BACKGROUND Those older than 65 years represent the fastest growing demographic in the United States. As such, their care has been emphasized by trauma entities such as the American College of Surgeons Committee on Trauma. Unfortunately, much of that focus has been of their care once they reach the hospital with little attention on the access of geriatric trauma patients to trauma centers (TCs). We sought to determine the rate of geriatric undertriage (UT) to TCs within a mature trauma system and hypothesized that there would be variation and clustering of the geriatric undertriage rate (UTR) within a mature trauma system because of the admission of geriatric trauma patient to nontrauma centers (NTCs). METHODS From 2003 to 2015, all geriatric (age >65 years) admissions with an Injury Severity Score of greater than 9 from the Pennsylvania Trauma Systems Foundation (PTSF) registry and those meeting trauma criteria (International Classification of Diseases, Ninth Revision: 800–959) from the Pennsylvania Health Care Cost Containment Council (PHC4) database were included. Undertriage rate was defined as patients not admitted to TCs (n = 27) divided by the total number of patients as from the PHC4 database. The PHC4 contains all inpatient admissions within Pennsylvania (PA), while PTSF reports admissions to PA TCs. The zip code of residence was used to aggregate calculations of UTR as well as other aggregate patient and census demographics, and UTR was categorized into lower, middle box, and upper quartiles. ArcGIS Desktop: Version 10.7, ESRI, Redlands, CA and GeoDa: Version 1.14.0, Open source license were used for geospatial mapping of UT with a spatial empirical Bayesian smoothed UTR, and Stata: Version 16.1, Stata Corp., College Station TX was used for statistical analyses. RESULTS Pennsylvania Trauma Systems Foundation had 58,336 cases, while PHC4 had 111,626 that met the inclusion criteria, resulting in a median (Q1–Q3) smoothed UTR of 50.5% (38.2–60.1%) across PA zip code tabulation areas. Geospatial mapping reveals significant clusters of UT regions with high UTR in some of the rural regions with limited access to a TC. The lowest quartile UTR regions tended to have higher population density relative to the middle or upper quartile UTR regions. At the patient level, the lowest UTR regions had more racial and ethnic diversity, a higher injury severity, and higher rates of treatment at a TC. Undertriage rate regions that were closer to NTCs had a higher odds of being in the upper UTR quartile; 4.48 (2.52–7.99) for NTC with less than 200 beds and 8.53 (4.70–15.47) for NTC with 200 beds or greater compared with zip code tabulation areas with a TC as the closest hospital. CONCLUSION There are significant clusters of geriatric UT within a mature trauma system. Increased emphasis needs to focus prehospital on identifying the severely injured geriatric patient including specific geriatric triage protocols. LEVEL OF EVIDENCE Epidemiological, Level III.

BACKGROUND Improved mortality as a result of appropriate triage has been well established in adult trauma and may be generalizable to the pediatric trauma population as well. We sought to determine the overall undertriage rate (UTR) in the pediatric trauma population within Pennsylvania (PA). We hypothesized that a significant portion of pediatric trauma population would be undertriaged. METHODS All pediatric (age younger than 15) admissions meeting trauma criteria (International Classification of Diseases, Ninth Revision: 800–959) from 2003 to 2015 were extracted from the Pennsylvania Health Care Cost Containment Council (PHC4) database and the Pennsylvania Trauma Systems Foundation (PTSF) registry. Undertriage was defined as patients not admitted to PTSF-verified pediatric trauma centers (n = 6). The PHC4 contains inpatient admissions within PA, while PTSF only reports admissions to PA trauma centers. ArcGIS Desktop was used for geospatial mapping of undertriage. RESULTS A total of 37,607 cases in PTSF and 63,954 cases in PHC4 met criteria, suggesting UTR of 45.8% across PA. Geospatial mapping reveals significant clusters of undertriage regions with high UTR in the eastern half of the state compared to low UTR in the western half. High UTR seems to be centered around nonpediatric facilities. The UTR for patients with a probability of death 1% or less was 39.2%. CONCLUSION Undertriage is clustered in eastern PA, with most areas of high undertriage located around existing trauma centers in high-density population areas. This pattern may suggest pediatric undertriage is related to a system issue as opposed to inadequate access. LEVEL OF EVIDENCE Retrospective study, without negative criteria, Level III.

In recent years, there has been an emphasis on evaluating the outcomes of patients who have experienced an intensive care unit (ICU) readmission. This may in part be due to the Patient Protection and Affordable Care Act’s Hospital Readmission Reduction Program which imposes financial sanctions on hospitals who have excessive readmission rates, informally known as bounceback rates. The financial cost associated with avoidable bounceback combined with the potentially preventable expenses can result in unnecessary financial strain. Within the hospital readmissions, there is a subset pertaining to unplanned readmission to the ICU. Although there have been studies regarding ICU bounceback, there are limited studies regarding ICU bounceback of trauma patients and even fewer proven strategies. Although many studies have concluded that respiratory complications were the most common factor influencing ICU readmissions, there is inconclusive evidence in terms of a broadly applicable strategy that would facilitate management of these patients. The purpose of this review is to highlight the outcomes of patients readmitted to the ICU and to provide an overview of possible strategies to aid in decreasing ICU readmission rates.

BACKGROUND With the recent birth of the Pennsylvania TQIP Collaborative, statewide data identified unplanned admissions to the intensive care unit (ICU) as an overarching issue plaguing the state trauma community. To better understand the impact of this unique population, we sought to determine the effect of unplanned ICU admission/readmission on mortality to identify potential predictors of this population. We hypothesized that ICU bounceback (ICUBB) patients would experience increased mortality compared with non-ICUBB controls and would likely be associated with specific patterns of complications. METHODS The Pennsylvania Trauma Outcome Study database was retrospectively queried from 2012 to 2015 for all ICU admissions. Unadjusted mortality rates were compared between ICUBB and non-ICUBB counterparts. Multilevel mixed-effects logistic regression models assessed the adjusted impact of ICUBB on mortality and the adjusted predictive impact of 8 complications on ICUBB. RESULTS A total of 58,013 ICU admissions were identified from 2012 to 2015. From these, 53,715 survived their ICU index admission. The ICUBB rate was determined to be 3.82% (2,054/53,715). Compared with the non-ICUBB population, ICUBB patients had a significantly higher mortality rate (12% vs. 8%; p < 0.001). In adjusted analysis, ICUBB was associated with a 70% increased odds ratio for mortality (adjusted odds ratio, 1.70; 95% confidence interval, 1.44–2.00; p < 0.001). Adjusted analysis of predictive variables revealed unplanned intubation, sepsis, and pulmonary embolism as the strongest predictors of ICUBB. CONCLUSION Intensive care unit bouncebacks are associated with worse outcomes and are disproportionately burdened by respiratory complications. These findings emphasize the importance of the TQIP Collaborative in identifying statewide issues in need of performance improvement within mature trauma systems. LEVEL OF EVIDENCE Epidemiological study, level III.

Coagulopathy in trauma patients is a known contributor to death due to hemorrhage. In fact, it seen as frequently as 35% of the time. The complexity of the coagulopathy pathway requires a deliberate and planned approach. The methods used to assess and detect if a patient is coagulopathic remain challenging, but tools have been developed to assist the practitioner to effectively manage and even quickly reverse the coagulopathy. The purpose of this review is to educate trauma and emergency medicine staff on the currently available diagnostic tools to assess coagulopathy, to provide an overview of the coagulopathy pathway, as well as provide examples of how to intervene and treat coagulopathy, including the use of crew resource management during mass transfusion protocol activations.

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This study aimed to help determine the effect of dietary supplements on symptom course and quality of life in patients with mild-to-moderate COVID-19 infection.Design: We modified the Wisconsin Upper Respiratory Symptom Survey (WURSS) to conduct a 3 arm, parallel, randomized, double-blind, placebo-controlled trial, enrolling patients with mild-to-moderate symptoms of COVID-19 infection. Patients took placebo (n = 34), vitamin C 1000 mg (n = 32), or melatonin 10 mg (n = 32) orally for 14 days.Outcomes: Ninety Eight (98 out of 104 recruited; mean age = 52 years) patients completed the study. Outcomes were calculated as differences from baseline scores on each of 2 WURSS-derived surveys and analyzed using a spline regression analysis. Regarding symptom progression, those patients taking placebo and vitamin C progressed at the same rate. When compared with those taking placebo (coefficient = -1.09 (95% confidence interval [CI] = -1.39 to -0.8) the group taking melatonin had a faster resolution of symptoms (coefficient = -0.63 [95% CI -1.02 to -0.21] P = .003). By day 14 all 3 groups had reached plateau.Quality-of-life impact analysis demonstrated that the group taking vitamin C improved at the same rate as the group taking placebo (coefficient = -0.71 (95% CI = -1.11 to -0.3)). The group taking melatonin (coefficient = -1.16 (95% CI = -1.75 to -0.57) P < .005) had a faster improvement in quality-oflife. By day 14 all 3 groups had reached plateau.Conclusion: Vitamin C 1000 mg once daily has no effect on disease progression. Melatonin 10 mg daily may have a statistically significant effect but it is unclear if this represents a clinically significant benefit to those with mild-to-moderate symptoms of COVID-19 infection. Further study is warranted. ( J Am Board Fam Med 2022;35:695-707.

BACKGROUND While issues regarding triage of severely injured trauma patients are well publicized, little information exists concerning the difference between triage rates for patients transported by advanced life support (ALS) and basic life support (BLS). We sought to analyze statewide trends in undertriage (UT) and overtriage (OT) to address this question, hypothesizing that there would be a difference between the UT and OT rates for ALS compared with BLS over a 13-year period. METHODS All patients submitted to Pennsylvania Trauma Outcomes Study database from 2003 to 2015 were analyzed. Undertriage was defined as not calling a trauma alert for patients with an Injury Severity Score (ISS) of 16 or greater. Overtriage was defined as calling a trauma alert for patients with an ISS of 9 or less. A logistic regression was used to assess mortality between triage groups in ALS and BLS. A multinomial logistic regression assessed the adjusted impact of ALS versus BLS transport on UT and OT versus normal triage while controlling for age, sex, Glasgow Coma Scale, systolic blood pressure (SBP), pulse, Shock Index and injury year. RESULTS A total of 462,830 patients met inclusion criteria, of which 115,825 had an ISS of 16 or greater and 257,855 had an ISS of 9 or less. Both ALS and BLS had significantly increased mortality when patients were undertriaged compared with the reference group. Multivariate analysis in the form of a multinomial logistic regression revealed that patients transported by ALS had a decreased adjusted rate of undertriage (relative risk ratio, 0.92; 95% confidence interval, 0.87–0.97; p = 0.003) and an increased adjusted rate of OT (relative risk ratio, 1.59; 95% confidence interval, 1.54–1.64; p < 0.001) compared with patients transported by BLS. CONCLUSION Compared with their BLS counterparts, while UT is significantly lower, OT is substantially higher in ALS—further increasing the high levels of resource (over)utilization in trauma patients. Undertriage in both ALS and BLS are associated with increased mortality rates. Additional education, especially in the BLS provider, on identifying the major trauma victim may be warranted based on the results of this study. LEVEL OF EVIDENCE Epidemiological, Level III.