Multiple Organ Dysfunction Score Is Superior to the Obstetric-Specific Sepsis in Obstetrics Score in Predicting Mortality in Septic Obstetric Patients
Bibliographic record
Abstract
OBJECTIVES: Mortality prediction scores have been used for a long time in ICUs; however, numerous studies have shown that they over-predict mortality in the obstetric population. With sepsis remaining a major cause of obstetric mortality, we aimed to look at five mortality prediction scores (one obstetric-based and four general) in the septic obstetric population and compare them to a nonobstetric septic control group. SUBJECT AND DESIGN: Women in the age group of 16-50 years with an admission diagnosis or suspicion of sepsis were included. In a multicenter obstetric population (n = 797), these included all pregnant and postpartum patients up to 6 weeks postpartum. An age- and gender-matched control nonobstetric population was drawn from a single-center general critical care population (n = 2,461). Sepsis in Obstetric Score, Acute Physiology and Chronic Health Evaluation II, Simplified Acute Physiology Score II, Sequential Organ Failure Assessment, and Multiple Organ Dysfunction Scores were all applied to patients meeting inclusion criteria in both cohorts, and their area under the receiver-operator characteristic curves was calculated to find the most accurate predictor. MEASUREMENTS AND MAIN RESULTS: A total of 146 septic patients were found for the obstetric cohort and 299 patients for the nonobstetric control cohort. The Sepsis in Obstetric Score, Acute Physiology and Chronic Health Evaluation II, Simplified Acute Physiology Score II, Sequential Organ Failure Assessment, and Multiple Organ Dysfunction Scores gave area under the receiver-operator characteristic curves of 0.67, 0.68, 0.72, 0.79, and 0.84 in the obstetric cohort, respectively, and 0.64, 0.72, 0.61, 0.78, and 0.74 in the nonobstetric cohort, respectively. The Sepsis in Obstetric Score performed similarly to all the other scores with the exception of the Multiple Organ Dysfunction Score, which was significantly better (p < 0.05). CONCLUSION: The Sepsis in Obstetric Score, designed specifically for sepsis in obstetric populations, was not better than general severity of illness scoring systems. Furthermore, the Sepsis in Obstetric Score performance was no different in an obstetric sepsis population compared to a nonobstetric sepsis population. The Multiple Organ Dysfunction Score is a simple organ-based score, and this result supports the use of organ-based outcome predictors in ICU even in an obstetric sepsis 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".