Inpatient palliative care referral and 9‐month hospital readmission in patients with congestive heart failure: a linked nationwide analysis
Bibliographic record
Abstract
OBJECTIVE: End-stage heart failure (HF) is characterized by high symptom burden and frequent hospitalization. Palliative care (PC) is recommended for advanced HF, and there is some evidence in other diseases that this may reduce readmission rates. We attempted examine the association of an inpatient PC visit on hospital readmission for patients admitted with HF. METHODS: Retrospective linked nationwide analysis from 2013 with 9-month follow-up for all hospital readmissions for patients admitted with HF exacerbations using the Nationwide Readmission Database (NRD). The NRD gathers all hospital admissions for patients from 22 states and tracks patients throughout the year, allowing for examination of readmission statistics. A propensity score model for PC visit was made, and patients were matched in a 1 : 1 fashion. RESULTS: There were 102 746 patients who survived an admission for HF in the first 3 months of 2013. Of these, 2287 (2.2%) patients had a PC visit as inpatients. After matching based on propensity for a PC visit during the index hospitalization, 2282 patients who received a PC visit were matched to 2282 patients who did not. Those receiving a PC visit were less likely to be readmitted for HF (9.3% vs. 22.4%, P < 0.01) or for any cause (29.0% vs. 63.2%, P < 0.01) during the 9-month follow-up period. The average hospital charges during the follow-up period for the non-PC cohort were $77 643 per patient. The average charges for PC patients were $23 200 (P < 0.01). CONCLUSIONS: Patients with HF who received an inpatient PC visit had significantly lower rates of all-cause and HF-specific readmission in the subsequent 9 months. Total 9-month hospital charges were also significantly lower for patients who received an inpatient PC visit.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".