Abstract 286: Factors Associated with Non-survival From In-hospital Maternal Cardiac Arrest: An Analysis of Get with the Guidelines (gwtg) Data
Bibliographic record
Abstract
Introduction: Maternal mortality has risen in the United States during the 21 st century. Factors influencing outcome of maternal cardiac arrest (MCA) remain largely unexplored. Hypothesis: We sought to further elucidate the factors affecting maternal death from in-hospital (IH) MCA. Methods: Our query of the American Heart Association’s GWTG ® -Resuscitation voluntary registry from 2000-2017 revealed 561 index cases of in IH MCA with complete outcome data. Logistic regression was performed using hospital death as the primary outcome and included variables with a p value = 0.1 or less based upon univariate analysis. Age, race, year of arrest, pre-existing conditions, first documented pulseless rhythm, and location of arrest were used in the model. Sensitivity analyses and assessment of variable interaction were also performed to test model stability. Results: Among 561 cases of MCA, 57.2% (321/561) did not survive to hospital discharge. In-hospital death was not associated with maternal age, race and year of event. In the final model (see table), IH death was significantly associated with prearrest hypotension/hypoperfusion (p=0.009). While MCA cases with a shockable vs non-shockable first documented pulseless rhythm had similar outcomes, those with an indeterminate rhythm were less likely to die, (p=0.014). The occurrence of MCA outside of the delivery suite (referent group) or operating room was associated with a significantly higher risk of death: ICU/Recovery Room (p=0.001) and ER/other (p= 0.012). In a sensitivity analysis, removal of the indeterminate group did not alter outcomes regarding first documented pulseless rhythm or arrest location. Area under the curve for the final model was 0.715 (95% CI 0.673-0.757). Conclusions: Our study identified several novel factors associated with IH death of our MCA cohort. More research is required to further understand the pathophysiologic dynamics affecting outcomes of IHCA in this unique population.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".