Epidemiologic Trends of Adoption of Do-Not-Resuscitate Status After Pediatric In-Hospital Cardiac Arrest*
Bibliographic record
Abstract
OBJECTIVES: To evaluate the prevalence of do-not-resuscitate status, assess the epidemiologic trends of do-not-resuscitate status, and assess the factors associated with do-not-resuscitate status in children after in-hospital cardiac arrest using large, multi-institutional data. DESIGN: Generalized estimating equations logistic regression model was used to evaluate the trends of do-not-resuscitate status and evaluate the factors associated with do-not-resuscitate status after cardiac arrest. SETTING: American Heart Association's Get With the Guidelines-Resuscitation Registry. PATIENTS: Children (< 18 yr old) with an index in-hospital cardiac arrest and greater than or equal to 1 minute of documented chest compressions were included (2006-2015). Patients with no return of spontaneous circulation after cardiac arrest were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: In total, 8,062 patients qualified for inclusion. Of these, 1,160 patients (14.4%) adopted do-not-resuscitate status after cardiac arrest. We found low rates of survival to hospital discharge among children with do-not-resuscitate status (do-not-resuscitate vs no do-not-resuscitate: 6.0% vs 69.7%). Our study found that rates of do-not-resuscitate status after cardiac arrest are highest in children with Hispanic ethnicity (16.4%), white race (15.0%), and treatment at institutions with larger PICUs (> 50 PICU beds: 17.8%) and at institutions located in North Central (17.6%) and South Atlantic/Puerto Rico (17.1%) regions of the United States. Do-not-resuscitate status was more common among patients with more preexisting conditions, longer duration of cardiac arrest, greater than 1 cardiac arrest, and among patients requiring extracorporeal cardiopulmonary resuscitation. We also found that trends of do-not-resuscitate status after cardiac arrest in children are decreasing in recent years (2013-2015: 13.8%), compared with previous years (2006-2009: 16.0%). CONCLUSIONS: Patient-, hospital-, and regional-level factors are associated with do-not-resuscitate status after pediatric cardiac arrest. As cardiac arrest might be a signal of terminal chronic illness, a timely discussion of do-not-resuscitate status after cardiac arrest might help families prioritize quality of end-of-life care.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".