Disparities in Palliative Care Among Critically Ill Patients With and Without COVID-19 at the End of Life: A Population-Based Analysis
Bibliographic record
Abstract
Background: The surge in critical illness and associated mortality brought by the coronavirus virus disease 2019 (COVID-19) pandemic, coupled with staff shortages and restrictions of family visitation, may have adversely affected delivery of palliative measures, including at the end of life of affected patients. However, the population-level patterns of palliative care (PC) utilization among septic critically ill patients with and without COVID-19 during end-of-life hospitalizations are unknown. Methods: A statewide dataset was used to identify patients aged ≥ 18 years with intensive care unit (ICU) admission and a diagnosis of sepsis in Texas, who died during hospital stay during April 1 to December 31, 2020. COVID-19 was defined by the International Classification of Diseases, 10th Revision (ICD-10) code U07.1, and PC was identified by ICD-10 code Z51.5. Multivariable logistic models were fitted to estimate the association of COVID-19 with use of PC among ICU admissions. A similar approach was used for sensitivity analyses of strata with previously reported lower and higher than reference use of PC. Results: There were 20,244 patients with sepsis admitted to ICU during terminal hospitalization, and 9,206 (45.5%) had COVID-19. The frequency of PC among patients with and without COVID-19 was 32.0% vs. 37.1%, respectively. On adjusted analysis, the odds of PC use remained lower among patients with COVID-19 (adjusted odds ratio (aOR): 0.84, 95% confidence interval (CI): 0.78 - 0.90), with similar findings on sensitivity analyses. Conclusions: PC was markedly less common among critically ill septic patients with COVID-19 during terminal hospitalization, compared to those without COVID-19. Further studies are needed to determine the factors underlying these findings in order to reduce disparities in use of PC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".