Predict Easy :Pre-eclampsia Risk Stratification Tool

1st International Congress for Innovation in Global Surgery

doi: 10.52648/ICIGS.1000_16

Predict Easy :Pre-eclampsia Risk Stratification Tool

Vaishali Sundaresan , Yaswin K S Sagar, Carolin Solomi, A Revanth
HTIC, IIT Madras Research Park, Chennai, India; Makunda Christian leprosy and General Hospital, Assam, India; St. Joseph’s Institute of Technology, Chennai, India; Healthcubed Pvt Ltd, Bengaluru, India

Problem Statement: Unavailability of accurate screening tool for pre-eclampsia in low resource settings leading to preventable maternal, perinatal mortality and morbidity.

Need of an innovation: Our product Predict Easy is a machine learning based software that provides risk stratification for pre-eclampsia based on routine antenatal markers. Our product will be placed on the cloud, clinicians can access it through the web interface. Input parameters are entered through the portal, risk analysis is performed on the cloud and risk report is generated.

Pre-eclampsia Opportunity: Pre-eclampsia is a multisystem disorder that typically affects 2% – 5% of pregnant women around the world. In India, the incidence is reported to be 8%-10 %. Globally,76,000 women and 500,000 babies die each year because of pre-eclampsia. Thus, identification of women who are at high risk of developing pre-eclampsia at early stage becomes necessary so that necessary measures can be initiated early enough to improve the pregnancy outcome. The current existing screening methods for pre-eclampsia using biophysical and biochemical markers are expensive, require sophisticated equipment and well-trained manpower which might not be available in all the resource settings.

Mean Arterial Pressure, Proteinuria, Red Blood Cell Distribution Width, Maternal Characteristics, Medical history are some of the markers that are routinely collected during antenatal care that are cost effective and require minimal equipment. Multiple studies have been performed to associate these markers with pre-eclampsia individually or in combination of two or three. We intend to combine all these markers and come up with a simple machine learning based risk stratification tool for pre-eclampsia. This tool could be implemented in low resource settings where women with possibility of developing pre-eclampsia can be identified early and frequent antenatal care can be provided to high risk women.

Novelty, Differentiation, Value Proposition:
• Easy to implement
• Simple, based on routine antenatal markers
• No additional tests and sophisticated equipment required
• Risk interpretation based of Indian population
• Best use of available resources
• Cost effective in comparison with the existing the methods.

Impact: Our product Predict Easy will address the need for an accurate screening tool for preeclampsia. In the resource limited settings, it will help the ANMs and healthcare workers in detecting the complication at early stage without having to go for other expensive tests. Product is currently in the development stage with data collection in progress.

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