Maternal Risk Modeling in Critical Care—Development of a Multivariable Risk Prediction Model for Death and Prolonged Intensive Care*
Bibliographic record
Abstract
OBJECTIVES: We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care. DESIGN: We used data from a high-quality prospectively collected national database, supported with literature review and expert opinion. We tested univariable associations between each risk factor and outcome. We then developed two separate multivariable logistic regression models for the outcomes of acute hospital mortality and death or prolonged ICU length of stay. We validated two parsimonious risk prediction models specific for a maternal population. SETTING: The Intensive Care National Audit and Research Centre Case Mix Programme is the national clinical audit for adult critical care in England, Wales, and Northern Ireland. PATIENTS: All female admissions to adult general critical care units, for the period January 1, 2007-December 31, 2016, 16-50 years old, and admitted either while pregnant or within 42 days of delivery-a cohort of 15,480 women. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care. For the primary outcome of acute hospital mortality, our parsimonious risk model consisting of eight variables had an area under the receiver operating characteristic of 0.96 (95% CI, 0.91-1.00); these variables are commonly available for all maternal admissions. For the secondary composite outcome of death or ICU length of stay greater than 48 hours, the risk model consisting of 17 variables had an area under the receiver operating characteristic of 0.80 (95% CI, 0.78-0.83). CONCLUSIONS: We developed risk prediction models specific to the maternal critical care population. The models compare favorably against general adult ICU risk prediction models in current use within this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".