Delivery of End-of-Life Care in Patients Requesting Withdrawal of a Left Ventricular Assist Device Using Intranasal Opioids and Benzodiazepines
Bibliographic record
Abstract
With the increasing prevalence of the left ventricular assist device (LVAD) in patients with end-stage cardiomyopathies, an increasing number of these patients are dying of noncardiac conditions. It is likely that the palliative care clinician will have an ever-increasing role in managing end of life for patients with LVADs, including discontinuation of LVAD support. There exists a paucity of literature describing strategies for effective delivery of palliative care in patients requesting discontinuation of LVAD therapy. Here, we present a case of a patient with metastatic cancer who requested LVAD discontinuation. Because of practical concerns and patient preference, the patient did not have intravenous (IV) access and medications requiring IV administration could not be used. Therefore, a strategy using intranasal midazolam and sufentanil was applied, the LVAD was deactivated, and the patient died comfortably. This case is, to our knowledge, the first to describe a strategy for delivery of palliative care in patients requesting discontinuation of LVAD support, particularly in the absence of IV access. Such a strategy may be applicable to patients wishing to die at home, and therefore allow greater latitude for patients and clinicians in their approach to the end of life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".