Distinct maternal and fetal pregnancy outcomes in women with sickle cell disease can be predicted using routine clinical and laboratory data
Bibliographic record
Abstract
We aimed to identify risk factors for adverse outcomes in pregnancies of women with sickle cell disease (SCD) and develop risk prediction models. Models were derived from a retrospective cohort of pregnant women with SCD and constructed using generalised estimating equation logistic regression, with clustering by woman. Maternal event(s) consisted of acute anaemia; cardiac, pulmonary, hepatobiliary, musculoskeletal, skin, splenic, neurological or renal complications, multi-organ failure, venous thromboembolism, admission-requiring vaso-occlusive events (VOE), red cell transfusion, mortality or hypertensive disorder of pregnancy. Fetal events included preterm birth, small-for-gestational-age or perinatal mortality. Of 199 pregnancies, 71% and 45% resulted in adverse maternal and fetal outcomes respectively. Low first-trimester haemoglobin, admission-requiring VOE in the year before pregnancy, multiple transfusions before pregnancy, SCD genotype and previous cardiac complications predicted maternal risk. Younger age and SCD genotype allowed early prediction of fetal risk (model-F1). Adding maternal event(s) and high lactate dehydrogenase enabled re-assessment of fetal risk with advancing gestation (model-F2). Models were well calibrated and moderately discriminative for maternal outcome (c-statistic 0·81, cross-validated value 0·79) and fetal outcome (model-F1 c-statistic 0·68, cross-validated value 0·65; model-F2 c-statistic 0·72, cross-validated value 0·68). The models will allow early identification of women with SCD at high risk of adverse events, permitting early targeted interventions and ongoing fetal risk re-assessment enabling intensification of surveillance and optimisation of delivery timing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".