Time to Computed Tomography and Association With Survival in Indian Trauma Patients
Time to Computed Tomography and Association With Survival in Indian Trauma Patients
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:

 -  Direct admission to the participating centre (not referrals)

 -  Patient is 15 years or older

 -  CT imaging was conducted as part of the trauma workup