Trends in Isolated Pelvic Fracture and 30-Day Survival during a Recent 15-Year Period: A Nationwide Study of the Japan Trauma Data Bank

Abstract

BackgroundThe epidemiology and treatment of isolated pelvic fracture is not well understood in Japan. This study aimed to evaluate epidemiological trends in isolated pelvic trauma and in-hospital survival rates over 15 years. MethodsThis retrospective cohort study analyzed data from the Japan Trauma Data Bank for 2004-2018. Patients of any age with isolated pelvic fracture were grouped according to time period: 2004-2008 (Phase 1), 2009-2013 (Phase 2), and 2014-2018 (Phase 3). The main outcome was 30-day in-hospital survival rate. The data were analyzed using chisquared, Kruskal-Wallis, and Mantel-Haenszel trend tests. We analyzed changes in the main outcome over time in a multiple logistic regression analysis fitted with a generalized estimating equation, accounting for the within-cluster association. ResultsIn total, 5348 isolated pelvic fractures occurred during the study period. There was no significant between-phase difference in proportions of patients who underwent resuscitative balloon occlusion of the aorta or external fixation. The proportion of patients who underwent transcatheter arterial embolization increased year by year (p=0.003).

Comparison of the effectiveness of pericardiocentesis and surgical pericardiotomy in the prognosis of patients with blunt traumatic cardiac tamponade: a multicenter study using the Japan Trauma Data Bank

“…To reduce bias due to incomplete data, we undertook multiple imputation using 20 datasets generated by replacing missing values with alternative values. 15 , 16 , 17 Finally, we examined the temporal trend of survival using the Kaplan–Meier method. The level of statistical significance was set at a P ‐value of <0.05.…”

“…Next, multivariable analyses were undertaken to examine the association of PCC and SP with in‐hospital mortality using variables considered to be independently associated with mortality (age, sex, time from emergency call to hospital arrival, sBP, respiratory rate, pulse rate, GCS score, head AIS, chest AIS, abdomen/pelvis AIS, and ISS). To reduce bias due to incomplete data, we undertook multiple imputation using 20 datasets generated by replacing missing values with alternative values 15,16,17 . Finally, we examined the temporal trend of survival using the Kaplan–Meier method.…”