quick shots presentations for on demand viewing ptsf

Quick Shots Presentations for On Demand Viewing PTSF Virtual Fall Conference October 19, 20, and 21, 2020

1. Highlighting the Burden of Firearm Injuries: A Ten-Year Trend of Gun Violence in Southeastern Pennsylvania

• Authors: Sirivan Seng MD1, Katherine Valles MS MPH 2, Jintong Hou MS3, Sandra Durgin RN MSN 1, Jennifer Collins MSN CRNP1, Danielle Gritenas BSN RN CEN1, Asanthi Ratnasekera DO FACS 1

• Organizational Affiliations: o Department of Surgery, Crozer Keystone Health System, Upland, PA o Drexel University College of Medicine, Philadelphia, PA o Dornsife School of Public Health, Drexel University, Philadelphia, PA

• Presenting Author: Sirivan Seng, MD, [email protected] 2. Outcomes of Penetrating Proximal Artery Extremity Injuries at Level 1 versus Level 2 Trauma Centers in the State of Pennsylvania

• Authors: Sirivan Seng MD1, Matilda Whitney BS, Matthew Sayegh MD1, Sandra Durgin RN MSN1, Alicia Lozano MS2, Danielle Sienko MS3, Alexandra Hanlon PhD2, Niels Martin MD4, Asanthi Ratnasekera DO FACS1

• Organizational Affiliations: • Department of Surgery, Crozer Keystone Health System, Upland PA • Department of Statistics, Virginia Polytechnic Institute & State University, Center for

Biostatistics and Health Data Science, Roanoke , VA • Beyond Inference Statistical Consulting, Meadowbrook, PA • Department of Surgery, University of Pennsylvania, Philadelphia, PA • Presenting Author: Sirivan Seng, MD. [email protected]

3. Benzodiazipines increase the likelihood of both infectious and thrombotic complications

• Authors: Edward Skicki, DO; Eric Bradburn, DO, FACS; Madison Morgan, BS; Frederick Rogers, MD, FACS

• Lancaster General Health/Penn Medicine, Lancaster, PA • Presenting Author: Edward Skicki DO, [email protected]

4. A comprehensive analysis of undertriage in a mature trauma system using geospatial analysis

• Authors: Frederick B. Rogers, MD, FACS; Michael A. Horst, PhD; Madison E. Morgan, BS; Eric H. Bradburn, DO, FACS; Alan D. Cook, MD, FACS; George O. Maish III, MD, FACS

• Lancaster General Health/Penn Medicine, Lancaster, PA • Presenting Author: George O. Maish III, MD, FACS,

[email protected] 5. Location, Location, Location: Place of injury matters for the unexpected geriatric survivor

• Authors: Madison Morgan, BS; Kellie Bresz, MS; Tamer Shtayyeh, DO; Eric Bradburn, DO; Tawnya Vernon, BA; Jennifer A.T. Schwartz, MD

• Lancaster General Health/Penn Medicine, Lancaster, PA • Presenting Author: Jennifer A.T. Schwartz, MD, [email protected]

mailto:[email protected]









6. An analysis of pediatric social vulnerability in the Pennsylvania Trauma System • Authors: Madison Morgan, BS; Michael A. Horst, PhD; Tawnya Vernon, BA; Mary E. Fallat,

MD, Amelia Rogers, MD; Eric H. Bradburn, DO, FACS; Frederick B. Rogers, MD, FACS • Lancaster General Health/Penn Medicine, Lancaster, PA • Presenting Author: Madison E. Morgan, BS, [email protected]

7. Police Transport for Penetrating Trauma

• Authors: Eric Winter, BS1, Allyson Hynes, MD1, Kaitlyn Shultz, BS3,4, Daniel N. Holena, MD1, Neil R. Malhotra, MD2,3, Jeremy W. Cannon, MD1

• Organizational Affiliations: o 1Division of Traumatology, Surgical Critical Care & Emergency Surgery, Perelman

School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA o 2Department of Neurosurgery, Perelman School of Medicine at the University of

Pennsylvania, Philadelphia, PA, USA o 3McKenna EpiLog Fellowship in Population Health, at the University of Pennsylvania,

Philadelphia, PA, USA o 4West Chester University, The West Chester Statistical Institute and Department of

Mathematics, 25 University Ave, West Chester, PA, USA • Presenting Author: Eric Winter, BS, [email protected]

8. Falling… From Buggies… Differences in mechanism of injury in the Amish population

• Authors: Larissa D. Whitney PA-C; Kelly F. Bonneville MHS, PA-C; Madison E. Morgan BS; Lindsey L. Perea DO

• Lancaster General Health/Penn Medicine, Lancaster, PA, USA • Presenting Author: Larissa D. Whitney PA-C, [email protected]

9. Subdural hematoma in the super elderly: Moderate range GCS protends poor prognosis

• Authors: Rachel Appelbaum, MD, Matelin Crosen, MD, Lehigh Valley Health Network, Devon Haggerty, Research Scholar, Lehigh Valley Health Network, Jill Krystofinski, CRNA, Lehigh Valley Health Network, Malia Eischen, MD, Department of Surgery, Queens Medical Center, Honolulu, HI, Joseph Stirparo, MD, LVHN Muhlenberg, Robert Barraco, MD, MPH, Lehigh Valley Health Network, Rovinder Sandhu, Lehigh Valley Health Network.

• Presenting Author: Rachel Appelbaum, MD, Rachel D. Appelbaum, MD, Surgical Critical Care Fellow, PGY6, Department of Surgery, Wake Forest Baptist Medical Center, Winston-Salem, NC, 27157, [email protected]

10. Tracheostomy dislodgement: are obese patients at increased long-term risk?

• Authors: Ryan Wan DO, Courtney Docherty DO, Hamza Bhatti DO, Hannah Shin DO, Chelsea Spector MD, Brian Thai BS, Alison Muller, MLS, MSPH, Anthony Martin, RN, BSN, Adrian Ong, MD

• Organizational Affiliation: Reading Hospital/Tower Health, Reading PA • Presenting Author: Ryan Wan, DO, [email protected]

11. Documentation of Cognitive Rest Following Concussion by Pediatric Primary Care Providers

• Authors: Susan Butler MSN, FNP-BC, Cynthia Dimovitz, MSN, CPNP, Temilolaoluwa Daramola, RN, BS, MS, Sahin Becirovic, BS, Louis Mancano, MD, Adrian Ong, MD, & Alison Muller MLS(ASCP) MSPH

• Organizational Affiliation: Reading Hospital/Tower Health, Reading, PA • Presenting Author: Susan Butler MSN, FNP-BC, [email protected]







12. Direct to OR Resuscitation: A reevaluation 30 yrs. after implementation • Author(s): Christian Pothering MD Lehigh Valley Health Network – Cedar Crest, Rovinder

Sandhu MD FACS Lehigh Valley Health Network – Cedar Crest, Farina Klocksieben University of South Florida Morsani College of Medicine, Joseph Stirparo MD FACS Lehigh Valley Health Network – Cedar Crest

• Presenting Author: Christian Pothering, [email protected].


Highlighting The Burden Of Firearm Injuries: A Trend Of Gun Violence In Southeastern Pennsylvania

Authors: Sirivan Seng MD1, Katherine Valles MS MPH 2, Jintong Hou MS3, Sandra Durgin RN MSN 1, Jennifer Collins MSN CRNP1, Danielle Gritenas BSN RN CEN1, Asanthi Ratnasekera DO FACS 1

1. Department of Surgery, Crozer Keystone Health System, Upland, PA 2. Drexel University College of Medicine, Philadelphia, PA 3. Dornsife School of Public Health, Drexel University, Philadelphia, PA

Presenting Author: Sirivan Seng, MD

Objectives

Firearm injury is a major public health concern in the United States. In 2015 alone, firearms killed over 35,000 people and injured over 200,000. The purpose of the study was to provide a trend of firearm injuries of Chester, Delaware and Gloucester counties, which encompasses a city with the 10th highest homicide rate in the country.

Methods

A retrospective review of patients with firearm injuries from 2009 to 2019 at a Level 2 Trauma Center was performed. All patients who met inclusion criteria for the Pennsylvania Trauma Outcome Study registry were included. Patients with firearm injuries were compared in mild to moderate (ISS<16) and severe injury (ISS≥16) cohorts based on injury severity score (ISS).

Results

1028 patients met inclusion criteria. The 11-year in-hospital mortality was 8.2%, decreasing from 14.3% in 2009 to 7.5% in 2019 (p=0.06). The most common demographic was African-American (84.3%) and male (90.2%), with a median age of 24 (20, 31). Overall, median ISS was 9 (4, 17). Patients with ISS≥16 had a higher rate of massive transfusion protocol activation (MTP) (p<0.0001) and mortality (p<0.0001) compared to those with ISS<16. Of the 331 (32.2%) patients with ISS≥16, 190 survived past 24 hours. Within the cohort with ISS≥16 that survived past 24 hours, MTP was activated in 48 cases (25.3%) and 143 (75.3%) went immediately to the operating room. Upon their discharge, 25.8% went to long term acute care, rehabilitation or skilled nursing facilities, while mortality was 6.3%.

Conclusions

The study highlights the burden of injuries related to gun violence. Results indicate gun violence disproportionally affects young, African-American men. While mortality has shown a downward trend, the number of firearm injuries has increased in the past

eleven years. Efforts to include hospital-based violence intervention programs should be pursued to decrease firearm-related injuries.

Figures or Graphs

Figure 1. Total Firearm Injuries and Annual Mortality Rate in Southeastern Pennsylvania from 2009 to 2019

Table 1. Outcomes of Patients with Severe Injuries with Survival > 24 hours

Year 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total Total Traumas (n)

16 17 18 17 11 20 14 15 23 17 22 190

Age* 23.5 (20.5-27.5)

23 (18-31)

23.5 (19-33)

24 (21-33)

25 (20-42)

21 (19.5-

23)

27 (23-34)

20 (18-24)

24 (22-32)

26 (22-31)

25 (20-32)

23 (20-31)

Gender (n, %)

Male 15 (93.8%)

16 (94.1%)

17 (94.4%)

15 (88.2%)

9 (81.8%)

16 (80.0%)

12 (85.7%)

15 (100%)

21 (91.3%)

15 (88.2%)

19 (86.4%)

170 (89.5%)

Female 1 (6.3%)

1 (5.9%)

1 (5.6%)

2 (11.8%)

2 (18.2%)

4 (20.0%)

2 (14.3%)

0 2 (8.7%)

2 (11.8%)

3 (13.6%)

20 (10.5%)

Race

(n, %)

White 2 (12.5%)

1 (5.9%)

2 (11.1%)

2 (11.8%)

1 (9.1%)

1 (5.0%)

0 1 (6.7%)

1 (4.3%)

4 (23.5%)

1 (4.5%)

16 (8.4%)

Black 12 (75.0%)

16 (94.1%)

15 (83.3%)

15 (88.2%)

10 (90.9%)

19 (95.0%)

13 (92.9%)

14 (93.3%

)

22 (95.7%)

12 (70.6%)

19 (86.4%)

167 (87.9%)

Asian 0 0 0 0 0 0 0 0 0 0 0 0 Other/NFS 1

(6.3%) 0 1

(5.6%) 0

0

0

1 (7.1%)

0

0

1 (5.9%)

0 4 (2.1%)

Unknown 1 (6.3%)

0 0

0

0 0

0

0 0

0

2 (9.1%)

3 (1.6%)

Injury Severity Score*

20 (17-25)

25 (19-26)

18.5 (16-24)

20 (17-25)

24 (17-29)

22 (17-28)

20 (18-29)

21 (17-29)

22 (18-27)

20 (18-25)

25 (17-27)

21 (17-26)

Massive Transfusion Protocol (n, %) **

--- --- --- --- --- 7 (35.0%)

5 (35.7%)

8 (53.3%

)

9 (39.1%)

8 (47.1%)

11 (50.0%)

48 (25.3%)

Total RBC Transfused*

6 (2-11)

5 (2-

17.5)

4 (4-11)

7.5 (4-10)

6 (5-15)

13.5 (3-

20.5)

7 (4-11)

9.5 (6-

13.5)

7 (4-20)

6 (4-12)

7 (4-13)

7 (4-14)

ICU Length of Stay*

3 (2-7)

5 (2-

15.5)

2 (1-6)

3 (1-6)

5 (2-25)

3 (2-7)

6.5 (2-9)

6 (1-13)

4 (3-9)

4 (2-9)

3 (2-10)

4 (2-9)

Ventilator Time*

2 (1-17)

4 (1-14)

2 (1-5)

2 (1-7)

7 (2-23)

3 (1-10)

3 (2-8)

7 (3.5-14)

4.5 (2-11)

2.5 (1-6)

2 (1-6)

3 (1-9)

Hospital Length of Stay*

6.5 (4.5-22)

11 (9-21)

8 (6-16)

10 (6-14)

10 (7-27)

12.5 (8-26)

14 (10-18)

14 (8-22)

11 (6-23)

8 (4-20)

10.5 (5-25)

11 (6-20)

Discharge Destination (n, %)

Home 10 10 14 12 7 11 10 7 16 11 12 120

(62.5%)

(58.8%)

(77.8%)

(70.6%)

(63.6%)

(55.0%)

(71.4%)

(46.7%)

(69.6%)

(64.7%)

(54.5%)

(63.2%)

Rehabilitation Center

3 (18.8%)

6 (35.3%)

2 (11.1%)

4 (23.5%)

3 (27.3%)

7 (35.0%)

3 (21.4%)

5 (33.3%)

4 (17.4%)

2 (11.8%)

5 (22.7%)

44 (23.2%)

Long Term Acute Care

0 0 0 0 0 1 (5.0%)

0 0 0 1 (5.9%)

0 2 (1.1%)

Legal Authority

1 (6.3%)

0 0 0 0 1 (5.0%)

1 (7.1%)

1 (6.7%)

0 1 (5.9%)

0 5 (2.6%)

Psychiatric Facility

0 0 0 0 0 0 0 0 0 1 (5.9%)

0 1 (0.5%)

PA Trauma Center

0 0 0 0 0 0 0 0 1 (4.3%)

0 1 (4.5%)

2 (1.1%)

Out of State Trauma Center

0 0 0 0 0 0 0 1 (6.7%)

0 0 0 1 (0.5%)

Skilled Nursing Facility

0 0 0 0 1 (9.1%)

0 0 0 1 (4.3%)

0 1 (4.5%)

3 (1.6%)

Hospice 0 0 0 0 0 0 0 0 0 0 1 (4.5%)

1 (0.5%)

Mortality

(n, %)

2 (12.5%)

1 (5.9%)

2 (11.1%)

1 (5.9%)

0 0 0 1 (6.7%)

1 (4.3%)

2 (11.8%)

2 (9.1%)

12 (6.3%)

* Median (25th percentile -75th percentile) ** Massive Transfusion Protocol data not available during 2009 – 2013

Outcomes of Penetrating Proximal Artery Extremity Injuries at Level 1 versus Level 2 Trauma Centers in the State of Pennsylvania

Authors

Sirivan Seng MD1, Matilda Whitney BS, Matthew Sayegh MD1, Sandra Durgin RN MSN1, Alicia Lozano MS2, Danielle Sienko MS3, Alexandra Hanlon PhD2, Niels Martin MD4, Asanthi Ratnasekera DO FACS1

1. Department of Surgery, Crozer Keystone Health System, Upland PA 2. Department of Statistics, Virginia Polytechnic Institute & State University, Center for

Biostatistics and Health Data Science, Roanoke , VA 3. Beyond Inference Statistical Consulting, Meadowbrook, PA 4. Department of Surgery, University of Pennsylvania, Philadelphia, PA

Presenting Author: Sirivan Seng, MD

Objectives

Extremity vascular injuries comprise approximately 0.5-4% of all civilian traumas, with 80% due to penetrating mechanisms. Mortality from bleeding from a penetrating extremity vascular injury is preventable, however is high. There is a paucity of literature on outcomes in regards to care of penetrating extremity vascular injuries at Level 1 versus Level 2 trauma centers. The objective of this study was to discern any differences in outcomes in mortality and complications for proximal artery extremity injuries in Level 1 versus Level 2 trauma centers.

Methods

A retrospective cohort study of adult patients (age >18) with penetrating proximal extremity artery injuries from January 2013 to December 2017 was performed using the Pennsylvania Trauma Outcome Study registry. Data from 14 Level 1 and 15 Level 2 Pennsylvania Trauma Systems Foundation accredited trauma centers were included.

Results

Of 404 patients, 326 (80.1%) patients were treated at a Level 1 and 78 (19.2%) at a Level 2 trauma center. The most common injury was to the brachial artery (34.4% and 30.8%, respectively). Median injury severity score for both Level 1 and Level 2 trauma centers were 10. The majority of injuries were repaired via a combination of open and endovascular approach (44.5% and 35.9%, respectively). There were no statistically significant differences between in-hospital mortality or complications for patients who underwent endovascular, open, combined repairs, or non-operative management. Overall, there were no differences for in-hospital mortality between Level 1 and Level 2 trauma centers (11.7%, 7.7%, p = 0.312).

Conclusion

There are no differences for in-hospital mortality or complications for patients with proximal artery extremity injuries being treated at a Level 1 versus Level 2 trauma center.

Tables

Table 1: Comparing Patient Characteristics for those with Proximal Artery Injuries by Level of Trauma Center

Characteristic Level 1 Trauma Center

(N = 326)

Level 2 Trauma Center

(N = 78)

P-value1

Age, Median (Q1, Q3) 27 (22, 35) 31 (22, 43) 0.0789 Gender, n (%) 0.7343

Male 297 (91.1%) 72 (92.3%) - Female 29 (8.9%) 6 (7.7%) -

Race, n (%) (n=285) (n=77) <.0001 White 65 (22.8%) 45 (58.4%) - Black 208 (73.0%) 31 (40.3%) - Asian 4 (1.4%) 0 (0.0%) - Other 8 (2.8%) 1 (1.3%) -

ISS, Median (Q1, Q3) 10 (9, 17) 10 (9, 17) 0.3316 TRISS, Median (Q1, Q3)

0.99 (0.95, 0.99) (n=300)

0.99 (0.96, 0.99) (n=73)

0.5729

AIS, Median (Q1, Q3) 3 (3, 4) 3 (3, 4) 0.4696 Hospital Days, Median (Q1, Q3)

6 (2, 13) 6 (2, 12) 0.7776

Ventilator Days, Median (Q1, Q3)

0 (0, 1) 0 (0, 1) 0.5228

ICU Days, Median (Q1, Q3)

1 (0, 3) 1 (0, 3) 0.9071

Transfusion Prior to Hospital Stay, Median (Q1, Q3)

0 (0, 0) (n=222)

0 (0, 0) (n=67)

0.8460

Transfusion During Hospital Stay, Median (Q1, Q3)

0 (0, 1) (n=322)

0 (0, 2) 0.2835

MTP Initiation, n (%) 30 (20.0%) (n = 150)

9 (29.0%) (n=31)

0.2655

Mortality, n (%) 38 (11.7%) 6 (7.7%) 0.3127 Vascular Injury repair > 24 hours, n (%)

4 (2.7%) (n=147)

2 (5.0%) (n=40)

0.6100

Procedure, n (%) 0.1583 Endovascular Repair 41 (12.6%) 12 (15.4%) -

Open Repair 111 (34.0%) 25 (32.0%) - Combined Approach 145 (44.5%) 28 (35.9%) -

Neither 29 (8.9%) 13 (16.7%) - Fasciotomy, n (%) 107 (32.8%) 18 (23.1%) 0.0944

Table 2: Comparing Clinical Outcomes for Patients with Proximal Artery Injuries Treated in a Level 1 versus Level 2 Trauma Center Grouped by Type of Repair.



P-Value1

Endovascular Repair N = 41 N = 12 Hospital Days, Median (Q1, Q3)

3 (1, 6) 4 (2.5, 10) 0.3046


0 (0, 1) 0 (0, 0) 0.4729


0 (0, 1) 1.5 (0, 3) 0.1265


0 (0, 0) (n=25)

0 (0, 0) (n=9)

0.5938


0 (0, 1) 0 (0, 0) 0.7037

MTP Initiation, n (%) 4 (23.5%) (n=17)

0 (0.0%) (n=4)

0.5455

Mortality, n (%) 7 (17.1%) 1 (8.3%) 0.6652 Re-Intervention Rate, n (%)

1 (2.4%) 0 (0.0%) >.9999

Complications, n (%) 4 (9.8%) 1 (8.3%) >.9999 Open Repair N = 111 N = 25 Hospital Days, Median (Q1, Q3)

5 (3, 8) 8 (3, 12) 0.1897


0 (0, 0) 0 (0, 1) 0.1258


1 (0, 2) 1 (1, 2) 0.0992


0 (0, 0) (n=82)

0 (0, 0) (n=24)

0.1430


0 (0, 1) 0 (0, 2) 0.1766


2 (25.0%) (n=8)

0.3199

Mortality, n (%) 6 (5.4%) 0 (0%) 0.5923 Re-Intervention Rate, n (%)

5 (4.5%) 0 (0.0%) 0.5840

Complications, n (%) 12 (10.8%) 2 (8.0%) >.9999 Combined Repair N = 145 N = 28 - Hospital Days, Median (Q1, Q3)

10 (4, 18) 10 (3.5, 14.5) 0.6814


0 (0, 1) 1 (0, 2) 0.2977


2 (1, 4) 2 (1, 4) 0.7703


0 (0, 0) (n=93)

0 (0, 0) (n=24)

0.1516


0 (0, 2) (n=143)

1.5 (0, 2) 0.1687


6 (42.9%) (n=14)

0.5758

Mortality, n (%) 15 (10.3%) 3 (10.7%) >.9999 Re-Intervention Rate, n (%)

22 (15.2%) 4 (14.3%) >.9999

Complications, n (%) 52 (35.9%) 10 (35.7%) 0.9881

BENZODIAZIPINES INCREASE THE LIKELIHOOD OF BOTH INFECTIOUS AND THROMBOTIC COMPLICATIONS

Edward Skicki, DO; Eric Bradburn, DO, FACS; Madison Morgan, BS; Frederick Rogers, MD, FACS

Presenting Author: Dr. Edward Skicki DO

Lancaster General Health/Penn Medicine, Lancaster, PA, USA

OBJECTIVES: Benzodiazepines (BZDs) modulate peripheral γ-amino-butyric acid type A on macrophages causing immunomodulation. They inhibit pro-inflammatory cytokines increasing infections. Prior studies have also shown that infections can increase thrombotic complications. We sought to examine this relationship in trauma patients. We hypothesized that the presence of BZDs on admission urine drug screen (UDS) would increase rates of both complications.

METHODS: All patients submitted to the Pennsylvania Trauma Outcome Study database from 2003-2018 were queried. Those with a positive UDS for BZDs were analyzed. Infectious complications were defined as: pneumonia, UTI, sepsis, wound and soft tissue infection; thrombotic complications were defined as the presence of pulmonary embolism or deep vein thrombosis. Logistic regressions controlling for demographic and injury covariates assessed the adjusted impact of BZDs on infectious and thrombotic complications.

RESULTS: 9,394 (1.56%) patients had infectious complications and 8,794 (1.46%) had thrombotic complications. 33,260 (5.52%) patients had a positive UDS for BZDs on admission. Univariate analysis showed that those positive for BZDs had higher rates of infectious (3.33% vs 1.45%, p<0.001) and thrombotic (2.84% vs 1.38%, p<0.001) complications. Multivariate analysis revealed that BZDs significantly increased the odds of infectious and thrombotic complications. Patients who tested positive for BZDs and subsequently developed infection had dramatically increased odds (AOR: 5.17, p<0.001) of developing thrombotic complications.

CONCLUSION: Trauma patients with a positive UDS for BZDs had higher odds of both infectious and thrombotic complications. Moreover, odds of thrombotic complications were higher in those with infections. This supports that BZD use is associated with more infections which may lead to more thrombotic events in trauma.

Table 1. Multivariate analysis of infectious and thrombotic complications in the PTOS database

Infectious Complications (n=9,394)

Thrombotic Complications (n=8,794)

Variable Adjusted Odds Ratio (95% CI) p-value Adjusted Odds

Ratio (95% CI) p-value

+ UDS for Benzodiazepines 1.41 (1.32-1.51) <0.001 1.24 (1.16-1.33) <0.001 AUROC:0.76 AUROC:0.78 *adjusted for injury severity score, systolic blood pressure, Glasgow coma score, age, gender, injury type

A COMPREHENSIVE ANALYSIS OF UNDERTRIAGE IN A MATURE TRAUMA SYSTEM USING GEOSPATIAL MAPPING

Frederick B. Rogers, MD, FACS; Michael A. Horst, PhD; Madison E. Morgan, BS; Eric H. Bradburn,

DO, FACS; Alan D. Cook, MD, FACS; George O. Maish III, MD, FACS

Presenting Author: Dr. George O. Maish III, MD, FACS

Lancaster General Health/Penn Medicine, Lancaster, PA, USA OBJECTIVES: The correct triage of trauma patients to trauma centers (TCs) is essential. We sought to determine the percentage of patients who were undertriaged (UTR) within the Pennsylvania (PA) trauma system and spatially analyze areas of UTR in PA for all age groups: pediatric, adult and geriatric. We hypothesized that there would be certain areas that had high UTR for all age groups.

METHODS: From 2003-2015, all admissions from the Pennsylvania Trauma Systems Foundation (PTSF) registry and those meeting trauma criteria (ICD-9: 800-959) from the Pennsylvania Health Care Cost Containment Council (PHC4) database were included. Admissions were divided into age groups: pediatric (>15y), adult (15-64y) and geriatric (≥65y). All pediatric trauma cases were included from the PTSF and PHC4 registry, while only cases with ISS>9 were included in adult and geriatric age groups. UTR was defined as patients not admitted to pediatric TCs (n=6) or Level I/II adult TCs (n=27) divided by the total number of patients from the PHC4 database. ArcGIS Desktop and GeoDa were used for geospatial mapping of UT with a spatial empirical Bayesian smoothed UTR by Zip Code Tabulation Area (ZCTA) and Stata for statistical analyses.

RESULTS: There were significant percentages of UTR for all age groups (Table 1). One area of high UTR for all age groups had TCs and large non-trauma centers(NTCs) in close proximity. There were high rates of undertriage for all ages in rural areas, specifically in the upper central regions of PA, with limited access to TCs.

CONCLUSION: It appears there are two patterns leading to undertriage. The first is in areas where TCs are in close proximity to large competing NTCs which may lead to inappropriate triage. The second has to do with lack of access to TCs. Geospatial mapping is a valuable tool that can be used to ascertain where trauma systems should focus scarce resources to decrease UTR.

Figure 1. Combinations of Top Quartile Pediatric All ISS Cases UTR, Adult ISS>9 UTR and Geriatric ISS>9 UTR

Table 1. Statewide Undertriage Percentages By Age Group Age Group ZCTA (n=1,798) Median (Q1-Q3) Smoothed

Percent Undertriaged Pediatric (age <15 years) All ISS 37.3 (21.6-46.2)

Adult (age 15-64 years) ISS > 9 22.9 (16.9-28.3)

Geriatric (age ≥65 years) ISS > 9 50.0 (36.2-59.7)

LOCATION, LOCATION, LOCATION: PLACE OF INJURY MATTERS FOR THE UNEXPECTED GERIATRIC SURVIVOR

Madison Morgan, BS; Kellie Bresz, MS; Tamer Shtayyeh, DO; Eric Bradburn, DO; Tawnya Vernon, BA;

Jennifer A.T. Schwartz, MD

Presenting Author: Dr. Jennifer A.T. Schwartz, MD

Lancaster General Health/Penn Medicine, Lancaster, PA, USA

OBJECTIVES: The geriatric population requires specialized trauma care due to altered anatomy and physiology associated with aging. Patients with a Trauma Injury Severity Score (TRISS) <0.5 are termed “Unexpected Survivors.” There is scarce information on this subset of geriatric patients whose survival is an anomaly. We hypothesized that location of injury would be associated with improved survival in the geriatric trauma patient.

METHODS: Utilizing the Pennsylvania Trauma Outcome Study database, a retrospective cross-sectional study sought to identify which factors lead to improved survival of patients aged >65 years with a TRISS <0.5 from 2013-2017. The outcome of interest was survival to discharge. Ten clinically relevant variables were identified as possible factors that may lead to improved survival. Logistic regression models adjusting for age, ISS, GCS, and systolic blood pressure with the trauma facility as the clustering variable were used to measure the effect of each variable, individually, on survival rates.

RESULTS: 1,336 patients met inclusion criteria and 29.6% were Unexpected Survivors (n=395). Factors that led to improved survival included: Place of Injury: Street/Highway (AOR: 0.51; p <0.001) and Residential Institution (AOR: 0.46; p=0.043); Positive Urine Drug Screen: Benzodiazepines (AOR: 0.49; p=0.002); Positive Serum Ethanol Level (AOR: 0.57; p=0.040). Hypotension (AOR: 8.59; p <0.001) and Hypothermia (AOR: 1.58; p=0.014) led to a decrease in survival.

CONCLUSION: Location of injury (Street/Highway and Residential Institution) as well as ethanol and benzodiazepine use lead to a significant increase in survival in severely injured geriatric patients with TRISS<0.5. Future studies are underway to investigate why these factors lead to improved survival in order to focus efforts in these areas to improve trauma care in the severely injured geriatric population.

AN ANALYSIS OF PEDIATRIC SOCIAL VULNERABILITY IN THE PENNSYLVANIA TRAUMA SYSTEM

Madison Morgan, BS; Michael A. Horst, PhD; Tawnya Vernon, BA; Mary E. Fallat, MD

Amelia Rogers, MD; Eric H. Bradburn, DO, FACS; Frederick B. Rogers, MD, FACS

Presenting Author: Madison E. Morgan, BS

Penn Medicine Lancaster General Health, Lancaster, PA, USA

OBJECTIVES: 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%). Pediatric undertriage was defined as the proportion of PHC4 cases that were not represented in the Pennsylvania Trauma Systems Foundation (PTSF) database 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.

Figure 1. Weighted SVI by Zip Code Area for Pennsylvania

Title: Police Transport for Penetrating Trauma: Lessons from 3,313 Patients in Philadelphia Authors: Eric Winter, BS1, Allyson Hynes, MD1, Kaitlyn Shultz, BS3,4, Daniel N. Holena, MD1, Neil R. Malhotra, MD2,3, Jeremy W. Cannon, MD1

Affiliations: 1Division of Traumatology, Surgical Critical Care & Emergency Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA 2Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

3McKenna EpiLog Fellowship in Population Health, at the University of Pennsylvania, Philadelphia, PA, USA 4West Chester University, The West Chester Statistical Institute and Department of Mathematics, 25 University Ave, West Chester, PA, USA Presenting Author: Eric Winter, BS Objectives: Philadelphia police routinely transport penetrating trauma patients to nearby trauma centers. This study hypothesizes that similarly injured patients have lower mortality with police transport compared to emergency medical services (EMS). Methods: This retrospective investigation utilizing the Pennsylvania Trauma Outcomes Study registry included penetrating trauma victims who presented directly to a Level I or Level II trauma center in Philadelphia (2014-2018). Mortality was assessed at multiple timepoints. Subgroup analysis was performed for patients with minor, moderate, and severe injuries, defined by injury severity score (ISS). Patient cohorts (police vs EMS) were compared with regression analysis, and coarsened exact matching (CEM) was used to control for confounding differences between groups. Results: Of 3,313 total patents who met inclusion criteria, 1,970 were transported by police and 1,343 were transported by EMS. Police transport volume increased significantly over the study period (p=0.041). On unadjusted analysis, patients transported by police had significantly higher overall mortality compared to those transported by EMS (p<0.001). This difference was also observed on hospital arrival, and at 1-hour, 6-hour, and 24-hour post-arrival timepoints (all p<0.001). CEM identified 870 patients in each transport group (police vs. EMS) with exactly matching characteristics. No difference in mortality rate was observed for matched cohorts during overall hospitalization (p=0.374), on arrival (p=0.115), or at 1-hour (p=0.195), 6-hour (p=0.561), or 24-hour (p=0.910) post-arrival timepoints. Patients with severe injuries (ISS 26-75) were significantly more likely to be alive on hospital arrival when transported by police, compared to EMS (p=0.032).

Conclusion: A majority of patients with penetrating trauma in Philadelphia are transported by police to area trauma centers. Patients with similar characteristics have comparable mortality when transported by police and EMS – timely transport to definitive trauma care should be emphasized over medical capability in the pre-hospital management of patients with penetrating trauma.

FALLING… FROM BUGGIES… DIFFERENCES IN MECHANISM OF INJURY IN THE AMISH POPULATION

Larissa D. Whitney MHS, PA-C; Kelly F. Bonneville MHS, PA-C; Madison E. Morgan BS; Lindsey L. Perea DO

Presenting Author: Larissa D. Whitney PA-C

Lancaster General Health/Penn Medicine, Lancaster, PA, USA OBJECTIVES: Individuals presenting with traumatic injury in rural populations have significantly different injury patterns than those in urban environs. With an increasing Amish population totaling over 33,000 in our catchment area, their unique way of life poses additional factors for injury. This study aims to evaluate differences in mechanism of injury, location of injury and demographic patterns within the Amish population. We hypothesize that there will be an increased incidence of agriculture related mechanisms of injury.

METHODS: All Amish trauma patients who presented and were captured by the trauma registry at our Level II trauma center over 20 years (1/2000-1/2020) were analyzed. Our registry subsequently submits their data to the Pennsylvania Trauma Outcome Study (PTOS) database. A retrospective chart review was subsequently performed. Mechanism and geographic location of injury were collected for this subset of patients. Demographic and clinical variables were compared between the age groups.

RESULTS: There were 1,740 patients included in the study with 36.4% (n= 634) ≤14 years. Only 10% (n=174) were ≥65 years. The most common mechanism across all ages was falls. However, when separating out the pediatric population (≤14 years), 27.8% (n=60) fell from a height on average >8-10 feet. The most common geographic location of injury was at home in all age groups, except for the 15-24 year group which was roadways. Table 1 demonstrates the most common mechanisms by age group. Table 2 demonstrates the most common geographic locations by age group.

CONCLUSION: The Amish population poses a unique set of mechanisms of injury and thus injury patterns to rural trauma centers. We have found the most common injuries to be falls, buggy accidents, animal-related injuries and farming across all age groups. Future research and collaboration with other rural trauma centers treating large Amish populations would be beneficial to maximize injury prevention in this population.

<1 year 1-4 years 5-9 years 10-14 years 15-24 years 25-34 years 35-44 years 45-54 years 55-64 years 65+ years All Ages (n=18) (n=261) (n=181) (n=174) (n=399) (n=174) (n=126) (n=113) (n=120) (n=174) (n=1740)

Rank 1 Fall Fall Fall Fall Fall Fall Fall Fall Fall Fall Fall 2 Buggy Buggy Animal Animal Buggy Buggy Buggy Buggy Buggy Buggy Buggy 3 Other Animal Buggy Farming Construction Animal Farming Farming Animal MVC Animal 4 n/a Sports Farming Buggy Animal Farming Animal Animal Stab/Cutting Animal Farming 5 n/a Other Pedestrian Stab/Cutting Farming Construction Construction Other Farming Other Other

Table 1. Top Five Leading Causes of Trauma in the Amish Population. (Animal- kick, strike, crush)

<1 year 1-4 years 5-9 years 10-14 years

15-24 years

25-34 years

35-44 years

45-54 years

55-64 years 65+ years All Ages

(n=18) (n=261) (n=181) (n=174) (n=399) (n=174) (n=126) (n=113) (n=120) (n=174) (n=1740) Rank

1 Home Home Home Home Street Home Home Home Home Home Home 2 Street Street Street Street Home Street Street Street Street Street Street 3 Unspecified Unspecified Unspecified Unspecified Unspecified Work Unspecified Unspecified Unspecified Unspecified Unspecified 4 Barn Barn Barn Farm Work Unspecified Farm Work Work Work Work 5 n/a Work School Barn Farm Farm Work Barn Farm/Barn Business Farm

Table 2. Top Five Leading Locations of Trauma in the Amish Population.

SUBDURAL HEMATOMA IN THE SUPER ELDERLY: MODERATE RANGE GCS PORTENDS POOR PROGNOSIS

Presenting Author: Rachel Appelbaum, MD

Lehigh Valley Hospital - Cedar Crest

Objectives: The purpose of this study was to evaluate the outcomes of super elderly patients, age greater than 80 years old, with isolated subdural hematomas (SDH) who presented with varying mentation as measured by Glasgow Coma Scale (GCS). We hypothesized that super elderly patients with a SDH and a GCS < 8 would have an overall poor prognosis (i.e. hospital, 30- and 60- day mortality, increased level of care at discharge). Methods: A retrospective chart review was conducted using the Trauma database out of our level 1 trauma center from December 2012 to December of 2017. The trauma database was evaluated for patients 80 years and older with an isolated SDH. To determine the relationship between risk factors and poor prognosis we built a multivariate logistic regression model. Results: Our trauma registry included 617 patients 80 years and older for this time period. The population was primarily Caucasian and the male to female gender was evenly distributed. The study group included 450 (72.9%) in the 80-89 group, 164 (26.6%) 90-99, and 3 (0.5%) of patients who were 100 years or older. The majority of the patients had an arrival and pre-trauma GCS of 13-15 and a moderate or serious injury based on ISS. Across all analyses, several factors emerged as dominant trends with poor prognosis and increased in-hospital mortality including GCS < 8, severe ISS, and warfarin. GCS < 8 had an Odds Ratio (OR) of 41.984 (95% CI 18.160-97.064) with p<0.001 and GCS 8-12 had an OR of 9.069 (95% CI 3.769-21.817) with p<0.001. Severe ISS (25-49) had an OR of 11.333 (95% CI 5.072-25.326) with p<0.001. Among the multivariate analysis for in-hospital mortality, GCS < 8 again was seen as a prognostic factor, OR 27.001 (95% CI 7.436-98.048) with p<0.001. The same trends for GCS < 8, severe ISS, and warfarin use were seen for the 30 - and 60 - day univariate and multivariate analysis of mortality. Conclusion: As the US population ages, there is an increase in the number of geriatric patients requiring trauma care; however, the current "one size fits all" approach often neglects the special issues of the older adult. Across all analyses, several factors emerged as dominant trends including GCS < 8, severe ISS, and warfarin use. We hope that earlier, accurate prognoses will lead to improved discussion of goals of care and palliation when deemed fit. Author(s) • Rachel Appelbaum, MD, Department of Surgery, Wake Forest Baptist Health, Winston-

Salem, NC 27103 • Matelin Crosen, MD, Department of Surgery, Lehigh Valley Health Network, Allentown, PA • Devon Haggerty, Research Scholar, Lehigh Valley Health Network, Allentown, PA 18103 • Jill Krystofinski, CRNA, Lehigh Valley Health Network, Allentown, PA 18103 • Malia Eischen, MD, Department of Surgery, Queens Medical Center, Honolulu, HI 96813 • Joseph Stirparo, MD, LVHN Muhlenberg Trauma Medical Director, Department of Surgery,

Lehigh Valley Health Network, Allentown, PA 18103

• Robert Barraco, MD, MPH, Department of Surgery, Chief Academic Officer, Lehigh Valley Health Network, Allentown, PA 18103

• Rovinder Sandhu, MD, Vice Chair, Department of Surgery, Quality and Patient Safety Surgery, Program Director Surgical Critical Care Fellowship, Lehigh Valley Health Network, Allentown, PA 18103

Presenting Author Rachel Appelbaum, MD, Department of Surgery, Wake Forest Baptist Health, Winston-Salem, NC 27103, USA [email protected]



Tracheostomy Dislodgement: Are obese patients at increased long-term risk?

Authors: Ryan Wan DO1, Courtney Docherty DO1, Hamza Bhatti DO1, Hannah Shin DO1, Chelsea Spector MD2, Brian Thai BS1, Alison Muller, MLS, MSPH3, Anthony Martin, RN, BSN3, Adrian Ong, MD3 1Department of Surgery, Philadelphia College of Osteopathic Medicine 2Drexel University College of Medicine 3Department of Surgery, Reading Hospital, Tower Health System

Objective: Unplanned tracheostomy decannulation or dislodgement (DD) can be a life-threatening event that has not been studied well. Obesity has been associated with tracheostomy complications. We hypothesized that obese patients have increased long-term risk of DD.

Methods: Patients undergoing tracheostomies from 2013-19 were reviewed retrospectively. Those who died or transferred out on/before postoperative day one were excluded. The primary outcome was DD within 12 months of tracheostomy. Body mass index (BMI) and Skin-To-Trachea distance (STT), defined as the distance between the anterior tracheal wall and the skin measured on computed tomography were recorded. The probability of DD was calculated using Kaplan-Meier (K-M) estimations with right censoring and the log-rank test was used to compare groups. A p-value of <0.05 was deemed significant.

Results: A total of 212 patients with 220 tracheostomies were included. The median age was 62 (interquartile range [IQR], 52-71) years, and 59% were male. Sixty-six (31%) had a BMI of > 35 kg/m2 and 14 (7%) had a STT of > 80 mm. BMI was associated with STT >80 mm (≤35 kg/m2 vs. > 35 kg/m2, 0.7% vs 21%, p<0.0001). Extended length tracheostomy tubes were more likely used if BMI was > 35 kg/m2 (26% vs. 4%, p<0.0001) and if STT was > 80 mm (33% vs 4%, p<0.0001). The median duration of tracheostomy was 29 (IQR 14-95) days. Fifty-three episodes of DD occurred in 31 (14%) patients, with 14 having > 1 DD. Of the 53 episodes, 22 (42%) occurred after discharge and 13 (42%) patients required an operation. One death was attributed to DD in a nursing home. Rates of DD were similar for BMI > 35 kg/m2 vs ≤ 35 kg/m2 (20% vs. 13%, p=0.2), but higher for STT> 80 mm vs. ≤ 80 mm (43% vs. 12%, p=0.007). K-M estimations of risk of DD were similar for BMI> 35 kg/m2 vs. ≤ 35 kg/m2 (p=0.6) but increased for STT of > 80 mm vs. ≤ 80 mm (p<0.0001).

Conclusion: DD occurred in 14% of patients within a year of tracheostomy, often after discharge with serious morbidity/mortality. Although STT correlated with BMI in this cohort, 12-month risk of DD was associated with a STT of > 80 mm but not BMI.

Presenting author: Ryan Wan, DO

Tracheostomy Dislodgement: Are obese patients at increased long-term risk?

Ryan Wan DO1, Courtney Docherty DO1, Hamza Bhatti DO1, Hannah Shin DO1, Chelsea Spector MD2, Brian Thai BS1, Alison Muller, MLS, MSPH3, Anthony Martin, RN, BSN3,

Adrian Ong, MD3

1Department of Surgery, Philadelphia College of Osteopathic Medicine 2Drexel University College of Medicine 3Department of Surgery, Reading Hospital, Tower Health System

Objective: Unplanned tracheostomy decannulation or dislodgement (DD) can be a life-threatening event that has not been studied well. Obesity has been associated with tracheostomy complications. We hypothesized that obese patients have increased long-term risk of DD.

Methods: Patients undergoing tracheostomies from 2013-19 were reviewed retrospectively. Those who died or transferred out on/before postoperative day one were excluded. The primary outcome was DD within 12 months of tracheostomy. Body mass index (BMI) and Skin-To-Trachea distance (STT), defined as the distance between the anterior tracheal wall and the skin measured on computed tomography were recorded. The probability of DD was calculated using Kaplan-Meier (K-M) estimations with right censoring and the log-rank test was used to compare groups. A p-value of <0.05 was deemed significant.

Results: A total of 212 patients with 220 tracheostomies were included. The median age was 62 (interquartile range [IQR], 52-71) years, and 59% were male. Sixty-six (31%) had a BMI of > 35 kg/m2 and 14 (7%) had a STT of > 80 mm. BMI was associated with STT >80 mm (≤35 kg/m2 vs. > 35 kg/m2, 0.7% vs 21%, p<0.0001). Extended length tracheostomy tubes were more likely used if BMI was > 35 kg/m2 (26% vs. 4%, p<0.0001) and if STT was > 80 mm (33% vs 4%, p<0.0001). The median duration of tracheostomy was 29 (IQR 14-95) days. Fifty-three episodes of DD occurred in 31 (14%) patients, with 14 having > 1 DD. Of the 53 episodes, 22 (42%) occurred after discharge and 13 (42%) patients required an operation. One death was attributed to DD in a nursing home. Rates of DD were similar for BMI > 35 kg/m2 vs ≤ 35 kg/m2 (20% vs. 13%, p=0.2), but higher for STT> 80 mm vs. ≤ 80 mm (43% vs. 12%, p=0.007). K-M estimations of risk of DD were similar for BMI> 35 kg/m2 vs. ≤ 35 kg/m2 (p=0.6) but increased for STT of > 80 mm vs. ≤ 80 mm (p<0.0001).

Conclusion: DD occurred in 14% of patients within a year of tracheostomy, often after discharge with serious morbidity/mortality. Although STT correlated with BMI in this cohort, 12-month risk of DD was associated with a STT of > 80 mm but not BMI.

Presenting author: Ryan Wan, DO

Documentation of Cognitive Rest Following Concussion by Pediatric Primary Care Providers

Susan Butler MSN, FNP-BC, Cynthia Dimovitz, MSN, CPNP, Temilolaoluwa Daramola, RN, BS, MS, Sahin Becirovic, BS, Louis Mancano, MD, Adrian Ong, MD, & Alison Muller MLS(ASCP) MSPH

Presenting Author: Susan Butler, MSN, FNP-BC, CRNP

Reading Hospital/Tower Health, Reading PA

Introduction: Pediatric Primary Care Physicians (PPCP) often provide the initial assessment of patients with concussion. PPCPs also guide ongoing concussion management which includes both cognitive and physical rest until all acute symptoms resolve. While return to play can be closely managed as an activity progression by coaches and parents, cognitive rest is more difficult to define and monitor. Cognitive rest requires limiting activities such as video games, social media, return to school and driving. Several studies have shown that while PPCPs have adequate knowledge of concussion management, there are gaps in the documentation upon discharge for cognitive rest and specifics regarding return to school. The goal of this study was to identify if there were gaps in provider documentation for elements of cognitive rest, looking specifically at the plan of care (POC) and discharge instructions (DCI). We also sought to assess the impact of the use of concussion documentation tools (CDTs) on completeness of documentation. Our hypothesis was that the use of CDTs by PPCPs improved documentation in the POC and DCI with respect to cognitive rest.

Methods: A retrospective chart review was completed of patients aged 5 to 18 with a diagnosis of concussion that presented to the Tower Health Medical Group Pediatric Practices from 1/1/2014 through 12/31/2017. Subcategories of cognitive rest were planned rest (no school, shortened school day, late start, rest periods), reduced exposure to electronics, reduced lights and noise, extra time for assignments and tests, and driving restrictions. For each subcategory, the presence or lack of documentation in both POC and DCI was recorded. The use of a CDT was also recorded. Differences between documentation in the POC and DCI when a CDT was used versus not used was determined using a chi-square test of independence. A p-value of < 0.05 was considered significant.

Results: A total of 144 patient charts were reviewed. 72 (50%) were female and median (interquartile range) age was 14 (5-18) years. The majority of patients (66%) sustained concussion due to sports. 102 (71%) were below driving age and hence excluded from the analysis of driving restrictions. Thirty-one percent of providers adopted a CDT, most commonly the Acute Concussion Evaluation tool. Use of a CDT was associated with improved documentation in the POC for planned rest (89% vs 71%, p<0.0001), reduced exposure to electronics (78% vs 31%, p<0.0001), reduced light and noise (49% vs 15%, p<0.0001), extra time for homework or tests (67% vs 27%, p<0.0001) but not driving restrictions (10% vs 0%, p=0.4). Similarly, use of CDT was associated with improved documentation in the DCI for planned rest (95% vs 41%, p<0.0001), reduced exposure to electronics (68% vs 13%, p<0.0001), reduced light and noise (68% vs 12%, p<0.0001), extra time for homework or tests (82% vs 19%, p<0.0001) but not driving restrictions (20% vs 3%, p=0.1).

Conclusion: While only a minority of PCPPs adopted an evidence-based CDT in their practice, these PCPPs were more likely than their counterparts not using a tool to incorporate vital recommendations in their documentation. Completeness of documentation of elements of cognitive rest likely enhances understanding and compliance by patients, parents, and provides schools with clear concussion management plans.

24th Annual PaCOT/PTSF Conference & Meeting

CALL FOR RESEARCH October 19-21, 2020 ▪ An Online “Virtual” Event

1

▪ ABSTRACT DETAILS ▪

1. The details of your abstract for oral podium/ poster submissions must include the following:

DIRECT TO OR RESUSCITATION: A REEVALUATION 30 YEARS AFTER IMPLEMENTATION OBJECTIVES: The highest level of trauma team activation at our institution, a Code Red, involves direct delivery of the patient into the operating room. This study was undertaken to compare outcomes between patients arriving as a Code Red to those evaluated in the trauma bay and were upgraded to the operating room. Objectives of this study include: Review mortality and morbidity of direct to operating room resuscitation (Code Red) as compared to those evaluated in the trauma bay prior to operating room upgrade (upgrade to Code Red). METHODS: This is a retrospective chart review at a Level 1 trauma center which began direct to OR resuscitation in 1988. Patients from January 1, 2008 to December 31, 2015 treated by the trauma service arriving as a direct to OR resuscitation (Code Red) or were upgraded to OR (upgrade to Code Red) were included for analysis. The primary endpoint was in-hospital mortality. The secondary endpoint was morbidity. The outcomes are reported as an odds ratio which is adjusted for potential confounders using multiple logistic regression. Additional sub-group analysis using univariate logistic regression was performed regarding number of operative interventions. RESULTS: 680 patients met inclusion criteria (585 Code Red, 95 upgrade). Patients upgraded to Code Red were older (48 vs 31 years old, p<0.001) and more severely injured (ISS 26 vs 14, p<0.001). Multivariate results demonstrated code red status did not have a statistically significant relationship with mortality (odds ratio 2.345 [95% CI 0.712-7.729], p 0.161) when adjusted for age, GCS, hospital LOS, ICU LOS, ISS and OR required status. For patients requiring emergent operative intervention, Code Red status had no effect on mortality (OR 1.880 [95% CI 0.33-10.709], p 0.477) when adjusted. There was a statistically significant difference in the number of overall operative interventions with those in the upgrade to OR receiving more interventions (3 vs 1, p <0.001). Figure 1: Demographics Code red (n=585) Upgrade to code red

(n=95) p-value

Age (median, IQR) 31 (23-47) 48 (28-68) <.001 Gender (n,%) .148



2

Male 469 (80.2%) 70 (73.7%) Female 116 (19.8%) 25 (26.3%) GCS on admission 15 (3-15) 11 (3-15) .002 Mortality <.001 Yes 122 (20.9%) 36 (37.9%) No 463 (79.1%) 59 (62.1%) Disposition Home 262 (44.8%) 17 (17.9%) Acute rehab 78 (13.3%) 20 (21.1%) Long term care 37 (6.3%) 10 (10.5%) Police custody 24 (4.1%) 2 (2.1%) Hospice 3 (0.5%) 1 (1.1%) Morgue 122 (20.9%) 36 (37.9%) Other 59 (10.1%) 9 (9.5%) Hospital LOS 4 (1-12) 7 (1-21) .047 ICU LOS 1 (0-5) 3 (1-15) .001 ISS on admission 14 (5-29) 26 (17-33) <.001 Operative intervention (OI)

<.001

Yes 449 (76.8%) 89 (93.7%) No 136 (23.2%) 6 (6.3%) Number of OIs 1 (1-3) 3 (1-4) <.001 OI require OR .053 Yes 238 (40.7%) 57 (60.0%) No 212 (36.2%) 32 (33.7%) N/A 135 (23.1%) 6 (6.3%) Initial lactate 4.3 (2.4-9.8) 3.9 (2.5-8.4) .622

Univariate logistic regression: Mortality

Clinical features p-value Odds ratio Exp(B) 95% Confidence Interval

Status Code red reference Upgraded to code red <.001 2.316 1.462 3.668

Age One-year increase <.001 1.022 1.013 1.031 Gender Male reference Female .343 1.229 .802 1.882



3

GCS One-point increase <.001 .748 .715 .783 Hospital LOS One-day increase <.001 .790 .741 .843 ICU LOS One-day increase <.001 .901 .863 .939 ISS One-point increase <.001 1.048 1.037 1.060 Initial lactate One-point increase <.001 1.002 1.001 1.002 OR required No reference Yes .038 1.532 1.023 2.294

Multivariate logistic regression: Mortality


Status Code red reference Upgraded to code red .161 2.345 .712 7.729

Age One-year increase .009 1.038 1.009 1.067 GCS One-point increase <.001 .757 .681 .841 Hospital LOS One-day increase <.001 .160 .078 .331 ICU LOS One-day increase <.001 4.667 2.299 9.475 ISS One-point increase <.001 1.104 1.060 1.149 OR required No reference Yes .05 3.299 1.001 10.869

Multivariate logistic regression: Mortality (OR required subgroup)



GCS One-point increase <.001 .701 .584 .841 Hospital LOS One-day increase <.001 .100 .033 .306 ICU LOS One-day increase <.001 6.708 2.394 18.797 ISS One-point increase <.001 1.249 1.127 1.384



4

Univariate logistic regression: Morbidity



CONCLUSION: Despite the fact that patients requiring an upgrade to the operating room were older, more severely injured and requiring more operative interventions there was no difference in mortality or morbidity as defined by the parameters of this study. Timely identification and movement of patients to an operating room provides similar outcomes of critically ill trauma patients.

2. Author(s):

Christian Pothering MD Lehigh Valley Health Network – Cedar Crest Level 1 Trauma Center Rovinder Sandhu MD FACS Lehigh Valley Health Network – Cedar Crest Level 1 Trauma Center Farina Klocksieben University of South Florida Morsani College of Medicine (Statistician) Joseph Stirparo MD FACS Lehigh Valley Health Network – Cedar Crest Level 1 Trauma Center

3. Presenting Author (if submitting abstract for Completed Research Award):

Christian Pothering MD Lehigh Valley Health Network – Cedar Crest Level 1 Trauma Center

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