Withdrawal of Life-Sustaining Treatment for Pediatric Patients With Severe Traumatic Brain Injury
Bibliographic record
Abstract
Importance: The decision to withdraw life-sustaining treatment for pediatric patients with severe traumatic brain injury (TBI) is challenging for clinicians and families with limited evidence quantifying existing practices. Given the lack of standardized clinical guidelines, variable practice patterns across trauma centers seem likely. Objective: To evaluate the factors influencing decisions to withdraw life-sustaining treatment across North American trauma centers for pediatric patients with severe TBI and to quantify any existing between-center variability in withdrawal of life-sustaining treatment practices. Design, Setting, and Participants: This retrospective cohort study used data collected from 515 trauma centers through the American College of Surgeons Trauma Quality Improvement Program between 2017 and 2020. Pediatric patients younger than 19 years with severe TBI and a documented decision for withdrawal of life-sustaining treatment were included. Data were analyzed from January to May 2023. Main Outcomes and Measures: A random intercept multilevel logistic regression model was used to quantify patient, injury, and hospital characteristics associated with the decision to withdraw life-sustaining treatment; the median odds ratio was used to characterize residual between-center variability. Centers were ranked by their conditional random intercepts and quartile-specific adjusted mortalities were computed. Results: A total of 9803 children (mean [SD] age, 12.6 [5.7]; 2920 [29.8%] female) with severe TBI were identified, 1003 of whom (10.2%) had a documented decision to withdraw life-sustaining treatment. Patient-level factors associated with an increase in likelihood of withdrawal of life-sustaining treatment were young age (younger than 3 years), higher severity intracranial and extracranial injuries, and mechanism of injury related to firearms. Following adjustment for patient and hospital attributes, the median odds ratio was 1.54 (95% CI, 1.46-1.62), suggesting residual variation in withdrawal of life-sustaining treatment between centers. When centers were grouped into quartiles by their propensity for withdrawal of life-sustaining treatment, adjusted mortality was higher for fourth-quartile compared to first-quartile centers (odds ratio, 1.66; 95% CI, 1.45-1.88). Conclusions and Relevance: Several patient and injury factors were associated with withdrawal of life-sustaining treatment decision-making for pediatric patients with severe TBI in this study. Variation in withdrawal of life-sustaining treatment practices between trauma centers was observed after adjustment for case mix; this variation was associated with differences in risk-adjusted mortality rates. Taken together, these findings highlight the presence of inconsistent approaches to withdrawal of life-sustaining treatment in children, which speaks to the need for guidelines to address this significant practice pattern variation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".