Intensive End-of-Life Care: Implementation of a Canadian Guideline-Based Order Set for the Withdrawal of Life-Sustaining Therapy in the Intensive Care Unit
Bibliographic record
Abstract
Background: An increasing number of patients receive end-of-life care in the intensive care unit (ICU). Death often occurs in the ICU after a decision has been made to withdraw life-sustaining therapies. In 2016, Downar et al. published Canadian consensus guidelines to standardize practices for withdrawal of life-sustaining therapy in the ICU. In this study, we sought to understand the feasibility and acceptability of implementing an order set, nursing flowsheet, and nursing care plan based on these guidelines in two ICUs in Saskatchewan, Canada. Methods: We used a hybrid effectiveness-implementation design, engaging a steering committee of ICU health care providers and leadership to guide implementation. We conducted a six-month pilot implementation. We collected data in the three months pre-implementation, during the six-month implementation period, and for three months post-implementation. To evaluate implementation outcomes, we used the Consolidated Framework for Implementation Research to develop semi-structured interviews and feasibility surveys. To measure effectiveness outcomes, bedside nurses completed Quality of Death and Dying surveys, and we performed a patient chart review. Results: The intervention materials added to the burden of paperwork of bedside health care providers but helped them provide quality end-of-life care, meet the needs of patients and their families, and lessen ethical tensions between symptom control and hastening death. There was no difference in cumulative sedative dosing and time to death after extubation in the pre-implementation, implementation, or post-implementation periods. A significant increase in symptom assessment (pain, dyspnea, and agitation) using standardized tools was observed during the implementation and post-implementation periods. There was an improvement in holistic care outcomes post-implementation. Conclusions: We implemented current Canadian best-practice guidelines for providing end-of-life care in the ICU using a multidisciplinary approach. This study offers insight into how standardized symptom assessment and medication titration can be incorporated into the complex ICU environment.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".