Brendan G Carr

University of Pennsylvania, Filadelfia, Pennsylvania, United States

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Publications (125)472.17 Total impact

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    ABSTRACT: Background: -Therapeutic hypothermia (TH) attenuates reperfusion injury in comatose survivors of cardiac arrest. The utility of TH in patients with non-shockable initial rhythms has not been widely accepted. We sought to determine whether TH improved neurologic outcome and survival in post-arrest patients with non-shockable rhythms. Methods and results: -We identified 519 patients after in- and out-of-hospital cardiac arrest with non-shockable initial rhythms from the Penn Alliance for Therapeutic Hypothermia (PATH) registry between 2000-2013. Propensity score matching was used. Patient and arrest characteristics used to estimate the propensity to receive TH were age, sex, location of arrest, witnessed arrest, and duration of arrest. To determine the association between TH and outcomes, we created two multivariable logistic models controlling for confounders. Of 201 propensity score matched pairs, mean age was 63±17 years; 51% were male; and 60% had an initial rhythm of pulseless electrical activity. Survival to hospital discharge was greater in patients who received TH (17.6% vs. 28.9%; p<0.01), as was discharge CPC of 1-2 (13.7% vs 21.4%; p= 0.04). In adjusted analyses, patients who received TH were more likely to survive (OR 2.8, 95% CI: 1.6-4.7) and have better neurologic outcome (OR 3.5, 95% CI: 1.8-6.6) than those that did not receive TH. Conclusions: -Using propensity score matching, we found patients with non-shockable initial rhythms treated with TH had better survival and neurologic outcome at hospital discharge than those who did not receive TH. Our findings further support the use of TH in patients with initial non-shockable arrest rhythms.
    Circulation 11/2015; DOI:10.1161/CIRCULATIONAHA.115.016317 · 14.43 Impact Factor
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    ABSTRACT: . Ischemic stroke is a time sensitive disease with the effectiveness of treatment decreasing over time. Treatment is more likely to occur at Primary Stroke Centers (PSC); thus rapid access to acute stroke care through stand-alone PSCs or telemedicine (TM) is vital for all Americans. The objective of this study is to determine if disparities exist in access to PSCs or the extended access to acute stroke care provided by TM. Methods . Data from the US Census Bureau and the 2010 Neilson Claritas Demographic Estimation Program, American Hospital Association annual survey, and The Joint Commission list of PSCs and survey response data for all hospitals in the state of Texas were used. Results . Over 64% of block groups had 60-minute ground access to acute stroke care. The odds of a block group having 60-minute access to acute stroke care decreased with age, despite adjustment for sex, race, ethnicity, socioeconomic status, urbanization, and total population. Conclusion . Our survey of Texas hospitals found that as the median age of a block group increased, the odds of having access to acute stroke care decreased.
    Stroke Research and Treatment 11/2015; 2015(4):813493. DOI:10.1155/2015/813493
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    ABSTRACT: Purpose: Age is a risk factor for death, adverse outcomes, and health care use following trauma. The American College of Surgeons' Trauma Quality Improvement Program (TQIP) has published "best practices" of geriatric trauma care; adoption of these guidelines is unknown. We sought to determine which evidence-based geriatric protocols, including TQIP guidelines, were correlated with decreased mortality in Pennsylvania's trauma centers. Methods: PA's level I and II trauma centers self-reported adoption of geriatric protocols. Survey data were merged with risk-adjusted mortality data for patients ≥65 from a statewide database, the Pennsylvania Trauma Systems Foundation (PTSF), to compare mortality outlier status and processes of care. Exposures of interest were center-specific processes of care; outcome of interest was PTSF mortality outlier status. Results: 26 of 27 eligible trauma centers participated. There was wide variation in care processes. Four trauma centers were low outliers; three centers were high outliers for risk-adjusted mortality rates in adults ≥65. Results remained consistent when accounting for center volume. The only process associated with mortality outlier status was age-specific solid organ injury protocols (p = 0.04). There was no cumulative effect of multiple evidence-based processes on mortality rate (p = 0.50). Conclusions: We did not see a link between adoption of geriatric best-practices trauma guidelines and reduced mortality at PA trauma centers. The increased susceptibility of elderly to adverse consequences of injury, combined with the rapid growth rate of this demographic, emphasizes the importance of identifying interventions tailored to this population. Level of evidence: III. Study type: Descriptive.
    European Journal of Trauma and Emergency Surgery 10/2015; DOI:10.1007/s00068-015-0586-9 · 0.35 Impact Factor

  • Journal of the American Heart Association 10/2015; 4(10):e001877. DOI:10.1161/JAHA.115.001877 · 4.31 Impact Factor
  • D.N. Karp · A. Baehr · M.K. Delgado · A.S. Kilaru · D.J. Wiebe · B.G. Carr ·

  • K.L. Rising · J.E. Hollander · B.G. Carr ·

  • D.N. Holena · A. Agarwal · S.R. Allen · N.D. Martin · H. Judd · J. Chung · B.G. Carr ·

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    ABSTRACT: Introduction: The failure to rescue (FTR) rate is the probability of death after a major complication and was defined in elective surgical cohorts. In elective surgery, the precedence rate (proportion of deaths preceded by major complications) approaches 100%, but recent studies in trauma report rates of only 20-25%. We hypothesised that use of high quality data would result precedence rates in higher than those derived from national datasets, and we further sought to characterise the nature of those deaths not preceded by major complications. Methods: Prospectively collected data from 2006 to 2010 from a single level I trauma centre were used. Patients age >16 years with AIS ≥2 who survived beyond the trauma bay were included. Complications, mortality, FTR, and precedence rates were calculated. Chart abstraction was performed for registry deaths without recorded complications to verify the absence of complications and determine the cause of death, after which outcomes were re-calculated. Results: A total of 8004 patients were included (median age 41 (IQR 25-75), 71% male, 82% blunt, median ISS 10 (IQR 5-18)). Using registry data the precedence rate was 55%, with 132/293 (45%) deaths occurring without antecedent major complications. On chart abstraction, 11/132 (8%) patients recorded in the registry as having no complication prior to death were found to have major complications. Complication and FTR rates after chart abstraction were statistically significantly different than those derived from registry data alone (complications 16.5% vs. 16.3, FTR 12.3 vs.13, p=0.001), but this difference was unlikely to be clinically meaningful. Patients dying without complications predominantly (87%) had neurologic causes of demise. Conclusions: Use of data with near-complete ascertainment of complications results in precedence rates much higher than those from national datasets. Patients dying without precedent complications at our centre largely succumbed to progression of neurologic injury. Attempts to use FTR to compare quality between centres should be limited to high quality data. Level of evidence: Level III. Retrospective cohort study: Outcomes.
    Injury 10/2015; DOI:10.1016/j.injury.2015.10.004 · 2.14 Impact Factor
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    ABSTRACT: We sought to improve public health surveillance by using a geographic analysis of emergency department (ED) visits to determine local chronic disease prevalence. Using an all-payer administrative database, we determined the proportion of unique ED patients with diabetes, hypertension, or asthma. We compared these rates to those determined by the New York City Community Health Survey. For diabetes prevalence, we also analyzed the fidelity of longitudinal estimates using logistic regression and determined disease burden within census tracts using geocoded addresses. We identified 4.4 million unique New York City adults visiting an ED between 2009 and 2012. When we compared our emergency sample to survey data, rates of neighborhood diabetes, hypertension, and asthma prevalence were similar (correlation coefficient = 0.86, 0.88, and 0.77, respectively). In addition, our method demonstrated less year-to-year scatter and identified significant variation of disease burden within neighborhoods among census tracts. Our method for determining chronic disease prevalence correlates with a validated health survey and may have higher reliability over time and greater granularity at a local level. Our findings can improve public health surveillance by identifying local variation of disease prevalence. (Am J Public Health. Published online ahead of print July 16, 2015: e1-e8. doi:10.2105/AJPH.2015.302679).
    American Journal of Public Health 07/2015; 105(9):e1-e8. DOI:10.2105/AJPH.2015.302679 · 4.55 Impact Factor
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    ABSTRACT: International Classification of Disease, Ninth Revision (ICD-9) diagnosis codes have not been validated for identifying cases of missed abortion where a pregnancy is no longer viable but the cervical os remains closed. Our goal was to assess whether ICD-9 code "632" for missed abortion has high sensitivity and positive predictive value (PPV) in identifying patients in the emergency department (ED) with cases of stable early pregnancy failure (EPF). We studied females ages 13-50 years presenting to the ED of an urban academic medical center. We approached our analysis from two perspectives, evaluating both the sensitivity and PPV of ICD-9 code "632" in identifying patients with stable EPF. All patients with chief complaints "pregnant and bleeding" or "pregnant and cramping" over a 12-month period were identified. We randomly reviewed two months of patient visits and calculated the sensitivity of ICD-9 code "632" for true cases of stable miscarriage. To establish the PPV of ICD-9 code "632" for capturing missed abortions, we identified patients whose visits from the same time period were assigned ICD-9 code "632," and identified those with actual cases of stable EPF. We reviewed 310 patient records (17.6% of 1,762 sampled). Thirteen of 31 patient records assigned ICD-9 code for missed abortion correctly identified cases of stable EPF (sensitivity=41.9%), and 140 of the 142 patients without EPF were not assigned the ICD-9 code "632"(specificity=98.6%). Of the 52 eligible patients identified by ICD-9 code "632," 39 cases met the criteria for stable EPF (PPV=75.0%). ICD-9 code "632" has low sensitivity for identifying stable EPF, but its high specificity and moderately high PPV are valuable for studying cases of stable EPF in epidemiologic studies using administrative data.
    The western journal of emergency medicine 07/2015; 16(4):551-6. DOI:10.5811/westjem.2015.4.24946
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    ABSTRACT: Racial and ethnic disparities have been previously reported in acute stroke care. We sought to determine the effect of telemedicine (TM) on access to acute stroke care for racial and ethnic minorities in the state of Texas. Data were collected from the US Census Bureau, The Joint Commission and the American Hospital Association. Access for racial and ethnic minorities was determined by summing the population that could reach a primary stroke centre (PSC) or telemedicine spoke within specified time intervals using validated models. TM extended access to stroke expertise by 1.5 million residents. The odds of providing 60-minute access via TM were similar in Blacks and Whites (prevalence odds ratios (POR) 1.000, 95% CI 1.000-1.000), even after adjustment for urbanization (POR 1.000, 95% CI 1.000-1.001). The odds of providing access via TM were also similar for Hispanics and non-Hispanics (POR 1.000, 95% CI 1.000-1.000), even after adjustment for urbanization (POR 1.000, 95% CI 1.000-1.000). We found that telemedicine increased access to acute stroke care for 1.5 million Texans. While racial and ethnic disparities exist in other components of stroke care, we did not find evidence of disparities in access to the acute stroke expertise afforded by telemedicine. © The Author(s) 2015.
    Journal of Telemedicine and Telecare 06/2015; DOI:10.1177/1357633X15589534 · 1.54 Impact Factor
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    ABSTRACT: Geographic variation in healthcare has been traditionally studied in large areas such as hospital referral regions or service areas. These analyses are limited by variation that exists within local communities.

  • Journal of the American Academy of Dermatology 05/2015; 72(5). DOI:10.1016/j.jaad.2015.02.1093 · 4.45 Impact Factor
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    Mark S. Zocchi · Renee Y. Hsia · Brendan G. Carr · Babak Sarani · Jesse M. Pines ·
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    ABSTRACT: We examine differences in inpatient mortality and hospitalization costs at trauma and nontrauma centers for injuries of minor and moderate severity. Inpatient data sets from the California Office of Statewide Health Planning and Development were analyzed for 2009 to 2011. The study population included patients younger than 85 years and admitted to general, acute care hospitals with a primary diagnosis of a minor or moderate injury. Minor injuries were defined as having a New Injury Severity Score less than 5 and moderate injuries as having a score of 5 to 15. Multivariate logistic regression and generalized linear model with log-link and γ distribution were used to estimate differences in adjusted inpatient mortality and costs. A total of 126,103 admissions with minor or moderate injury were included in the study population. The unadjusted mortality rate was 6.4 per 1,000 admissions (95% confidence interval [CI] 5.9 to 6.8). There was no significant difference found in mortality between trauma and nontrauma centers in unadjusted (odds ratio 1.2; 95% CI 0.97 to 1.48) or adjusted models (odds ratio 1.1; 95% CI 0.79 to 1.57). The average cost of a hospitalization was $13,465 (95% CI $12,733 to $14,198) and, after adjustment, was 33.1% higher at trauma centers compared with nontrauma centers (95% CI 16.9% to 51.6%). For patients admitted to hospitals for minor and moderate injuries, hospitalization costs in this study population were higher at trauma centers than nontrauma centers, after adjustments for patient clinical-, demographic-, and hospital-level characteristics. Mortality was a rare event in the study population and did not significantly differ between trauma and nontrauma centers. Copyright © 2015 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.
    Annals of emergency medicine 05/2015; DOI:10.1016/j.annemergmed.2015.04.021 · 4.68 Impact Factor
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    ABSTRACT: Objectives This study explored what smartphone health applications (apps) are used by patients, how they learn about health apps, and how information about health apps is shared.Methods Patients seeking care in an academic ED were surveyed about the following regarding their health apps: use, knowledge, sharing, and desired app features. Demographics and health information were characterized by summary statistics.ResultsOf 300 participants, 212 (71%) owned smartphones, 201 (95%) had apps, and 94 (44%) had health apps. The most frequently downloaded health apps categories were exercise 46 (49%), brain teasers 30 (32%), and diet 23 (24%). The frequency of use of apps varied as six (6%) of health apps were downloaded but never used, 37 (39%) apps were used only a few times, and 40 (43%) health apps were used once per month. Only five apps (2%) were suggested to participants by health care providers, and many participants used health apps intermittently (55% of apps ≤ once a month). Participants indicated sharing information from 64 (59%) health apps, mostly within social networks (27 apps, 29%) and less often with health care providers (16 apps, 17%).Conclusions While mobile health has experienced tremendous growth over the past few years, use of health apps among our sample was low. The most commonly used apps were those that had broad functionality, while the most frequently used health apps encompassed the topics of exercise, diet, and brain teasers. While participants most often shared information about health apps within their social networks, information was less frequently shared with providers, and physician recommendation played a small role in influencing patient use of health apps.
    Academic Emergency Medicine 05/2015; 22(6). DOI:10.1111/acem.12675 · 2.01 Impact Factor
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    ABSTRACT: Effective measurement of health care quality, access, and cost for populations requires an accountable geographic unit. Although Hospital Service Areas (HSAs) and Hospital Referral Regions (HRRs) have been extensively used in health services research, it is unknown whether these units accurately describe patterns of hospital use for patients living within them. To evaluate the ability of HSAs, HRRs, and counties to define discrete health care populations. Cross-sectional geographic analysis of hospital admissions. All hospital admissions during the year 2011 in Washington, Arizona, and Florida. The main outcomes of interest were 3 metrics that describe patient movement across HSA, HRR, and county boundaries: localization index, market share index, and net patient flow. Regression models tested the association of these metrics with different HSA characteristics. For 45% of HSAs, fewer than half of the patients were admitted to hospitals located in their HSA of residence. For 16% of HSAs, more than half of the treated patients lived elsewhere. There was an equivalent degree of movement across county boundaries but less movement across HRR boundaries. Patients living in populous, urban HSAs with multiple, large, and teaching hospitals tended to remain for inpatient care. Patients admitted through the emergency department tended to receive care at local hospitals relative to other patients. HSAs and HRRs are geographic units commonly used in health services research yet vary in their ability to describe where patients receive hospital care. Geographic models may need to account for differences between emergent and nonemergent care.
    Medical care 04/2015; 53(6). DOI:10.1097/MLR.0000000000000356 · 3.23 Impact Factor
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    ABSTRACT: Sudden hospital closures displace patients from usual sources of care and force them to access facilities that lack their prior medical records. For patients with complex needs and for nearby hospitals already strained by high volume, disaster-related hospital closures induce a public health emergency. Our objective was to analyze responses of patients from public versus private emergency departments after closure of their usual hospital after Hurricane Sandy. Using a statewide database of emergency visits, we followed patients with an established pattern of accessing 1 of 2 hospitals that closed after Hurricane Sandy: Bellevue Hospital Center and NYU Langone Medical Center. We determined how these patients redistributed for emergency care after the storm. We found that proximity strongly predicted patient redistribution to nearby open hospitals. However, for patients from the closed public hospital, this redistribution was also influenced by hospital ownership, because patients redistributed to other public hospitals at rates higher than expected by proximity alone. This differential response to hospital closures demonstrates significant differences in how public and private patients respond to changes in health care access during disasters. Public health response must consider these differences to meet the needs of all patients affected by disasters and other public health emergencies. (Disaster Med Public Health Preparedness. 2015;00:1-9).
    Disaster Medicine and Public Health Preparedness 03/2015; 9(3):1-9. DOI:10.1017/dmp.2015.11 · 0.70 Impact Factor
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    ABSTRACT: The location of comprehensive stroke centers (CSCs) is critical to ensuring rapid access to acute stroke therapies; we conducted a population-level virtual trial simulating change in access to CSCs using optimization modeling to selectively convert primary stroke centers (PSCs) to CSCs. Up to 20 certified PSCs per state were selected for conversion to maximize the population with 60-minute CSC access by ground and air. Access was compared across states based on region and the presence of state-level emergency medical service policies preferentially routing patients to stroke centers. In 2010, there were 811 Joint Commission PSCs and 0 CSCs in the United States. Of the US population, 65.8% had 60-minute ground access to PSCs. After adding up to 20 optimally located CSCs per state, 63.1% of the US population had 60-minute ground access and 86.0% had 60-minute ground/air access to a CSC. Across states, median CSC access was 55.7% by ground (interquartile range 35.7%-71.5%) and 85.3% by ground/air (interquartile range 59.8%-92.1%). Ground access was lower in Stroke Belt states compared with non-Stroke Belt states (32.0% vs 58.6%, p = 0.02) and lower in states without emergency medical service routing policies (52.7% vs 68.3%, p = 0.04). Optimal system simulation can be used to develop efficient care systems that maximize accessibility. Under optimal conditions, a large proportion of the US population will be unable to access a CSC within 60 minutes. © 2015 American Academy of Neurology.
    Neurology 03/2015; 84(12). DOI:10.1212/WNL.0000000000001390 · 8.29 Impact Factor
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    ABSTRACT: Emergency visits are rising nationally, whereas the number of emergency departments is shrinking. However, volume has not increased uniformly at all emergency departments. It is unclear what factors account for this variability in emergency volume growth rates. The objective of this study was to test the association of hospital and population characteristics and the effect of hospital closures with increases in emergency department volume. The study team analyzed emergency department volume at New York State hospitals from 2004 to 2010 using data from cost reports and administrative databases. Multivariate regression was used to evaluate characteristics associated with emergency volume growth. Spatial analytics and distances between hospitals were used in calculating the predicted impact of hospital closures on emergency department use. Among the 192 New York hospitals open from 2004 to 2010, the mean annual increase in emergency department visits was 2.7%, but the range was wide (-5.5% to 11.3%). Emergency volume increased nearly twice as fast at tertiary referral centers (4.8%) and nonurban hospitals (3.7% versus urban at 2.1%) after adjusting for other characteristics. The effect of hospital closures also strongly predicted variation in growth. Emergency volume is increasing faster at specific hospitals: tertiary referral centers, nonurban hospitals, and those near hospital closures. This study provides an understanding of how emergency volume varies among hospitals and predicts the effect of hospital closures in a statewide region. Understanding the impact of these factors on emergency department use is essential to ensure that these populations have access to critical emergency services. (Population Health Management 2015;xx:xxx-xxx).
    Population Health Management 02/2015; DOI:10.1089/pop.2014.0123 · 1.51 Impact Factor

Publication Stats

2k Citations
472.17 Total Impact Points


  • 2010-2015
    • University of Pennsylvania
      • • Center for Clinical Epidemiology and Biostatistics
      • • Department of Emergency Medicine
      Filadelfia, Pennsylvania, United States
    • Emory University
      • Department of Emergency Medicine
      Atlanta, Georgia, United States
    • Mayo Clinic - Rochester
      • Department of Emergency Medicine
      Рочестер, Minnesota, United States
  • 2007-2015
    • Thomas Jefferson University
      • Department of Emergency Medicine
      Filadelfia, Pennsylvania, United States
  • 2007-2014
    • William Penn University
      Filadelfia, Pennsylvania, United States
  • 2013
    • Treatment Research Institute, Philadelphia PA
      Filadelfia, Pennsylvania, United States
  • 2009-2013
    • The Children's Hospital of Philadelphia
      • Department of Emergency Medicine
      Philadelphia, Pennsylvania, United States
  • 2007-2011
    • Hospital of the University of Pennsylvania
      • Department of Emergency Medicine
      Philadelphia, Pennsylvania, United States
  • 2008
    • Robert Wood Johnson Foundation
      Princeton, New Jersey, United States
  • 2007-2008
    • Robert Wood Johnson University Hospital
      Нью-Брансуик, New Jersey, United States
  • 2006
    • Carolinas Medical Center University
      Charlotte, North Carolina, United States