Microbiome-Based Risk Stratification for Cystic Fibrosis
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Solution Overview
Problem
Current methods lack effective ways to identify patients with cystic fibrosis at high risk for upper respiratory infections and systemic inflammation, and to provide therapeutic interventions to mitigate these risks.
Innovation Solution
A method using machine learning models trained on stool microbiota data to classify patients with cystic fibrosis as having high risks for upper respiratory infections or systemic inflammation, based on relative abundances of specific genera such as Faecalibacterium, Butyricoccus, and Lactococcus, and providing therapeutic interventions like bacterial compositions, probiotics, and anti-inflammatory medications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to identify high-risk patients based on stool microbiota data, then early identification capability is improved, but system complexity increases
Solution Approach 1:
The machine learning model is trained in advance on stool microbiota data from cystic fibrosis patients to establish predictive patterns. This preliminary training enables the system to automatically identify high-risk patients for upper respiratory infections and systemic inflammation without requiring complex real-time analysis during clinical decision-making.
Solution Approach 2:
The patent introduces an intermediary classification system that translates complex microbiota data into simplified risk categories (high-risk vs. low-risk). This intermediary layer bridges the gap between complex microbiome analysis and clinical decision-making, making the system more manageable while maintaining identification accuracy.
2Reliability
If therapeutic interventions are provided to reduce infection risk, then patient health outcomes are improved, but treatment cost increases
Solution Approach 1:
The patent applies therapeutic interventions selectively only to high-risk patients identified by the machine learning model, rather than treating all cystic fibrosis patients uniformly. This localized approach targets resources to those who most need them, improving health outcomes while reducing overall treatment costs compared to universal intervention.
Solution Approach 2:
The system changes the parameter of patient selection from universal treatment to risk-stratified treatment. By using microbiota-based risk classification, the patent enables differential treatment strategies where high-risk patients receive preventive therapeutic interventions (such as probiotics or antimicrobial treatments) while low-risk patients receive standard care, optimizing resource allocation.
Data Source
AI summary
The present disclosure relates to computer-implemented systems for identifying a patient with cystic fibrosis as having a high risk for upper respiratory infections or systemic inflammation, based on the patient's stool microbiota. The systems use machine learning models trained with data comprising (a) risk classifications for subjects with cystic fibrosis and (b) stool microbiota information for each of the subjects. The present disclosure also relates to methods of reducing frequency and/or number of upper respiratory infections in a patient with cystic fibrosis. The present disclosure also relates to methods of identifying a patient with cystic fibrosis (CF) as having a high risk for frequent upper respiratory infections or high systemic inflammation based on the patient's stool microbiota.