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

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Bibtex

@article{a674fa76c68b401fbb88ec57ec0b9a42,
title = "Predictive value of traction force measurement in vacuum extraction: Development of a multivariate prognostic model",
abstract = "Objective: To enable early prediction of strong traction force vacuum extraction. Design: Observational cohort. Setting: Karolinska University Hospital delivery ward, tertiary unit. Population and sample size: Term mid and low metal cup vacuum extraction deliveries June 2012 - February 2015, n = 277. Methods: Traction forces during vacuum extraction were collected prospectively using an intelligent handle. Levels of traction force were analysed pairwise by subjective category strong versus non-strong extraction, in order to define an objective predictive value for strong extraction. Statistical analysis: A logistic regression model based on the shrinkage and selection method lasso was used to identify the predictive capacity of the different traction force variables. Predictors: Total (time force integral, Newton minutes) and peak traction (Newton) force in the first to third pull; difference in traction force between the second and first pull, as well as the third and first pull respectively. Accumulated traction force at the second and third pull. Outcome: Subjectively categorized extraction as strong versus non-strong. Results: The prevalence of strong extraction was 26{\%}. Prediction including the first and second pull: AUC 0,85 (CI 0,80-0,90); specificity 0,76; sensitivity 0,87; PPV 0,56; NPV 0,94. Prediction including the first to third pull: AUC 0,86 (CI 0,80-0,91); specificity 0,87; sensitivity 0,70; PPV 0,65; NPV 0,89. Conclusion: Traction force measurement during vacuum extraction can help exclude strong category extraction from the second pull. From the third pull, two-thirds of strong extractions can be predicted.",
author = "Kristina Pettersson and Khurram Yousaf and Jonas Ranstam and Magnus Westgren and Gunilla Ajne",
year = "2017",
month = "3",
day = "1",
doi = "10.1371/journal.pone.0171938",
language = "English",
volume = "12",
pages = "1--10",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "3",

}