Predictive Model for Multiple Birth Risk in Infertility Treatments

Solution Overview

Problem

Current methods for assessing the risk of multiple births during infertility treatments are inadequate, lacking validated prediction tools to determine the likelihood of multiple gestations and compromising live birth rates, especially when selecting between elective single embryo transfer (eSET) and transferring multiple embryos.

Innovation Solution

A method that analyzes patient-specific data including age, medical history, hormonal responses, embryo quality, and treatment protocols using logistic regression, regression tree analysis, and machine learning to predict the risk of multiple births, allowing for personalized counseling and embryo viability testing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If multiple embryos are transferred to increase live birth rates, then the probability of successful pregnancy increases, but the risk of multiple gestations and preterm birth increases

Engineering Contradiction:

Improvelive birth rateVSAvoidmultiple gestation risk

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The patent applies preliminary action by developing and applying a predictive model before embryo transfer to assess the risk of multiple gestations. The model analyzes patient-specific factors (age, BMI, diagnosis, treatment protocol) and embryo factors (number, quality, developmental stage) to predict multiple birth risk in advance, allowing clinicians to make informed decisions about the optimal number of embryos to transfer before the procedure occurs.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies parameter changes by using a multivariate predictive model that quantifies multiple parameters (patient age, BMI, infertility diagnosis, treatment protocol, embryo number, embryo quality) to dynamically determine the optimal number of embryos for transfer. The model calculates a predicted probability of multiple gestation based on these parameters, allowing customization of the embryo transfer strategy based on the specific combination of parameters for each patient.

Inventive Principle:

Principle #35Parameter changes

2Object-affected harmful factors

If elective single embryo transfer (eSET) is used to eliminate multiple gestation risk, then multiple birth risk is reduced, but live birth rates may be compromised

Engineering Contradiction:

Improvemultiple birth riskVSAvoidlive birth rate

Core Design Contradiction:

Object-affected harmful factorsVSReliability

Solution Approach 1:

The patent applies parameter changes by using a multivariate predictive model that quantifies multiple parameters (patient age, BMI, infertility diagnosis, treatment protocol, embryo number, embryo quality) to dynamically determine the optimal number of embryos for transfer. The model calculates a predicted probability of multiple gestation based on these parameters, allowing customization of the embryo transfer strategy based on the specific combination of parameters for each patient.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent applies local quality by providing personalized risk assessment for each patient based on their specific characteristics rather than applying a uniform approach. The model identifies which specific parameters contribute most to multiple gestation risk for each individual patient, allowing tailored counseling and decision-making specific to that patient's situation and embryo quality.

Inventive Principle:

Principle #3Local quality

3Ease of manufacture

If current embryo transfer guidelines are followed, then standardization is maintained, but personalized risk assessment for multiple births is insufficient

Engineering Contradiction:

Improveguideline implementationVSAvoidmultiple birth risk prediction

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent applies parameter changes by using a multivariate predictive model that quantifies multiple parameters (patient age, BMI, infertility diagnosis, treatment protocol, embryo number, embryo quality) to dynamically determine the optimal number of embryos for transfer. The model calculates a predicted probability of multiple gestation based on these parameters, allowing customization of the embryo transfer strategy based on the specific combination of parameters for each patient.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent applies preliminary action by developing and applying a predictive model before embryo transfer to assess the risk of multiple gestations. The model analyzes patient-specific factors (age, BMI, diagnosis, treatment protocol) and embryo factors (number, quality, developmental stage) to predict multiple birth risk in advance, allowing clinicians to make informed decisions about the optimal number of embryos to transfer before the procedure occurs.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS9348972B2Method of assessing risk of multiple births in infertility treatments

Publication Date: 2016.05.24 UNIVFY INC

AI summary

Provided is a multiple birth prognostic tool that is used to analyze data in order to predict a multiple birth event in a female human patient undergoing an infertility treatment. The MBP prognostic tool may also be used to enhance the accuracy of diagnostic or prognostic tests that predict embryo viability. The MBP prognostic tool of the present invention may be clinic specific or it may be modified to be used in a multi-clinic approach.