-dependent variables and extrapolation of data
The HTS infusion was modelled as a time-dependent indicator variable. To avoid the possibility that HTS administration might be interpreted as a marker of TBI severity rather than as a predictor of mortality, the coding of the variable was changed from 0 to 1 starting from the day on which HTS was first administered. The use of desmopressin was also expressed as a time-varying variable and coded in a similar manner. The presence of hypernatremia on each day was also modelled as a time-dependent indicator variable. Daily hypernatremia was defined as daily Na >145 mmol·L−1. The daily Na level is a single daily determination that was measured at 8 AM every day during the14-day ICU period. The daily use of HTS and desmopressin as well as daily Na levels were measured and recorded only during the first 14-day ICU period. The status of these variables were not known after discharge from the ICU. To account for these variables beyond this period, we extrapolated the data such that the subsequent values for these variables took on those of the final day of the ICU period. Multicollinearity was assessed by calculating the variance inflation factor for all predictor variables in the final multivariable model. All variables had a variance inflation factor below 2, indicating an absence of multicollinearity.
Survival analysis
For all survival analyses, hospital mortality was the main outcome. Using univariable and multivariable Cox proportional-hazard regression models, we explored the relationship between HTS and hospital mortality. To account for baseline risk of death, known determinants of outcome in patients with TBI were selected a priori to be included as covariates in the eventual multivariable model. These included age, Rotterdam CT score (interval continuous variable), APACHE II score, abnormal pupil reactivity (one or both eyes), admission motor GCS, admission hypotension (systolic blood pressure < 90 mmHg), and site of admission. The use of desmopressin was also included as a time-varying covariate to adjust for potential confounding due to diabetes insipidus, which may be associated with a higher risk of death. Censoring was done at the time of discharge for patients who did not experience death during the hospital stay.
Linearity of continuous variables was tested using residual-based plots. Deviations from proportional assumptions were examined using the method proposed by Grambsch and Therneau, which is based on Schoenfeld residuals. The Efron method was used to handle tied failures. The P values, profile likelihood for point estimates, and 95% confidence intervals (CI) were calculated using the likelihood ratio test.
To assess the association between HTS-induced hypernatremia and hospital mortality, we examined whether the association between HTS and mortality was modified by hypernatremia and whether the association between hypernatremia and mortality was altered by HTS. In two separate analyses, HTS and hypernatremia were chosen as stratum variables, and a stratified Cox proportional-hazard model with an interaction between HTS and hypernatremia was fitted in each instance.
Relationship between HTS and ICP
To explore the relationship between HTS and ICP, linear mixed models (with patients included as a random effect) adjusted for baseline risk of death were constructed for both HTS and non-HTS groups. The within-subject (random effects) and between-subject (fixed effects) relationships between HTS use and ICP were determined by maximizing the restricted maximum likelihood. The Wald test was used to assess the overall change in ICP over the first 14-day ICU period for the two groups.