Timing and Outcomes After Coronary Angiography Following Out-of-Hospital Cardiac Arrest Without Signs of ST-Segment Elevation Myocardial Infarction.

BACKGROUND: There is broad consensus that resuscitated out-of-hospital cardiac arrest (OHCA) patients with ST-segment elevation myocardial infarction (STEMI) should receive immediate coronary angiography (CAG); however, factors that guide patient selection and optimal timing of CAG for post-arrest patients without evidence of STEMI remain incompletely described. OBJECTIVE: We sought to describe the timing of post-arrest CAG in actual practice, patient characteristics associated with decision to perform immediate vs. delayed CAG, and patient outcomes after CAG. METHODS: We conducted a retrospective cohort study at seven U.S. academic hospitals. Resuscitated adult patients with OHCA were included if they presented between January 1, 2015 and December 31, 2019 and received CAG during hospitalization. Emergency medical services run sheets and hospital records were analyzed. Patients without evidence of STEMI were grouped and compared based on time from arrival to CAG performance into "early" (≤ 6 h) and "delayed" (> 6 h). RESULTS: Two hundred twenty-one patients were included. Median time to CAG was 18.6 h (interquartile range [IQR] 1.5-94.6 h). Early catheterization was performed on 94 patients (42.5%) and delayed catheterization was performed on 127 patients (57.5%). Patients in the early group were older (61 years [IQR 55-70 years] vs. 57 years [IQR 47-65] years) and more likely to be male (79.8% vs. 59.8%). Those in the early group were more likely to have clinically significant lesions (58.5% vs. 39.4%) and receive revascularization (41.5% vs. 19.7%). Patients were more likely to die in the early group (47.9% vs. 33.1%). Among survivors, there was no significant difference in neurologic recovery at discharge. CONCLUSIONS: OHCA patients without evidence of STEMI who received early CAG were older and more likely to be male. This group was more likely to have intervenable lesions and receive revascularization.

Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors.

PURPOSE: Predicting 30-day readmission risk is paramount to improving the quality of patient care. In this study, we compare sets of patient-, provider-, and community-level variables that are available at two different points of a patient's inpatient encounter (first 48 hours and the full encounter) to train readmission prediction models and identify possible targets for appropriate interventions that can potentially reduce avoidable readmissions. METHODS: Using electronic health record data from a retrospective cohort of 2,460 oncology patients and a comprehensive machine learning analysis pipeline, we trained and tested models predicting 30-day readmission on the basis of data available within the first 48 hours of admission and from the entire hospital encounter. RESULTS: Leveraging all features, the light gradient boosting model produced higher, but comparable performance (area under receiver operating characteristic curve [AUROC]: 0.711) with the Epic model (AUROC: 0.697). Given features in the first 48 hours, the random forest model produces higher AUROC (0.684) than the Epic model (AUROC: 0.676). Both models flagged patients with a similar distribution of race and sex; however, our light gradient boosting and random forest models were more inclusive, flagging more patients among younger age groups. The Epic models were more sensitive to identifying patients with an average lower zip income. Our 48-hour models were powered by novel features at various levels: patient (weight change over 365 days, depression symptoms, laboratory values, and cancer type), hospital (winter discharge and hospital admission type), and community (zip income and marital status of partner). CONCLUSION: We developed and validated models comparable with the existing Epic 30-day readmission models with several novel actionable insights that could create service interventions deployed by the case management or discharge planning teams that may decrease readmission rates over time.

A Pilot of a Randomized Control Trial of Melatonin and Vitamin C for Mild-to-Moderate COVID-19.

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-of-life. 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.

Radiographic Reread Protocols to Identify Clinically Relevant Errors in Initial Trauma Evaluations.

INTRODUCTION: Diagnostic radiology interpretive errors in trauma patients can lead to missed diagnoses, compromising patient care. Due to this, our level II trauma center implemented a reread protocol of all radiographic imaging within 24 hours on our highest trauma activation level (Code T). We sought to determine the efficacy of this reread protocol in identifying missed diagnoses in Code T patients. We hypothesized that a few, but clinically relevant errors, would be identified upon reread. METHODS: All radiographic study findings (initial read and reread) performed for Code T admissions from July 2015 to May 2016 were queried. The reviewed radiological imaging was given one of four designations: agree with interpretation, minor (non-life threatening) nonclinically relevant error(s)-addendum/correction required or clinically relevant error(s) (major [life threatening] and minor)-addendum/correction required, and trauma surgeon notified. The results were compiled, and the number of each type of error was calculated. RESULTS: Of the 752 radiological imaging studies reviewed on the 121 Code T patients during this period, 3 (0.40%) contained minor clinically relevant errors, 11 (1.46%) contained errors that were not clinically relevant, and 738 (98.1%) agreed with the original interpretation. The three clinically relevant errors included a right mandibular fracture found on X-ray and a temporal bone fracture that crossed the clivus and bilateral rib fractures found on computerized tomography. DISCUSSION: Clinically relevant errors, although minimal, were discovered during rereads for Code T patients. Although the clinical errors were significant, none affected patient outcomes. We propose that the implementation of reread protocols should be based upon institution-specific practices.

Unplanned Readmissions to the Intensive Care Unit Among Geriatric Trauma Patients.

BACKGROUND: Unplanned readmission/bounceback to the intensive care unit (ICUBB) is a prevalent issue in the medical community. The geriatric population is incompletely studied in regard to ICUBB. We sought to determine if ICUBB in older patients was associated with higher risk of mortality. We hypothesized that, of those who were older, those with ICUBB would have higher mortality compared to those with no ICUBB. Further, we hypothesized that of those with ICUBB, older age would lead to higher mortality. METHODS: The Pennsylvania Trauma Outcome Study database was retrospectively queried from 2003 to 2018 for all trauma patients of age ≥40 years. Those with advance directives were excluded. Adjusted analysis in the form of logistic regressions controlling for demographic and injury covariates and clustering by facility were used to assess the adjusted impact of ICUBB and age on mortality. RESULTS: 363,778 patients were aged ≥40 years. When comparing mortalities between the age 40 and 49 years group and those in older groups, a dramatic increase in mortality was observed between those in each respective age category with ICUBB vs non-ICUBB. This trend was most prominent in those in the 90+ years age group (ICUBB: AOR: 34.78, P < .001; non-ICUBB: AOR: 9.08, P < .001). A second model only including patients who had ICUBB found that patients of age ≥65 years had significantly higher odds of mortality (AOR: 4.10, P < .001) when compared to their younger counterparts (age <65 years). DISCUSSION: An ICUBB seems to exacerbate mortality rates as age increases. This profound increase in mortality calls for strategies to be developed, especially in the older population, to attempt to mitigate the factors leading to ICUBB.

Clinical factors associated with significant coronary lesions following out-of-hospital cardiac arrest.

OBJECTIVES: Out-of-hospital cardiac arrest (OHCA) afflicts >350,000 people annually in the United States. While postarrest coronary angiography (CAG) with percutaneous coronary intervention (PCI) has been associated with improved survival in observational cohorts, substantial uncertainty exists regarding patient selection for postarrest CAG. We tested the hypothesis that symptoms consistent with acute coronary syndrome (ACS), including chest discomfort, prior to OHCAs are associated with significant coronary lesions identified on postarrest CAG. METHODS: We conducted a multicenter retrospective cohort study among eight regional hospitals. Adult patients who experienced atraumatic OHCA with successful initial resuscitation and subsequent CAG between January 2015 and December 2019 were included. We collected data on prehospital documentation of potential ACS symptoms prior to OHCA as well as clinical factors readily available during postarrest care. The primary outcome in multivariable regression modeling was the presence of significant coronary lesions (defined as >50% stenosis of left main or >75% stenosis of other coronary arteries). RESULTS: Four-hundred patients were included. Median (interquartile range) age was 59 (51-69) years; 31% were female. At least one significant stenosis was found in 62%, of whom 71% received PCI. Clinical factors independently associated with a significant lesion included a history of myocardial infarction (adjusted odds ratio [aOR] = 6.5, [95% confidence interval {CI} = 1.3 to 32.4], p = 0.02), prearrest chest discomfort (aOR = 4.8 [95% CI = 2.1 to 11.8], p ≤ 0.001), ST-segment elevations (aOR = 3.2 [95% CI = 1.7 to 6.3], p < 0.001), and an initial shockable rhythm (aOR = 1.9 [95% CI = 1.0 to 3.4], p = 0.05). CONCLUSIONS: Among survivors of OHCA receiving CAG, history of prearrest chest discomfort was significantly and independently associated with significant coronary artery lesions on postarrest CAG. This suggests that we may be able to use prearrest symptoms to better risk stratify patients following OHCA to decide who will benefit from invasive angiography.

Location of Injury and Other Factors Associated With Increased Survival Among Severely Injured Geriatric Trauma Patients.

BACKGROUND: Patients with a Trauma Injury Severity Score (TRISS) < .5 are termed "unexpected survivors." There is scarce information published on this subset of geriatric patients whose survival is an anomaly. METHODS: This is a retrospective case-control study examining all geriatric patients (age ≥65) not expected to survive (TRISS<.5) in the Pennsylvania Trauma Outcome Study (PTOS) database from 2013 to 2017. Primary outcome was survival to discharge. We selected 10 clinically important variables for logistic regression analysis as possible factors that may improve survival. RESULTS: 1336 patients were included, 395 (29.6%) were unexpected survivors. Factors that improved survival odds are the following: Place of injury: street/highway (AOR:0.51; 95% CI: .36-.73, P < .001) and residential institution (AOR:0.46; 95% CI: .21-.98, P = .043); and presence of Benzodiazepines (AOR:0.49; 95% CI: .31-.77, P = .002) or ethanol (AOR:0.57; 95% CI: .34-.97, P = .040). Factors that decreased survival odds are the following: Hypotension (AOR: 8.59; 95% CI: 4.33-17.01, P < .001) and hypothermia (AOR: 1.58; 95% CI: 1.10-2.28, P = .014). Gender, race/ethnicity, blood transfusion in first 24 hours, shift of presentation to Emergency Department, place of injury (farm, industrial, recreational, or public building), use of Tetrahydrocannabinol, amphetamines or opioids, and level of trauma activation did not impact survival. DISCUSSION: Location of injury (street/highway and residential institution) and ethanol or benzodiazepine use led to a significant increased survival in severely injured geriatric patients. Hypotension and hypothermia led to decreased survival. Future studies should determine possible reasons these factors lead to survival (and identify additional factors) to focus efforts in these areas to improve outcomes in geriatric trauma.

The effect of the Affordable Care Act on rates on inpatient rehabilitation hospital admission in a mature trauma system.

BACKGROUND: The beneficial effects of acute rehabilitation for trauma patients are well documented but can be limited because of insurance coverage. The Patient Protection and Affordable Care Act (ACA) went into effect on March 23, 2010. The ACA allowed patients who previously did not have insurance to be fully incorporated into the health system. We sought to analyze the likelihood of discharge to rehab for trauma patients before and after the implementation of the ACA. We hypothesized that there would be a higher rate of inpatient rehabilitation hospital (IRH) admission after the ACA was put into effect. METHODS: The Pennsylvania Trauma Outcome Study (PTOS) database was retrospectively queried from 2003 to 2017 for all trauma patients admitted to accredited trauma centers in Pennsylvania, who also had a functional status at discharge (FSD). Admission to an IRH was determined using discharge destination. Two categories were created to represent periods before and after ACA was implemented, 2003 to 2009 (pre-ACA) and 2010-2017 (post-ACA). A multilevel mixed-effects logistic regression model controlling for demographics, injury severity, and FSD assessed the adjusted impact of ACA implementation on IRH admissions. RESULTS: From the Pennsylvania Trauma Outcome Study query, 341,252 patients had FSD scores and of these patients, 47,522 (13.9%) were admitted to IRH. Patients who were severely injured were more likely to be admitted to IRH. Compared with FSD scores signifying complete independence at discharge, those with lower FSD had significantly increased odds of IRH admission. The odds of IRH admission post-ACA implementation significantly increased when compared with pre-ACA years (adjusted odds ratio, 1.14; 95% confidence interval, 1.12-1.17; p < 0.001; area under the receiver operating curve, 0.818). CONCLUSION: The implementation of the ACA significantly increased the likelihood of discharge to IRH for trauma patients. LEVEL OF EVIDENCE: Care management, level III.

Geriatric Trauma Mortality: Does Trauma Center Level Matter?

BACKGROUND: Given their mostly rural/suburban locations, level II trauma centers (TCs) may offer greater exposure to and experience in managing geriatric trauma patients. We hypothesized that geriatric patients would have improved outcomes at level II TCs compared to level I TCs. METHODS: The Pennsylvania Trauma Outcome Study (PTOS) database was retrospectively queried from 2003 to 2017 for geriatric (age ≥65 years) trauma patients admitted to level I and II TCs in Pennsylvania. Patient demographics, injury severity, and clinical outcomes were compared to assess differences in care between level I and II TCs. A multivariate logistic regression model assessed the adjusted impact of care at level I vs II TCs on mortality, complications, and functional status at discharge (FSD). The National Trauma Data Bank (NTDB) was retrospectively queried for geriatric (age ≥65 years) trauma admissions to state-accredited level I or level II TCs in 2013. RESULTS: 112 648 patients met inclusion criteria. The proportion of geriatric trauma patients across level I and level II TCs were determined to be 29.1% and 36.2% (P <.001), respectively. In adjusted analysis, there was no difference in mortality (adjusted odds ratio [AOR]: 1.13; P = .375), complications (AOR: 1.25; P = .080) or FSD (AOR: 1.09; P = .493) when comparing level I to level II TCs. Adjusted analysis from the NTDB (n = 144 622) also found that mortality was not associated with TC level (AOR: 1.04; P = .182). DISCUSSION: Level I and level II TCs had similar rates of mortality, complications, and functional outcomes despite a higher proportion (but lower absolute number) of geriatric patients being admitted to level II TCs. Future consideration for location of centers of excellence in geriatric trauma should include both level I and II TCs.

Massive Transfusion Protocol and Subsequent Development of Venous Thromboembolism: Statewide Analysis.

BACKGROUND: Massive transfusion protocols (MTP) are a routine component of any major trauma center's armamentarium in the management of exsanguinating hemorrhages. Little is known about the potential complications of those that survive a MTP. We sought to determine the incidence of venous thromboembolism (VTE) following MTP. We hypothesized that MTP would be associated with a higher risk of VTE when compared with a risk-adjusted control population without MTP. METHODS: The Pennsylvania Trauma Outcome Study database was retrospectively queried from 2015 to 2018 for trauma patients who developed VTE and survived until discharge at accredited trauma centers in Pennsylvania. Patient demographics, injury severity, and clinical outcomes were compared to assess differences in VTE development between MTP and non-MTP patients. A multivariate logistic regression model assessed the adjusted impact of MTP on VTE development. RESULTS: 176 010 patients survived until discharge, meeting inclusion criteria. Of those, 1667 developed a VTE (pulmonary embolism [PE]: 662 [0.4%]; deep vein thrombosis [DVT]: 1142 [0.6%]; PE and DVT: 137 [0.1%]). 1268 patients (0.7%) received MTP and, of this subset of patients, 171 (13.5%) developed a VTE during admission. In adjusted analysis, patients who had a MTP and survived until discharge had a higher odds of developing a VTE (adjusted odds ratio: 2.62; 95% CI: 2.13-3.24; P < .001). DISCUSSION: MTP is a harbinger for higher risk of VTE in those patients who survive. This may, in part, be related to the overcorrection of coagulation deficits encountered in the hemorrhagic event. A high index of suspicion for the development of VTE as well as aggressive VTE prophylaxis is warranted in those patients who survive MTP.

The Effects of Fasting Status on the Relative Risk of Pulmonary Aspiration in Acute Care Surgery Patients.

BACKGROUND: Acute care surgery patients are often unfasted at the time of surgery, presenting a unique opportunity to study the effects of fasting on the risk of pulmonary aspiration. We aimed to determine the relative risk of aspiration in patients who were fasted at the time of surgery according to guidelines versus those in an unfasted state. METHODS: A retrospective chart review of 100 patients who underwent appendectomy (n = 76) or exploratory laparotomy (n = 24) was conducted at a single institution in 2016-2017. Using the American Society of Anesthesiologists (ASA) Practice Guidelines for Preoperative Fasting, patients were stratified into study and control groups according to whether they were unfasted (nothing by mouth for <8 hours prior to surgery) or fasted (nothing by mouth for >8 hours prior to surgery). Data controlled for patients' age, sex, body mass index (BMI), most recent hemoglobin A1c, presence of gastroesophageal reflux disease (GERD), and presence of hiatal hernia. RESULTS: Of the 76 patients who underwent appendectomy, 15% were unfasted with a total of 0 aspiration events (P < .001). Of the 24 patients who underwent exploratory laparotomy, 42% were unfasted with a total of 0 aspiration events (P < .001). This yields a relative risk of pulmonary aspiration of 1.0 (absolute risk of 0) in both the study and control groups. DISCUSSION: In an acute care surgery population including patients who were not fasted according to guidelines, there was no increase in the risk of pulmonary aspiration. LEVEL OF EVIDENCE: Epidemiological study; Level III.

Predictors of Trauma High Resource Consumers in a Mature Trauma System.

BACKGROUND: Extended hospital length of stay (LOS) is widely associated with significant healthcare costs. Since LOS is a known surrogate for cost, we sought to evaluate outliers. We hypothesized that particular characteristics are likely predictive of trauma high resource consumers (THRC) and can be used to more effectively manage care of this population. METHODS: The Pennsylvania Trauma Outcome Study database was retrospectively queried from 2003-2017 for all adult (age ≥15) trauma patients admitted to accredited trauma centers in Pennsylvania. THRC were defined as patients with hospital LOS two standard deviations above the population mean or ≥22 days (p<0.05). Patient demographics, comorbid conditions and clinical variables were compared between THRC and non-THRC to identify potential predictor variables. A multilevel mixed-effects logistic regression model controlling for age, gender, injury severity, admission Glasgow coma score, systolic blood pressure, and injury year assessed the adjusted impact of clinical factors in predicting THRC status. The National Trauma Data Bank (NTDB) was retrospectively queried from 2014-2016 for all adult (age ≥15) trauma patients admitted to state-accredited trauma centers and likewise were assessed for factors associated with THRC. RESULTS: A total of 465,601 patients met inclusion criteria [THRC: 16,818 (3.6%); non-THRC 448,783 (96.4%)]. Compared to non-THRC counterparts, THRC patients were significantly more severely injured (median ISS: 9 vs. 22, p<0.001). In adjusted analysis, gunshot wound (GSW) to the abdomen, undergoing major surgery and reintubation along with injury to the spine, upper or lower extremities were significantly associated with THRC. From the NTDB, 2 323 945 patients met inclusion criteria. In adjusted analysis, GSW to the abdomen was significantly associated with THRC. Penetrating injury overall was associated with decreased risk of being a THRC in the NTDB dataset. Those who had either GSW to abdomen, surgery, or reintubation required significantly longer LOS (p<0.001). CONCLUSIONS: Reintubation, major surgery, gunshot wound to abdomen, along with injury to the spine, upper or lower extremities are all strongly predictive of THRC. Understanding the profile of the THRC will allow clinicians and case management to proactively put processes in place to streamline care and potentially reduce costs and LOS.

An analysis of pediatric social vulnerability in the Pennsylvania trauma system.

BACKGROUND: The social vulnerability index (SVI) is used to assess resilience to external influences that may affect human health. Social vulnerability has been noted to be a barrier to healthcare access for pediatric patients. We hypothesized that Pennsylvania (PA) pediatric trauma patients high on the social vulnerability index would have significantly lower rates of rehab admission following admission to a hospital for traumatic injury. METHODS: The SVI was determined for each PA zip code area utilizing the census tract based 2014 SVI provided by the CDC along with a weighted crosswalk between census tracts and zip code areas using the Housing and Urban Development zip code crosswalk files. The rate of the uninsured population was extracted from the CDC SVI files in addition to other US Census variables based upon estimates from the 2014 American Community Survey (ACS). We also included the individual primary payer status of each subject. Pediatric (age <15 years) trauma admissions with in-hospital mortality excluded, were extracted from the PA Healthcare Cost Containment Council (PHC4) for all hospital admissions for the period of 2003-2015 (n = 63,545). Complete case analysis was conducted based upon the final model providing a sample of 52,794. Cases were coded as rehab patients based upon discharge status (n = 603; 1.1%). A multi-level logistic model was used to determine if subjects had a higher odds of being discharged to rehab based on SVI, undertriage rates of their zip code area of residence and their own primary payer status; this was adjusted for age, multi-system injury and a head, chest or abdomen injury with abbreviate injury scale (AIS) severity > = 3. RESULTS: SVI and undertriage rates of the zip code areas of residence were not significantly associated with admission to rehab. The individual primary payer status of the subject was significantly associated with admission to rehab (OR 95%CI vs. self/uninsured; Medicaid 3.65 1.84-7.24; Commercial = 3.09 1.56-6.11; other/unknown = 2.85 1.02-7.93). Admission to rehab was also significantly associated with age, injury severity (ISS), head or chest injury with AIS scores > = 3, year of admission and hospital type. CONCLUSION: Individual patient level factors (primary payer of patient) may be associated with the odds of rehab admission rather than neighborhood factors. LEVEL OF EVIDENCE: Epidemiologic: Level III.

An analysis of overtriage and undertriage by advanced life support transport in a mature trauma system.

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.

An analysis of outcomes and predictors of intensive care unit bouncebacks in a mature trauma system.

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.

The geriatric trauma patient: A neglected individual in a mature trauma system.

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.

A comparison of adolescent penetrating trauma patients managed at pediatric versus adult trauma centers in a mature trauma system.

BACKGROUND: While there is little debate that pediatric trauma centers (PTC) are uniquely equipped to manage pediatric trauma patients, the extent to which adolescents benefit from treatment there remains controversial. We sought to elucidate differences in management approach and outcome between PTC and adult trauma centers (ATC) for the adolescent penetrating trauma population. We hypothesized that improved mortality would be observed at ATC for this subset of patients. METHODS: Adolescent patients (age, 15-18 years), presenting to Pennsylvania-accredited trauma centers between 2003 and 2017 with penetrating injury, were queried from the Pennsylvania Trauma Outcome Study database. Dead on arrival, transfer patients, and those admitted to a Level III or Level IV trauma center were excluded from analysis. Patient length of stay, number of complications, surgical intervention, and mortality were compared between ATC and PTC. Multilevel mixed effects logistic regression models with trauma center as the clustering variable were used to assess the impact of center type (ATC/PTC) on management approach and mortality adjusted for appropriate covariates. RESULTS: A total of 2,630 adolescent patients met inclusion criteria (PTC: n = 428 [16.3%]; ATC: n = 2,202 [83.7%]). Pediatric trauma centers had a lower adjusted odds of mortality (adjusted odds ratio [AOR], 0.35; 95% confidence interval [CI], 0.17-0.74; p = 0.006) and a lower adjusted odds of surgery (AOR, 0.67; 95% CI, 0.0.48-0.93; p = 0.016) than their ATC counterparts. There were no differences in complication rates (AOR, 0.94; 95% CI, 0.57-1.55; p = 0.793) or length of stay longer than 4 days (AOR, 0.95; 95% CI, 0.61-1.48; p = 0.812) between the PTCs and ATCs. There were also differences in penetrating injury type between PTC and ATC. CONCLUSION: The adolescent penetrating trauma patient population treated at PTC had less surgery performed with improved mortality compared with ATC. LEVEL OF EVIDENCE: Therapeutic, Level IV.

A Comprehensive Review of the Outcome for Patients Readmitted to the ICU Following Trauma and Strategies to Decrease Readmission Rates.

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.

Practice Variation in Vena Cava Filter Use Among Trauma Centers in the National Trauma Database.

BACKGROUND: Agreement regarding indications for vena cava filter (VCF) utilization in trauma patients has been in flux since the filter's introduction. As VCF technology and practice guidelines have evolved, the use of VCF in trauma patients has changed. This study examines variation in VCF placement among trauma centers. MATERIALS AND METHODS: A retrospective study was performed using data from the National Trauma Data Bank (2005-2014). Trauma centers were grouped according to whether they placed VCFs during the study period (VCF+/VCF-). A multivariable probit regression model was fit to predict the number of VCFs used among the VCF+ centers (the expected [E] number of VCF per center). The ratio of observed VCF placement (O) to expected VCFs (O:E) was computed and rank ordered to compare interfacility practice variation. RESULTS: In total, 65,482 VCFs were placed by 448 centers. Twenty centers (4.3%) placed no VCFs. The greatest predictors of VCF placement were deep vein thrombosis, spinal cord paralysis, and major procedure. The strongest negative predictor of VCF placement was admission during the year 2014. Among the VCF+ centers, O:E varied by nearly 500%. One hundred fifty centers had an O:E greater than one. One hundred sixty-nine centers had an O:E less than one. CONCLUSIONS: Substantial variation in practice is present in VCF placement. This variation cannot be explained only by the characteristics of the patients treated at these centers but could be also due to conflicting guidelines, changing evidence, decreasing reimbursement rates, or the culture of trauma centers.