Predictors for Withdrawal of Life-Sustaining Therapies in Patients With Traumatic Brain Injury: A Retrospective Trauma Quality Improvement Program Database Study

Abstract

Many patients with severe traumatic brain injuries (TBIs) undergo withdrawal of life-sustaining therapies (WLSTs) or transition to comfort measures, but noninjury factors that influence this decision have not been well characterized. We hypothesized that WLST would be associated with institutional and geographic noninjury factors. All patients with a head Abbreviated Injury Scale score ≥3 were identified from 2016 Trauma Quality Improvement Program data. We analyzed factors that might be associated with WLST, including procedure type, age, sex, race, insurance, Glasgow Coma Scale score, mechanism of injury, geographic region, and institutional size and teaching status. Adjusted logistic regression was performed to examine factors associated with WLST. Sixty-nine thousand fifty-three patients were identified: 66% male, 77% with isolated TBI, and 7.8% had WLST. The median age was 56 years (34-73). A positive correlation was found between increasing age and WLST. Women were less likely to undergo WLST than men (odds ratio 0.91 [0.84-0.98]) and took more time to for WLST (3 vs 2 days, P < .001). African Americans underwent WLST at a significantly lower rate (odds ratio 0.66 [0.58-0.75]). Variations were also discovered based on US region, hospital characteristics, and neurosurgical procedures. WLST in severe TBI is independently associated with noninjury factors such as sex, age, race, hospital characteristics, and geographic region. The effect of noninjury factors on these decisions is poorly understood; further study of WLST patterns can aid health care providers in decision making for patients with severe TBI.

The final decision among the injured elderly, to stop or to continue? Predictors of withdrawal of life supporting treatment

“…Withdrawal of life supporting treatment is common in severe TBI and has been studied by multiple researchers. 4,15 In our study, severe TBI was found to be an independent predictor of WLST in 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.…”

“…Withdrawal of life supporting treatment is common in severe TBI and has been studied by multiple researchers 4,15 . In our study, severe TBI was found to be an independent predictor of WLST in geriatric trauma patients.…”

“…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][17][18][19][20][21][22][23][24][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.…”

Admitting Hospital Influences on Withdrawal of Life-Sustaining Treatment Decision for Patients With Severe Traumatic Brain Injury

“…3,4 Withdrawal of life-sustaining treatment (WLST) decisions are complex in this population; providers must often rely on patient factors, including age and premorbid functional status, and injury characteristics, including the extent of neurologic deficit. 5,6 Despite advances in TBI outcome prediction and care pathways, there remains a relative paucity of tools to navigate withdrawal-of-care decision-making and prognostication resulting in variable practice patterns. 7 The harm of premature WLST is potential loss of recovery opportunity; alternatively, continued invasive support in the face of a poor prognosis leads to excess suffering, healthcare system inefficiencies, and potential opportunity costs.…”

“…Previous groups that have examined WLST in the context of severe TBI have largely focused on associations between patient characteristics, such as insurance status, race, comorbidities, and injury factors, and hospital characteristics. 5,10,11 We postulate that additional unmeasurable attributes pertaining to hospital culture and practice patterns contribute to residual observed practice variability. Estimating the magnitude of this unexplained variation might direct further investigation into these contextual factors to increase adherence to equitable end-of-life care standards.…”