How is Time to Computed Tomography Associated With Survival in Adult Trauma Patients in an Urban Lower-middle Income Setting?
How is Time to Computed Tomography Associated With Survival in Adult Trauma Patients in an Urban Lower-middle Income Setting?
The study aims to assess whether time to CT is associated with survival in adult trauma patients in an urban lower-middle income setting.
Background
Trauma is major threat to population health worldwide, each year killing more people than malaria, tuberculosis, HIV/AIDS and maternal conditions combined. Almost five millions deaths occur annually as a result of injuries and of these approximately 90 percent occur in low- and middle-income countries (LMIC). An increase in road traffic deaths has been seen in many LMIC where motorization and urbanization has not been accompanied sufficiently by improved road safety strategies. In fact, in the age group 15-29 years, road traffic injuries are the leading cause of death worldwide. With these changing patterns in global health, trauma is now a condition needing greater priority to reduce avoidable mortality in young and middle-aged adults.
Early detection of potentially lethal or disabling injuries is crucial to reduce trauma mortality and morbidity. Imaging is at the core of such detection, and computed tomography (CT) is standard in trauma systems all over the world. Studies comparing whole body CT (WBCT) to selective CT imaging suggest that WBCT is associated with better outcomes and lower mortality rates. In a well structured environment, WBCT during trauma resuscitation was associated with significantly decreased mortality in haemodynamically stable as well as in haemodynamically unstable major trauma patients.
There is a strong push to perform CT as early as possible as part of the diagnostic workup. Immediate CT and rapid bleeding control without patient transfer, close distance of the CT scanner to the trauma room, as well as immediate WBCT after initial examination compared to selective CT imaging according to the Advanced Trauma Life Support (ATLS) guidelines was associated with improved probability of survival of severely injured patients in high income countries. However, no similar studies have been conducted in LMIC. There are concerns about such investigations delaying time-critical interventions. In low-resource settings the CT may be located far from the resuscitation and surgical resources, and the expertise needed to keep patients stable during the transfer to and from the CT may be limited. Therefore, whether time to CT is associated with survival remains unknown in low-middle income settings disproportionally affected by trauma.
Aim
To assess whether time to CT is associated with survival in adult trauma patients in an urban lower-middle income setting.
Study Design
This is a retrospective analysis of the cohort study Towards Improved Trauma Care Outcomes in India (TITCO).
Setting
The de-identified TITCO cohort includes a total of 16,000 patients enrolled from four public university hospitals in urban India between July 2013 and December 2015. The hospitals are located in the megacities Mumbai (two centres), Delhi and Kolkata. One project officer at each site performed the data collection. Data was gathered prospectively on-admission on a standardized intake form for eight hours per day by directly observing the staff delivering trauma care. They rotated daily through each eight-hour shift (morning, evening, night), including public holidays. For patients admitted outside the eight-hour "observed shift", the data was retrospectively retrieved from patient records within days. Time to first CT was recorded within the first 24 hours of arrival to a participating centre.
Source and method of participant selection
The one-site project officer included patients from participating hospitals, either by prospective observation or by retrospective data retrieval from patient records.
Exposure
Time from injury to CT imaging in hours, extracted from patient records.
Covariates
Age in years, sex, whether the participant was transferred from another health facility, mechanism of injury recorded as road traffic injury, railway injury, fall, assault or other, all extracted from patient records or reported by participants. Vital signs on arrival to participating centre including systolic blood pressure (SBP), heart rate (HR), and Glasgow coma scale (GCS). Vital sign data was extracted from patient records. Anatomical injury severity quantified using the injury severity score (ISS), calculated by a single accredited coder based on text injury descriptions.
Bias
All project officers observing and collecting the data had a health science master degree. They were not employed by participating centres but by the project administration centrally. In addition, they were continuously trained and supervised through out the study period.
Quantitative variables
Quantitative variables will be handled as continuous. Variables for which a non-linear association with survival can be assumed, such as age, systolic blood pressure, heart rate and time between arrival and CT will be modelled using restricted cubic splines with three knots placed at equally spaced percentiles.
Statistical methods
R, a language and environment for statistical computing, will be used for all statistical analyses. A predictive approach will be employed to test the internal validity of the findings by temporally splitting the sample in two parts, henceforth referred to as the test and validation samples. The earlier half of observations from each participating centre will form the test sample whereas the later half from each centre will form the validation sample, ensuring that the relative contribution of each centre is approximately the same in both samples.
The following procedures will then be conducted in both samples. First, sample characteristics will be presented using medians and inter-quartile ranges (IQR) for quantitative variables and counts and percentages for qualitative variables. Second, to assess how time to CT is associated with survival a logistic regression model will be used. A minimal model including only time to CT modelled using restricted cubic splines will be built to generate a crude estimate of the association. A full model including all covariates listed above in addition to time to CT will then be built to generate an adjusted estimate.
Finally, the differences and associated 95% confidence intervals (CI) between the time to CT parameter coefficients in the test and validation samples will be assessed using bootstrapping, to produce an estimate of the findings robustness. When relevant, a 5% significance level will be used.
Strategy to handle missing data
If the required sample size is reached if only patients with complete data on the outcome, explanatory variable, and covariates are included then a complete case analysis will be conducted. If not then missing data will be handled with multiple imputation using chained equations. The number of imputed datasets will be equal to the percentage of incomplete observations. The analysis will be conducted separately in each imputed dataset and the main results presented as medians with IQR across imputations. For confidence intervals the most extreme values of pooled upper and lower bounds will be reported.
Study size
Simulation studies of proportional hazard models' sample size requirements indicate a need for at least ten events per parameter (see below) in the hypothetically most complex model for the model to produce reliable coefficient estimates. An event here is an observation with the outcome. Each of time to CT, age, SBP and HR will contribute with two parameters when modelled using restricted cubic splines. Sex, transfer status, GCS, and ISS each accounts for one parameter. Mechanism of injury contributes with four parameters. Taken together the full model will include 16 parameters and hence require 160 events. Assuming an outcome prevalence of 20% based on previous research each of the test and validation samples need to include at least 800 observations. The minimum total sample size required is therefore 1600 observations.
Inclusion Criteria:
Mumbai, Maharashtra 400012, India
New Delhi, National Capital Territory of Delhi 110029, India