Variability in intensive care unit admission among pregnant and postpartum women in Canada: a nationwide population-based observational study
Bibliographic record
Abstract
BACKGROUND: Pregnancy-related critical illness results in approximately 300,000 deaths globally each year. The objective was to describe the variation in ICU admission and the contribution of patient- and hospital-based factors in ICU admission among acute care hospitals for pregnant and postpartum women in Canada. METHODS: A nationwide cohort study between 2004 and 2015, comprising all pregnant or postpartum women admitted to Canadian hospitals. The primary outcome was ICU admission. Secondary outcomes were severe maternal morbidity (a potentially life-threatening condition) and maternal death (during and within 6 weeks after pregnancy). The proportion of total variability in ICU admission rates due to the differences among hospitals was described using the median odds ratio from multi-level logistic regression models, adjusting for individual hospital clusters. RESULTS: There were 3,157,248 identifiable pregnancies among women admitted to 342 Canadian hospitals. The overall ICU admission rate was 3.2 per 1000 pregnancies. The rate of severe maternal morbidity was 15.8 per 1000 pregnancies, of which 10% of women were admitted to an ICU. The most common severe maternal morbidity events included postpartum hemorrhage (n = 16,364, 0.52%) and sepsis (n = 11,557, 0.37%). Of the 195 maternal deaths (6.2 per 100,000 pregnancies), only 130 (67%) were admitted to ICUs. Patients dying in hospital, without admission to ICU, included those with cardiovascular compromise, hemorrhage, and sepsis. For 2 pregnant women with similar characteristics at different hospitals, the average (median) odds of being admitted to ICU was 1.92 in 1 hospital compared to another. Hospitals admitting the fewest number of pregnant patients had the highest incidence of severe maternal morbidity and mortality. Patient-level factors associated with ICU admission were maternal comorbidity index (OR 1.88 per 1 unit increase, 95%CI 1.86-1.99), urban residence (OR 1.09, 95%CI 1.02-1.16), and residing at the lowest income quintile (OR 1.44, 95%CI 1.34-1.55). CONCLUSIONS: Most women who experience severe maternal morbidity are not admitted to an ICU. There exists a wide hospital-level variability in ICU admission, with patients living in urban locations and patients of lowest income levels most likely to be admitted to ICU. Cardiovascular compromise, hemorrhage, and sepsis represent an opportunity for improved patient care and outcomes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".