title: Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?
abstract: 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.
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Australia.
authors.forename: Wei
authors.initials: W
authors.lastname: Luo
authors.affiliation: Womens & Children's Services, Barwon Health - The Geelong Hospital, Australia.
authors.forename: Emily Y-S
authors.initials: EY
authors.lastname: Huning
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Australia; Department of Computing, Curtin University, Australia.
authors.forename: Truyen
authors.initials: T
authors.lastname: Tran
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Australia.
authors.forename: Dinh
authors.initials: D
authors.lastname: Phung
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Australia.
authors.forename: Svetha
authors.initials: S
authors.lastname: Venkatesh
keywords: Medicine