C-Reactive Protein and Erythrocyte Sedimentation Rate Do Not Correlate With Disease Activity in Pregnant Women With IBD
Bibliographic record
Abstract
Introduction: Non-invasive biomarkers of a disease flare in pregnant women with inflammatory bowel diseases (IBD) are essential to optimize outcomes. In healthy pregnancies, the erythrocyte sedimentation rate (ESR) rises, while C-reactive protein (CRP) does not have a consistent pattern of change. Our hypothesis is that CRP will correlate with disease activity during pregnancy in IBD. Methods: Patients were prospectively enrolled between September 2012 and May 2014. A disease flare was defined as a Harvey BradShaw index of ≥5 or a simple clinical colitis activity index score of ≥3. The inflammatory markers (CRP and ESR) were measured each trimester and patients were included if they had two such measurements during the pregnancy. Median CRP and ESR values with interquartile ranges (IQR) were calculated for patients who flared and for those who did not flare. Median values were compared using Wilcoxon signed rank test. A mixed model analysis was performed to investigate change in CRP and ESR over time. Results:Tables 1 and 2 compare the median CRP and ESR between IBD patients who had a flare compared to those who did not flare stratified by trimester. CRP and ESR levels did not differ between the two groups. ESR levels increased (p<.0001) across the three trimesters of pregnancy (Figure 1); however, the rise in ESR was not different between flaring and non-flaring IBD patients. In contrast, CRP levels were stable throughout pregnancy in both flaring and non-flaring patients. Analyses were consistent when ESR and CRP were studied separately for Crohn’s disease and ulcerative colitis.Figure 1Table 1Table 2Conclusion: CRP and ESR did not differentiate disease activity between flaring and non-flaring IBD patients across the trimesters of pregnancy. Future studies should evaluate more sensitive biomarkers of disease activity for pregnant women with IBD. Disclosure - Dr. Kaplan has served as a speaker for Jansen, Merck, Schering-Plough, Abbott, and UCB Pharma. He has participated in advisory board meetings for Jansen, Abbott, Merck, Schering-Plough, Shire, and UCB Pharma. Dr. Kaplan has received research support from Merck, Abbott, and Shire. Dr. Panaccione has served as a speaker, a consultant and an advisory board member for Abbott Laboratories, Merck, Schering-Plough, Shire, Centocor, Elan Pharmaceuticals, and Procter and Gamble. He has served as a consultant and speaker for Astra Zeneca. He has served as a consultant and an advisory board member for Ferring and UCB. He has served as a consultant for Glaxo-Smith Kline and Bristol Meyers Squibb. He has served as a speaker for Byk Solvay, Axcan, Jansen, and Prometheus. He has received research funding from Merck, Schering-Plough, Abbott Laboratories, Elan Pharmaceuticals, Procter and Gamble, Bristol Meyers Squibb, and Millennium Pharmaceuticals. He has received educational support from Merck, Schering-Plough, Ferring, Axcan, and Jansen. Dr. Ghosh has served as a speaker for Merck, Schering-Plough, Centocor, Abbott, UCB Pharma, Pfizer, Ferring, and Procter and Gamble. He has participated in ad-hoc advisory board meetings for Centocor, Abbott, Merck, Schering-Plough, Proctor and Gamble, Shire, UCB Pharma, Pfizer, and Millennium. He has received research funding from Procter and Gamble, Merck, and Schering-Plough. Dr. Barkema has served as a speaker for Merck, Pfizer and Schering-Plough. Dr. Seow has served as a speaker for Merck and Schering-Plough. She has participated in advisory board meetings for Abbott, Merck, and Schering-Plough. She has received research support from Jansen. Dr. Leung has received research support from Jansen. She has served as a speaker for Jansen. She has participated in advisory board meetings for Abbott, Jansen and Shire. The other authors do not have relevant conflict of interests to disclose.
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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 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".