1. Same Level Falls and TBIs in Older Adults: The Research Journey Linda J. Scheetz, EdD, RN, FAEN Lehman College and the Graduate Center City University of New York, NY, USA 1

2. Acknowledgement This study was funded by a grant from Lehman College, City University of New York 2

3. My Research Journey Early studies evaluated undertriage of older injured adults NJ hospital discharge data Results – NJ counties with TCs 18% older men undertriaged 15% older women undertriaged – NJ statewide 40% older men and women undertriaged 3

4. My Research Journey National study of undertriage of MVC patients comparing undertriage before and after ACS field triage guidelines changed in 2006 NASS CDS study of patients 55 years and older Results – 2004: 42% undertriaged – 2008: 22% undertriaged 4

5. My Research Journey 2010 study compared AIS 3, 4, 5 injuries in patients 65 years and older who were correctly triaged with those who were undertriaged NASS CDS data (motor vehicle injuries) Brain injuries were the most commonly undertriaged injury (31.1% of all injuries) 5

6. Background for Today’s Study All cause trauma incidence is lower for older adults compared to young and middle-aged adults (NTDB, 2014) All cause case fatality rates are highest among older adults (NTDB, 2014) Falls and MVCs account for most trauma incidents in older adults (NTDB, 2014) 6

7. Background Falls are the leading cause of fatal (54.1%)and nonfatal (62.5%) injuries among older adults, (CDC, 2012) 1 in 3 people age 65 and older falls each year (CDC, 2012) 20-30% sustain moderate to severe injuries that decrease their functional ability (CDC, 2012) At all ISS levels, fatality rates are higher for older adults (NTDB, 2014) 7

8. Background 8

9. Falls are the most common cause of TBIs among older adults (CDC, 2014) The number of TBIs among older adults has increased sharply in the past decade (CDC, 2014) 9

10. 10 TBI Incidence from Falls

11. Study Purpose This study examined the epidemiology of fall injuries among older adults who sustained TBIs from same level falls Specific aims: Identify incidence and type of TBIs Identify additional injuries Identify predictors of LOS, mortality, TC admission 11

12. Significance Same level falls are low energy injuries The potential for serious injury may be overlooked by bystanders, EMS responders and ED staff The potential for serious, life-threatening injuries must be considered 12

13. Methods Sample – Records extracted from the Healthcare Cost & Utilization Project N.Y. State Inpatient Database (HCUP SID), Agency for Healthcare Research and Quality – Inclusion criteria Age 65 years and older Primary E-code of same level fall Primary diagnosis of traumatic brain injury 13

14. Outcome Variables Short-term mortality Hospital length of stay Trauma center admission 14

15. Predictor Variables Sociodemographic TBI severity Number of diagnoses Chronic diseases Primary and secondary payers Patient residency location (urban, rural, etc.) Major surgery (LOS model) LOS (mortality LR model) TC admission (mortality model) 15

16. Data Analysis PASW (SPSS) Statistics 18 Descriptive analysis of sample Chi square analysis – Fall mechanism and type of TBI – Fall mechanism and injury outcomes Logistic regression to identify predictors of: – LOS – Short-term mortality – TC admission 16

17. Incidence 57,196 patients with primary injury mechanism of fall 20,876 patients who experienced same level fall (36.5%) 3,331 patients with same level fall and TBI – 5.82 % of all patients who fell and were hospitalized – 16.0% of all patients who had same level fall and were hospitalized 17

18. Results – Sample Description 3,331 patients admitted for TBI (same level fall) 52.6% (n=1752) were female Mean age = 81.1 years (SD = 8.1) Median LOS = 5 days Mean number of chronic conditions = 4.5 (SD 2.2) 347 persons (10.4%) died 18

19. 19

20. Results: Additional Injuries Hundreds of additional injuries – Additional TBIs (diagnoses 2-15) – Fractures of the facial bones, upper and lower extremities, hip, pelvis, vertebral column (with and without SCI), ribs, and sternum – Strains, sprains, contusions with skin intact – Open wounds – Hemothorax, pneumothorax 20

21. Predictors of Mortality To select variables for the logistic regression model, all sociodemographic, clinical and comorbidity variables were individually evaluated for their significance in predicting mortality Variables that individually predicted mortality (p=/<.05) were added to the LR model Model fit was verified 21

22. Results: Predictors of Mortality 22 PredictorsO.R.InterpretationC.I. Lymphoma2.788279% more likely to die1.233 - 6.304 Brain injury severity 2.608261% more likely to die1.602 - 4.245 Weight loss2.483248% more likely to die1.491 - 4.137 Metastatic cancer2.336234% more likely to die1.222 - 4.468 Solid tumor cancer 2.114211% more likely to die1.131 - 3.953 Coagulation disorder 1.993Twice as likely to die1.321- 3.009 CHF1.55255% more likely to die1.134-2.124 TC admission1.48849% more likely to die1.174-1.885 Age1.0323% more likely to die1.016-1.047

23. Predictors of LOS To select variables for the logistic regression model, all sociodemographic, clinical and comorbidity variables were individually evaluated for their significance in predicting LOS Variables that individually predicted LOS (p=/<.05) were added to the LR model Model fit was verified 23

24. Results: Predictors of LOS 24 PredictorsO.R.InterpretationC.I. Race, Black*major surgery 9.090 Nine times more likely longer LOS 4.604 - 17.949 Major surgery5.807 Nearly 6 times more likely longer LOS 4.673 - 7.216 Race, Black2.066Twice as likely longer LOS1.504 - 2.837 Race, Other, mixed 1.59059% more likely longer LOS1.100 - 2.297 Number diagnoses 1.369 Each additional diagnosis, 37% more likely longer LOS 1.320 - 1.420 Age1.012Each year older, 1% more likely longer LOS 1.002 - 1.022

25. Predictors of TC Admission To select variables for the logistic regression model, all sociodemographic, clinical and comorbidity variables were individually evaluated for their significance in predicting TC admission Variables that individually predicted TC admission (p=/<.05) were added to the LR model Model fit was verified 25

26. Results: Predictors of TC Admission 26 PredictorsO.R.InterpretationC.I. Medicaid5.803Nearly 6 times as likely1.555 - 21.659 Wealthiest quartile3.913Nearly 4 times as likely3.072 - 4.985 Other insurance2.782Nearly 3 times as likely1.439 - 5.381 2nd wealthiest quartile2.205More than twice as likely1.730 - 2.810 Race, mixed1.909Nearly twice as likely1.274 - 2.860 Location, 10-50k pop1.76376% more likely1.230 - 2.527 Hispanic ethnicity1.60160% more likely1.149 - 2.232 Age, 75-841.58258% more likely1.074 - 1.518 2nd poorest quartile1.56657% more likely1.226 - 1.998 Age, 65-741.27828% more likely1.033 - 1.582 Number diagnoses1.0455% more likely each diag1.012 - 1.079 Private insurance1.478Nearly 50% more likely1.014 - 2.153

27. Discussion Brain atrophy and other neuro changes predispose older persons to TBI Prehospital identification of TBIs difficult – 48 % transported to NTCs Age and chronic diseases contribute to frailty Mortality and LOS increase as age increases TC admits decrease as age increases Further investigation needed re: sociodemographic factors 27

28. Implications for ED Nurses Maintain high index of suspicion for TBI Detailed history on all older fall patients Serial neurological assessments Appropriate diagnostic workup Monitor effectiveness of ED triage protocols Consider developing protocol for evaluation and treatment of older fall patients Clear and written discharge instructions, verify understanding 28

29. The Journey Continues My current study examines time to deterioration of the GCS – Study collaborators are Michael Horst and Richard Arbour – We are about to begin data analysis! 29

30. References Centers for Disease Control and Prevention, Falls Among Older Adults: An Overview, 2013. Retrieved from: http://www.cdc.gov/HomeandRecreationalSafety/Falls/a dultfalls.html http://www.cdc.gov/HomeandRecreationalSafety/Falls/a dultfalls.html Centers for Disease Control and Prevention, WISQARS. Retrieved from: http://www.cdc.gov/injury/wisqars/index.html http://www.cdc.gov/injury/wisqars/index.html American College of Surgeons, National Trauma Data Bank Report. Retrieved from: https://www.facs.org/~/media/files/quality%20programs /trauma/ntdb/ntdb%20annual%20report%202014.ashx 30

31. References World Health Organization, WHO Global Report on Falls Prevention in Older Age, 2007. Available: http://www.who.int/ageing/publications/Falls_preve ntion7March.pdf http://www.who.int/ageing/publications/Falls_preve ntion7March.pdf Healthcare Cost and Utilization Project (HCUP), State Inpatient Databases, Agency for Healthcare Research and Quality (AHRQ), 2013. Available: http://www.hcup-us.ahrq.gov/sidoverview.jsp http://www.hcup-us.ahrq.gov/sidoverview.jsp 31