SNP-Based DVT Risk Prediction Algorithm
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting the risk of deep vein thrombosis (DVT) and pulmonary embolism (PE) in women undergoing hormonal changes, such as those using combined contraceptives or hormone replacement therapy, have low sensitivity and specificity, failing to accurately identify individuals at risk.
Innovation Solution
A prognostic method that determines the genotype of specific single nucleotide polymorphisms (SNPs) and combines this data with clinical risk factors using a decision support algorithm to calculate a risk score, specifically considering SNPs like rs1799853, rs4379368, rs6025, rs1799963, rs8176719, rs8176750, rs9574, rs2289252, and rs710446, along with smoking status, BMI, age, and familial history, to assess the likelihood of blood clotting diseases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional risk assessment methods (medical questionnaires focusing on age, BMI, smoking, and familial history) are used, then the approach is simple and easy to operate, but the sensitivity and specificity for predicting DVT risk are suboptimal
Solution Approach 1:
The patent combines multiple types of data (genetic SNPs, clinical risk factors, and hormonal parameters) into a unified risk prediction model. The decision support algorithm integrates these diverse data sources to generate a comprehensive risk score, thereby improving predictive accuracy while maintaining usability through automated processing
Solution Approach 2:
The patent introduces genetic testing as an intermediary mechanism between conventional clinical assessment and final risk prediction. By adding genetic data (SNPs in coagulation factors) as an intermediate layer, the system enhances predictive capability without requiring direct complex interactions between all clinical parameters
2Reliability
If genetic testing for thrombophilic status is added to conventional assessment, then predictive accuracy improves, but the complexity and cost of assessment increases
Solution Approach 1:
The patent segments the genetic assessment into specific single nucleotide polymorphisms (SNPs) rather than requiring complete genomic sequencing. By focusing on specific loci (Factor V, Factor II, Protein C, Protein S, and other coagulation-related genes), the system achieves enhanced prediction accuracy with reduced testing complexity and cost
Solution Approach 2:
The patent changes the parameter of genetic assessment from traditional thrombophilic screening to a broader panel including SNPs in multiple coagulation factors and hormonal genes. This parameter expansion allows the system to capture more nuanced genetic risks while the automated algorithm handles the complexity of interpreting multiple genetic markers
3Measurement precision
If comprehensive genetic and clinical parameters are combined, then the sensitivity and specificity for identifying high-risk women improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements a decision support algorithm that automatically processes and integrates genetic data, clinical risk factors, and hormonal parameters to generate risk scores. This self-service approach eliminates the need for manual analysis of complex interactions between multiple parameters, thereby achieving high measurement precision while reducing the operational difficulty of data collection and analysis
Data Source
AI summary
Specific single nucleotide polymorphisms (SNPs) in the human genome, and their association with deep vein thrombosis (DVT) and related pathologies, such as pulmonary embolism (PE), in relation with hormonal preparations (i.e. combined contraceptives, hormone replacement therapeutics) and hormone levels (i.e. during pregnancy and post-partum).