EP01.50: Diagnostic algorithm for the evaluation of threatened preterm labour with the novel fine birth device
Year: 2024
Type: conference-abstract
Abstract: Fine Birth (FB) is an in vivo, handheld medical device that aims to differentiate between false and true threatened preterm labour (TPTL). It uses torsional shear waves to determine the stiffness of the patient's cervical tissue. FB-CT1 study was conducted to develop a diagnostic algorithm for TPTL using the FB device in combination with clinical variables. The main objective of the present presentation is reporting the predictive value of true TPTL defined as giving birth 0 to 14 days after being evaluated for TPTL. The FB-CT1 study is a prospective, blind, multicentre, non-randomised and non-interventional study with the FB device in standard hospital care settings. Ten maternity units in Spain took part in this study. The study population were women ≥ 18 years, with a singleton pregnancy between 28+0 to 36+6 weeks, presenting to the emergency department with suspected TPTL. The sample size was estimated in 258, expecting a 15% of the event, with a competitive recruitment starting in June 2022. The algorithm will be created using a predictive logistic regression model and its diagnostic accuracy will be compared to PAMG1 testing and short cervical length (<15mm). It has been analysed the 72% of the sample (n = 185). Mean gestational age was 31 weeks (range 25-36) and the average days to labour after recruiting was 40.68 ± 20.68. True TPTL accounted for 13 cases of the 33 expected. The created algorithm uses 7 variables of the 16 that were tested, with a diagnostic accuracy of 86.44% (NPV 92.3%, PPV 42.86%). PAMG-1 or short cervix had a diagnostic accuracy of 81.08% (NPV 94.63%, PPV 25%). Current outcomes may suggest superior performance of the algorithm compared to other standardised methodologies. However, there are insufficient cases experiencing preterm labour after the TPTL for FB's comprehensive analyses. In the future, an international multicentre study has been designed to incorporate a larger dataset (FB-CT2).
DOI: 10.1002/uog.28064
Authors: A. Aiartzaguena, A. Samper, M. Muñoz Gonzalez, A. Urones, P. Prats, A. Jiménez, D. Boada Diaz, E. Del Barco, S. Ferrero
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4402358159