Developing Artificial Intelligence Based solutions to predict preterm delivery and related adverse outcome to baby

Not yet recruiting

Conditions: Premature rupture of membranes, unspecified as to length of time between rupture and onset of labor,

Registration Number: CTRI/2021/12/038654

Lead Sponsor: Philips Research

Brief Summary: This study aims to acquire data from around 1500 unselected pregnant women attending antenetal OPD in a tertiary care referral center in South India (single center). Data acquired includes risk factors from history, examination, blood/urine reports, Ultrasound reports - all relevant to prediction of preterm labour. In addition, regularly performed Non-stress tests and uterine activity monitoring are digitally captured during their antenatal visits at or beyond 26 weeks of pregnancy. Once data is collected, study aims to develop and test Artificial Intelligence Algorithms to predict preterm labour in obstetric population.

Preterm labour/delivery (PTL/PTD) is a major health burden and contributes significantly to perinatal mortality/morbidity as well as long term health problems in the children. With a worldwide prevalence of 5-18%, it is an urgent need to predict and prevent preterm delivery. Multiple individual risk factors and risk scoring algorithms have been developed to predict PTL/PTD but none have reached a level of accuracy to be able to be employ in general obstetric practice. In addition, over half of PTD occur in women with no apparent risk factors. Complex interplay of risk factors is also a well-known phenomenon â€“ which is difficult to be interpreted during routine obstetric practice. Hence this study aims to see if incorporating all these multiple risk factors in addition to periodic observation of fetal heart rate/uterine activity patterns â€“ into an AI tool â€“ to see if we can develop a prediction model for preterm delivery that has a better prediction value than the multiple existing risk factors/risk scoring systems. A large data set on fetal heart rate pattern along with uterine activity has not so far been studied by an AI algorithm in an attempt to predict preterm delivery. Also, incorporating multiple clinical, laboratory, imaging parameters to fetal heart+uterine activity patterns may help us develop a more robust tool to predict PTL/PTD - this information is not available in clinical literature. An accurate prediction may help employ preventive interventions in a suitable subset of vulnerable pregnant women in order to prevent PTL/PTD.

Detailed Description: Not available

Recruitment & Eligibility

Status: Not Yet Recruiting

Sex: Female

Target Recruitment: 1500

Inclusion Criteria

1.Adult (>18 years) pregnant women (from 26th week of gestation), who have regular antenatal care at Department of OBG, KMC Manipal, willing and able to provide informed consent.

Exclusion Criteria

1.Pregnant women aged < 18 years.
2.Women admitted to labour room/HDU/ICU due to obstetric/non-obstetric problem 3.Women in need of emergency management 4.Critical non-pregnancy related health issues (Road traffic accidents etc.) 5.Currently displaying COVID-19-related symptoms, namely fever, cough and/or difficulty breathing or having been positively tested as infected with COVID-19 in the past 14 days.

Study & Design

Study Type: Observational

Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Primary outcome that the study intends to predict is preterm labour, delivery and the related adverse perinatal outcome. Using the acquired data from relevant clinical details, lab and imaging data, as well as regularly performed NSTs â€“ It is intended to develop a predictive AI model in order to predict the occurrence of preterm labour, delivery and the related adverse perinatal outcome. Study aims to develop as well as test the accuracy of such a predictive AI model in terms of sensitivity, specificity, positive and negative predictive value.	3 years

Secondary Outcome Measures

Name	Time	Method

Trial Locations

Locations (1): Department of OBG,KMC Manipal, MAHE
🇮🇳
Udupi, KARNATAKA, India
Department of OBG,KMC Manipal, MAHE
🇮🇳Udupi, KARNATAKA, India
Akhila Vasudeva
Principal investigator
9591614792
akhila.vasudeva@manipal.edu