Repeated cervical length measurements for the verification of short cervical length

Hermans, Frederik; Koullali, Bouchra; Os, M. A. van; Ven, Jeanine E.M. van der; Kazemier, Brenda M.; Woiski, Mallory; Willekes, Christine; Kuiper, Petra N.; Roumen, Frans J.M.E.; Groot, Christianne M. de; Miranda, Esteriek de; Verhoeven, Corine; Haak, Monique C.; Pajkrt, Eva; Schuit, Ewoud; Mol, Ben Willem J.

doi:10.1002/ijgo.12321

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Some studies advocate for dynamic cervical examination regardless of whether a subject is highor low-risk [50,51]. On the other hand, a greater number of studies either oppose or do not recommend large-scale cervical screening, for reasons that include but are not limited to the material cost, the time required, the lack of unified standards, and the professional training of laboratory personnel [13,14,[52][53][54][55], which may lead to costs that do not conform to health economics. The prediction model in this study effectively predicts the early development of preterm labor based on demographic factors and prenatal laboratory data.…”

Section: Principal Findingsmentioning

confidence: 99%

The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study (Preprint)

Zhang¹,

Lu²,

Wu³

et al. 2021

Preprint

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BACKGROUND Globally, the trends of preterm birth rate have been increasing over time. Ultrasonography cervical length assessment is considered to be the most effective screening method, however, universal cervical length screening in the whole population remains controversial because of the cost budget. OBJECTIVE In this work, obstetric data are used to analyze and assess the risk of preterm birth. The purpose is to screen high-risk groups of preterm birth in the early and second trimester of pregnancy, and targeted cervical screening is more in line with health economics. METHODS This study attempts to use continuous electronic medical records(EMRs) data of pregnant women to construct a preterm birth predicting classifier based on long short-term memory (LSTM) networks. The clinical data were collected from 5187 Chinese pregnant women with natural vaginal delivery, including more than 25,000 obstetric EMRs during the early trimester to 28 weeks of gestation. The area under ROC curve, accuracy, sensitivity and specificity were used to assess the performance of prediction model. RESULTS Compared with traditional cross-sectional study, LSTM model in time series study has better overall prediction ability, which has a lower misdiagnosis rate with the same detection rate, and the accuracy was 0.739, sensitivity was 0.407, specificity was 0.982, and AUC was 0.651. Feature importance identification indicated that blood pressure, blood glucose, lipids, uric acid and other metabolic factors were the important factors related to preterm birth. CONCLUSIONS The results of this study are helpful to the formulation of guidelines for the prevention and treatment of preterm birth, and it can assist the clinicians to make correct decisions during the obstetric examinations. For the preterm birth prediction scenario, time series model has certain advantages.

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Section: Principal Findingsmentioning

confidence: 99%

The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study (Preprint)

Zhang¹,

Lu²,

Wu³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study

Zhang

et al. 2022

JMIR Med Inform

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Background Globally, the preterm birth rate has tended to increase over time. Ultrasonography cervical-length assessment is considered to be the most effective screening method for preterm birth, but routine, universal cervical-length screening remains controversial because of its cost. Objective We used obstetric data to analyze and assess the risk of preterm birth. A machine learning model based on time-series technology was used to analyze regular, repeated obstetric examination records during pregnancy to improve the performance of the preterm birth screening model. Methods This study attempts to use continuous electronic medical record (EMR) data from pregnant women to construct a preterm birth prediction classifier based on long short-term memory (LSTM) networks. Clinical data were collected from 5187 pregnant Chinese women who gave birth with natural vaginal delivery. The data included more than 25,000 obstetric EMRs from the early trimester to 28 weeks of gestation. The area under the curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the prediction model. Results Compared with a traditional cross-sectional study, the LSTM model in this time-series study had better overall prediction ability and a lower misdiagnosis rate at the same detection rate. Accuracy was 0.739, sensitivity was 0.407, specificity was 0.982, and the AUC was 0.651. Important-feature identification indicated that blood pressure, blood glucose, lipids, uric acid, and other metabolic factors were important factors related to preterm birth. Conclusions The results of this study will be helpful to the formulation of guidelines for the prevention and treatment of preterm birth, and will help clinicians make correct decisions during obstetric examinations. The time-series model has advantages for preterm birth prediction.

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Repeated cervical length measurements for the verification of short cervical length

Abstract: Among patients with singleton pregnancies, verification of short cervical length did not improve the identification of short cervical length.

Cited by 2 publications

References 23 publications

The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study (Preprint)

The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study (Preprint)

The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study

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