Over the past four decades, the geriatric population in the United States has risen significantly and is estimated that it will comprise up to 24% of the total US population by 2060.1 2 frailty , defined as a state of decreased physiologic reserve and loss of resistance to stressors, is becoming increasingly prominent among trauma surgeons, and has proven to be a valuable tool in predicting the outcomes of geriatric trauma patients.3
Physicians may offer interventions, such as ventilator support, renal support, medications to support cardiac function, or hemorrhage control surgery as a lifesaving measure. However, some geriatric patients and their families may not choose to opt for such heroic life-sustaining measures. The severity of the situation often complicates this decision and may make it difficult for the family members to make a substituted judgment.4
Withdrawal of life supporting treatment (WLST) has been well established in oncology but has not been extensively studied in geriatric trauma . Understanding the factors predicting WLST is important to improve overall care in geriatric trauma patients. Simply stated, WLST too early in the hospital course may lead to a missed opportunity to improve a patient's recovery, while, conversely, trying to continue life supporting treatment in severely injured trauma patients may not provide a good quality of life despite survival.5 frailty in the WLST in geriatric trauma patients.
METHODS
Study Design and Population
We performed a 3-year (2017–2019) retrospective analysis of the American College of Surgeons-Trauma Quality Improvement Program (ACS-TQIP). This study was exempted from the University Institutional Review Board approval since the ACS-TQIP contains only de-identified data.
Inclusion and Exclusion Criteria
We included all severely injured (Injury Severity Score [ISS] > 15) geriatric trauma patients (≥65 years). We identified frail patients using the 11-variable modified frailty index (mFI-11).6 frailty index >0.27 were considered frail. Flow diagram of patients included in the final analysis is included in the Supplemental Digital Content 1 (SDC 1, https://links.lww.com/TA/C931
Data Points
Demographics (age, sex, and race), injury parameters (mechanism of injury, ISS, each body region Abbreviated Injury Score [AIS]), emergency department (ED) vital parameters (systolic blood pressure [SBP], heart rate, Glasgow Coma Scale [GCS] score), comorbidity status, frailty index (by using the 11-factor modified frailty index), presence of advance directive limiting care (ADLC), transfusion parameters (packed red blood cells, platelets, and fresh frozen plasma), insurance status, ventilator requirement, total ventilator days, intensive care unit length of stay (LOS), hospital LOS, in-hospital complications, WLST, and mortality were collected and recorded.
ACS-TQIP database tracks the primary method of payment in the following categories: Medicaid, Medicare, other government insurance, self-pay, private/commercial insurance. Medicare, Medicaid, and other government insurance were included under the variable “government insurance.” Patients coded “self-pay” were considered as uninsured patients. Patients with private/commercial insurance were set as the reference in the regression model.
The ACS-TQIP provides the variable “WithdrawalLST” which identifies the decision of WLST in patients if the decision was documented in the medical record. Withdrawal of life supporting treatment also includes a limit to escalation of treatments such as ventilator support, dialysis, medications to support blood pressure or cardiac function, and specific procedures.
Patient Stratification
Patients were stratified based on WLST into two groups: those with WLST and those without WLST (No-WLST).
Missing Data Analysis
Nearly 8% of the patients that underwent WLST had missing data regarding time to WLST. Missing data were treated as missing completely at random and analyzed using Little’s missing completely at random test. Multiple imputations were performed using the Markov Chain Monte Carlo method to reduce bias and preserve sample size.
Statistical Analysis
We performed tests of normality including the Shapiro-Wilk test, the Kolmogorov-Smirnov test, visual inspection of the histogram for continuous variables, and Q-Q plots to assess alignment with normal theoretical quantiles. Continuous parametric data were reported as means (with standard deviations), continuous nonparametric data were reported as medians (with interquartile ranges), and categorical data were reported as proportions. To analyze the differences between two groups on a univariable level, we used the χ2 tests for categorical variables, the Mann-Whitney U tests for continuous nonparametric data, and the Student's t tests for continuous parametric data.
A multivariable logistic regression (MLR) model was used to perform the analysis to determine the predictors of WLST, while adjusting for measurable confounding variables. We performed a univariable analysis to assess the association between each potential dependent variable and the binary outcome. All the binary variables with a p < 0.2 on univariable analysis were then included in the MLR model. On MLR analysis, variables were considered significant at p < 0.05.
In our study, alpha was set at 5%, and p < 0.05 was considered statistically significant. All the analyses were performed using the Statistical Package for Social Services (SPSS, version 28; SPSS, Inc, Armonk, NY). We adhered to Strengthening the Reporting of Observational Studies in Epidemiology guidelines for reporting observational studies (please see Supplemental Digital Content 2 [SDC 2, https://links.lww.com/TA/C932
RESULTS
A total of 155,583 severely injured geriatric trauma patients were identified. The mean (±standard deviation [SD]) age was 77 ± 7 years, 54.8% were male, 84.1% were White, and 97% sustained blunt injuries. The mean (±SD) ED SBP was 146 ± 33 mm Hg, mean (±SD) ED pulse rate was 83 ± 20 beats per minute, and median [interquartile range {IQR}] ED GCS score was 15 [14–15]. The median [IQR] ISS was 17 [16–25], head AIS was 4 [4], thorax AIS was 0 [0–1], abdomen AIS was 0 [0–0], and extremity AIS was 0 [0–2], and 2,542 (1.6%) patients underwent hemorrhage control surgery. Overall, 16,812 (10.8%) patients underwent WLST, and the median [IQR] time to WLST was 2.4 days [0.6–6.8 days].
Patients that underwent WLST were older, male, White, had lower mean ED SBP, median ED GCS score, and were more likely to have ADLC (p < 0.05). Patients in the WLST cohort also had higher rates of penetrating injuries, higher ISS, were more likely to be treated at higher level trauma centers and had higher rates of 24-hour and overall mortality when compared with the no-WLST cohort. The detailed comparison of the baseline characteristics of WLST and No-WLST cohorts are given in Table 1 .
TABLE 1 -
Comparison of the Study Population
Variables
WLST (n = 16,812)
No-WLST (n = 139,041)
p
Demographics
Age: mean ± SD, y
78 ± 7
77 ± 7
<0.001
Male, n (%)
10,451 (62)
74,892 (54)
<0.001
White, n (%)
14,401 (86)
116,626 (84)
<0.001
Vital signs in ED
SBP, mean (SD), mm Hg
143 ± 39
147 ± 33
<0.001
HR, mean (SD), bpm
87 ± 25
82 ± 20
<0.001
GCS, median [IQR]
8 [3–14]
15 [14–15]
<0.001
Injury parameters
Blunt MOI, n (%)
15,925 (95)
135,227 (97)
<0.001
ISS, median [IQR]
24 [17–26]
17 [16–21]
<0.001
Head-AIS ≥ 3, n (%)
15,819 (94)
121,925 (88)
<0.001
Thorax-AIS ≥ 3, n (%)
2,765 (16)
18,479 (13)
<0.001
Abdomen-AIS ≥ 3, n (%)
714 (4)
4,499 (3)
<0.001
Extremity-AIS ≥ 3, n (%)
1,059 (6)
13,761 (10)
<0.001
Comorbidities
Hypertension, n (%)
10,204 (61)
91,090 (66)
<0.001
Diabetes mellitus, n (%)
4,473 (27)
37,610 (27)
0.221
COPD, n (%)
1,937 (12)
14,458 (10)
<0.001
CHF, n (%)
1,891 (11)
11,495 (8)
<0.001
CKD, n (%)
778 (5)
4,639 (3)
<0.001
Liver disease, n (%)
357 (2)
1,729 (1)
<0.001
Alcohol use disorder, n (%)
727 (4)
6,133 (4)
0.605
Smoking, n (%)
1,123 (7)
11,018 (8)
<0.001
Frailty , n (%)3,462 (21)
25,599 (18)
<0.001
ACS trauma center verification level
<0.001
Level I, n (%)
8,707 (52)
59,075 (43)
Level II, n (%)
4,405 (26)
34,897 (25)
Level III and lower, n (%)
3,700 (22)
45,069 (32)
Hemorrhage control surgery, n (%)
576 (3)
1,966 (1)
<0.001
Ventilator requirement, n (%)
13,383 (80)
22,714 (16)
<0.001
HR, heart rate; MOI, mechanism of injury; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; CHF, congestive heart failure.
On multivariable regression analysis for the predictors of WLST, every 5-year increase in age was independently associated with a nearly 35% increase in the odds of WLST. In addition to age, male gender, White race, presence of advance directive limiting care (ADLC), severe traumatic brain injury (TBI), frailty , ventilator requirement, government insurance, and treatment at higher level trauma centers were found to be independently associated with WLST. The predictors of WLST in geriatric trauma patients are summarized in Table 2 .
TABLE 2 -
Independent Predictors of WLST in
Geriatric Trauma Patients
Variable
aOR
95% CI
p
Age (every 5-y increase)
1.35
1.33–1.37
<0.001
Male gender
1.14
1.09–1.18
<0.001
White race
1.44
1.36–1.52
<0.001
Advance directive limiting care 2.55
2.40–2.70
<0.001
Severe TBI
1.80
1.66–1.95
<0.001
Frailty 1.42
1.34–1.50
<0.001
Ventilator requirement
12.73
12.09–13.39
<0.001
Government insurance
1.27
1.20–1.33
<0.001
ACS trauma center verification level
Level I
1.49
1.42–1.57
<0.001
Level II
1.43
1.35–1.51
<0.001
Level III or lower
Reference
—
—
Variables controlled in the regression: age, sex, race, emergency department SBP, Glasgow coma score, frailty , mechanism of injury, injury severity score, head abbreviated injury scale score, advance directive limiting care , ventilator requirement, hemorrhage control surgery type, insurance status, and ACS trauma center verification level.
With every 5-year increase in age beyond 69 years, there was a significant increase in the odds of WLST, with patients 85 years or older having a nearly 340% increase in the odds of WLST compared with patients aged 65 to 69 years. The association between age and WLST is shown in Figure 1 .
Figure 1: The independent effect of increasing age on WLST.
To confirm the predictive ability of the variables, we performed sensitivity subanalyses by selecting patients aged 65 years to 69 years, 70 years to 74 years, 75 years to 79 years, 80 years to 84 years and 85 years or older, and found that all the significant predictors identified on the overall regression maintained the significance in every age group. We also performed sensitivity subanalyses by selecting patients based on the level of trauma centers, and frailty status, and the predictors remained significant in each of the subanalyses, except government insurance, which maintained the trend but was not statistically significant in the frail group. Level I trauma centers were independently associated with WLST in both frail (adjusted odds ratio [aOR], 1.55; confidence interval [CI],1.39–1.72) and nonfrail (aOR, 1.49; 95% CI, 1.30–1.48) patients. The sensitivity analysis is provided as Supplemental Digital Content 3 (SDC 3, https://links.lww.com/TA/C933
Frail patients had higher rates of WLST compared with nonfrail patients (11.9% vs. 10.5%; p < 0.001). The median [IQR] time to WLST was longer in frail patients compared with nonfrail patients (3 [1–8] vs. 2 [0.5–7] days; p < 0.001). Also, frail patients were more likely to have ADLC (17% vs. 7%; p < 0.001) compared with nonfrail patients.
The mortality in WLST group was 81% as compared with 5% in the no-WLST group. The outcomes between WLST and No-WLST groups are summarized in Table 3 . Overall, survivors were younger, male, White, had higher SBP, more likely to have sustained blunt injury, and had lower ISS, less hemorrhage control surgery, and lower rates of WLST when compared with nonsurvivors. The detailed comparison of the characteristics between survivors and nonsurvivors is given in Table 4 .
TABLE 3 -
Univariate Analysis of Outcomes in WLST vs. No-WLST Patients
Outcome
WLST (n = 16,812)
No-WLST (n = 139,041)
p
Hospital LOS: median [IQR], d
3.6 [0.9–8.9]
4 [1.9–7.9]
<0.001
ICU LOS: median [IQR], d
2 [0–6]
2 [0–4]
0.09
Ventilator days, median [IQR]
2 [1–5]
0 [0–0]
<0.001
Discharge to SNF or rehab, n (%)
210 (1.2)
57,974 (42)
<0.001
Discharge to hospice care, n (%)
2,111 (13)
4,112 (3)
<0.001
Overall mortality, n (%)
13,619 (81)
6,490 (5)
<0.001
ICU, intensive care unit; SNF, skilled nursing facility; Rehab, rehabilitation center.
TABLE 4 -
Comparison Between Survivors and Nonsurvivors
Variables
Survivors (n = 135,744)
Nonsurvivors (n = 20,109)
p
Demographics
Age, mean ± SD
77 ± 7
78 ± 7
<0.001
Male, n (%)
63,192 (47)
7,318 (36)
<0.001
White, n (%)
114,271 (84)
16,756 (83)
0.002
Vital signs in ED
SBP, mean (SD), mm Hg
147 ± 32
140 ± 40
<0.001
HR, mean (SD), bpm
82 ± 19
88 ± 24
<0.001
GCS, median [IQR]
15 [14–15]
8 [3–14]
<0.001
Injury parameters
Blunt MOI, n (%)
59,513 (44)
8,282 (41)
<0.001
ISS, median [IQR]
17 [16–21]
24 [17–27]
<0.001
Head-AIS ≥ 3, n (%)
119,314 (88)
18,430 (92)
<0.001
Thorax-AIS ≥ 3, n (%)
17,262 (13)
3,982 (20)
<0.001
Abdomen-AIS ≥ 3, n (%)
3,951 (3)
1,262 (6)
<0.001
Extremity-AIS ≥ 3, n (%)
13,233 (10)
1,587 (8)
<0.001
Comorbidities
Hypertension, n (%)
89,539 (66)
11,755 (59)
<0.001
Diabetes mellitus, n (%)
36,694 (27)
5,389 (27)
0.488
COPD, n (%)
14,012 (10)
2,383 (12)
<0.001
CHF, n (%)
11,128 (8)
2,258 (11)
<0.001
CKD, n (%)
4,418 (3)
999 (5)
<0.001
Liver disease, n (%)
1,652 (1)
434 (2)
<0.001
Alcohol use disorder, n (%)
6,003 (4)
857 (4)
0.300
Smoking, n (%)
10,775 (8)
1,366 (7)
<0.001
ACS trauma center verification level
<0.001
Level I, n (%)
57,754 (43)
10,028 (50)
Level II, n (%)
34,342 (25)
4,960 (25)
Level III and lower, n (%)
43,648 (32)
5,121 (25)
Hemorrhage control surgery, n (%)
1,382 (1)
1,160 (6)
<0.001
Ventilator requirement, n (%)
20,083 (15)
16,014 (80)
<0.001
WLST, n (%)
3,193 (2)
13,619 (68)
<0.001
In the WLST group, survivors were older, female, White, had higher SBP, lower ISS, and less hemorrhage control surgery when compared with nonsurvivors. The detailed comparison of characteristics between survivors and nonsurvivors of the WLST group is given in Table 5 . Among survivors, 83% were discharged to hospice care, 7% were discharged to skilled nursing facility, 6% were discharged to home, and 1% were discharged to inpatient rehab.
TABLE 5 -
Comparison Between Survivors and Nonsurvivors in WLST Group
Variables
Survivors (n = 3,193)
Nonsurvivors (n = 13,619)
p
Demographics
Age, mean ± SD
80 ± 7
78 ± 7
<0.001
Male, n (%)
1,812 (57)
8,639 (63)
<0.001
White, n (%)
2,818 (88)
11,583 (85)
<0.001
Vital signs in ED
SBP, mean (SD), mm Hg
145 ± 38
142 ± 39
<0.001
HR, mean (SD), bpm
85 ± 23
87 ± 24
<0.001
GCS, median [IQR]
11 [3–14]
7 [3–14]
<0.001
Injury parameters
Blunt MOI, n (%)
3,029 (95)
12,896 (95)
0.695
ISS, median [IQR]
20 [16–25]
25 [17–27]
<0.001
Head-AIS ≥ 3, n (%)
3,066 (96)
12,753 (94)
<0.001
Thorax-AIS ≥ 3, n (%)
314 (10)
2,451 (18)
<0.001
Abdomen-AIS ≥ 3, n (%)
64 (2)
650 (5)
<0.001
Extremity-AIS ≥ 3, n (%)
147 (5)
912 (7)
<0.001
Comorbidities
Hypertension, n (%)
1,986 (62)
8,218 (60)
0.053
Diabetes mellitus, n (%)
789 (25)
3,684 (27)
0.007
COPD, n (%)
308 (10)
1,629 (12)
<0.001
CHF, n (%)
358 (11)
1,533 (11)
0.943
CKD, n (%)
110 (3)
668 (5)
<0.001
Liver disease, n (%)
60 (2)
297 (2)
0.287
Alcohol use disorder, n (%)
117 (4)
610 (5)
0.042
Smoking, n (%)
181 (6)
942 (7)
0.011
ACS trauma center verification level
<0.001
Level I, n (%)
1,426 (45)
7,281 (54)
Level II, n (%)
909 (29)
3,496 (26)
Level III and lower, n (%)
858 (27)
2,842 (21)
Hemorrhage control surgery, n (%)
31 (1)
545 (4)
<0.001
Ventilator requirement, n (%)
1,842 (58)
11,541 (85)
<0.001
DISCUSSION
In this retrospective study of a national trauma database, we found that increasing age, male gender, White race, frailty , presence of ADLC, severe head injury, ventilator requirement, government insurance, and treatment at higher level trauma centers were independent predictors of WLST in geriatric trauma patients. Nearly 81% of patients that underwent WLST died as compared with 5% of the patients that had continued life supporting treatment. This difference in mortality rate between the two groups highlights the need to understand the factors associated with WLST in geriatric trauma patients. This study is one of the very few studies evaluating the predictors of WLST focused on geriatric trauma patients.
In a retrospective study of geriatric trauma patients, Trunkey et al.2 7 8 9
Racial disparities in the outcomes of trauma patients have been well established in the literature. African American and Hispanic ethnicity have been identified as predictors of late WLST by Hornor et al.10 8
Government insurance was found to be an independent predictor of WLST in geriatric trauma patients. The reason behind this finding is not clear but the disproportionate differences in hospital expenditure and Medicare reimbursement might play a crucial role in decision making.11 geriatric trauma patients.
Frailty has been gaining importance among trauma surgeons in the last decade as a tool to predict outcomes in geriatric trauma patients. We found that frailty is an independent predictor of WLST in geriatric trauma patients across all the subcategories of age in addition to its proven ability to predict unfavorable outcomes, discharge disposition, and failure to rescue in geriatric trauma patients.12,13 14
Withdrawal of life supporting treatment is common in severe TBI and has been studied by multiple researchers.4,15 geriatric trauma patients. It is well known that severe TBI patients have poorer outcomes, and the families may choose WLST in these patients because of the uncertainty of their progress and return to preinjury health. On the other hand, mechanical ventilation was found to increase the odds of undergoing WLST by nearly 13 times. Intubation might interfere with the close interaction between patient and the family members.16 17
We found that care at higher level trauma centers was an independent predictor of WLST. This observation is contrary to the results of Williamson et al.4 10 18 geriatric trauma patients. Identifying palliative care processes among TCs is an important quality marker for TCs.19
One of the complexities of caring for elderly trauma patients is a failure to appreciate their treatment goals. In our study, only 9% of geriatric trauma patients across the nation had a documented request to limit life sustaining therapy or similar advanced directives prior to their arrival at the trauma center. There needs to be more effort to have open discussions with patients before they are hospitalized with injury or illness to clearly document their wishes. Ongoing discussion through routine family meetings is also important, as patient and/or surrogate desires may change based on changes in clinical status. Medical students and residents need to be trained to make them comfortable while dealing with end-of-life conversations. Siddiqui and Holley20
LIMITATIONS
This study has its own limitations. Because it is a large national database study, it lacks granularity, specifically in terms of the details regarding the reason for WLST. We could not determine if the decision to WLST was due to the severity of injury, or advance directives of patient. Trunkey et al.2
CONCLUSION
We found that increasing age, male sex, White race, frailty , presence of advance directive limiting care , severe TBI, ventilator requirement, government insurance, and management at higher level trauma centers are independent predictors of WLST in severely injured geriatric trauma patients. Patients with the above identified factors might be more approachable for decisions for withdrawal of support when the treating physicians deem the need for WLST. Early decisions about WLST may not be ideal because of inaccurate prognostication. Further studies are warranted to identify the subset of patients in which WLST may be reasonable to avoid futile resuscitation, and which subset of patients need to continue life supporting treatment to improve their chances of survival.
AUTHORSHIP
S.B., L.M., T.A., A.N. and B.J. designed this study. S.B., C.C., A.S., K.E., and B.J. searched the literature. S.B., H.H., and B.J. collected the data. S.B., L.M., H.H., R.F., and B.J. analyzed the data. All authors participated in data interpretation and article preparation.
DISCLOSURE
The authors declare no funding or conflicts of interest.
REFERENCES
1. Mccusker A, Khan M, Kulvatunyou N, Zeeshan M, Sakran JV, Hayek H, et al. Sarcopenia defined by a computed tomography estimate of the psoas muscle area does not predict
frailty in
geriatric trauma patients.
Am J Surg . 2019;218(2):261–265.
2. Trunkey DD, Cahn RM, Lenfesty B, Mullins R. Management of the
geriatric trauma patient at risk of death: therapy withdrawal decision making.
Arch Surg . 2000;135(1):34–38.
3. Joseph B, Pandit V, Zangbar B, Kulvatunyou N, Hashmi A, Green DJ, et al. Superiority of
frailty over age in predicting outcomes among
geriatric trauma patients: a prospective analysis.
JAMA Surg . 2014;149(8):766–772.
4. Williamson T, Ryser MD, Ubel PA, Abdelgadir J, Spears CA, Liu B, et al. Withdrawal of life-supporting treatment in severe traumatic brain injury.
JAMA Surg . 2020;155(8):723–731.
5. Livingston DH, Tripp T, Biggs C, Lavery RF. A fate worse than death? Long-term outcome of trauma patients admitted to the surgical intensive care unit.
J Trauma Acute Care Surg . 2009;67(2):341–348.
6. Velanovich V, Antoine H, Swartz A, Peters D, Rubinfeld I. Accumulating deficits model of
frailty and postoperative mortality and morbidity: its application to a national database.
J Surg Res . 2013;183(1):104–110.
7. Plaisier BR, Blostein PA, Hurt KJ, Malangoni MA. Withholding/withdrawal of life support in trauma patients: is there an age bias?
Am Surg . 2002;68(2):159–162.
8. Cooper Z, Rivara FP, Wang J, MacKenzie EJ, Jurkovich GJ. Withdrawal of life-sustaining therapy in injured patients: variations between trauma centers and nontrauma centers.
J Trauma . 2009;66(5):1327–1335.
9. Manara AR, Pittman JA, Braddon FE. Reasons for withdrawing treatment in patients receiving intensive care.
Anaesthesia . 1998;53(6):523–528.
10. Hornor MA, Byrne JP, Engelhardt KE, Nathens AB. Examining racial disparities in the time to withdrawal of life-sustaining treatment in trauma.
J Trauma Acute Care Surg . 2018;84(4):590–597.
11. Qureshi AI, Adil MM, Suri MF. Rate of use and determinants of withdrawal of care among patients with subarachnoid hemorrhage in the United States.
World Neurosurg . 2014;82(5):e579–e584.
12. Joseph B, Pandit V, Rhee P, Aziz H, Sadoun M, Wynne J, et al. Predicting hospital discharge disposition in
geriatric trauma patients: is
frailty the answer?
J Trauma Acute Care Surg . 2014;76(1):196–200.
13. Joseph B, Orouji Jokar T, Hassan A, Azim A, Mohler MJ, Kulvatunyou N, et al. Redefining the association between old age and poor outcomes after trauma: the impact of
frailty syndrome.
J Trauma Acute Care Surg . 2017;82(3):575–581.
14. Montgomery CL, Rolfson DB, Bagshaw SM.
Frailty and the association between long-term recovery after intensive care unit admission.
Crit Care Clin . 2018;34(4):527–547.
15. DeMario BS, Stanley SP, Truong EI, Ladhani HA, Brown LR, Ho VP, et al. Predictors for withdrawal of life-sustaining therapies in patients with traumatic brain injury: a retrospective trauma quality improvement program database study.
Neurosurgery . 2022;91(2):e45–e50.
16. Kirchhoff KT, Anumandla PR, Foth KT, Lues SN, Gilbertson-White SH. Documentation on withdrawal of life support in adult patients in the intensive care unit.
Am J Crit Care . 2004;13(4):328–334.
17. Gerstel E, Engelberg RA, Koepsell T, Curtis JR. Duration of withdrawal of life support in the intensive care unit and association with family satisfaction.
Am J Respir Crit Care Med . 2008;178(8):798–804.
18. American College of Surgeons Committee on Trauma. Resources for optimal care of the injured patient.
https://www.facs.org/media/yu0laoqz/resources-for-optimal-care.pdf . Published 2014. Accessed December 24, 2022.
19. ACS TQIP Palliative Care Best Practices Guidlines. Available at:
https://www.facs.org/quality-programs/trauma/quality/best-practices-guidelines/ . Published October 2013. Accessed December 24, 2022.
20. Siddiqui MF, Holley JL. Residents’ practices and perceptions about do not resuscitate orders and pronouncing death: an opportunity for clinical training.
Am J Hosp Palliat Care . 2011;28(2):94–97.
