Predictive biomarkers of preeclampsia severity in a low resource setting: Role of red blood cell indices, NLR, and albumin-to-creatinine ratio
Bibliographic record
Abstract
Preeclampsia (PE), a serious medical condition with substantial maternal and perinatal implications, poses a significant challenge, particularly in high-incidence countries like Indonesia. Red blood cell (RBC) indices, neutrophil-to-lymphocyte ratio (NLR), and microalbuminuria (albumin-to-creatinine ratio (ACR)) may signal systemic inflammation and endothelial dysfunction, recently recognized as potential indicators for diagnosing and predicting disease severity. The aim of this study was to analyze RBC indices, NLR, and ACR changes in women with PE and their potential for predicting disease severity. A cross-sectional study was conducted at multi-center hospitals across Medan, Indonesia, from June 2022 to June 2023. The patients were grouped into PE cases with and without severe features. Demographic characteristics and complications were recorded while blood and urine were tested. The Chi-squared test, Fisher's exact test and Mann-Whitney test were used to determine biomarkers associated with severe PE. A total of 208 PE patients were included in the study (104 patients for each PE with and without severe features). Our data indicated that PE patients with severe features had higher red cell distribution width (18.5% vs 13.7%; p<0.001), NLR (5.66% vs 4.1%; p<0.001), and ACR (755.97 mg/dL vs 468.63 mg/dL; p<0.001) compared to those without severe features. In contrast, the platelet count was lower in severe features than those without (21.9 × 106/µL vs 27.0 × 106/µL; p=0.002). This study highlighted that PE patients with severe features predominantly had higher levels of RDW, NLR, and ACR and lower platelet counts compared to those without severe features. Therefore, basic tests such as complete blood count and urinalysis, which are inexpensive and feasible in primary care settings with limited resources, offer hope as valuable diagnostic biomarkers for pregnant women diagnosed with PE in a low resource setting.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".