ArticleCureus2024
Generation of Trauma Rush Call in Managing Patients With Road Traffic Injuries Visiting the Emergency Department: A Retrospective Analysis.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRoad traffic injuries (RTIs) are currently the ninth most common cause of mortality and are expected to increase in the future. RTIs rank in the top three reasons why young people die. Because of the high incidence and mortality risk, proper trauma care has been prioritized for RTI patients who present to the emergency department. Making appropriate and prompt trauma emergency calls can save lives. Poor resource use and an increased burden of inquiry can result from the inappropriate development of trauma rush calls (TRCs). Therefore, this study aims to evaluate the appropriate generation of TRCs to manage patients with RTIs presenting to the emergency department (ED).
methodsA retrospective analysis was conducted at the ED of the private tertiary care hospital in Karachi, Pakistan. Patients with RTIs and TRCs generated for the management were included. Dead on arrival was excluded. A total of 322 patients were enrolled using convenient sampling. A standard Performa was developed to collect the data from medical records, including demographics, clinical characteristics, radiological findings, TRC characteristics, and disposition. Multivariable logistic regression analysis was applied to determine the TRC justified using odds ratios, considering a p-value ≤0.05 as significant.
resultsOf the total 322 patients, the majority were males (n=266, 82.61%) between 21 and 40 years of age. Most patients reported injury/accident through high-speed vehicle crash (n=102, 31.68%). A Glasgow Coma Scale (GCS) score of <14 was recorded in 119 (36.96%) patients, and of 208 patients, 153 (73.55%) had positive computed tomography (CT) head findings, majorly subdural hematoma (n=43, 28.10%). CT cervical spine showed fractures in 14 patients (11.29%). Only one patient had both a positive CT abdomen and focused assessment with sonography in trauma (FAST), with a low systolic blood pressure (SBP) of 85 mmHg, and the CT abdomen showed a grade 3 spleen laceration. TRC was justified in 248 (77.01%) patients. Multivariable analysis showed that a GCS <14 [adjusted odds ratio (aOR) 3.03 (95% CI: 2.58-5.04)] and cervical collar [aOR 3.12 (95% CI: 1.63-5.98)] were the significant predictors for the TRC justified.
conclusionA GCS <14 and cervical collar significantly justify the generation of TRC. These findings are crucial in educating ED staff regarding optimal care delivery. The future deployment of resources depends on these findings for skill development.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.