Abstract
Object
To determine if traumatic brain injury (TBI) patients in low and middle income countries receiving surgery have better outcomes than TBI patients not receiving surgery and whether this differs with severity of injury.
Methods
We generated a series of Kaplan-Meier (KM) plots and performed multiple Cox proportional hazard models (CPHM) to assess the relationship between TBI surgery and TBI severity. We categorized TBI severity using admission GCS: mild (14–15), moderate (9–13), severe (3–8). We investigated outcomes from admission to hospital day 14. The outcome considered was the Glasgow Outcome Scale-Extended (GOSe), categorized as poor outcome (1–4) and good outcome (5–8). We used TBI registry data collect from 2013–2017 at a regional referral hospital in Tanzania.
Results
Of the final 2502 patients, 609 (24%) received surgery and 1893 (76%) did not receive surgery. There were significantly less road traffic injuries and more violent causes of injury in those receiving surgery. Those receiving surgery were also more likely to receive care in the intensive care unit, have a poor outcome, have a moderate or severe TBI, and stay in the hospital longer. The hazard ratio for operated vs. non-operated TBI patients was .17 (95% CI .06 to .49; p = <.001) in moderate TBI patients,.2 (95% CI .06 to .64; p = .01) for moderates and .47 (95% CI .24 to .89; p = .02). for severe TBIs.
Conclusions
Those that received surgery for their TBI had a lower hazard for poor outcome than those who did not. Surgical intervention was associated with the greatest improvement in outcomes for moderate head injuries, followed by mild and severe injuries. Our findings suggest a reprioritization of moderate TBI patients, a drastic change to the traditional practice within low-and-middle-income-countries in which the most severely injured patients are prioritized for care.
Keywords: brain injuries, survival analysis, developing countries, critical care outcomes
INTRODUCTION
At an estimated 69 million cases annually, traumatic brain injury (TBI) is a devastating global health pathology with a growing burden of disease.7 This burden disproportionately occurs in low and middle income countries (LMICs), where the incidence of TBI is nearly 3 times the incidence in high income countries (HICs). 7 In sub-Saharan Africa (SSA), an estimated 801 people per 100,000 suffer TBI, with in-hospital mortality rates as high as 47% for severe TBI.23 In HICs, neurosurgical interventions have been shown to prevent morbidity and mortality in certain TBI patients.25 However, in SSA, a region with an estimated 1 neurosurgeon per 9 million people, hospitals must balance limited surgical capacity with expansive clinical need.12 This demands investigation of the surgical impact on TBI outcomes, which can then support triage and surgical intervention decisions. Despite the need, the impact of surgery on TBI outcomes remains unexplored in low resource settings.
One major cause of in-hospital death for TBI patients is secondary brain injury resulting from increased intracranial pressure. Neurosurgical decompression, including craniectomies, and craniotomies, can relieve elevated intracranial pressure and prevent disability and death in certain TBI patients. TBI morbidity and mortality in SSA remains high due in part to limited neurosurgical resources and a TBI burden which exceeds capacity.. In a comparison study, patients with a severe TBI in LMICs had over twice the odds of death compared to patients in HICs.6 TBI surveillance registries from the SSA countries of Tanzania, Malawi, Uganda and Ethiopia reported mortality rates of 47%, 41%, 26% and 21%, respectively, for severe TBI.1,9,23,28 The causes of these mortality rates are multifactorial, including the pre-hospital and inpatient care settings. While pre-hospital data quality in low resource settings are scarce, the availability of quality inpatient TBI registries from LMICs invites exploration of in-hospital care for TBI patients in SSA.23
A survival analysis using a Cox regression model is a powerful statistical technique to quantify the association of a treatment and outcome for two groups.5 For TBI, this technique has potential to measure associations between in-hospital clinical management (e.g. surgical intervention) and outcomes. As of 2010, there were only 12 papers examining TBI management in non-high income settings, only 1 of which was from a low income country.2,22 Despite a recent increase in published data on TBI management and outcomes in LMICs 9,10,13,23,28, there are no previous studies applying survival analyses to inpatient TBI outcomes data.
TBI outcomes data from an area of the world where trauma is an endemic disease can help plan interventions to improve TBI management. This study’s objective was to assess the outcomes of TBI patients at a tertiary referral center in Moshi, Tanzania. Specifically, we wanted to determine if TBI patients receiving surgery had better outcomes than TBI patients not receiving surgery and whether this differs with severity of injury. A greater understanding of the impact of surgery by TBI severity, particularly in a resource-scarce setting, can guide the prioritization of limited resources.
METHODS
Methodological approach
In this study, we compared the outcomes between those who received surgical intervention and those who did not using a survival analysis. A survival analysis is a statistical technique well-suited for time to event data. For calculating the time to event for surgical patients in our study, we could have selected date of admission to outcome or date of surgery to outcome. We tested both options as a sensitivity analysis and found no difference between the two methodological approaches. Since we are interested in the overall health system process and outcome of patients and not just post-surgical outcomes, we chose date of admission for our time to event analysis. One potential confounder with this approach is whether delay to surgery varied by patient GCS. We calculated the time to surgery (median [IQR]) for each TBI severity and performed a logistic regression to determine if time to surgery impacted patient outcomes. This variable was not a significant predictor of outcomes, thus supporting our approach.
Study design
We conducted a retrospective, cross-sectional observational study. In designing the analysis, we followed the STROBE reporting guidelines 31 and incorporated best-practice recommendations published for time-to-event outcomes and survival analyses. 18,24,30
Study Setting
The data for this study were from Kilimanjaro Christian Medical center (KCMC), a tertiary level hospital in northern Tanzania serving a catchment population of over 15 million people. KCMC treats about 1000 TBI patients annually.23 At the time of data collection, KCMC did not have any neurosurgery-trained physicians. General surgery attendings and residents perform all neurosurgical cases. The most common procedures are emergency burr holes or, less frequently, craniectomies/craniotomies for subdural and epidural hematomas. Although burr holes are not the preferred surgical intervention for acute epidural hematomas, this technique may still be used in some lower resource settings like KCMC8,11,14.
In 2013, KCMC established a prospective TBI registry as part of a quality improvement process. 23 The registry consecutively enrolled all patients presenting to KCMC Emergency Department for treatment of their acute (<24 hours) TBI. Trained research nurses collected data on the enrolled patients’ injury details, acute care, hospitalization care and condition at discharge. The research team and study PI performed quality analysis on data, entering the registry at time of data entry and during weekly meetings. 23
Patient population
The study included patients enrolled in the KCMC registry between May 2013 and July 2017. We excluded patients with missing data for the following key variables: admission date, discharge date, received surgery for TBI, and admission Glasgow Coma Scale (GCS) score (Figure 1).
Figure 1.
Diagram illustrating how we arrived at final study population.
Explanatory variables
The analysis included the following variables: age; gender; self-reported consumption of alcohol before the injury; admission GCS26; mechanism of injury; whether the patient had surgery for TBI; and whether the patient was transferred from the surgical ward to the intensive care unit (ICU), representing a worsening in their condition or a missed triage from the emergency department. The TBI surgeries variable included mostly emergency burr holes and some craniotomies/craniectomies. The KCMC registry did not include the exact procedure performed.
We stratified patients into 3 groups based on admission GCS : mild (14–15), moderate (9–13), and severe (3–8) TBI. Since the data included predominantly mild injuries, we used the aforementioned GCS cutoffs to create more balanced subgroups (i.e. mild, moderate and severe TBIs) for the survival analysis.15 We transformed age from a continuous to an ordinal variable with the following categories: <18, 18–29, 30–39, 40–49 and >50. Mechanism of injury included road traffic injury, assault, falls, and other. Examples of other mechanism of injury included accidental trauma to head with object. For mild, moderate and severe TBI patients, we also calculated time from admission to surgery. This allowed us to compare delays to surgery by TBI severity and ensure one group did not have significantly different delays to the others. We present these delays to surgery data but did not include in the analysis, as the primary comparison of this study was patient outcomes between non-surgery and surgery patients, not timely versus delayed surgery.
Outcome
We selected the discharge Glasgow Outcome Scale (GOS) as the outcome measure for this study (Table 1). GOS ranges from 1 to 5, each number representing a different level of recovery as described in Table 1. We dichotomized this variable into good outcome (GOS of 4 to 5) and poor outcome (GOS of 1 to 3). Approximately halfway through the data collection process, we switched to the Glasgow Outcome Scale-Extended (GOSe) to improve our descriptions of patients’ outcome disabilities. This scale ranges from 1 to 8. We converted those with a GOSe outcome to a GOS score as shown in Table 1. The registry did not include outcomes after discharge.
Table 1.
GOS-GOSe conversion.
| Outcome | GOSe score | GOS score |
|---|---|---|
| Death | 1 | 1 |
| Persistent Vegetative State | 2 | 2 |
| Severe Disability | 3 or 4 | 3 |
| Moderate Disability | 5 or 6 | 4 |
| Good Recovery | 7 or 8 | 5 |
Data analysis
The data analysis compared the two study groups: TBI patients (1) receiving and (2) not receiving surgery. We followed each patient from admission (hospital day [HD] 0) to discharge, inpatient death, or HD 14. We selected hospital day 14 due to its precedent in the literature to assess acute outcomes for TBI.16,20 Additionally, survival plots should continue until 10–20% of the data remains.18 In our study, 13% of patients remained without an outcome at HD 14. These patients with a poor outcome after HD 14 did not count as an event in the survival analysis (i.e. right censored). We used R Language for Statistical Computing 3.4.1, including the survminer and tidyverse packages, for data management and statistical analyses.19 We set significance at a p-value less than 0.05.
Descriptive Analysis
We calculated descriptive statistics for demographic and clinical care data, including means, standard deviations, medians and IQR. We used chi-square tests to compare categorical variables.
Kaplan-Meier (KM) Survival Plots
We used the KM plots to present the survival curves and test the crude survival between the two study groups.24 One KM curve depicted the entire study population and a second set of KM curves depicted mild, moderate and severe TBI patients separately. We presented the statistical uncertainty with 95% confidence intervals (shaded region on plots). Our KM plots travel up with sliding y-axis scales to improve comparison between study groups.18 We displayed the number of patients still at risk at a given time below the time axis. The KM plots cannot provide an effect estimate for surgery on outcomes, which led us to build a Cox proportional hazard model.
Cox Proportional Hazard Model (Cox model)
We built Cox models to examine the association of TBI surgery with outcomes for the overall study population and by TBI severity. The Cox model is a powerful method to analyze survival data, which can produce a hazard ratio (interpreted similar to an incident rate ratio or relative risk) for poor outcome while controlling for possible confounders. 30
For appropriate use and inference of a Cox model, the survival curves must be proportional throughout the study period. To assess for proportionality, we (1) checked if the survival curves crossed and (2) used the proportional hazards assumption of a Cox regression test (Coxph). If the proportional hazard assumption was violated, we used step functions to add time interaction terms.27 We used the Coxph output to determine at which HD to add an interaction term. We produced unadjusted and adjusted effect estimates for surgery.
Institutional Review Board
We obtained ethical approval from KCMC Ethics Committee and had an exemption from the Duke University Institutional Review Board as this was a de-identified secondary data analysis of a quality improvement registry.
RESULTS
The initial cohort comprised all 3209 patients enrolled in the TBI registry. After the exclusion of cases with key missing data, 2502 TBI patients remained for inclusion in the analysis (Figure 1).
Of the final 2502 patients, 609 (24%) received TBI surgery and 1893 (76%) did not receive TBI surgery. There were significantly less road traffic injuries and more violent causes of injury in those receiving surgery. Those receiving TBI surgery were also more likely to receive care in the ICU, have a poor outcome, have a moderate or severe TBI, and stay in the hospital longer. There was no significant difference between the two study groups with regard to age, gender, or presence of alcohol.
Of the 609 TBI surgeries performed, severe TBI patients received the highest proportion (121/316, 38%), followed by moderate TBI patients (109/352, 31%) and mild TBI patients (379/1834, 21%). The median (IQR) for days to surgery was 0.58 (0.29, 1.35). On regression analysis (not shown), time to surgery was not significantly associated with patient outcomes.
Figure 2 is the KM plot for the entire study population. TBI patients not receiving surgery, the blue line, had more events of poor outcome compared to those receiving surgery. Around HD 11, the lines cross, and those receiving surgery had more events of poor outcome.
Figure 2.
Kaplan Meier plot comparing outcomes between those receiving and not receiving TBI surgery for the entire study population.
Figure 3 includes the KM plots for each TBI severity level separately. The slope of the survival curves increases from mild to severe, reflecting an increase in poor outcome rates as severity increases. In each of the 3 plots, the survival curves between patients receiving and not receiving surgery separate initially, converge between HD five and 10, and then separate again. This fluctuation resulted in visible and statistical violations of the proportionality assumption.
Figure 3.
Kaplan Meier curves comparing outcomes between TBI patients receiving and not receiving surgery, stratified by GCS severity groups. We used a sliding y-axis scale.
The overall study population and severe TBI populations required time interaction terms at HD 3 and HD 7 to ensure proportionality. Mild and moderate TBI populations required a time interaction at HD 7 only.
In the unadjusted model (Table 1 in the Supplement), TBI surgery was associated with a reduced hazard for poor outcome for all study groups before HD 8. This finding was only significant for moderate TBI patients before HD 8 and severe TBI patients before HD 4. Surgery was associated with an increased hazard for poor outcome for the overall, mild and moderate TBI populations after HD 7; however, this finding was insignificant. In the fully adjusted model (Table 3), surgery was associated with a significantly reduced hazard for poor outcome for all study groups before HD 8. Moderate TBI patients receiving surgery had the greatest reduction in hazard, an 83% decrease.
Table 3.
Fully adjusted Cox proportional hazard ratios (HR) for TBI surgery by injury severity with time interactions.
| Fully Adjusted Cox Proportional Hazard Model | ||||||||
|---|---|---|---|---|---|---|---|---|
| Hospital Day | Overall | Mild | Moderate | Severe | ||||
| HR (CI) | p-value | HR (CI) | p-value | HR (CI) | p-value | HR (CI) | p-value | |
| 0–3 | 0.32 (0.19, 0.54) | <0.001 | 0.20 (0.06, 0.64) | .006 | 0.17 (0.06, 0.49) | <.001 | 0.47 (0.24, 0.89) | .02 |
| 4 –7 | 0.46 (0.25, 0.86) | .02 | 0.65 (0.30, 1.39) | .26 | ||||
| 8–14 | 0.77 (0.36, 1.66) | .51 | 0.69 (0.14, 3.31) | .64 | 1.23 (0.23, 6.68) | .81 | 0.70 (0.23, 2.13) | .53 |
Covariates included, but not shown, are mechanism of injury, gender, alcohol, surgery to ICU, age, and GCS (for overall only).
Table 4 provides the Cox model output for the fully adjusted Cox model of the overall study population. Moderate TBI, severe TBI, and transfer from the ward to the ICU significantly increased the hazard for poor outcome. Patients receiving surgical intervention and having an outcome by HD 3 had a 68% reduction in their hazard ratio (HR). Similarly, patients with a surgery and outcome between HD 4 and 7 had a 54% reduction in their hazard ratio. Time from patient admission to surgery was lowest for those with an outcome between HD 0 and 3, and highest for those with an outcome between HD 8 and 14.
Table 4.
Fully adjusted Cox model results for entire study population.
| Cox Model Output | |||
|---|---|---|---|
| Variable | Hazard Ratio (CI) | P-value | |
| Gender | |||
| Male | 1.51 (0.98, 2.35) | .06 | |
| Age | |||
| < 18 | ref | Ref | |
| 18–29 | 1.91 (1.04, 3.52) | .04 | |
| 30–39 | 2.45 (1.29, 4.64) | .006 | |
| 40–49 | 2.51 (1.28, 4.91) | .007 | |
| 50+ | 1.92 (0.99, 3.72) | .05 | |
| MOI | |||
| Road Traffic Crash | ref | Ref | |
| Assault | 0.73 (0.41, 1.32) | .30 | |
| Fall | 1.55 (0.96, 2.51) | .07 | |
| Other | 1.24 (0.67, 2.28) | .49 | |
| Alcohol | |||
| No | ref | Ref | |
| Yes | 0.90 (0.59, 1.38) | .63 | |
| Unknown | 1.03 (0.70, 1.52) | .88 | |
| TBI surgery (Hospital Day [HD] of outcome)* | |||
| HD 0–3 | 0.32 (0.19, 0.54) | <.001 | |
| HD 4–7 | 0.46 (0.25, 0.86) | .01 | |
| HD 8–14 | 0.77 (0.36, 1.66) | .51 | |
| Surgery to ICU Transfer | |||
| Yes Transferred | 3.43 (2.32, 5.05) | <.001 | |
| GCS | |||
| Mild | ref | ref | |
| Moderate | 3.21 (1.99, 5.17) | <.001 | |
| Severe | 8.01 (5.18, 12.4) | <.001 | |
This variable indicates patient had surgery and an outcome within the specified time frame. This variable correlates with time to surgery (Table 2 in the Supplement).
DISCUSSION
This study is the first analysis of acute TBI outcomes using a survival analysis in an LMIC and one of the largest single-center studies on TBI outcome. About a quarter of the 2502 patients in our study received surgery for TBI; those that did had a lower hazard for poor outcome than those who did not. To varying degrees, we observed a benefit of surgery for all TBI severities. Surgical intervention was associated with the greatest improvement in outcomes for moderate TBI, followed by mild and severe TBI. The fully adjusted models revealed variables significantly associated with both poor and good outcome. This study leveraged advanced statistical methods and a robust dataset from an LMIC to investigate the association of surgery on acute TBI outcomes and how that association changes according to TBI severity.
Impact of surgery
We found surgery associated with reduced HR for poor outcome for all TBI severity groups. Impressively, given that KCMC relies on general surgeons and does not have trained neurosurgeons, the positive association of TBI surgery on outcomes is a critical finding for LMICs experiencing shortages in highly skilled professionals. This finding supports the potential of task-sharing neurosurgical emergencies with general surgeons while efforts are ongoing to increase the number of trained neurosurgeons globally. Additionally, patients with moderate TBI who received surgery had the greatest reduction in HR, followed by mild and then severe TBI. Our findings, together with previous reports on the overall effects of surgery on severe TBI patients, should encourage increased attention towards triaging limited resources, especially in regard to focusing resources on those who will receive the greatest impact on their outcome. A retrospective review on post-surgical outcomes of severe TBI patients found patients presenting with a GCS of 3–5 were nearly 5 times more likely to have a poor outcome at 30 days compared to patients with a GCS of 6–8.17 Cooper et al., in a prospective randomized controlled trial, found severe TBI patients receiving decompressive craniectomy compared to standard level of care had higher odds of unfavorable outcome and similar death rates at 6 months.4 These previous findings, along with our data, should encourage research on the reprioritization of care to patients with less severe injuries, a drastic change to the traditional practice in which the most severely injured patients are prioritized for care.
Predictors of outcomes
Predictors associated with poor outcome included male gender, patient transfer from surgery ward to ICU, increased age, and moderate and severe TBI. Assault, an intentional cause of TBI, was not significantly associated with poor outcome in our study. However, previous research has found intentional injuries are a predictor of poor outcome.32 The predictor associated with improved outcomes was undergoing TBI surgery. This finding was insignificant for patients with a discharge past HD 7. A statistical explanation is the limited number of patients remaining in the study after HD 7 ( < 25% of the study population). Clinically, this finding suggests those with a later hospital discharge, and longer delay to surgical intervention, had poorer outcomes. We found those with an outcome before HD, a group with the shortest time from admission to surgery, received the greatest benefit from surgery. While the impact of delays to surgery on outcomes was not the primary objective of this study, this association delays to care with poor outcomes is well-supported by previous research.21,29
Considerations for Survival analysis
The lessons learned by applying survival analysis, including KM plots and a Cox model, to our data have important clinical and statistical considerations. First, our data exhibited significant non-proportionality: the effect of surgery varied depending on day of hospital discharge and TBI severity. Statistically, this finding required the use of time interaction terms in the Cox model to avoid erroneous inferences from the data.3,27 For example, patients with severe TBI receiving surgery, compared to those with moderate and mild TBI, had the lowest HR when a time interaction term was not included. This finding was the opposite of the association seen when we used time interaction terms. Clinically, the non-proportionality of our data may represent temporal differences to care (i.e., delays to surgery) and/or clinical deterioration for patients with protracted hospital stays. These potential explanations warrant further investigation. For moderate TBI, the positive association of surgery with outcomes decreased at HD 8. This finding was not seen in mild and severe TBI patients. Patients with a moderate TBI may therefore be more prone to clinical deterioration compared to mild and severe TBI patients, for whom good and poor outcomes, respectively, are more confidently expected. Further research surrounding inpatient care for this specific patient population is necessary to explore this finding. We believe survival analysis techniques, when applied to TBI outcomes, can improve our assessment of inpatient and outpatient clinical care.
Limitations
A robust statistical analysis on registry data from a low resource setting needs careful consideration of limitations. First, the decision to operate or not is influenced by factors which may also contribute to the patient outcome. For example, a patient may not receive surgery due to major comorbidities or other negative prognostic indicators. As result, a patient suitable for surgery may be better positioned to have a good outcome. To mitigate this limitation, we used the data available to control for predictor of outcome (e.g. GCS, age). Also. previous research in similar contexts of care has found limited resources may be focused on more severely injured patients.29 The TBI registry used in this study did not include the GCS at the time of surgery because this information was not always readily available to the research team. The type of surgery was also not documented in the database; however, the general surgery staff endorsed performing mostly burr holes and some hemicraniectomies/craniotomies. The registry included fewer patients who suffered severe and moderate TBI than mild injuries. The smaller sample size limited our ability to draw inferences from the survival analyses at later HDs. Other than the admission GCS, we did not have data on the injury itself (e.g. computed tomography [CT] results) to understand severity of TBI. We also did not have information on other injuries that might have caused poor outcomes. Finally, we did not have patient outcome data past hospitalization due to the limited post-discharge contact and care in this clinical setting. This limited our analysis to in-hospital outcomes only.
CONCLUSION
A detailed understanding of TBI in-hospital outcomes in a low resource setting is needed to support prudent use of limited, life-saving resources. This paper provides an analysis of surgical management and outcomes data from a tertiary referral center in Moshi, Tanzania. Surgical intervention improved outcomes for all patients with mild, moderate and severe TBI. The positive impact of surgery to improve outcomes, however, was greatest for patients with moderate and mild TBI injuries. Future research should investigate which TBI patients are the best candidates for surgery and what factors contribute to clinical deterioration for patients with longer hospital stays, especially in the low resource setting.
Supplementary Material
Table 2.
Descriptive analysis of the variables considered.
| Variables | Patient Population | |||
|---|---|---|---|---|
| All patients (n = 2502) | Yes TBI Surgery (n = 609) | No TBI Surgery (n = 1893) | P-value | |
| Age | .33 | |||
| <18 | 368 (15) | 98 (16) | 270 (14) | |
| 18–29 | 881 (36) | 195 (32) | 686 (37) | |
| 30–39 | 554 (22) | 139 (23) | 415 (22) | |
| 40–49 | 325 (13) | 87 (14) | 238 (13) | |
| 50+ | 347 (14) | 87 (14) | 260 (14) | |
| missing | 27 | 3 | 24 | |
| Gender | .72 | |||
| Male | 2084 (83) | 510 (84) | 1574 (83) | |
| Female | 417 (17) | 98 (16) | 319 (17) | |
| missing | 1 | 1 | 0 | |
| Presence of Alcohol | .22 | |||
| Yes | 662 (27) | 147 (24) | 515 (27) | |
| No | 1211 (49) | 299 (49) | 912 (49) | |
| Unknown | 607 (24) | 160 (26) | 447 (24) | |
| missing | 22 | 3 | 19 | |
| Mechanism of Injury | <.001 | |||
| RTI | 1693 (76) | 339 (64) | 1354 (79) | |
| Assault | 362 (16) | 122 (23) | 240 (14) | |
| Fall | 252 (11) | 72 (14) | 180 (11) | |
| Other | 187 (8) | 71 (13) | 116 (7) | |
| missing | 8 | 5 | 3 | |
| Surgical Ward to ICU | ||||
| Yes | 357 (15) | 230 (40) | 127 (7) | <.001 |
| No | 2029 (85) | 338 (60) | 1691 (93) | |
| Missing | 116 | 41 | 75 | |
| Time to Surgery | ||||
| Days Median (IQR) | 0.58 (0.29, 1.35) | 0.58 (0.29, 1.35)* | NA | |
| Outcome | <.001 | |||
| Poor outcome (GOSe 1–4 or GOS 1–3) | 253 (10) | 85 (14) | 168 (9) | |
| Good outcome (GOSe 5–8 or GOS 4–5) | 2249 (90) | 524 (86) | 1725 (91) | |
| Days to outcome median (IQR) | 3.4 (1.8 – 7.2) | 6.2 (3.6 – 11.2) | 2.8 (1.5 – 5.7) | |
| TBI | <.001 | |||
| Mild (14–15) | 1834 (73) | 379 (62) | 1455 (77) | |
| Moderate (9–13) | 352 (14) | 109 (18) | 243 (13) | |
| Severe (3–8) | 316 (13) | 121 (20) | 195 (10) | |
The days to surgery for mild, moderate and severe TBI injuries were 0.53 (0.26, 1.44), 0.76 (0.41, 1.34) and 0.60 (0.26, 1.02) respectively.
Acknowledgments
Funding: Dr. Staton would like to acknowledge salary support from the Fogarty International Center of the U.S. National Institutes of Health under Award Number K01TW010000 (PI, Staton) and the Duke Division of Emergency Medicine.
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