Shoulder Dystocia Risk Estimation Using Composite Maternal-Fetal Data
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Solution Overview
Problem
Current methods for predicting shoulder dystocia in obstetrics are plagued by high false positive rates, leading to unnecessary interventions and inadequate prediction of risk, as they rely heavily on fetal weight, which is not sufficiently reliable.
Innovation Solution
A method and apparatus that estimate the risk of shoulder dystocia by processing data elements including maternal weight, height, and fetal weight, using a predictive model to generate a ranking of risk levels, along with false and true positive detection rates, to provide a more accurate assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a hard threshold of estimated fetal weight (4500g or 5000g) is used to predict shoulder dystocia risk, then intervention rate increases to catch more potential cases, but false positive rate becomes unacceptably high leading to unnecessary cesarean sections
Solution Approach 1:
The patent transforms the single-parameter threshold approach (fetal weight only) into a multi-parameter composite risk score system. By incorporating maternal BMI, fetal weight, and their interaction term, the system dynamically adjusts risk assessment based on multiple factors rather than relying on a fixed weight threshold, thereby improving prediction accuracy while reducing false positives
Solution Approach 2:
The patent creates a composite risk assessment model that combines multiple data elements (maternal BMI, fetal weight, interaction term) into a unified risk score. This composite approach integrates different physiological factors to provide a more nuanced prediction of shoulder dystocia risk, allowing clinicians to identify true high-risk cases without triggering unnecessary interventions in low-risk patients
2Ease of operation
If fetal weight alone is used as the prediction criterion, then the prediction method is simple and easy to implement, but the reliability and clinical value of the prediction is insufficient
Solution Approach 1:
The patent extends the prediction from a single parameter (fetal weight) to multiple parameters (maternal BMI, fetal weight, and their interaction). This expansion captures the complex relationship between maternal and fetal factors in shoulder dystocia risk, significantly improving prediction reliability while maintaining computational simplicity through a linear scoring system
Solution Approach 2:
The patent creates a multi-functional prediction system that simultaneously assesses multiple risk factors and their interactions. The same model can be applied across different patient populations and clinical settings, providing universal applicability while capturing the nuanced interplay between maternal obesity and fetal size in shoulder dystocia risk
Data Source
AI summary
A method and an apparatus for estimating a level of risk of shoulder dystocia with neonatal injury associated to an obstetrics patient are provided. A set of information data elements associated to an obstetrics patient is received including information derived from a maternal weight component, a maternal height component and a fetal weight component. The set of information data elements is processing to derive a ranking data element associated to the obstetrics patient. The ranking data element conveys a level of risk of shoulder dystocia associated to the obstetrics patient. The ranking data element is then released.