The effect of digital cervical examination on group B streptococcal culture

Knudtson, Eric; Lorenz, Landon B.; Skaggs, Valerie J.; Peck, Jennifer D.; Goodman, Jean R.; Elimian, Andrew A.

doi:10.1016/j.ajog.2009.08.021

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…This disruption, coupled with the potential damage to the vaginal mucosa during these examinations, creates conditions favorable for the invasion and upward movement of pathogenic bacteria. Consequently, this can lead to violations of the endometrium, and in severe cases, the myometrium, resulting in infections [ 9 , 10 ]. Prior research has established a connection between reproductive tract infections and histologically confirmed endometritis [ 11 ].…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a predictive model for postpartum endometritis

Wang,

Shao,

Liu

et al. 2024

PLoS ONE

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Objective The aim was to develop a predictive tool for anticipating postpartum endometritis occurrences and to devise strategies for prevention and control. Methods Employing a retrospective approach, the baseline data of 200 women diagnosed with postpartum endometritis in a tertiary maternity hospital in Zhejiang Province, spanning from February 2020 to September 2022, was examined. Simultaneously, the baseline data of 1,000 women without endometritis during the same period were explored with a 1:5 ratio. Subsequently, 1,200 women were randomly allocated into a training group dataset and a test group dataset, adhering to a 7:3 split. The selection of risk factors for postpartum endometritis involved employing random forests, lasso regression, and traditional univariate and multifactor logistic regression on the training group dataset. A nomogram was then constructed based on these factors. The model’s performance was assessed using the area under the curve (AUC), calculated through plotting the receiver operating characteristic (ROC) curve. Additionally, the Brier score was employed to evaluate the model with a calibration curve. To gauge the utility of the nomogram, a clinical impact curve (CIC) analysis was conducted. This comprehensive approach not only involved identifying risk factors but also included a visual representation (nomogram) and thorough evaluation metrics, ensuring a robust tool for predicting, preventing, and controlling postpartum endometritis. Results In the multivariate analysis, six factors were identified as being associated with the occurrence of maternal endometritis in the postpartum period. These factors include the number of negative finger tests (OR: 1.159; 95%CI: 1.091–1.233; P < 0.05), postpartum hemorrhage (1.003; 1.002–1.005; P < 0.05), pre-eclampsia (9.769; 4.64–21.155; P < 0.05), maternity methods (2.083; 1.187–3.7; P < 0.001), prenatal reproductive tract culture (2.219; 1.411–3.47; P < 0.05), and uterine exploration (0.441; 0.233–0.803; P < 0.001).A nomogram was then constructed based on these factors, and its predictive performance was assessed using the area under the curve (AUC). The results in both the training group data (AUC: 0.803) and the test group data (AUC: 0.788) demonstrated a good predictive value. The clinical impact curve (CIC) further highlighted the clinical utility of the nomogram. Conclusion The development of an individualized nomogram for postpartum endometritis infection holds promise for doctors in screening high-risk women, enabling early intervention and ultimately reducing the rate of postpartum endometritis infection. This comprehensive approach, integrating key risk factors and predictive tools, enhances the potential for timely and targeted medical intervention.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a predictive model for postpartum endometritis

Wang,

Shao,

Liu

et al. 2024

PLoS ONE

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“…Pre- and post-intervention experiments have been widely used in medical and social behavioral studies (Spleen et al 2012, Rossi et al 2010, Wajnberg et al 2010, Knudtson et al 2010, Zieschang et al 2010). One distinct feature of a pre-post study is that each patient contributes a pair of observations (observations at pre-intervention and post-intervention).…”

Section: Introductionmentioning

confidence: 99%

A GEE approach to determine sample size for pre- and post-intervention experiments with dropout

Zhang

Ahn

2014

Computational Statistics & Data Analysis

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Pre- and post-intervention experiments are widely used in medical and social behavioral studies, where each subject is supposed to contribute a pair of observations. In this paper we investigate sample size requirement for a scenario frequently encountered by practitioners: All enrolled subjects participate in the pre-intervention phase of study, but some of them will drop out due to various reasons, thus resulting in missing values in the post-intervention measurements. Traditional sample size calculation based on the McNemar’s test could not accommodate missing data. Through the GEE approach, we derive a closed-form sample size formula that properly accounts for the impact of partial observations. We demonstrate that when there is no missing data, the proposed sample size estimate under the GEE approach is very close to that under the McNemar’s test. When there is missing data, the proposed method can lead to substantial saving in sample size. Simulation studies and an example are presented.

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