scholarly journals The Best Prediction Model for Trauma Outcomes of the Current Korean Population: a Comparative Study of Three Injury Severity Scoring Systems

2016 ◽  
Vol 31 (3) ◽  
pp. 221-228 ◽  
Author(s):  
Kyoungwon Jung ◽  
John Cook-Jong Lee ◽  
Rae Woong Park ◽  
Dukyong Yoon ◽  
Sungjae Jung ◽  
...  
Injury ◽  
1982 ◽  
Vol 14 (1) ◽  
pp. 2-6 ◽  
Author(s):  
J.P. Bull

2002 ◽  
Vol 23 (5) ◽  
pp. 268-273 ◽  
Author(s):  
Silom Jamulitrat ◽  
Montha Na Narong ◽  
Somchit Thongpiyapoom

Objectives:To describe the patterns of nosocomial infections in patients with traumatic injuries and to compare the associations between injury severity, derived from various severity scoring systems, and subsequent nosocomial infections.Design:Prospective observational study.Setting:A 750-bed university hospital serving as a medical school and referral center for the southern part of Thailand.Participants:All trauma patients admitted to the hospital for more than 3 days during 1996 to 1999 were eligible for this study.Methods:The severity of injuries was measured in terms of injury severity score (ISS), revised trauma score (RTS), new injury severity score (NISS), and trauma injury severity score (TRISS). Infections acquired during hospitalization were categorized using Centers for Disease Control and Prevention criteria. The association between severity of injury and nosocomial infection was examined with Poisson regression models.Results:There were 222 nosocomial infections identified among 146 patients, yielding an infection rate of 0.8 infections per 100 patient-days. Surgical-site infection was the most common site-specific infection, accounting for 31.1% of all infections. The incidence of intravenous catheter–related bloodstream infection was 1.6 infections per 100 catheter-days. The bladder catheter–related urinary tract infection rate was 2.8 infections per 100 catheter-days. The rate of ventilator-associated pneumonia was 3.2 infections per 100 ventilator-days. The incidence of infection correlated well with injury severity. The infection incidence rate ratios for one severity category increment of ISS, NISS, RTS, and TRISS were 1.65 (95% confidence interval [CI95, 1.42 to 1.92), 1.79 (CI95, 1.55 to 2.05), 1.64 (CI95, 1.43 to 1.88), and 1.32 (CI95, 1.14 to 1.52), respectively.Conclusions:Surgical-site infection was the most common site-specific nosocomial infection. The NISS might be the most appropriate severity scoring system for adjustment of infection rates in trauma patients.


Trauma ◽  
2021 ◽  
pp. 146040862098226
Author(s):  
Will Kieffer ◽  
Daniel Michalik ◽  
Jason Bernard ◽  
Omar Bouamra ◽  
Benedict Rogers

Introduction Trauma is one of the leading causes of mortality worldwide, but little is known of the temporal variation in major trauma across England, Wales and Northern Ireland. Proper workforce and infrastructure planning requires identification of the caseload burden and its temporal variation. Materials and Methods The Trauma Audit Research Network (TARN) database for admissions attending Major Trauma Centres (MTCs) between 1st April 2011 and 31st March 2018 was analysed. TARN records data on all trauma patients admitted to hospital who are alive at the time of admission to hospital. Major trauma was classified as an Injury Severity Score (ISS) >15. Results A total of 158,440 cases were analysed. Case ascertainment was over 95% for 2013 onwards. There was a statistically significant variation in caseload by year (p < 0.0001), times of admissions (p < 0.0001), caseload admitted during weekends vs weekdays, 53% vs 47% (p < 0.0001), caseload by season with most patients admitted during summer (p < 0.0001). The ISS varied by time of admission with most patients admitted between 1800 and 0559 (p < 0.0001), weekend vs weekday with more severely injured patients admitted during the weekend (p < 0.0001) and by season p < 0.0001). Discussion and Conclusion: There is a significant national temporal variation in major trauma workload. The reasons are complex and there are multiple theories and confounding factors to explain it. This is the largest dataset for hospitals submitting to TARN which can help guide workforce and resource allocation to further improve trauma outcomes.


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