Risk factors for preterm delivery: do they add to fetal fibronectin testing and cervical length measurement in the prediction of preterm delivery in symptomatic women?

Baaren, Gert Jan Van; Bruijn, Merel; Vis, Jolande Y.; Wilms, Femke F.; Oudijk, Martijn A.; Kwee, Anneke; Porath, Martina; Oei, Guid; Scheepers, Hubertina C.J.; Spaanderman, Marc E. A.; Bloemenkamp, Kitty W. M.; Haak, Monique C.; Bolte, Antoinette C.; Bax, Caroline J.; Cornette, Jérôme; Duvekot, Johannes J.; Bijvanck, Bas W.A. Nij; Eijck, Jim van; Franssen, Maureen; Sollie, Krystyna M.; Vandenbussche, Frank P.H.A.; Woiski, Mallory; Bossuyt, Patrick M.; Opmeer, Brent C.; Mol, Ben Willem J.

doi:10.1016/j.ejogrb.2015.05.004

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…As applicability was already included in the eligibility assessment before the qualitative analysis, eligible studies were not found to use data sets from either primary care or hospital settings, such as from a house-to-house survey or a screening program. Most used data sets were from hospital settings, whereas only a few of those were from primary care settings in the LR (6/77, 8%) [65,69,73,77,78,87], non-LR (6/50, 12%) [119,122,132,135,148,153], or both algorithms (1/15, 7%) [162]. A detailed description of this is also given in Multimedia Appendix 1.…”

Section: Characteristics Of the Studiesmentioning

confidence: 99%

“…This is because their AUROCs were outliers compared with those for the same outcome and algorithm. We also excluded 3 LR studies [32,63,87]. In those studies, preterm delivery was defined as delivering within 1 to 2 weeks of preterm labor presentation.…”

Section: Comparison Of the Predictive Performancementioning

confidence: 99%

“…The non-LR models significantly outperformed the LR models in preterm delivery (4/5 non-LR models), CS (3/5 non-LR models), pre-eclampsia (1/2 non-LR models), and gestational diabetes (2/3 non-LR models). From those that examined preterm delivery, a prediction model did not include a non-LR high ROB study [115] compared with those from 7 LR studies [32,44,60,63,75,87,96]. This model applied a random forest (differences in logit AUROC 2.51; 95% CI 1.49-3.53).…”

Section: Comparison Of the Predictive Performancementioning

confidence: 99%

“…32,44,60,63,75,87,96,111,112,115,118,119,121,125,130,141,143,156,163,169], 5 for CS (7/62,11%)[79,90,106,117,142,166,167], 2 for pre-eclampsia (6/62, 10%)[31,48,65,76,123,147], 3 for gestational diabetes (9/62, 15%)[33,45,84,94,100,104,108,139,157], 5 for ongoing pregnancy (13/62, 21%)[73,78,99,110,132,[134][135][136]…”

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confidence: 99%

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Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

et al. 2020

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Background Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. Objective This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians’ decision making. Methods Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians’ decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2. Results Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07). Conclusions Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106

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Section: Characteristics Of the Studiesmentioning

confidence: 99%

Section: Comparison Of the Predictive Performancementioning

confidence: 99%

Section: Comparison Of the Predictive Performancementioning

confidence: 99%

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confidence: 99%

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Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

et al. 2020

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Time-to-Birth Prediction Models and the Influence of Expert Opinions

Vandewiele

Dehaene

Janssens

et al. 2019

Artificial Intelligence in Medicine

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Preterm birth is the leading cause of death among children under five years old. The pathophysiology and etiology of preterm labor are not yet fully understood. This causes a large number of unnecessary hospitalizations due to high-sensitivity clinical policies, which has a significant psychological and economic impact. In this study, we present a predictive model, based on a new dataset containing information of 1,243 admissions, that predicts whether a patient will give birth within a given time after admission. Such a model could provide support in the clinical decision-making process. Predictions for birth within 48 hours or 7 days after admission yield an Area Under the Curve of the Receiver Operating Characteristic (AUC) of 0.72 for both tasks. Furthermore, we show that by incorporating predictions made by experts at admission, which introduces a potential bias, the prediction effectiveness increases to an AUC score of 0.83 and 0.81 for these respective tasks.

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The fetal fibronectin test: 25 years after its development, what is the evidence regarding its clinical utility? A systematic review and meta-analysis

Faron

Balepa

Parra

et al. 2018

The Journal of Maternal-Fetal & Neonatal Medicine

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The fetal fibronectin test should not be used as a screening test for asymptomatic women. For high-risk asymptomatic women, and especially for women with multiple pregnancies, the performance of the fetal fibronectin test was also too low to be clinically relevant. Consensual use as a diagnostic tool for women with suspected preterm labor, the best use policy probably still depends on local contingencies, future cost-effectiveness analysis, and comparison with other more recent available biochemical markers.

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Risk factors for preterm delivery: do they add to fetal fibronectin testing and cervical length measurement in the prediction of preterm delivery in symptomatic women?

Cited by 4 publications

References 35 publications

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Time-to-Birth Prediction Models and the Influence of Expert Opinions

The fetal fibronectin test: 25 years after its development, what is the evidence regarding its clinical utility? A systematic review and meta-analysis

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