Cytokine Predictive Model for Low Back Pain Treatment Selection
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Solution Overview
Problem
Current treatments for low back pain, particularly those involving non-surgical methods like epidural steroid injections, lack reliable predictive methods to determine patient responsiveness, leading to variable treatment outcomes and increased economic burden.
Innovation Solution
A system and method utilizing a predictive model based on stepwise multiple linear regression analysis of cytokine levels and clinical data to categorize patients as treatment-responsive, guiding the administration of appropriate non-surgical treatments such as epidural steroid injections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-surgical treatments are administered without predictive assessment, then treatment accessibility is improved, but treatment effectiveness deteriorates due to variable patient responsiveness
Solution Approach 1:
The patent applies preliminary action by measuring cytokine levels (such as IL-6, TNF-alpha, and other inflammatory markers) before administering non-surgical treatment. This pre-assessment allows the system to predict patient responsiveness and select appropriate treatments in advance, thereby improving treatment effectiveness without reducing accessibility. The cytokine profiling is performed as a preliminary step that guides subsequent treatment decisions.
Solution Approach 2:
The patent utilizes parameter changes by measuring specific biochemical parameters (cytokine levels) that correlate with treatment responsiveness. By monitoring changes in these biological parameters, the system can predict which patients will respond well to non-surgical treatments versus those who may require surgical intervention, thus improving treatment reliability while maintaining ease of operation through standardized biomarker assessment.
2Measurement precision
If predictive modeling with multiple cytokines is implemented, then treatment prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex predictive modeling task into separate, independent cytokine measurements. Each cytokine (IL-6, TNF-alpha, CRP, etc.) is measured and analyzed as an independent parameter, allowing the system to build prediction accuracy through multiple discrete measurements rather than requiring a single complex assay. This modular approach improves prediction accuracy while managing system complexity through standardized, individual biomarker assessments.
3Object-affected harmful factors
If cytokine profiling is performed before treatment, then adverse effect risk is reduced, but measurement time increases
Solution Approach 1:
The patent implements preliminary action by performing cytokine profiling measurements before treatment administration. This pre-assessment of inflammatory markers and cytokine levels allows clinicians to identify patients at risk of adverse reactions and select appropriate treatments in advance. The measurements are conducted as a preliminary screening step that prevents harmful outcomes while the time investment is offset by avoiding costly adverse events and treatment failures.
Data Source
AI summary
A method for treating a patient suffering from a pain condition is provided by obtaining a plurality of biometric measurement values for the patient, wherein the plurality of biometric measurement values includes at least (i) a plurality of cytokines measurement levels, and (ii) at least one clinical data value; providing, using a computer configured by code executing therein, the plurality of biometric measurement values as inputs to a predictive model. The predictive model is configured to output a pain responsive likelihood value in response to the input values. The method also includes the step of comparing, using the computer, the pain responsive likelihood value to a pre-determined threshold value, and categorizing, using the computer, the patient as treatment positive where the pain responsive likelihood value is equal to or greater than the threshold value.