278: PRACTICE OF DO-NOT-RESUSCITATE ORDERS IN A TERTIARY CARE HOSPITAL IN SAUDI ARABIA
Bibliographic record
Abstract
Introduction: The objective of the study is to describe DNR practices in Saudi Arabia. Methods: This is a retrospective cohort study based on a prospectively collected intensive care unit (ICU) database between 2002-2017. We compared patients who had DNR order during the ICU stay with patients with “full code” admitted to a tertiary medical-surgical ICU in Riyadh, Saudi Arabia. Results: Among 24790 patients admitted to the ICU over the 16-year period, 3217 (13%) patients had DNR orders during the ICU stay. Patients with DNR orders, compared to patients with “full code”, were older (median 67 years (Q1,Q3: 55,76) vs. 57 years (Q1,Q3: 33,71), P < 0.0001), were more likely to be females 1374 (43%) vs. 8200 (38%), P < 0.0001) and had higher APACHE II score (median 28 (Q1,Q3: 23, 34) vs. 19 (Q1,Q3: 13, 25), P < 0.0001). Patients with DNR orders, compared to patients with “full code”, were more likely to be mechanically ventilated (2660 (83%) vs. 11871 (55%), P < 0.0001), to have comorbid conditions and had a lower functional status (modified Rankin scores 4-5: 608 (18.9%) vs. 1897 (8.8%), P < 0.0001). Patients with DNR orders, compared to patients with “full code”, were more likely to die in the ICU (2168/3217 (67.8%) vs. 1807/21573 (8.5%) P < 0.0001); and in the hospital (2650/3217 (82.4%) vs. 3908/21573 (18.1%) P < 0.0001). On multivariate logistic regression analysis, the following were associated with an increased likelihood of DNR status: increasing age (OR 1.01, 95% CI 1.009, 1.014, P < 0.0001), higher APACHE II score (OR 1.09, 95% CI 1.008, 1.095, P < 0.0001), and increasing modified Rankin score. Patients admitted in the recent years (2012-2017 vs. 2002-2005) were less likely to have DNR orders (OR 0.36, 95% CI 0.33, 0.40, P < 0.0001). Conclusions: In a tertiary care ICU in Saudi Arabia, about 13% of patients had DNR orders. The study identified several predictors that were associated with the likelihood of DNR orders, including severity of illness and poor baseline functional status.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".