Abstract
OBJECTIVE
Given the high burden of neurotrauma in low- and middle-income countries (LMICs), in this observational study, the authors evaluated the treatment and outcomes of patients with severe traumatic brain injury (TBI) accessing care at the national neurosurgical institute in Tanzania.
METHODS
A neurotrauma registry was established at Muhimbili Orthopaedic Institute, Dar-es-Salaam, and patients with severe TBI admitted within 24 hours of injury were included. Detailed emergency department and subsequent medical and surgical management of patients was recorded. Two-week mortality was measured and compared with estimates of predicted mortality computed with admission clinical variables using the Corticoid Randomisation After Significant Head Injury (CRASH) core model.
RESULTS
In total, 462 patients (mean age 33.9 years) with severe TBI were enrolled over 4.5 years; 89% of patients were male. The mean time to arrival to the hospital after injury was 8 hours; 48.7% of patients had advanced airway management in the emergency department, 55% underwent cranial CT scanning, and 19.9% underwent surgical intervention. Tiered medical therapies for intracranial hypertension were used in less than 50% of patients. The observed 2-week mortality was 67%, which was 24% higher than expected based on the CRASH core model.
CONCLUSIONS
The 2-week mortality from severe TBI at a tertiary referral center in Tanzania was 67%, which was significantly higher than the predicted estimates. The higher mortality was related to gaps in the continuum of care of patients with severe TBI, including cardiorespiratory monitoring, resuscitation, neuroimaging, and surgical rates, along with lower rates of utilization of available medical therapies. In ongoing work, the authors are attempting to identify reasons associated with the gaps in care to implement programmatic improvements. Capacity building by twinning provides an avenue for acquiring data to accurately estimate local needs and direct programmatic education and interventions to reduce excess in-hospital mortality from TBI.
Keywords: neurotrauma, global health, capacity building, traumatic brain injury, low- and middle-income countries
DEATHS from injuries and violence account for 9% of global mortality and disproportionately affect young males.1 Among patients who sustain trauma, traumatic brain injuries (TBIs) contribute to 30% of all deaths.2 While in high-income countries (HICs) neurotrauma incidence and mortality are decreasing, the incidence in middle-income countries (MICs) and prevalence in low- and middle-income countries (LMICs) are increasing, and data on mortality are sparse.3,4
Rapid urbanization in low-income countries (LICs) and LMICs has led to an unprecedented growth in motor vehicle traffic and resultant trauma from road traffic injuries. In sub-Saharan Africa, in addition to increased incidences of injuries and underdeveloped trauma management systems, specialized neurosurgical skills are also scarce.5 It is estimated that per 10 million population, there are 2 neurosurgeons in LICs and 11 in LMICs compared with 124 in HICs.6 Specialized neurosurgical units and specialty-trained nurses are rare.
The Lancet Neurology Commission on TBI concluded that there is an undeniable need for increased financial resourcing and organizational improvements across the chain of trauma care, and importantly, robust data are needed to quantify current management and outcomes of TBI patients accessing the healthcare system.7 In such an effort to identify discrete gaps in the acute management of patients with severe TBI, in this prospective observational cohort study, we describe the management and short-term mortality at a national neurosurgical institute, Muhimbili Orthopaedic Institute (MOI) in Dar-es-Salaam, Tanzania. In addition, we estimate excess in-hospital mortality that may be reduced by programmatically targeting specific gaps in care.
Methods
Study Design
In partnership with the Department of Neurological Surgery at the MOI hospital, a registry of patients with severe TBI was established as a quality improvement initiative as part of a neurotrauma capacity building program. The TBI-trac® database from the Brain Trauma Foundation was used. It is a web-based platform with data sections on monitoring and treatment details in prehospital, emergency department (ED), and the first 10 in-hospital days. In addition, data on head CT scanning, surgical intervention, and 2-week mortality are recorded. All patient data are anonymized at the moment of data entry, and no identifiers are stored anywhere for subject privacy protection. This database has been previously deployed in New York State (1997–2007) with success and leading to several high-impact findings.3,8,9
As a quality improvement study, this database was exempt from research institutional review board approval at both institutions. Data collection occurred between March 2014 and October 2018. A research assistant position was created for neurosurgical doctors to fill in rotation and was frequently occupied by a neurosurgery house officer. The research assistant identified patients with severe TBI who were admitted to the ED, operating room, or ICU daily. Patients who had sustained a severe TBI and arrived at the hospital within the first 24 hours were enrolled. All patients who had a Glasgow Coma Scale (GCS) score ≤ 8 at any time point in the first 24 hours were included. As stated above, data on prehospital, ED, and ICU care in addition to CT findings, surgery, and 2-week mortality were recorded. Patients were excluded if they presented more than 24 hours after injury to avoid bias on analyses of in-hospital treatment. Patients were also excluded if they were moribund with a GCS score of 3 and with dilated and unreactive pupils, died on day 1, or had personally expressed or per family no desire for heroic interventions. While every effort was made to include all patients presenting with the inclusion and exclusion criteria, it is possible that this did not always occur if patients were transferred to a private hospital from the ED, died in the ED, or if TBI was not listed as a primary diagnosis. Such short-comings of trauma registries in LMICs are described.10,11
Outcomes and Statistical Analyses
We present proportions of patients who underwent basic clinical monitoring and had prehospital and in-hospital data recorded. In the clinical care of patients, we evaluated the proportion of patients who received basic therapies such as oxygen and intravenous fluids in the ED, mechanical ventilation, sedation, advanced diagnostic techniques such as brain CT scanning and intracranial monitoring, and advanced treatment, including hyperosmolar therapies, craniotomy, or craniectomy. Outcomes were measured as death within 2 weeks.
To evaluate whether observed mortality was different from that predicted by established models, we utilized the Corticoid Randomisation After Significant Head Injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic models.12–16 The CRASH model predicts the probability of 2-week mortality and 6-month unfavorable outcome using age, GCS score, pupillary reactivity, and presence of major extracranial injury and is also validated for application in LMICs. The IMPACT model is more appropriate for developed countries, and utilizes age, the motor component of the GCS, and pupillary reactivity to predict the probability of 6-month mortality and unfavorable outcome. We also constructed multivariate models using clinical variables that were associated with mortality in univariate analysis and tested them against the established models.
We calculated summary statistics using frequencies and proportions for categorical variables, and means, standard deviations, medians, and interquartile ranges for continuous variables. Demographics and clinical characteristics were compared between patients who died within 2 weeks and those who did not using the chi-square test, two-sample t-test, or rank-sum test, as appropriate. To identify independent risk factors for 2-week mortality, multivariable logistic regression models were fitted. We assessed performance of the models using receiver operating characteristic curves and the C-statistic.
Data proportions were calculated, and univariate and multivariate analyses were performed. Data were analyzed using SAS version 9.4 software (SAS Institute Inc.). All statistical tests were 2-sided with a significance level of p ≤ 0.05. The IMPACT and CRASH model calculations were obtained and applied to the data set.12–14
Results
Epidemiology
A total of 462 patients who had a GCS score ≤ 8 in the first 24 hours after admission to the ED, operating room, or ICU at MOI were enrolled over 4.5 years. It is unlikely that all eligible patients were included in the registry; however, in triangulation with operative records, it appears that close to 70% of eligible patients were enrolled. We did not identify any systematic bias in patients who were not enrolled.
The mean patient age was 33.9 years, and 89% of patients were male; 79% were involved in a road traffic accident, and 15% sustained an assault. Overall, 290 patients (62.8%) patients died in the first 2 weeks (Table 1), and the outcome of 29 patients (6.2%) was unknown. The latter patients were excluded from univariate and multivariate analyses.
TABLE 1.
Admission characteristics of patients included in the study
| Admission Data | All | Survivors | Nonsurvivors | p Value |
|---|---|---|---|---|
| No. of patients | 462 | 143 (31.0) | 290 (62.8) | |
| Age in yrs, mean (SD) | 33.93 (16.4) | 30.61 (15.2) | 35.73 (16.9) | 0.002 |
| Age range in yrs | 0.01 | |||
| <16 | 30 (6.5) | 16 (11.2) | 13 (4.5) | |
| 16–59 | 390 (84.4) | 118 (82.5) | 245 (84.5) | |
| ≥60 | 42 (9.1) | 9 (6.3) | 32 (11.0) | |
| Sex | 0.62 | |||
| Male | 410 (88.7) | 129 (90.2) | 257 (88.6) | |
| Female | 52 (11.3) | 14 (9.8) | 33 (11.4) | |
| Mechanism of injury | 0.99 | |||
| Traffic-related | 367 (79.4) | 114 (79.7) | 228 (78.6) | |
| Violence | 67 (14.5) | 20 (14.0) | 44 (15.2) | |
| Fall | 15 (3.3) | 5 (3.5) | 10 (3.5) | |
| Other | 13 (2.8) | 4 (2.8) | 8 (2.7) |
Values represent the number of patients (%) unless stated otherwise. Boldface type indicates statistical significance.
Prehospital Data
Data and comparison between survivors and nonsurvivors are summarized in Table 2. The median time to arrival at MOI was 8 hours after injury; 89.2% of patients were transferred from another hospital. Forty-four percent of patients had a prehospital GCS score recorded, 26% had oxygen saturation documented, 36% had systolic blood pressure (SBP) recorded, and 42% had documented intravenous fluid administration. From the data recorded, the prehospital GCS score was the only variable that differed significantly between survivors and nonsurvivors (median score 7 [IQR 5–8] in survivors vs 6 [IQR 4–7] in nonsurvivors; p = 0.002).
TABLE 2.
Prehospital monitoring and treatment of patients with severe TBI
| Prehospital Data | All | Survivors | Nonsurvivors | p Value |
|---|---|---|---|---|
| Median time to arrival, hrs | 8 (4–13) | 7 (4–12) | 8 (4–13) | 0.53 |
| GCS score (n = 202) | ||||
| Median | 6 (5–7) | 7 (5–8) | 6 (4–7) | 0.002 |
| 3–5 | 82 (40.6) | 20 (29.0) | 60 (48.4) | 0.05 |
| 6–8 | 96 (47.5) | 38 (55.1) | 53 (42.7) | |
| 9–12 | 18 (8.9) | 8 (11.6) | 8 (6.5) | |
| 13–15 | 6 (3.0) | 3 (4.4) | 3 (2.4) | |
| Mean lowest SpO2, % (n = 118) | 94.01 (7.2) | 95.23 (4.0) | 93.34 (8.5) | 0.10 |
| Mean lowest SBP, mm Hg (n = 168) | 120.86 (21.8) | 120.1 (20.3) | 121.9 (22.9) | 0.60 |
| Intravenous fluid administration | ||||
| Yes | 195 (42.2) | 68 (47.6) | 118 (40.7) | 0.40 |
| No | 9 (2.0) | 3 (2.1) | 6 (2.1) | |
| Unknown | 258 (55.8) | 72 (50.4) | 166 (57.2) | |
SpO2 = peripheral saturation of oxygen.
Values represent the number of patients (%) unless stated otherwise. Mean values are presented as the mean (SD), and median values are presented as the median (IQR). Boldface type indicates statistical significance.
Emergency Department Data
In the ED, the GCS score was recorded in 98% of patients, oxygen saturation in 89%, SBP in 95%, pupillary reactivity in 82%, and intravenous fluid administration in 98% of all patients (Table 3). However, oxygen administration was not recorded. Intravenous fluids were administered to 87% of patients, and advanced airway using a laryngeal mask or endotracheal intubation was established in 49%. GCS score and advanced airway management were the only variables that were statistically significantly different between survivors and nonsurvivors. Survivors had a higher median GCS score (7 [IQR 6–8] vs 6 [IQR 4–7], p < 0.001) and received advanced airway management more frequently (57.3% vs 44.5%, p = 0.01) than nonsurvivors.
TABLE 3.
ED basic critical care monitoring and treatment in patients with severe TBI
| ED Data | All | Survivors | Nonsurvivors | p Value |
|---|---|---|---|---|
| GCS score (n = 454) | ||||
| Median | 6 (4–7) | 7 (6–8) | 6 (4–7) | <0.001 |
| 3–5 | 181 (39.9) | 33 (23.2) | 134 (47.4) | <0.001 |
| 6–8 | 226 (49.8) | 91 (64.1) | 122 (43.1) | |
| 9–12 | 39 (8.6) | 15 (10.6) | 22 (7.8) | |
| 13–15 | 8 (1.8) | 3 (2.1) | 5 (1.8) | |
| Pupillary reactivity | 0.63 | |||
| Normal | 215 (46.5) | 68 (47.6) | 129 (44.5) | |
| Abnormal | 165 (35.7) | 47 (32.9) | 109 (37.6) | |
| Missing | 82 (17.8) | 28 (19.6) | 52 (17.9) | |
| Mean lowest SpO2, % (n = 409) | 93.65 (10.6) | 94.64 (8.4) | 92.95 (11.9) | 0.11 |
| Mean lowest SBP, mm Hg (n = 438) | 122.95 (24.2) | 121.9 (21.9) | 124.1 (25.2) | 0.38 |
| Hypoxia: SpO2 <90% | 0.26 | |||
| Yes | 68 (14.7) | 20 (14.0) | 47 (16.2) | |
| No | 341 (73.8) | 113 (79.0) | 210 (72.4) | |
| Unknown | 53 (11.5) | 10 (7.0) | 33 (11.4) | |
| Hypotension: SBP <90 mm Hg | 0.17 | |||
| Yes | 34 (7.4) | 9 (6.3) | 23 (7.9) | |
| No | 404 (87.5) | 131 (91.6) | 250 (86.2) | |
| Unknown | 24 (5.2) | 3 (2.1) | 17 (5.9) | |
| Intravenous fluid administration | 0.49 | |||
| Yes | 402 (87.0) | 128 (89.5) | 247 (85.2) | |
| No | 52 (11.3) | 13 (9.1) | 38 (13.1) | |
| Unknown | 8 (1.7) | 2 (1.4) | 5 (1.7) | |
| Advanced airway management | 0.01 | |||
| Yes | 226 (48.9) | 82 (57.3) | 129 (44.5) | |
| No | 236 (51.1) | 61 (42.7) | 161 (55.5) | |
Values represent the number of patients (%) unless stated otherwise. Mean values are presented as the mean (SD), and median values are presented as the median (IQR). Boldface type indicates statistical significance.
Neuroimaging
Fifty-five percent of patients underwent CT scanning of the head in toto, with 91% of scans revealing abnormal findings; more survivors than nonsurvivors underwent CT scanning (68.5% vs 49.7%, p < 0.001), while more nonsurvivors than survivors had abnormal CT findings of closed cisterns (52.1% vs 22.5%, p < 0.001) and midline shift (57.6% vs 39.7%, p = 0.007). The median time from admission at MOI until CT was performed was 3 hours (IQR 1.3–9.1 hours) and not different between survivors and nonsurvivors. Midline shift and closed cisterns were the only two CT findings that were associated with outcome (Table 4).
TABLE 4.
Neuroimaging data and findings in patients with severe TBI
| CT Scan Data | All | Survivors | Nonsurvivors | p Value |
|---|---|---|---|---|
| Underwent CT scanning | <0.001 | |||
| Yes | 254 (55.0) | 98 (68.5) | 144 (49.7) | |
| No | 208 (45.0) | 45 (31.5) | 146 (50.3) | |
| Median time from admission to CT, hrs | 3.0 (1.3–9.1) | 3.0 (1.5–11.1) | 3.07 (1.3–7.8) | 0.49 |
| Any abnormality on CT | 0.02 | |||
| Yes | 230 (90.6) | 85 (86.7) | 137 (95.1) | |
| No | 20 (7.9) | 12 (12.2) | 5 (3.5) | |
| Unknown | 4 (1.6) | 1 (1.0) | 2 (1.4) | |
| Basal cisterns | <0.001 | |||
| Open | 51 (20.1) | 29 (29.6) | 20 (13.9) | |
| Partially open | 98 (38.6) | 46 (46.9) | 48 (33.3) | |
| Closed | 102 (40.2) | 22 (22.5) | 75 (52.1) | |
| Unknown | 3 (1.2) | 1 (1.0) | 1 (0.7) | |
| Midline shift, cm | 0.007 | |||
| None | 120 (47.2) | 56 (57.1) | 59 (41.0) | |
| <0.5 | 40 (15.8) | 18 (18.4) | 20 (13.9) | |
| 0.5–1.5 | 67 (26.4) | 17 (17.4) | 48 (33.3) | |
| >1.5 | 19 (7.5) | 4 (4.1) | 15 (10.4) | |
| Unknown | 8 (3.2) | 3 (3.1) | 2 (1.4) | |
| Subarachnoid hemorrhage | 0.19 | |||
| Yes | 90 (35.4) | 29 (29.6) | 58 (40.3) | |
| No | 158 (62.2) | 67 (68.4) | 84 (58.3) | |
| Unknown | 6 (2.4) | 2 (2.0) | 2 (1.4) | |
| Intraventricular hemorrhage | 0.33 | |||
| Yes | 38 (15.0) | 11 (11.2) | 26 (18.1) | |
| No | 213 (83.9) | 86 (87.8) | 117 (81.3) | |
| Unknown | 3 (1.2) | 1 (1.0) | 1 (0.7) | |
| Multiple parenchymal lesions | 0.64 | |||
| Yes | 164 (64.6) | 66 (67.4) | 93 (64.6) | |
| No | 87 (34.3) | 32 (32.7) | 49 (34.0) | |
| Unknown | 3 (1.2) | 0 (0) | 2 (1.4) |
Values represent the number of patients (%) unless stated otherwise. Median values are presented as the median (IQR). Boldface type indicates statistical significance.
ICU Monitoring and Treatment
All comatose patients (GCS score < 9) were admitted to the ICU. The median GCS scores for survivors (score 6, IQR 5–8) and nonsurvivors (score 5, IQR 3–7) were significantly different (p < 0.001) and also worse from the prehospital and ED scores. Pupillary abnormalities increased to 55% on day 1 of admission from 36% in the ED and were also greater in nonsurvivors (p = 0.002). The incidence of hypotension during admission was 40% in nonsurvivors compared with 14% in survivors (p < 0.001). Mechanical ventilation was performed in 62% on day 1 and 78% at any time during admission and was not associated with outcome. Deep vein thrombosis prophylaxis was rarely administered, while nutrition was provided by nasogastric tube to 69% of all patients and 94% of survivors compared with 56% of nonsurvivors (p < 0.001) (Table 5).
TABLE 5.
Basic critical care therapies in patients with severe TBI
| In-Hospital Data | All | Survivors | Nonsurvivors | p Value |
|---|---|---|---|---|
| Day 1 GCS score (n = 456) | ||||
| Median | 6 (4–7) | 6 (5–8) | 5 (3–7) | <0.001 |
| 3–5 | 208 (45.6) | 40 (28.0) | 162 (56.3) | <0.001 |
| 6–8 | 239 (52.4) | 99 (69.2) | 125 (43.4) | |
| 9–12 | 9 (2.0) | 4 (2.8) | 1 (0.4) | |
| Pupils on day 1 | 0.002 | |||
| Normal | 196 (42.4) | 76 (53.2) | 104 (35.9) | |
| Abnormal | 256 (55.4) | 66 (46.2) | 181 (62.4) | |
| Missing | 10 (2.2) | 1 (0.7) | 5 (1.7) | |
| Hypotension on day 1: SBP <90 mm Hg | <0.001 | |||
| Yes | 67 (14.5) | 5 (3.5) | 59 (20.3) | |
| No | 364 (78.8) | 125 (87.4) | 218 (75.2) | |
| Missing | 31 (6.7) | 13 (9.1) | 13 (4.5) | |
| Any hypotension: SBP <90 mm Hg | <0.001 | |||
| Yes | 140 (30.3) | 20 (14.0) | 116 (40.0) | |
| No | 322 (69.7) | 123 (86.0) | 174 (60.0) | |
| Mechanical ventilation on day 1 | 0.69 | |||
| Yes | 288 (62.3) | 87 (60.8) | 183 (63.1) | |
| No | 142 (30.7) | 49 (34.3) | 89 (30.7) | |
| Missing | 32 (7.0) | 7 (4.9) | 18 (6.2) | |
| Any mechanical ventilation | 0.22 | |||
| Yes | 361 (78.1) | 107 (74.8) | 232 (80.0) | |
| No | 101 (21.9) | 36 (25.2) | 58 (20.0) | |
| DVT prophylaxis | ||||
| Compression stockings | 0.23 | |||
| Yes | 7 (1.5) | 4 (2.8) | 3 (1.0) | |
| No | 455 (98.5) | 139 (97.2) | 287 (99.0) | |
| Heparin | 0.04 | |||
| Yes | 5 (1.1) | 4 (2.8) | 1 (0.3) | |
| No | 457 (98.9) | 139 (97.2) | 289 (99.7) | |
| LMWH | 0.07 | |||
| Yes | 28 (6.1) | 12 (8.4) | 12 (4.1) | |
| No | 434 (93.9) | 131 (91.6) | 278 (95.9) | |
| Any nutrition | <0.001 | |||
| Yes | 319 (69.1) | 135 (94.4) | 162 (55.9) | |
| No | 143 (31.0) | 8 (5.6) | 128 (44.1) | |
DVT = deep vein thrombosis; LMWH = low-molecular-weight heparin.
Values represent the number of patients (%) unless stated otherwise. Median values are presented as the median (IQR). Boldface type indicates statistical significance.
Specific neurological therapies were also recorded. External ventricular drains were rarely placed (2.8%), and intracranial pressure was not monitored. Hypertonic saline was rarely used (< 1%), while mannitol was used in 44% of patients on day 1 and close to 50% of patients during their ICU stay, significantly more often in survivors (60.1% vs 46.2%, p = 0.006). Even though seizures were observed only in 7%, antiseizure prophylaxis was used in 88%. Utilization of analgesia, high-dose propofol or barbiturates, vasopressor, or paralytic agents was also low (Table 6). Survivors had sedation, CSF drainage, mannitol administration, antiseizure prophylaxis, and high-dose barbiturate administration more frequently than nonsurvivors.
TABLE 6.
Neurology-specific therapies for patients with severe TBI
| No. of Patients (%) | ||||
|---|---|---|---|---|
| In-Hospital Data | All | Survivors | Nonsurvivors | p Value |
| Any CSF drainage | 0.003 | |||
| Yes | 13 (2.8) | 9 (6.3) | 3 (1.0) | |
| No | 449 (97.2) | 134 (93.7) | 287 (99.0) | |
| Mannitol on day 1 | 0.09 | |||
| Yes | 205 (44.4) | 74 (51.8) | 121 (41.7) | |
| No | 251 (54.3) | 69 (48.3) | 167 (57.6) | |
| Missing | 6 (1.3) | 0 (0.0) | 2 (0.7) | |
| Any mannitol | 0.006 | |||
| Yes | 230 (49.8) | 86 (60.1) | 134 (46.2) | |
| No | 232 (50.2) | 57 (39.9) | 156 (53.8) | |
| Hypertonic saline on day 1 | 0.55 | |||
| Yes | 2 (0.4) | 1 (0.7) | 1 (0.3) | |
| No | 460 (99.6) | 142 (99.3) | 289 (99.7) | |
| Any hypertonic saline | 0.55 | |||
| Yes | 2 (0.4) | 1 (0.7) | 1 (0.3) | |
| No | 460 (99.6) | 142 (99.3) | 289 (99.7) | |
| Any antiseizure prophylaxis | <0.001 | |||
| Yes | 408 (88.3) | 140 (97.9) | 246 (84.8) | |
| No | 54 (11.7) | 3 (2.1) | 44 (15.2) | |
| Any seizure activity | 0.32 | |||
| Yes | 30 (6.5) | 12 (8.4) | 17 (5.9) | |
| No | 432 (93.5) | 131 (91.6) | 273 (94.1) | |
| Any steroids | 0.49 | |||
| Yes | 9 (2.0) | 4 (2.8) | 5 (1.7) | |
| No | 453 (98.0) | 139 (97.2) | 285 (98.3) | |
| Opioid analgesia | 0.08 | |||
| Yes | 201 (43.5) | 71 (49.7) | 118 (40.7) | |
| No | 261 (56.5) | 72 (50.3) | 172 (59.3) | |
| Sedative agents | 0.02 | |||
| Yes | 118 (25.5) | 48 (33.6) | 66 (22.8) | |
| No | 344 (74.5) | 95 (66.4) | 224 (77.2) | |
| Paralytic agents | 0.67 | |||
| Yes | 42 (9.1) | 12 (8.4) | 28 (9.7) | |
| No | 420 (90.9) | 131 (91.6) | 262 (90.3) | |
| Vasopressor agents | 0.49 | |||
| Yes | 9 (2.0) | 4 (2.8) | 5 (1.7) | |
| No | 453 (98.0) | 139 (97.2) | 285 (98.3) | |
| High-dose barbiturates | <0.001 | |||
| Yes | 74 (16.0) | 37 (25.9) | 35 (12.1) | |
| No | 388 (84.0) | 106 (74.1) | 255 (87.9) | |
| High-dose propofol | 0.35 | |||
| Yes | 24 (5.2) | 10 (7.0) | 14 (4.8) | |
| No | 438 (94.8) | 133 (93.0) | 276 (95.2) | |
| Surgery performed | 0.004 | |||
| Yes | 92 (19.9) | 40 (28.0) | 44 (15.2) | |
| No | 364 (78.8) | 103 (72.0) | 244 (84.1) | |
| Unknown | 6 (1.3) | 0 (0.0) | 2 (0.7) | |
| Surgery type | 0.003 | |||
| Craniectomy | 43 (9.3) | 17 (11.9) | 25 (8.6) | |
| Craniotomy | 49 (10.6) | 23 (16.1) | 19 (6.6) | |
Boldface type indicates statistical significance.
Surgery
Neurosurgical intervention was mostly performed for subdural or epidural hematomas. In the overall cohort, 20% underwent surgery, with survivors having surgery almost twice as often as nonsurvivors (28.0% vs 15.2%, p = 0.004). Approximately half of the surgeries were craniotomies, while the other half were converted to craniectomy based on intraoperative decisions (Table 6).
Outcome and Determinants
After excluding 29 patients (6.2%) with an unknown outcome, the overall 2-week mortality in the cohort was 67%. Based on the patients’ admission clinical characteristics, using the CRASH predictive model, the predicted 2-week mortality was 43.3% and the predicted 6-month unfavorable outcome was 64.8%. The IMPACT core model predicted a 6-month mortality of 32.2% and 6-month unfavorable outcome of 49.1%.
In univariate analyses, age (OR 1.02, 95% CI 1.01–1.04; p = 0.003), GCS score of 3–5 in the ED (OR 2.71, 95% CI 1.33–5.49; p = 0.006), pupillary reactivity loss in the ED (OR 2.59, 95% CI 1.15–5.84; p = 0.02), advanced airway management in the ED (OR 1.68, 95% CI 1.12–2.51; p = 0.01), CT scanning (OR 2.21, 95% CI 1.45–3.37; p < 0.001), abnormal findings on CT (OR 3.87, 95% CI 1.32–11.37; p = 0.01), and surgery (OR 2.15, 95% CI 1.32–11.37; p = 0.002) were significantly associated with death (Table 7).
TABLE 7.
Univariate analysis of predictors of outcome
| Total | Nonsurvivors | OR (95% CI) | p Value | |
|---|---|---|---|---|
| 2-wk mortality | 433 | 290 (67.0) | ||
| Mean age, yrs | 34.04 (16.54) | 35.73 (16.93) | 1.02 (1.01–1.04) | 0.003 |
| ED GCS score | ||||
| 3–5 | 167 (39.3) | 134 (80.2) | 2.71 (1.33–5.49) | 0.006 |
| 6–8 | 213 (50.1) | 122 (57.3) | 0.89 (0.46–1.72) | 0.74 |
| 9–15 | 45 (10.6) | 27 (60.0) | Ref | |
| ED pupillary reactivity | ||||
| Both reactive | 199 (56.1) | 130 (65.3) | Ref | |
| 1 reactive | 109 (30.7) | 70 (64.2) | 0.95 (0.59–1.55) | 0.85 |
| Neither reactive | 47 (13.2) | 39 (83.0) | 2.59 (1.15–5.84) | 0.02 |
| ED hypoxia: SpO2 <90% | ||||
| Yes | 67 (17.2) | 47 (70.2) | 1.27 (0.71–2.24) | 0.42 |
| No | 323 (82.8) | 210 (65.0) | Ref | |
| ED hypotension: SBP <90 mm Hg | ||||
| Yes | 32 (7.8) | 23 (71.9) | 1.34 (0.60–2.98) | 0.47 |
| No | 381 (92.3) | 250 (65.6) | Ref | |
| ED advanced airway management | ||||
| Yes | 211 (48.7) | 129 (61.1) | Ref | |
| No | 222 (51.3) | 161 (72.5) | 1.68 (1.12–2.51) | 0.01 |
| Mechanical ventilation | ||||
| Yes | 339 (78.3) | 232 (68.4) | Ref | |
| No | 94 (21.7) | 58 (61.7) | 0.74 (0.46–1.20) | 0.22 |
| Received CT scan | ||||
| Yes | 242 (55.9) | 144 (59.5) | Ref | |
| No | 191 (44.1) | 146 (76.4) | 2.21 (1.45–3.37) | <0.001 |
| Time to CT from arrival, hrs | ||||
| 0–3 | 140 (57.9) | 85 (60.7) | Ref | |
| 4–6 | 33 (13.6) | 21 (63.6) | 1.13 (0.52–2.49) | 0.76 |
| ≥7 | 69 (28.5) | 38 (55.1) | 0.79 (0.44–1.42) | 0.44 |
| Abnormal CT scan | ||||
| Yes | 222 (92.9) | 137 (61.7) | 3.87 (1.32–11.37) | 0.01 |
| No | 17 (7.1) | 5 (29.4) | Ref | |
| Surgery | ||||
| Yes | 84 (19.5) | 44 (52.4) | Ref | |
| No | 347 (80.5) | 244 (70.3) | 2.15 (1.32–3.50) | 0.002 |
| Time to surgery from arrival, days | ||||
| <1 | 44 (52.4) | 21 (47.7) | Ref | |
| >1 | 40 (47.6) | 23 (57.5) | 1.48 (0.63–3.51) | 0.37 |
| Major extracranial injury | ||||
| Yes | 76 (17.6) | 55 (72.4) | 1.36 (0.79–2.35) | 0.27 |
| No | 357 (82.5) | 235 (65.8) | Ref | |
Values represent the number of patients (%) unless stated otherwise. Mean values are presented as the mean (SD). Boldface type indicates statistical significance.
In multivariate models, we first included all variables that had an association with outcome from univariate analysis, namely, age; GCS score, pupillary reactivity, hypoxia, hypotension, and advanced airway management in the ED; CT scanning; and undergoing surgery (model 1, n = 321, C-statistic 0.7097) (Table 8). In model 2, when we removed hypoxia and hypotension, as they were not significantly correlated with outcome in this cohort, the model did not change for the worse (n = 351, C-statistic 0.7143). These models were comparable to the CRASH model (n = 342, C-statistic 0.6959).
TABLE 8.
Explanatory multivariate models using different variables associated with outcome
| Model 1 (n = 321) | Model 2 (n = 351) | CRASH model (n = 342) | |||||
|---|---|---|---|---|---|---|---|
| Variable | OR (95% CI) | p Value | OR (95% CI) | p Value | Variable | OR (95% CI) | p Value |
| Age | 1.02 (1.00–1.04) | 0.02 | 1.02 (1.00–1.03) | 0.04 | Age | 1.02 (1.00–1.05) | 0.07 |
| ED GCS score | ED GCS motor score | ||||||
| 3–5 | 2.14 (0.83–5.54) | 0.12 | 2.32 (0.93–5.79) | 0.07 | M1 | 6.07 (2.51–14.68) | <0.001 |
| 6–8 | 0.81 (0.34–1.95) | 0.65 | 0.77 (0.33–1.79) | 0.55 | M2 | 2.21 (1.02–4.80) | 0.05 |
| 9–12 | Ref | Ref | M3 | 2.25 (1.12–4.55) | 0.02 | ||
| M4 | 0.88 (0.46–1.70) | 0.71 | |||||
| M5/6 | Ref | ||||||
| ED pupillary reactivity | ED pupillary reactivity | ||||||
| Both | Ref | Ref | Both | Ref | |||
| One | 1.22 (0.69–2.14) | 0.50 | 1.21 (0.71–2.07) | 0.48 | One | 0.98 (0.58–1.65) | 0.93 |
| None | 1.66 (0.69–4.03) | 0.26 | 1.81 (0.76–4.30) | 0.18 | None | 1.60 (0.66–3.83) | 0.30 |
| CT scan | MEI | ||||||
| Yes | Ref | Ref | 1.38 (0.74–2.57) | 0.31 | |||
| No | 1.65 (0.96–2.84) | 0.07 | 1.58 (0.95–2.63) | 0.08 | |||
| Surgery | |||||||
| Yes | Ref | Ref | |||||
| No | 1.42 (0.75–2.69) | 0.28 | 1.63 (0.90–2.96) | 0.11 | |||
| ED advanced airway | |||||||
| Yes | Ref | Ref | |||||
| No | 1.83 (1.07–3.11) | 0.03 | 1.71 (1.03–2.83) | 0.04 | |||
| ED hypoxia | |||||||
| Yes | 1.36 (0.68–2.73) | 0.39 | |||||
| No | Ref | ||||||
| ED hypotension | |||||||
| Yes | 1.47 (0.54–4.06) | 0.45 | |||||
| No | Ref | ||||||
| C-statistic | 0.7097 | 0.7143 | 0.6959 | ||||
MEI = major extracranial injury.
Boldface type indicates statistical significance.
Discussion
Our findings demonstrate gaps in the acute care of patients with severe TBI at a tertiary center in Tanzania at several sequential points. Two-week mortality among survivors with severe TBI who were admitted to the hospital and had outcomes recorded was high at 67%, while the predicted mortality was 43.3%. A further estimated 20% of survivors would be expected to die between 2 weeks and 6 months based on prior clinical trial data from developed trauma care settings, thus potentially bringing the total mortality to approximately 75%.17–19 This level of mortality after severe TBI was seen in HICs around 1930 and conveys the urgent nature of the problem of head trauma care in LICs.20 Moreover, it is likely that our findings are not isolated to just this location but rather widespread in LICs and LMICs, given that over 143 countries have no evidence of adoption of WHO trauma guidelines.21 With the burden of TBI, estimated at 2.6 million and 8 million in LICs and LMICs, respectively,4 and increasing with time, the excess mortality that can be prevented is enormous.
Mortality and Unfavorable Outcome
Determinants of mortality after severe TBI may be categorized into prehospital care and transport, hospital-based surgical and medical care, and posthospital rehabilitation. To apportion the setting for increased mortality, we utilized the CRASH and IMPACT head injury prognosis models and calculated predicted mortality based on admission clinical features, thus accounting for prehospital neurological worsening.12,14 Predicted 2-week mortality using the CRASH model was 43.3% compared with the observed mortality of 67%. The predicted mortality in itself is significantly higher than the short-term mortality of 13%–20% seen in HICs.3,17,22 In-hospital mortality from severe TBI in the region has been reported in the range of 55%,23,24 while in the CRASH trial (2004), the 6-month mortality in LMICs was estimated at 51%.25 The higher predicted mortality is likely due to lack of prehospital systems and timely transport of patients to a suitable hospital and appears to contribute an excess mortality of 23%–30% compared with HICs and 10% compared with the region. The difference between the observed mortality rates (67%) and the predicted mortality (43.3%) yields an approximate 24% excess mortality from gaps in in-hospital care at MOI.
Unfavorable outcome at 6 months with the CRASH model was predicted to be 64.8% and, with the IMPACT model, 49%; the latter underestimates outcomes in LMICs since it is based solely on HIC data, whereas the CRASH model includes data from LMICs and is likely to be closer to reality. However, as observed in our sample, the actual unfavorable outcome rates are significantly higher than the predicted rates and likely approximate 90% at 6 months if the extra 24% mortality is simply added to the predicted unfavorable outcomes.
Without minimizing the need for prevention and development of prehospital care systems, targeted improvement programs at neurotrauma centers such as MOI could carry an enormous benefit in reducing as much as 24% of mortality and likely a greater risk of unfavorable outcome after severe TBI in LMICs.
Prehospital Care
In Dar-es-Salaam, a city of nearly 4.5 million inhabitants, while ambulances are available, there is no systematic network for prehospital care and transfer of patients with severe trauma to tertiary hospitals. In our data set, 89% of patients were initially taken to local hospitals before being referred to a tertiary center, and the median time to arrival at the tertiary center was 8 hours. Development of prehospital trauma management systems paralleling those in HICs has been shown to be cost-effective in LMICs, yet they remain severely underdeveloped.26,27 The lack of infield resuscitation and monitoring predisposes TBI patients to secondary insults such as hypoxia and hypotension as well as delays in transport, which are associated with adverse outcomes.28–30 In our cohort, monitoring for prehospital hypoxia and hypotension was documented in less than 35% of patients. Direct transport to specialized trauma centers is associated with lower mortality compared with initial transport to nonspecialized centers; however, 89.2% of patients in our study arrived from a lower-level healthcare facility.31–33 Downstream effects of prehospital delays increase time from injury to craniotomy in patients who are secondarily referred.34 With subdural hematomas requiring timely evacuation, there is evidence that delays beyond 3–4 hours may cause significantly worse outcomes.24,35–37 Overall impacts of delays between arrival, initial neurosurgical consultation, CT imaging, and subsequent surgery up to 70 hours have been described and were adversely associated with outcome in similar settings in Uganda.38
In-Hospital Care
Resuscitation must continue on arrival to the hospital at the ED and requires specialized systems to recognize and treat critically ill patients in a systematic manner. In our cohort, only 49% of comatose TBI patients had advanced airway established in the ED, implying a lack of systems in place; this correlated with short-term mortality. Further in-hospital care is guided by urgent neuroimaging and specialist review, followed by prompt surgery if required. Neuroimaging rates were low at 55%, and, as a consequence, surgical rates were also low (20%)—less than half of the rates quoted in the literature (40%–45%) from LMICs.17 Preliminary results from a separate study we conducted examining factors influencing low utilization of CT found financing and insurance as the most important cause rather than technological and resource factors.39 Since infrastructure and human resources already exist, no additional funds are spent in acquiring neuroimaging, and the imposition of this cost and subsequent delays in treatment must be considered in the context of medical and societal costs of worse outcomes.
Finally, ICU critical care was limited and asynchronous. Patient monitoring was inadequate, and several available low-cost treatments were underutilized. The incidence of hypotension remained high, with up to 30% of patients experiencing it at some point during their ICU stay, while rates of mechanical ventilation remained low, at 62% on day 1 and 78% at any time. Low-intensity high-impact approaches, including basic cardiorespiratory resuscitation, monitoring, and treatment for hypoxia and hypotension, are fundamental in the care of comatose TBI patients. These approaches require minimal expertise or specialized equipment and accurately performing them can reduce mortality significantly. Improved neurological monitoring of the patients with prompt as-needed hyperosmotic therapy can help to provide a time window for surgical intervention, which may further improve outcomes. Even though pupillary abnormalities were seen in 55% of patients, mannitol was used at any point in less than 50% of patients. Mannitol is a readily available, inexpensive, easily administered medication with few direct side effects, although caution should be exercised in patients who are at risk for hypotension. It is included in the WHO’s essential medication list, albeit as a diuretic, and should be available at all tertiary centers.40 CT imaging and surgery must be performed when required, but also in a timely manner for the interventions to be meaningful due to a high direct opportunity cost for both the patient and healthcare system.41
Systems Improvement and Capacity Building
Capacity building is at the heart of bridging gaps in medical care in low-resource areas. Tertiary hospitals need an organized and systematic approach to trauma patients, including formation of trauma teams and protocols. Stakeholder involvement is essential, with inclusion of emergency physicians, surgeons, anesthesiologists, nurses, hospital administrators, and health ministry officials. With the paucity in the availability of anesthesiologists and trained surgeons, the care of the 60%–70% nonsurgical TBI patients as well as perioperative management in surgical TBI patients must be delegated to a competent specialty. Training nurses in recognizing signs of neurological deterioration is vital, as is training junior doctors to respond expeditiously to the deterioration of such patients.
Capacity building by twinning between institutions with an aim to bridge knowledge and skill gap is an effective method for systems improvement. We have partnered with MOI in research and education and have established a neurotrauma program through which we have trained several surgical fellows and faculty, creating quality improvement databases and organizing annual regional educational courses in neurological surgery and neurointensive care of TBI and spine trauma. Additionally, weekly Skype conferences have been held for the past 5 years to have continual contact and knowledge transfer. In the past 2 years of the registry, we have seen mechanical ventilation and CT imaging rates double, and we anticipate seeing a consequent increase in surgical rates and reduction in mortality. Similar institutional twinning and training of neurosurgeons and other academic endeavors have been demonstrated in the region, although there are little data from the region on impact of capacity building in improvement of outcomes for neurotrauma.42,43
Our study provides comprehensive detail of existing care and quantifies the impact of gaps in the continuum of care of TBI patients in terms of excess mortality and carries a very specific social narrative. The typical TBI victim is a young male involved in a traffic accident with a high probability of dying. Given that the victims are at the peak of their economic contribution to their family and society, improvement in trauma care (along with improved prevention) is of paramount social importance. While this seems to be an enormous challenge, utilizing data to pinpoint exact gaps and measures of gaps is essential to build a successful program that provides meaningful care to severely ill patients in resource-limited settings. In this study, we have merely performed mapping of treatment currently utilized. We continue to work toward identifying reasons behind gaps in each segment of the treatment continuum, including examining effect of ability to pay, availability of drugs, protocols, and equipment, in detail. The current data provide an estimate of the magnitude of excess mortality of 24% that we must aim to reduce with our institutional twinning program.
Limitations
There are several limitations in our study. First, we found no association of early hypoxia and hypotension with outcome, which is contrary to known evidence.28–30 It is likely that hypotension and hypoxia were grossly underestimated since their measurements were not obtained continuously but once every few hours using portable devices, resulting in measurement error. This in itself is a serious issue to resolve in the monitoring of patients, since interventions can only be directed when monitoring is appropriate, reliable, and of good quality. Missing data is another limitation as is frequent in such high-intensity registries.44 It was not feasible to ensure year-round coverage for enrollment, and we estimate that 30% of patients were missed. However, given that data are collected over a protracted period of time, we hope the bias is minimized. There also appears to be a systematic bias in the selection of patients for aggressive therapy as evidenced by correlation of utilization of antiepileptic drug use and nutrition in survivors. The bias may also mask nonselection of therapies in patients deemed at high risk rather than gaps in care. A systematic evidence-based approach is essential to minimize selection bias and assist systems development. Resource implications must be considered as every investment comes at an opportunity cost while maximizing outcomes is essential. The CRASH model we used was borne out of clinical trial data with early enrollment of patients. In our study, time to arrival to the hospital is longer; therefore, it is possible that the actual estimates may vary somewhat. However, at the moment, the CRASH calculator is one of the few reliable and validated tools that exists for TBI patients in LMICs, and it is imperative to estimate the magnitude of excess mortality before implementing changes. Lastly, few data exist on posthospital care and continued survival of TBI victims in LMICs. We did not have the resources to collect these data, which ultimately demonstrates that work in global neurotrauma still illuminates merely the tip of the iceberg.
Conclusions
The short-term mortality in patients with severe TBI admitted to the hospital at a tertiary medical center in Tanzania was 67%, with excessive mortality from gaps in in-hospital acute care of more than 20%. Gaps exist along the entire continuum of management of the patient with severe TBI, including low rates of cardiorespiratory monitoring, resuscitation, airway management, mechanical ventilation, intensive care therapies, and surgical interventions. Further work is being done to identify causes of gaps at each step as well as bottleneck analyses. Such data derived from local settings are imperative to help guide specific interventions to maximize patient outcomes. Capacity building by twinning provides a means of obtaining critical data and providing programmatic improvements.
Acknowledgments
Xian Wu and Linda Gerber were partially funded by support from Clinical Translational Science Center, National Center for Advancing Translational Sciences, grant no. UL1-TR000457-06.
We would like to acknowledge the Brain Trauma Foundation for providing the TBI-trac registry for this work.
ABBREVIATIONS
- CRASH
Corticoid Randomisation After Significant Head Injury
- ED
emergency department
- GCS
Glasgow Coma Scale
- HIC
high-income country
- IMPACT
International Mission for Prognosis and Analysis of Clinical Trials
- LIC
low-income country
- LMIC
low- and middle-income country
- MOI
Muhimbili Orthopaedic Institute
- SBP
systolic blood pressure
- TBI
traumatic brain injury
Footnotes
Disclosures
Dr. Hartl: consultant for DePuy Synthes, Ulrich, and Brainlab; royalties from Zimmer Biomet; and investor in RealSpine.
Supplemental Information
Previous Presentations
Preliminary results of this work were previously presented at NeuroTrauma 2018: The 3rd Joint Symposium of the International and National Neurotrauma Societies and AANS/CNS Section on Neurotrauma and Critical Care, Toronto, Ontario, Canada, August 11–16, 2018.
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