Prehospital Trauma Scoring Systems for Evaluation of Trauma Severity and Prediction of Outcomes.

INTRODUCTION: Trauma scoring systems in prehospital settings are supposed to ensure the most appropriate in-hospital treatment of the injured. AIM OF THE STUDY: To determine the sensitivity and specificity of the CRAMS scale (circulation, respiration, abdomen, motor and speech), RTS score (revised trauma score), MGAP (mechanism, Glasgow Coma Scale, age, arterial pressure) and GAP (Glasgow Coma Scale, age, arterial pressure) scoring systems in prehospital settings in order to evaluate trauma severity and to predict the outcome. MATERIALS AND METHODS: A prospective, observational study was conducted. For every trauma patient, a questionnaire was initially filled in by a prehospital doctor and these data were subsequently collected by the hospital. RESULTS: The study included 307 trauma patients with an average age of 51.7 ± 20.9. Based on the ISS (injury severity score), severe trauma was diagnosed in 50 (16.3%) patients. MGAP had the best sensitivity/specificity ratio when the obtained values indicated severe trauma. The sensitivity and specificity were 93.4 and 62.0%, respectively, for an MGAP value of 22. MGAP and GAP were strongly correlated with each other and were statistically significant in predicting the outcome of treatment (OR 2.23; 95% Cl 1.06-4.70; p = 0.035). With a rise of one in the MGAP score value, the probability of survival increases 2.2 times. CONCLUSION: MGAP and GAP, in prehospital settings, had higher sensitivity and specificity when identifying patients with a severe trauma and predicting an unfavorable outcome than other scoring systems.

Getting a grippe on severity: a retrospective comparison of influenza-related hospitalizations and deaths captured in reportable disease and administrative data sources in Ontario, Canada.

BACKGROUND: Since 2009, in Ontario, reportable disease surveillance data has been used for timely in-season estimates of influenza severity (i.e., hospitalizations and deaths). Due to changes in reporting requirements influenza reporting no longer captures these indicators of severity, necessitating exploration of other potential sources of data. The purpose of this study was to complete a retrospective analysis to assess the comparability of influenza-related hospitalizations and deaths captured in the Ontario reportable disease information system to those captured in Ontario's hospital-based discharge database. METHODS: Hospitalizations and deaths of laboratory-confirmed influenza cases reported during the 2010-11 to 2013-14 influenza seasons were analyzed. Information on hospitalizations and deaths for laboratory-confirmed influenza cases were obtained from two databases; the integrated Public Health Information System, which is the provincial reportable disease database, and the Discharge Abstract Database, which contains information on all in-patient hospital visits using the International Classification of Diseases, 10th Revision, Canada (ICD-10-CA) coding standards. Analyses were completed using the ICD-10 J09 and J10 diagnosis codes as an indicator for laboratory-confirmed influenza, and a secondary analysis included the physician-diagnosed influenza J11 diagnosis code. RESULTS: For each season, reported hospitalizations for laboratory-confirmed influenza cases in the reportable disease data were higher compared to hospitalizations with J09 and J10 diagnoses codes, but lower when J11 codes were included. The number of deaths was higher in the reportable disease data, whether or not J11 codes were included. For all four seasons, the weekly trends in the number of hospitalizations and deaths were similar for the reportable disease and hospital data (with and without J11), with seasonal peaks occurring during the same week or within 1 week of each other. CONCLUSION: In our retrospective analyses we found that hospital data provided a reliable estimate of the trends of influenza-related hospitalizations and deaths compared to the reportable disease data for the 2010-11 to 2013-14 influenza seasons in Ontario, but may under-estimate the total seasonal number of deaths. Hospital data could be used for retrospective end-of-season assessments of severity, but due to delays in data availability are unlikely to be timely estimates of severity during in-season surveillance.

Evaluation of risk factors for prognosticating blunt trauma chest.

<b>Introduction:</b> Blunt trauma chest contributes to significant number of trauma admissions globally and is a cause of major morbidity and mortality. Many scoring systems and risk factors have been defined in past for prognosticating blunt trauma chest but, none is considered to be gold standard. </br> <b>Aim:</b> This study was conducted to reassess the significance of available scoring systems and others indicators of severity in prognosticating blunt trauma chest patients. </br> <b>Materials and Methods:</b> In this prospective observational study from November 2016 till March 2018, 50 patients with age more than 12 years with blunt chest trauma who required hospitalization were included. Nine risk factors were assessed namely- age of the patient, duration of presentation after trauma, number of ribs fractured, bilateral thoracic injury, evidence of lung contusion, associated extra thoracic injury, need for mechanical ventilation, Revised trauma Score (RTS) and Modified Early Warning Sign Score (MEWS). Severity of blunt thoracic trauma was assessed on following outcomes-SIRS, ARDS and Death. The inferences were drawn with the use of statistical software package SPSS v22.0. </br> <b>Results:</b> The age of 50 patients included in our study with a range of 15 to 76 years, the median age was 35.5 years. Statistically significant association was observed between occurrence of SIRS and multiple ribs fractured (p-value- 0.049), associated extra-thoracic injury (p-value-0.016) and higher MEWS score (p-value-0.025). ARDS occurrence was statistically significantly associated with all the risk factors except age.Death occurred more in patients with delayed duration of presentation to hospital (p-value <0.001), multiple ribs fractured (p-value-0.001), bilateral thoracic injury(p-value<0.001), associated extra-thoracic injury (p-value-0.004), patients who required ventilatory support (p-value<0.001), low RTS (p-value-0.006) and high MEWS (p-value-0.005) on admission. This association was found statistically significant. </br> <b>Conclusion:</b> High MEWS, associated extra-thoracic injuries and multiple rib fractured can very well predict poor outcome in terms of SIRS, ARDS and death. Aggressive treatment protocols should be established for better outcome in these patients with blunt trauma chest.