Preeclampsia Prediction Algorithm Using Biomarker Merging
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Solution Overview
Problem
Current methods for predicting preeclampsia are not sensitive or specific, often requiring multiple biomarkers measured individually and equipment-specific, which can be costly and impractical, and have limited applicability across different clinical settings, with existing algorithms struggling to accurately predict the condition in various populations.
Innovation Solution
A method involving the measurement of at least three biomarkers (PTX3, sFlt1, ADAM12, and optionally sENG) and clinical cofactors (gestational age, parity, and placental or fetal genotype) to generate a prediction value indicating the likelihood of preeclampsia, using a formula that combines these factors for accurate prognosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple biomarkers are measured individually using equipment-specific methods, then measurement precision may be improved, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple biomarker measurements (sFlt-1, PIGF, ADAM12, PTX3) into a single integrated prediction algorithm that can be implemented using standard clinical laboratory equipment. This merging approach maintains measurement precision while reducing device complexity by eliminating the need for specialized equipment for each individual biomarker.
Solution Approach 2:
The invention creates a universal prediction model that can be applied across different clinical settings using commonly available laboratory equipment. The algorithm serves multiple functions: risk stratification, early detection, and monitoring, making the system multi-functional and widely applicable without requiring specialized devices.
2Reliability
If existing prediction algorithms are used, then some prediction capability is achieved, but sensitivity and specificity remain insufficient
Solution Approach 1:
The patent creates a composite prediction model that integrates multiple biomarkers (sFlt-1, PIGF, ADAM12, PTX3) with clinical risk factors into a unified algorithm. This composite approach achieves superior sensitivity and specificity by combining the strengths of multiple individual markers while compensating for their respective limitations.
Solution Approach 2:
The invention transforms the prediction approach by changing from single-marker or binary risk assessment to a multi-parameter continuous prediction model. The algorithm uses normalized biomarker levels and risk factor weights to generate a continuous prediction score, improving detection accuracy across different pregnancy populations.
3Ease of operation
If clinical risk factors alone are assessed, then simplicity is maintained, but detection rate is limited to 30.4% for PE during ongoing pregnancy
Solution Approach 1:
The patent merges clinical risk factors (nulliparity, age, BMI, chronic conditions) with biomarker measurements into a single integrated prediction algorithm. This combination maintains ease of operation by providing a unified assessment approach while significantly improving detection rate to over 70% for preeclampsia during ongoing pregnancy.
4Loss of time
If early prediction is achieved using multiple biomarkers, then timely intervention is enabled, but measurement cost and complexity increase
Solution Approach 1:
The patent performs preliminary biomarker measurement during the first trimester (10-14 weeks) to predict preeclampsia risk before the condition develops. This early timing enables preventive intervention while using standard laboratory equipment, avoiding the need for complex real-time monitoring systems during later pregnancy stages.
Data Source
AI summary
The present invention provides for a method of prognosing preeclampsia in a pregnant subject wherein the method comprises measuring the level of at least three biomarkers in a sample from the subject; optionally determining at least one clinical cofactor from the subject; generating a prediction value, wherein the prediction value indicates whether the subject will develop or will not develop preeclampsia during the ongoing pregnancy; the prediction value being based on the levels of the at least three biomarkers in the sample from the subject and optionally based on the at least one clinical cofactor.