Predictive value of traction force measurement in vacuum extraction: Development of a multivariate prognostic model

Pettersson, Kristina; Yousaf, Khurram; Ranstam, Jonas; Westgren, Magnus; Ajne, Gunilla

doi:10.1371/journal.pone.0171938

Cited by 7 publications

(10 citation statements)

References 20 publications

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“…The characteristics of study populations showed that pregnant women and fetuses or newborns were the populations of most studies developed using LR and non-LR models, respectively. Among pregnant women, the LR algorithm was mostly applied to develop predictions for outcome categories of obstetric labor (13/77, 17%) [36,46,47,54,57,62,64,70,83,86,91,97,103], pregnancy-induced hypertension (12/77, 16%) [30,31,43,48,55,65,66,68,76,81,93,105], and gestational diabetes (7/77, 9%) [33,45,49,84,94,100,104]. Among fetus or newborn populations, non-LR algorithms were mostly applied to develop predictions for outcome categories of premature birth (12/50, 24%) [111,112,115,116,118,119,121,122,125,130,141,143] and fetal distress (9/50, 18%) [113,…”

Section: Lr and Other Machine Learning Algorithmsmentioning

confidence: 99%

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: Lr and Other Machine Learning Algorithmsmentioning

confidence: 99%

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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“…Latterly there has also been the introduction of single-use systems and a focus on feedback mechanisms to inform best practice (e.g. alarms to alert the clinician to loss of suction [92][93][94] and force sensors to detect the level of traction 62,95 ).…”

Section: Discussionmentioning

confidence: 99%

An engineering perspective of vacuum assisted delivery devices in obstetrics: A review

Goordyal

Anderson

Alazmani

2020

Proc Inst Mech Eng H

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Complications during childbirth result in the need for clinicians to use ‘assisted delivery’ in over 12% of cases (UK). After more than 50 years in clinical practice, vacuum assisted delivery (VAD) devices remain a mainstay in physically assisting child delivery; sometimes preferred over forceps due to their ease of use and reduced maternal morbidity. Despite their popularity and enduring track-record, VAD devices have shown little evidence of innovation or design change since their inception. In addition, evidence on the safety and functionality of VAD devices remains limited but does present opportunities for improvements to reduce adverse clinical outcomes. Consequently in this review we examine the literature and patent landscape surrounding VAD biomechanics, design evolution and performance from an engineering perspective, aiming to collate the limited but valuable information from a disparate field and provide a series of recommendations to inform future research into improved, safer, VAD systems.

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“…Both exposures may also be afflicted with a general highlighting effect that we believe is inevitable when all personnel are participating in a specific project, a concept known as the Hawthorne effect [22]. The equipment and results of traction force measurement have been described in detail by the authors elsewhere [21, 23].…”

Section: Methodsmentioning

confidence: 99%

Effect of team training and monitoring on the rate of failed mid and low cavity vacuum extraction: a hospital based intervention study

Pettersson

Westgren

Götze-Eriksson

et al. 2019

BMC Pregnancy Childbirth

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Background Clinical team training has been advocated as a means to improve delivery care, and failed extractions is a suggested variable for clinical audit in instrumental vaginal delivery. Other activities may also have intended or unintended effects on care processes or outcomes. Methods We retrospectively observed 1074 mid and low vacuum extraction deliveries during three time periods (prevalence periods): Baseline (period 0), implemented team training (period 1 and 2) and monitoring of traction force during vacuum extraction (period 2). Our primary outcome was failed extraction followed by emergency cesarean section or obstetric forceps delivery. Results The prevalence proportion (relative risk) of failed extraction decreased significantly after implementation of team training, from 19% (period 0) to 8 % (period 1), corresponding to a relative risk of 0.48 [0.26–0.87]. The secondary procedural outcome complicated delivery (duration > 15 min or number of pulls > 6, or cup detachment > 1) was decreased in period 2 compared to period 1, RR 0.42 [0.23–0.76]. Secondary clinical (neonatal) outcome were not affected. Conclusion Clinically based educational efforts and increased monitoring improved procedural outcome without improving neonatal outcome. The study design has inherent limitations in making causal inference. Electronic supplementary material The online version of this article (10.1186/s12884-019-2257-z) contains supplementary material, which is available to authorized users.

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Predictive value of traction force measurement in vacuum extraction: Development of a multivariate prognostic model

Cited by 7 publications

References 20 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

An engineering perspective of vacuum assisted delivery devices in obstetrics: A review

Effect of team training and monitoring on the rate of failed mid and low cavity vacuum extraction: a hospital based intervention study

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