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Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?

Luo, Wei, Huning, Emily Yu Sum, Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2016, Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?, Heliyon, vol. 2, no. 6, Article number: e00119, pp. 1-15, doi: 10.1016/j.heliyon.2016.e00119.

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Title Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?
Author(s) Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Huning, Emily Yu Sum
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name Heliyon
Volume number 2
Issue number 6
Season Article number: e00119
Start page 1
End page 15
Total pages 15
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-06
ISSN 2405-8440
Keyword(s) medicine
Summary BACKGROUND: Preterm birth is a clinical event significant but difficult to predict. Biomarkers such as fetal fibronectin and cervical length are effective, but the often are used only for women with clinically suspected preterm risk. It is unknown whether routinely collected data can be used in early pregnancy to stratify preterm birth risk by identifying asymptomatic women. This paper tries to determine the value of the Victorian Perinatal Data Collection (VPDC) dataset in predicting preterm birth and screening for invasive tests.

METHODS: De-identified VPDC report data from 2009 to 2013 were extracted for patients from Barwon Health in Victoria. Logistic regression models with elastic-net regularization were fitted to predict 37-week preterm, with the VPDC antenatal variables as predictors. The models were also extended with two additional variables not routinely noted in the VPDC: previous preterm birth and partner smoking status, testing the hypothesis that these two factors add prediction accuracy. Prediction performance was evaluated using a number of metrics, including Brier scores, Nagelkerke's R(2), c statistic.

RESULTS: Although the predictive model utilising VPDC data had a low overall prediction performance, it had a reasonable discrimination (c statistic 0.646 [95% CI: 0.596-0.697] for 37-week preterm) and good calibration (goodness-of-fit p = 0.61). On a decision threshold of 0.2, a Positive Predictive Value (PPV) of 0.333 and a negative predictive value (NPV) of 0.941 were achieved. Data on previous preterm and partner smoking did not significantly improve prediction.

CONCLUSIONS: For multiparous women, the routine data contains information comparable to some purposely-collected data for predicting preterm risk. But for nulliparous women, the routine data contains insufficient data related to antenatal complications.
Language eng
DOI 10.1016/j.heliyon.2016.e00119
Field of Research 080109 Pattern Recognition and Data Mining
111716 Preventive Medicine
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution Non-Commercial No-Derivatives licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085448

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.