P–393 The relationship of cigarette smoking with gestational diabetes. An evaluation of a database of more than nine million deliveries
Bibliographic record
Abstract
Abstract Study question Given the common pathophysiology between type 2 DM (risk of which is increased by smoking) and GDM we sought to assess whether an association between smoking and GDM exists? Summary answer After controlling for confounding effects, women who smoke during pregnancy are at an increased risk of developing GDM. What is known already Smoking is well associated with type 2 diabetes mellitus (DM) in multiple studies. It has remained unclear whether there is also an association between smoking and GDM as publications report conflicting results. In a meta-analysis of 1,364,468 pregnancies (22,811 smokers) there was no association between cigarette smoking and the risk of GDM. While a study from the Pregnancy Risk Assessment Monitoring System, on 222,408 patients (54,114 smoked during pregnancy) found a higher risk for GDM among smokers. Study design, size, duration A retrospective population-based study utilizing data from the Healthcare Cost and Utilization Project—Nationwide Inpatient Sample (HCUP-NIS). A dataset of all deliveries between 2004 and 2014 inclusively, was created. Within this group, all deliveries to women who smoked during pregnancy were identified as part of the study group (n = 443,590), and the remaining deliveries were categorized as non smoker births and comprised the reference group (n = 8,653,198). Participants/materials, setting, methods The HCUP-NIS is the largest inpatient sample database in the USA, and it is comprised of hospitalizations throughout the country. It provides information relating to 20% of US admissions and represents over 96% of the American population. Multivariate logistic regression analysis, controlling for confounding effects, was conducted to explore associations between smoking and delivery and neonatal outcomes. According to Tri-Council Policy statement (2018), IRB approval was not required, given data was anonymous and publicly available. Main results and the role of chance Our study identified 9,096,788 births between 2004–2014, of which 443,590 (4.8%) had a documented diagnosis of maternal smoking. Smokers were more likely to be young (53% vs 37.2% under the age of 35), white (78% vs 51.1%), of lower income (39.1% vs 26.6%), delivered in a rural hospital (28.7% vs 13.2%), suffer from obesity (6.4% vs 3.4%), have pregestational diabetes (1.2% vs 0.9%) and chronic hypertension (2.5% vs 1.8%) and to have undergone a previous caesarean section (17.7% vs 5.9%) (all p value <0.0001, all were controlled for in the logistic regression analysis). An increased risk for GDM among smokers was detected with an adjusted odds ratio (aOR) of 1.10 (95%CI:1.07–1.14 p < 0.0001), when controlling for the factors above. A significant higher risk of preterm delivery (aOR1.39, 95%CI:1.35–1.43, p < 0.0001), PPROM (aOR 1.52 ,95%CI:1.43–1.62, p < 0.0001), wound complications (aOR1.24,95%CI:1.09–1.41, p < 0.0001), and the need for hysterectomy (aOR1.32,95%CI:1.0–1.64,p< 0.0001) among the smokers was found as well. Limitations, reasons for caution The limitations of our study are its retrospective nature and the fact that it relies on an administrative database. Wider implications of the findings: The public health implications of confirming smoking as a risk for GDM are many. This can lead to earlier screening in pregnancy of smokers for GDM. The earlier initiation of interventions could decrease fetal complications and possibly have impact on the life and long-term health of that offspring. 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 | 0.000 | 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".