Multi-variable Logistic Regression Model for Lysosomal Storage Disorder Diagnosis
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Solution Overview
Problem
Lysosomal storage disorders, such as mucopolysaccharidoses, pose significant diagnostic challenges due to their rarity and variability in symptoms, often leading to misdiagnosis and ineffective or unsafe treatments, as conventional detection methods are invasive, expensive, and not suitable for widespread screening.
Innovation Solution
A multi-variable logistic regression statistical model is developed to detect and classify lysosomal storage disorders using a composite biomarker comprising physiological variables, integrated into a decision-support system for early detection and severity assessment, enabling timely and cost-effective diagnosis within electronic health records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional detection methods are used for lysosomal storage disorders, then diagnostic accuracy may be maintained, but the methods are invasive, expensive, and not suitable for widespread screening
Solution Approach 1:
The patent replaces invasive mechanical detection methods (enzyme assays, genetic testing) with an information-processing system that analyzes routine clinical data. The decision support tool substitutes complex laboratory procedures with computational analysis of existing physiological variables, making screening non-invasive and widely applicable.
Solution Approach 2:
The patent introduces a multi-variable composite biomarker as an intermediary that connects routine clinical measurements to disease diagnosis. This composite indicator serves as a mediator that translates common physiological data into meaningful diagnostic information without requiring direct measurement of enzyme activity or genetic mutations.
2Measurement precision
If conventional detection methods are used, then diagnostic precision may be maintained, but the cost and complexity increase significantly
Solution Approach 1:
The patent merges multiple routine clinical measurements into a single multi-variable composite biomarker. By combining several physiological variables that are already collected during standard care into one integrated indicator, the system maintains diagnostic precision while avoiding the complexity of individual specialized tests.
Solution Approach 2:
The decision support tool is designed to be universally applicable across different clinical settings by using routine physiological variables that are commonly measured in standard care. The system performs multiple functions: screening, risk stratification, and diagnostic guidance, all through a single platform that leverages existing data infrastructure.
3Reliability
If early screening is implemented, then patient outcomes improve, but the rarity of cases makes it challenging for clinicians to recognize patterns
Solution Approach 1:
The decision support tool implements feedback by continuously monitoring clinical data and providing real-time alerts when the composite biomarker indicates elevated risk. This feedback mechanism compensates for clinicians' inability to recognize rare disease patterns by automatically highlighting cases that warrant further investigation based on subtle deviations in routine measurements.
Solution Approach 2:
The system performs preliminary risk assessment by calculating the composite biomarker from existing routine data before definitive diagnosis is made. This preliminary action enables early identification of at-risk patients, allowing clinicians to prioritize further testing and intervention before disease progression occurs.
Data Source
AI summary
A diagnostic and decision support technology is provided for determining the presence, identity, and/or severity of an inherited lysosomal storage disorder. In particular, a mechanism is provided to detect and classify a lysosomal storage disorder in a human patient, which utilizes a logistic regression classifier determined based on a multi-variable-composite-biomarker comprising a specific set of physiological variables of the patient. This multi-variable statistical predictive biomarker approach may be employed for identifying persons whose attributes are consistent with features or lysosomal storage diseases, such as attenuated mucopolysaccharidosis Type 1 (Hurler-Scheie or Scheie syndromes).