Palliative Care Utilization Among Hospitalized Patients With Common Chronic Conditions in the United States
Bibliographic record
Abstract
Objective: Limited data exist around the receipt of palliative care (PC) in patients hospitalized with common chronic conditions. We studied the independent predictors, temporal trends in rates of PC utilization in patients hospitalized with acute exacerbation of common chronic diseases. Methods: Population-based cohort study of all hospitalizations with an acute exacerbation of heart disease (HD), cerebrovascular accident (CVA), cancer (CA), and chronic lower respiratory disease (CLRD). Patients aged ≥18 years or older between January 1, 2004, and December 31, 2017, referred for inpatient PC were extracted from the National Inpatient Sample. Poisson regression analyses were used to estimate temporal trends. Results: Between 2004 and 2017, of 91,877,531 hospitalizations, 55.2%, 13.9%, 17.2%, and 13.8% hospitalizations were related to HD, CVA, CA, and CLRD, respectively. There was a temporal increase in the uptake of PC across all disease groups. Age-adjusted estimated rates of PC per 100,000 hospitalizations/year were highest for CA (2308 (95% CI 2249–2366) to 10,794 (95% CI 10,652–10,936)), whereas the CLRD cohort had the lowest rates of PC referrals (255 (95% CI 231–278) to 1882 (95% CI 1821–1943)) between 2004 and 2017, respectively. In the subgroup analysis of patients who died during hospitalization, the CVA group had the highest uptake of PC per 100,000 hospitalizations/year (4979 (95% CI 4918–5040)) followed by CA (4241 (95% CI 4189–4292)), HD (3250 (95% CI 3211–3289)) and CLRD (3248 (95% CI 3162–3405)). Conclusion: PC service utilization is increasing but remains disparate, particularly in patients that die during hospital admission from common chronic conditions. These findings highlight the need to develop a multidisciplinary, patient-centered approach to improve access to PC services in these patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".