Transitioning to Palliative Care in an Italian Cardiac Intensive Care Unit Network
Bibliographic record
Abstract
BACKGROUND: Recent data indicate that end-of-life management for patients affected by acute decompensated heart failure in cardiac intensive care units is aggressive, with late or no engagement of palliative care teams. OBJECTIVE: To assess current palliative care and end-of-life practices in a contemporary Italian multicenter registry of patients with cardiogenic shock due to acute decompensated heart failure. METHODS: A survey-based approach was used to collect data on palliative care and end-of-life management practices. The AltShock-2 registry enrolled patients with cardiogenic shock from 12 participating centers. A subset of 153 patients with cardiogenic shock due to acute decompensated heart failure enrolled between March 2020 and March 2023 was analyzed, with a focus on early engagement of palliative care teams and deactivation of implantable cardioverter-defibrillators (ICDs). RESULTS: "Do not resuscitate" orders were documented in patient records in only 5 of 12 centers (42%). Palliative care teams were engaged for 21 of 153 enrolled patients (13.7%). Among the 51 patients with ICDs, 6 of 17 patients who died (35%) had defibrillator deactivation. Of the 17 patients who died, 13 died in the hospital and 4 died within 6 months after discharge; 1 patient had ICD deactivation supported by palliative care services at home. CONCLUSIONS: Therapy-limiting practices, including ICD deactivation, are not routine in the Italian centers participating in this study. The results emphasize the importance of integrating palliative care as a simultaneous process with intensive care to address the unmet needs of these patients and their families.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".