Disparities in Unilateral Do Not Resuscitate Order Use During the COVID-19 Pandemic*
Bibliographic record
Abstract
OBJECTIVES: A unilateral do-not-resuscitate (UDNR) order is a do-not-resuscitate order placed using clinician judgment which does not require consent from a patient or surrogate. This study assessed how UDNR orders were used during the COVID-19 pandemic. DESIGN: We analyzed a retrospective cross-sectional study of UDNR use at two academic medical centers between April 2020 and April 2021. SETTING: Two academic medical centers in the Chicago metropolitan area. PATIENTS: Patients admitted to an ICU between April 2020 and April 2021 who received vasopressor or inotropic medications to select for patients with high severity of illness. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The 1,473 patients meeting inclusion criteria were 53% male, median age 64 (interquartile range, 54-73), and 38% died during admission or were discharged to hospice. Clinicians placed do not resuscitate orders for 41% of patients ( n = 604/1,473) and UDNR orders for 3% of patients ( n = 51/1,473). The absolute rate of UDNR orders was higher for patients who were primary Spanish speaking (10% Spanish vs 3% English; p ≤ 0.0001), were Hispanic or Latinx (7% Hispanic/Latinx vs 3% Black vs 2% White; p = 0.003), positive for COVID-19 (9% vs 3%; p ≤ 0.0001), or were intubated (5% vs 1%; p = 0.001). In the base multivariable logistic regression model including age, race/ethnicity, primary language spoken, and hospital location, Black race (adjusted odds ratio [aOR], 2.5; 95% CI, 1.3-4.9) and primary Spanish language (aOR, 4.4; 95% CI, 2.1-9.4) had higher odds of UDNR. After adjusting the base model for severity of illness, primary Spanish language remained associated with higher odds of UDNR order (aOR, 2.8; 95% CI, 1.7-4.7). CONCLUSIONS: In this multihospital study, UDNR orders were used more often for primary Spanish-speaking patients during the COVID-19 pandemic, which may be related to communication barriers Spanish-speaking patients and families experience. Further study is needed to assess UDNR use across hospitals and enact interventions to improve potential disparities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".