Search

Purpose: Approximately 8 % of injuries in the elderly are from penetrating mechanisms. The natural history of potentially survivable penetrating torso wounds in the elderly is not well studied. Older adults with penetrating injuries to the torso may have worse outcomes than matched, younger patients due to a failure to rescue after complications. Methods: A retrospective chart review of all patients ≥55 (older) with a penetrating injury (GSW or SW) to the torso over 20 years was performed. All patients with a maximum AIS chest or abdomen >1 and <6 were included. A matched cohort (mechanism, AIS chest and abdomen, ISS and sex) of patients between the ages of 20-40 years (young) was created (3 young, 1 older). Differences in hemodynamics, complications, length of stay and mortality were analyzed. Results: 105 older met inclusion criteria were compared to 315 young patients. Hemodynamic status was similar between the groups. Older patients required ICU care more often than younger patients, p < 0.05. Older patients required longer ICU stays, p < 0.001 and longer hospitalizations, p = 0.0012. More older patients (41.0 %) suffered post-injury complications compared to the young (26.4 %), p = 0.005. Older patients who suffered a complication had a higher mortality (30.2 %) than the young after a complication (10.8 %), p = 0.007. Conclusions: While uncommon, penetrating injuries to older adults are associated with higher rates of post-injury complications and increased mortality. This may represent a 'failure to rescue' and represent an opportunity for improved post-injury care in older adults who suffer potentially survivable penetrating torso injuries. [ABSTRACT FROM AUTHOR]

• A extreme gradient boost prediction model can accurately predict mortality fall patients. • A extreme gradient boost model outperforms random forest and logistic regresion. • Machine learning algorithms can be trained with existing trauma data to potentially aide in resource utilization. Falls are the most common cause of injury-related death for patients older than 45. We hypothesized that a machine learning algorithm developed from state-level registry data could make accurate outcome predictions at a level 1 trauma hospital. Data for all patients admitted for fall injury during 2009 – 2019 in the state of Pennsylvania were derived from the state trauma registry. Thirteen variables that were immediately available upon patient arrival were used for prediction modeling. Data for the test institution were withheld from model creation. Algorithms assessed included logistic regression (LR), random forest (RF), and extreme gradient boost (XGB). Model discrimination for mortality was assessed with area under the curve (AUC) for each algorithm at our level 1 trauma center. 180,284 patients met inclusion criteria. The mean age was 69 years ± 18.5 years with a mortality rate of 4.0%. The AUC for predicting mortality in patients that fall for LR, RF, and XGB were 0.797, 0.876, and 0.880, respectively. The variables which contributed to the prediction in descending order of importance for XGB were respiratory rate, pulse, systolic blood pressure, ethnicity, weight, sex, age, temperature, Glasgow Coma Scale (GCS) eye, race, GCS voice, GCS motor, and blood alcohol level. An extreme gradient boost model developed using state-wide trauma data can accurately predict mortality after fall at a single center within the state. This machine learning model can be implemented by local trauma systems within the state of Pennsylvania to identify patients injured by fall that require greater attention, transfer to a higher level of care, and higher resource allocation. [ABSTRACT FROM AUTHOR]

Background: Damage control laparotomy emphasizes physiologic stabilization of critically injured patients and allows staged surgical management. However, there is little consensus on the optimal criteria for damage control laparotomy. We examined variability between centers and over time in Pennsylvania., Methods: We analyzed the Pennsylvania Trauma Outcomes Study data between 2000 and 2018, excluding centers performing <10 laparotomies/year. Laparotomy was defined using International Classification of Diseases codes, and damage control laparotomy was defined by a code for "reopening of recent laparotomy" or a return to the operating room >4 hours from index laparotomy that was not unplanned. We examined trends over time and by center. Multivariable logistic regression models were developed to predict both damage control laparotomy and mortality, generate observed:expected ratios, and identify outliers for each. We compared risk-adjusted mortality rates to center-level damage control laparotomy rates., Results: In total, 18,896 laparotomies from 22 centers were analyzed; 3,549 damage control laparotomies were performed (18.8% of all laparotomies). The use of damage control laparotomy in Pennsylvania varied from 13.9% to 22.8% over time. There was wide variation in center-level use of damage control laparotomy, from 11.1% to 29.4%, despite adjustment. Factors associated with damage control laparotomy included injury severity and admission vital signs. Center identity improved the model as demonstrated by likelihood ratio test (P < .001), suggesting differences in center-level practices. There was minimal correlation between center-level damage control laparotomy use and mortality., Conclusion: There is wide center-level variation in the use of damage control laparotomy among centers, despite adjustment for patient factors. Damage control laparotomy is both resource intensive and highly morbid; regional resources should be allocated to address this substantial practice variation to optimize damage control laparotomy use., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Higher center-level operative volume is associated with lower mortality after complex elective surgeries, but this relationship has not been robustly demonstrated for operative trauma. We hypothesized that trauma centers in Pennsylvania with higher operative trauma volumes would have lower risk-adjusted mortality rates than lower volume institutions., Methods: We queried the Pennsylvania Trauma Outcomes Study database (2017-2019) for injured patients 18 years or older at Level I and II trauma centers who underwent an International Classification of Diseases, Tenth Revision (ICD-10), procedure code -defined operative procedure within 6 hours of admission. The primary exposure was tertile of center-level operative volume. The primary outcome of interest was inpatient mortality. We entered factors associated with mortality in univariate analysis (age, injury severity, mechanism, physiology) into multivariable logistic regression models with tertiles of volume accounting for center-level clustering. We conducted secondary analyses varying the form of the association between the volume and mortality to including dichotomous and fractional polynomial models., Results: We identified 3,650 patients at 29 centers meeting the inclusion criteria. Overall mortality was 15.9% (center-level range, 6.7-34.2%). Operative procedure types were cardiopulmonary (7.3%), vascular (20.1%), abdominopelvic (24.3%), and multiple (48.3%). The mean annual operative volume over the 3 years of data was 10 to 21 operations for low-volume centers, 22 to 47 for medium-volume centers, and 47 to 158 for high-volume centers. After controlling for patient demographics, physiology, and injury characteristics, there was no significant difference in mortality between highest and lowest tertile centers (odds ratio, 0.92; confidence interval, 0.57-1.49). Secondary analyses similarly demonstrated no relationship between center operative volume and mortality in key procedure subgroups., Conclusion: In a mature trauma system, we found no association between center-level operative volume and mortality for patients who required early operative intervention for trauma. Efforts to standardize the care of seriously injured patients in Pennsylvania may ensure that even lower-volume centers are prepared to generate satisfactory outcomes., Level of Evidence: Prognostic and Epidemiological; Level III., (Copyright © 2022 American Association for the Surgery of Trauma.)

Objective: We aimed to determine whether gentrification predicts the movement of shooting victims over time and if this process has decreased access to care., Background: Trauma centers remain fixed in space, but the populations they serve do not. Nationally, gentrification has displaced disadvantaged communities most at risk for violent injury, potentially decreasing access to care. This process has not been studied, but an increase of only 1 mile from a trauma center increases shooting mortality up to 22%., Methods: We performed a cross-sectional study utilizing Philadelphia Police Department (PPD) and Pennsylvania trauma systems outcome (PTOS) data 2006-2018. Shootings were mapped and grouped into census tracts. They were then cross-mapped with gentrification data and hospital location. PPD and PTOS shooting data were compared to ensure patients requiring trauma care were captured. Census tracts with ≥500 residents with income and median home values in the bottom 40th percentile of the metropolitan area were eligible to gentrify. Tracts were gentrified if residents ≥25 with a bachelor's degree increased and home price increased to the top third in the metropolitan area. Change in distribution of shootings and its relation to gentrification was our primary outcome while proximity of shootings to a trauma center was our secondary outcome., Results: Thirty-two percent (123/379) of eligible tracts gentrified and 31,165 shootings were captured in the PPD database. 9090 (29.2%) patients meeting trauma criteria were captured in PTOS with an increasing proportion over time. The proportion of shootings within gentrifying tracts significantly dropped 2006-2018 (40%-35%, P < 0.001) and increased in non-gentrifying tracts (52%-57%, P < 0.001). In evaluation of shooting densities, a predictable redistribution occurred 2006-2018 with incident density decreasing in gentrified areas and increasing in non-gentrified areas. Shootings within 1 mile of a trauma center increased overall, but proportional access decreased in gentrified areas., Conclusions: Shootings in Philadelphia predictably moved out of gentrified areas and concentrated in non-gentrified ones. In this case study of a national crisis, the pattern of change paradoxically resulted in an increased clustering of shootings around trauma centers in non-gentrified areas. Repetition of this work in other cities can guide future resource allocation and be used to improve access to trauma care., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Pulmonary complications are the most common adverse event after injury and second greatest cause of failure to rescue (death after pulmonary complications). It is not known whether readily accessible trauma center data can be used to stratify center-level performance for various complications. Performance variation between trauma centers would allow sharing of best practices among otherwise similar hospitals. We hypothesized that high-, average-, and low-performing centers for pulmonary complication and failure to rescue could be identified and that hospital factors associated with success and failure could be discovered., Methods: Pennsylvania state trauma registry data (2007-2015) were abstracted for pulmonary complications. Burns and age <17 were excluded. Multivariable logistic regression models were developed for pulmonary complication and failure to rescue, using demographics, comorbidities, and injuries/physiology. Expected event rates were compared with observed rates to identify outliers. Center-level variables associated with outcomes of interest were taken from the American Hospital Association Annual Survey Database and assessed for inclusion., Results: Included in the study were 283,121 patients (male [60%] blunt trauma [92%]). Of these patients, 3% (8,381 of 283,121) developed pulmonary complications (center-level range 0.18%-5.8%). The percentage of failure-to-rescue patients was 13.4% (1,120/8,381, center-level range 0.0%-22.6%). For pulmonary complications, 13 out of 27 centers were high performers (95% CI for O:E ratio <1) and 7 out of 27 were low (95% CI for an O:E ratio >1). For failure-to-rescue patients, 2 out of 27 centers were low performers and the remainder average. There was little concordance between performance for pulmonary complications and failure to rescue. Research programs, large non-teaching hospitals, those with advanced practice providers, and those with health maintenance organizations had reduced failure-to-rescue patients., Conclusion: Factors associated with complications were distinct from those affecting failure to rescue and center-level success in reducing complications often did not translate into success in preventing death once they occurred. Our data demonstrate that high- and low-performing centers and the factors driving success or failure are identifiable. This work serves as a guide for comparing practices and improving outcomes with readily available data., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Introduction: Cardiac complications (CC) after injury are rare but contribute disproportionately to mortality. Variability in rates of CC and failure to rescue (FTR) after CC (FTR-C) within trauma systems may suggest opportunities for improvement, but we have not yet demonstrated the ability to identify high and low performers. We examined center-level rates of CC and FTR-C in a mature trauma system with the hypothesis that high-performing centers for each of these outcomes could be identified., Methods: Using a statewide trauma registry from 2007-2015, we developed multivariable logistic regression models on CC and FTR-C including patient demographics, physiology, comorbidity, and injury data. Predicted probabilities of each outcome were summed to generate expected event rates, which were compared to observed event rates to generate centerlevel observed-to-expected (O:E) ratios. We measured internal consistency between CC and FTR-C for centers using Cronbach's alpha., Results: Cardiac complications occurred in 5,079/278,042 (1.8%; center-level range: 0.9-3.8%) of included patients (median age 55 (IQR 34-76), 84% Caucasian, 60% male, 92% blunt, median ISS 9 (IQR5-16)). Death after CC occurred in 982/5,097 patients for an FTR-C rate of 19.3% (center-level range: 7.8-30.4%). 10/27 centers were high-performers (95% confidence interval for O:E ratio <1) for CC and 2/27 centers were high-performers for FTR-C, but internal consistency between these metrics was poor (alpha = 0.31)., Conclusion: Rates of CC and FTR-C vary significantly between hospitals in mature trauma systems but high-performing centers can be identified. Inconsistent performance between metrics suggests unknown institutional factors underlie performance for CC and FTR., (Copyright © 2020. Published by Elsevier Ltd.)