O-269 Reproductive and Neonatal Outcomes in Women with Adenomyosis: A Population-based Study
Bibliographic record
Abstract
Abstract Study question Does adenomyosis impact reproductive and neonatal outcomes? Summary answer Adenomyosis increases risk of infertility and pregnancy/delivery complications. Increased surveillance may aid in prevention and/or decreasing risk of maternal and neonatal morbidity and mortality. What is known already Patients with adenomyosis experience greater adverse obstetric and gynecological events which include but are not limited to, infertility and preterm delivery, as well as other adverse gynecological conditions such as endometriosis. Currently, existing literature provides a limited guide regarding obstetric and gynecological surveillance and management of patients diagnosed with adenomyosis. The current study aims at providing an in-depth analysis of a population database to expand and provide evidence-based insight on maternal, pregnancy and neonatal outcomes in women with adenomyosis. Study design, size, duration Retrospective population-based study. Data from the Health Care Cost and Utilization Project-Nationwide Inpatient Sample database (HCUP-NIS) were extracted from 2004 through 2014 using the ICD-9 codes to create a delivery cohort. Code 617.0 was used to identify women with adenomyosis, and reproductive outcomes were then compared to pregnancies without adenomyosis. A multivariate logistic regression model was utilized to adjust for statistically significant variables (P-value <0.05) Participants/materials, setting, methods Data was obtained from HCUP-NIS databse. Delivery records for the study and control groups were extracted using international classification of diseases Clinical Modification diagnostic codes(ICD-9-CM). The study group consisted of patients with adenomyosis and the control was patients without a diagnosis of adenomyosis. Logistic-regression-analysss was conducted to explore associations between adenomyosis and maternal&neonatal obstetrical outcomes through the estimation of odds ratio (OR) and 95%-confidence intervals(CI). The regression models were adjusted for the potential confounding factors. Main results and the role of chance Of the 9,096,788 deliveries in the study period, 2467 women had adenomyosis. Women with adenomyosis were more likely to be older (P < 0.0001), obese (P < 0.0001), have chronic hypertension (P < 0.0001), thyroid disease (P < 0.0001), pre-gestational diabetes mellitus (P < 0.0001), to have had previous caesarean section (P < 0.0001) and in-vitro fertilization (IVF) (P < 0.0001). Patients with adenomyosis had a statistically significant risk of developing pregnancy induced hypertension, preeclampsia, and placenta previa relative to the controls, after controlling for baseline risk factors; aOR 1.55 (95% CI 1.33-1.82), aOR 1.69 (95% CI 1.38-2.08), and aOR 5.86 (95% CI 4.62-7.43), respectively. Moreover, the rates of abruptio placenta, caesarean section, hysterectomy, post-partum hemorrhage, wound complications, blood transfusions and maternal infection were higher in the adenomyosis group relative to the controls, after controlling for confounding factors, aOR 2.17 (95% CI 1.60-2.95), aOR 21.63 (95% CI 17.99-26.02), aOR 6.39 (95% CI 4.22-9.68), aOR 1.97 (95% CI 1.60-2.42), aOR 2.37 (95% CI 1.52-3.69), aOR 2.25 (95% CI 1.71-2.95), and 1.82 (95% CI 1.39-2.39), respectively. At birth 1.5% of neonates born to adenomyosis patients had congenital anomalies, relative to 0.4% within the controls (aOR 2.94 (95% CI 2.02-4.28)). Limitations, reasons for caution Code were utilized to identify patients with adenomyosis. This may present as a limitation given the possibility that a number of adenomyosis patients were grouped within the controls. Inability to differentiate between severity and extent of adenomyosis, presents as a limitation within the HCUP-NIS database. Wider implications of the findings This is the first study of its kind to provide an in-depth analysis with substantial statistical power and over nine-million deliveries, in order to provide clinical guidance for patient surveillance and management. Trial registration number Not applicable
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".