Article Text

PDF
The development of an iphone application to predict preterm birth in high risk women
  1. E M Smout,
  2. P T Seed,
  3. A H Shennan
  1. King's College London, London, UK

Abstract

Introduction Fetal fibronectin (fFN) and cervical length are the best predictors of preterm birth (PTB). No studies have attempted to predict PTB in asymptomatic high risk women by combining demographics, past obstetric history and biochemical markers. We have developed an algorithm for predicting PTB before 30, 34 and 37 weeks' gestation.

Methods We analysed 219 women attending the Preterm Surveillance Clinic at our institution between 1 September 2007 and 31 August 2009. These women were at high risk of PTB following previous PTB (16–37/40) or cervical surgery. Multiple linear logistic regression analysis was performed using Stata.

Results fFN, shortest cervical length and gestation of fFN test were the only variables that influenced PTB risk when combined; previous PTB, ethnicity, smoking and BMI had no effect on ability to predict PTB. The area under the ROC curve for the prediction models was 0.96, 0.84 and 0.77 for delivery before 30, 34 and 37 weeks' gestation respectively. We have produced a formula for calculating percentage risk of delivering before 30, 34 and 37 weeks', and within 2 and 4 weeks of the calculation. This has been developed into a freely available iPhone application.

Conclusion Predicting PTB in high risk women is principally based upon cervical length, fFN result and gestation of fFN test; women with a positive fFN at an earlier gestation, with a shorter cervix are at greatest risk. Additional demographic information and past obstetric history are superseded by these variables and need not be applied to a high risk prediction model.

Statistics from Altmetric.com

Request permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.