Predictive model for spontaneous preterm labor among pregnant women with contractions and intact amniotic membranes

Oliveira, Ricardo Villar Barbosa de; Martins, Marília da Glória; Rios, Lívia Teresa Moreira; Júnior, Edward Araujo; Nardozza, Luciano Marcondes Machado

doi:10.1007/s00404-012-2397-0

Cited by 6 publications

(8 citation statements)

References 26 publications

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“…Model performance was mostly judged by receiver operating curves, specificity, sensitivity, positive predictive value (PPV) and negative predictive value (NPV). The best performing model predicted spontaneous preterm labour and had an area under the curve (AUC) of 0.91 (sensitivity 88% and specificity 93%) 18. Studies were conducted in 14 different countries, mostly in Brazil (n=7) and 4 studies were conducted in multiple LMICs 19–22.…”

Section: Resultsmentioning

confidence: 99%

Prognostic models for adverse pregnancy outcomes in low-income and middle-income countries: a systematic review

et al. 2019

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IntroductionNinety-nine per cent of all maternal and neonatal deaths occur in low-income and middle-income countries (LMIC). Prognostic models can provide standardised risk assessment to guide clinical management and can be vital to reduce and prevent maternal and perinatal mortality and morbidity. This review provides a comprehensive summary of prognostic models for adverse maternal and perinatal outcomes developed and/or validated in LMIC.MethodsA systematic search in four databases (PubMed/Medline, EMBASE, Global Health Library and The Cochrane Library) was conducted from inception (1970) up to 2 May 2018. Risk of bias was assessed with the PROBAST tool and narratively summarised.Results1741 articles were screened and 21 prognostic models identified. Seventeen models focused on maternal outcomes and four on perinatal outcomes, of which hypertensive disorders of pregnancy (n=9) and perinatal death including stillbirth (n=4) was most reported. Only one model was externally validated. Thirty different predictors were used to develop the models. Risk of bias varied across studies, with the item ‘quality of analysis’ performing the least.ConclusionPrognostic models can be easy to use, informative and low cost with great potential to improve maternal and neonatal health in LMIC settings. However, the number of prognostic models developed or validated in LMIC settings is low and mirrors the 10/90 gap in which only 10% of resources are dedicated to 90% of the global disease burden. External validation of existing models developed in both LMIC and high-income countries instead of developing new models should be encouraged.PROSPERO registration numberCRD42017058044.

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Section: Resultsmentioning

confidence: 99%

Prognostic models for adverse pregnancy outcomes in low-income and middle-income countries: a systematic review

et al. 2019

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“…In the predictive model, P(Y = 1) is the probability that a woman undergoes CDMR; this probability is related to the independent variables X 1 and X 2 in the binary logistic analysis model [ 26 ]. The predictive model for performing CDMR [ 27 ] is where α refers to the intercept; X i and β i (i = 1,2) refer to the independent variables and their coefficients in the binary logistic regression, respectively; and P(CDMR) captures the probability that a woman undergoes CDMR according to different periods (X 1 ) and different age groups (X 2 ).…”

Section: Methodsmentioning

confidence: 99%

Reducing the rate of cesarean delivery on maternal request through institutional and policy interventions in Wenzhou, China

Zhang

Sun

et al. 2017

PLoS ONE

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The objective of this study was to evaluate the effect of institutional and policy interventions on reducing the rate of cesarean delivery on maternal request (CDMR) in Wenzhou, China. Institutional interventions included health education, painless delivery introduction, and doula care. Additionally, a series of health policies were developed by the Chinese central and local governments to control cesarean section rates, mostly through controlling CDMR rates. We conducted a pre-/post-intervention study using 131,312 deliveries between 2006 and 2014 in three tertiary-level public hospitals in Wenzhou, China. Chi-square tests and predictive models were used to examine changes in the CDMR rate before and after institutional and policy interventions. After institutional interventions were introduced, the overall CDMR rate increased from 15.76% to 16.34% (p = 0.053), but the average annual growth rate (AAGR) of the overall CDMR rate quickly declined from 20.11% to -4.30%. After policy interventions were introduced, the overall CDMR rate, the AAGR of the overall CDMR rate, and the probability of performing CDMR declined. Further, the overall probability of a woman undergoing CDMR decreased in all three age groups (group one: <24; group two: 24–34; group three: >34) after institutional and policy interventions. These results show that institutional and policy interventions can reduce the CDMR rate. Additionally, the CDMR rate should be included in hospitals’ performance assessment matrix to reduce the CDMR rate further.

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“…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%

“…All predictors were features extracted from the multichannel EHG obtained at around 22 and 32 weeks of gestation to predict delivery after 39 and 34 to <37 weeks of gestation for term and preterm delivery, respectively. Compared with their counterparts, LR models used predictors consisting of maternal demographics or lifestyle [44,60,75,96,163], medical or obstetric histories [44,75,96,156,163], clinical predictors from obstetrical examinations [44,163], EHG [169], and biomarkers [75]. These were obtained before pregnancy [60,96,156,163], at 11 to 14 weeks of gestation [75], 18 to 34 weeks of gestation [44,163,169], or near events within 1 to 2 weeks [44].…”

Section: Descriptive Analysis Of Predictorsmentioning

confidence: 99%

“…Compared with their counterparts, LR models used predictors consisting of maternal demographics or lifestyle [44,60,75,96,163], medical or obstetric histories [44,75,96,156,163], clinical predictors from obstetrical examinations [44,163], EHG [169], and biomarkers [75]. These were obtained before pregnancy [60,96,156,163], at 11 to 14 weeks of gestation [75], 18 to 34 weeks of gestation [44,163,169], or near events within 1 to 2 weeks [44]. The LR models were developed to predict preterm delivery at 20 to <37 weeks of gestation [44,75,96,163,169] and any delivery at <37 weeks of gestation (predictors could be taken before pregnancy) [60,156].…”

Section: Descriptive Analysis Of Predictorsmentioning

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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Predictive model for spontaneous preterm labor among pregnant women with contractions and intact amniotic membranes

Abstract: The model presented good accuracy with correspondence between predictions and observations, and has the capacity to become a useful tool for management of pregnant women with preterm labor and intact amniotic membranes.

Cited by 6 publications

References 26 publications

Prognostic models for adverse pregnancy outcomes in low-income and middle-income countries: a systematic review

Prognostic models for adverse pregnancy outcomes in low-income and middle-income countries: a systematic review

Reducing the rate of cesarean delivery on maternal request through institutional and policy interventions in Wenzhou, China

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

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