Risk of preterm birth after prior term cesarean

Visser, Laura; Slaager, Ciska; Kazemier, Brenda M.; Rietveld, Anna L.; Oudijk, Martijn A.; Groot, Christianne de; Mol, Ben W.; Boer, Marjon A. de

doi:10.1111/1471-0528.16083

Cited by 23 publications

(19 citation statements)

References 41 publications

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“…A Dutch population study found that overall caesarean births at term was associated with a small increased risk in preterm birth, compared with vaginal birth, and this was mainly driven by an increase in spontaneous preterm birth. However, the study did not differentiate between the stage of labour at which the caesarean section was performed 7 …”

Section: Discussionmentioning

confidence: 99%

“…A number of risk factors have been associated with preterm birth including nulliparity, previous preterm birth, cervical surgery, short interpregnancy interval, social deprivation and smoking 4 . Caesarean delivery has been associated with preterm birth in a subsequent pregnancy in some studies, 5–7 but not others 8,9 . The stage in labour at which the caesarean section is performed may be crucial, with some studies suggesting preterm birth risk is higher if the caesarean section is performed in the second stage of labour, when the cervix is fully (10 cm) dilated, compared with caesarean before labour onset 2,5,10–12 .…”

Section: Introductionmentioning

confidence: 99%

“…However, the study did not differentiate between the stage of labour at which the caesarean section was performed. 7 Caesarean birth is increasingly used for operative birth in the second stage of labour, with an accompanying reduction in attempted and successful assisted vaginal births. [21][22][23] A large US study found that nearly one-quarter of primary caesarean births in 2000 were in the second stage of labour.…”

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confidence: 99%

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Caesarean birth and risk of subsequent preterm birth: a retrospective cohort study

Williams

Fong

Murray

et al. 2020

BJOG

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Objective To determine the risk of spontaneous and medically indicated preterm birth associated with mode of birth in previous term‐born pregnancy. Design Retrospective cohort study. Setting Two UK maternity units. Population or sample A total of 16 340 women with first two consecutive singleton births and the first birth at term. Methods Retrospective cohort study using routinely collected clinical data. Main outcome measures Incidence of spontaneous preterm birth and medically indicated preterm birth at less than 37 weeks of gestation after term birth, in relation to mode of birth in first pregnancy. Subgroup analysis on cervical dilatation at the time of first caesarean birth. Results Compared with vaginal birth, emergency caesarean birth at full dilatation was associated with an increase in spontaneous preterm birth (2.3% vaginal birth versus 4.5% full dilatation caesarean; adjusted odds ratio [aOR] 3.29, 95% CI 2.02–5.13, P < 0.001). Elective caesarean, emergency caesarean at <4 cm dilatation, and emergency caesarean at 4–9 cm dilatation were associated with increased medically indicated preterm birth (0.8% vaginal births versus 1.9% elective caesarean, 3.3% <4 cm caesarean, 1.3% 4–9 cm caesarean; aOR 2.30, 95% CI 1.19–4.15, P = 0.009; aOR 4.68, 95% CI 2.98–7.24, P < 0.001; and aOR 2.43, 95% CI 1.43–4.00, P = 0.001, respectively). Conclusions Term caesarean in the first stage of labour or performed prelabour is associated with medically indicated preterm birth. Term caesarean in the second stage of labour is associated with spontaneous preterm birth. Tweetable abstract Caesarean in the second stage of labour is associated with spontaneous preterm birth.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Caesarean birth and risk of subsequent preterm birth: a retrospective cohort study

Williams

Fong

Murray

et al. 2020

BJOG

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“…In addition, the codes included in this visit indicate a long-term and current use of aspirin during the previous pregnancy, probably to prevent preterm preeclampsia. Moreover, the codes on this visit report that this patient had a previous cesarean delivery, which might also increase the risk of preterm birth in later pregnancies [ 37 , 38 ]. This patient is a great example demonstrating the ability of the attention mechanism to go beyond recent events and model long-range dependencies among medical events to the predicted outcome.…”

Section: Experiments and Resultsmentioning

confidence: 99%

PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks

2022

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Background Early identification of pregnant women at risk for preterm birth (PTB), a major cause of infant mortality and morbidity, has a significant potential to improve prenatal care. However, we lack effective predictive models which can accurately forecast PTB and complement these predictions with appropriate interpretations for clinicians. In this work, we introduce a clinical prediction model (PredictPTB) which combines variables (medical codes) readily accessible through electronic health record (EHR) to accurately predict the risk of preterm birth at 1, 3, 6, and 9 months prior to delivery. Methods The architecture of PredictPTB employs recurrent neural networks (RNNs) to model the longitudinal patient’s EHR visits and exploits a single code-level attention mechanism to improve the predictive performance, while providing temporal code-level and visit-level explanations for the prediction results. We compare the performance of different combinations of prediction time-points, data modalities, and data windows. We also present a case-study of our model’s interpretability illustrating how clinicians can gain some transparency into the predictions. Results Leveraging a large cohort of 222,436 deliveries, comprising a total of 27,100 unique clinical concepts, our model was able to predict preterm birth with an ROC-AUC of 0.82, 0.79, 0.78, and PR-AUC of 0.40, 0.31, 0.24, at 1, 3, and 6 months prior to delivery, respectively. Results also confirm that observational data modalities (such as diagnoses) are more predictive for preterm birth than interventional data modalities (e.g., medications and procedures). Conclusions Our results demonstrate that PredictPTB can be utilized to achieve accurate and scalable predictions for preterm birth, complemented by explanations that directly highlight evidence in the patient’s EHR timeline.

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“…In addition, the codes included in this visit indicate a long-term and current use of aspirin during the previous pregnancy, probably to prevent preterm preeclampsia. Moreover, the codes on this visit report that this patient had a previous cesarean delivery, which might also increase the risk of preterm birth in later pregnancies [34,35]. This patient is a great example demonstrating the ability of the attention mechanism to go beyond recent events and model longrange dependencies among medical events to the predicted outcome.…”

Section: Code-level and Visit-level Interpretationsmentioning

confidence: 93%

PredictPTB: An Interpretable Preterm Birth Prediction Model Using Attention-based Recurrent Neural Networks

AlSaad

Malluhi

Boughorbel

2021

Preprint

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Background: Early identification of pregnant women at risk for preterm birth (PTB), a major cause of infant mortality and morbidity, has a significant potential to improve prenatal care. However, we lack effective predictive models which can accurately forecast PTB and complement these predictions with appropriate interpretations for clinicians. In this work, we introduce a clinical prediction model (PredictPTB) which combines variables (medical codes) readily accessible through electronic health record (EHR) to accurately predict the risk of preterm birth at 1, 3, 6, and 9 months prior to delivery. Methods: The architecture of PredictPTB employs recurrent neural networks (RNNs) to model the longitudinal patient's EHR visits and exploits a single code-level attention mechanism to improve the predictive performance, while providing temporal code-level and visit-level explanations for the prediction results. We compare the performance of different combinations of prediction time-points, data modalities, and data windows. We also present a case-study of our model's interpretability illustrating how clinicians can gain some transparency into the predictions. Results: Leveraging a large cohort of 222,436 deliveries, comprising a total of 27,100 unique clinical concepts, our model was able to predict preterm birth with an ROC-AUC of 0.82, 0.79, 0.78, and PR-AUC of 0.40, 0.31, 0.24, at 1, 3, and 6 months prior to delivery, respectively. Results also confirm that observational data modalities (such as diagnoses) are more predictive for preterm birth than interventional data modalities (e.g., medications and procedures). Conclusions: Our results demonstrate that PredictPTB can be utilized to achieve accurate and scalable predictions for preterm birth, complemented by explanations that directly highlight evidence in the patient's EHR timeline.

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Risk of preterm birth after prior term cesarean

Cited by 23 publications

References 41 publications

Caesarean birth and risk of subsequent preterm birth: a retrospective cohort study

Caesarean birth and risk of subsequent preterm birth: a retrospective cohort study

PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks

PredictPTB: An Interpretable Preterm Birth Prediction Model Using Attention-based Recurrent Neural Networks

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