Pivotal, Prospective, Multi-centre, Single-arm Study to Evaluate the Safety and Effectiveness of Oli for Identification of Patients, During the Intrapartum Period, Who Are at Higher Risk of Developing Abnormal Postpartum Uterine Bleeding, Including Postpartum Haemorrhage

Description

The World Health Organization (WHO) recognizes postpartum haemorrhage as a blood loss of 500ml or more within 24 hours after delivery and severe PPH as a blood loss of 1000ml within the same timeframe [2]. The American College of Obstetricians and Gynaecologists (ACOG) defines PPH as a cumulative blood loss of greater than or equal to 1000ml or blood loss accompanied by signs and symptoms of hypovolemia within 24 hours after the birth process [7] and recognizes a blood loss greater than 500ml as abnormal, and should be treated as a Stage 1 Obstetric Haemorrhage [17]. Further, the CMQCC Obstetric Haemorrhage Care Guidelines identifies a blood loss greater than 500ml as the trigger for the activation of haemorrhage protocols [18]. The Royal Australin and New Zealand College of Obstetricians and Gynaecologists (RANZCOG) also recognises postpartum haemorrhage as a blood loss of 500ml or more within 24 hours after delivery [26]. Consistent with these standards, Baymatob for the purpose of this study has defined Abnormal Postpartum Uterine Bleeding as postpartum blood loss ≥ 500ml.

Oli is a non-invasive device that uses several different parameters to identify those individuals who are likely to experience an APUB, including PPH, prior to birth. Oli has been granted FDA Breakthrough Device Designation for use by healthcare providers to identify a patient, during the intrapartum period, who is at higher risk of developing abnormal postpartum uterine bleeding, including postpartum haemorrhage, in advance of delivery/birth.

Oli functions by capturing physiological signals (a combination of biopotentials, skin temperature, movement and deformation of the maternal abdomen) via a wearable sensor attached to the maternal abdomen by electrodes during the intrapartum period. The physiological signals are processed and analysed via a fixed machine learning algorithm against a pre-defined threshold that indicates whether an individual is at an increased risk of APUB including PPH occurring.