Diagnostic Analyzer Calibration Using Patient Sample Centroids
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Solution Overview
Problem
Diagnostic instruments in veterinary and human applications face challenges in maintaining calibration accuracy due to species-specific responses and reagent lot variability, particularly when external factors like reagent lot changes occur, leading to system shifts and convoluted patient sample data sets.
Innovation Solution
An automated system and method that employs a centroid algorithm to calculate species and lot-specific median values from patient sample diagnostic results, compares these to population data to determine accuracy bias, and applies optimization factors to adjust the analyzer's parameters in real-time, ensuring results remain within acceptable limits without altering pre-set parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed optical references or control materials are used to determine instrument performance, then measurement precision is maintained, but device complexity increases and cost increases
Solution Approach 1:
The patent extracts the calibration function from dedicated control materials and references, instead using actual patient sample data to perform calibration. This eliminates the need for separate calibration reagents and controls while maintaining measurement precision through statistical analysis of patient population data.
Solution Approach 2:
Patient samples serve multiple functions: they are used for both diagnostic testing and calibration purposes. The same patient data used for clinical decisions is simultaneously utilized to establish instrument performance and detect drift, eliminating the need for separate calibration operations.
2Measurement precision
If species-specific algorithms are employed for different veterinary species, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies species-specific calibration parameters and statistical thresholds tailored to each veterinary species (dogs, cats, horses, etc.). Each species has its own reference ranges and control limits, ensuring optimal measurement precision for each species while managing complexity through standardized implementation across the platform.
3Reliability
If manual control tests are performed frequently to ensure instrument performance, then reliability is improved, but productivity decreases and cost increases
Solution Approach 1:
The calibration process operates continuously in the background using patient sample data as it is being analyzed. Instead of periodic interruptive control tests, the system continuously monitors instrument performance through statistical analysis of patient results, eliminating downtime and maintaining full productivity.
Solution Approach 2:
The system implements continuous feedback through statistical process control, where patient sample results are constantly analyzed to detect instrument drift. When drift is detected, the system automatically alerts for calibration, providing ongoing reliability assurance without requiring frequent manual intervention.
4Measurement precision
If reagent lot changes are implemented to improve assay performance, then measurement precision is improved, but system stability deteriorates due to lot variability
Solution Approach 1:
The system performs preliminary characterization of each reagent lot using patient sample data before the lot is fully deployed. Reference ranges and control parameters are established for each lot based on statistical analysis of patient results, ensuring that lot-to-lot variability is accounted for and does not compromise measurement precision or stability.
Data Source
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AI summary
An automated method for calibrating in real time a diagnostic analyzer includes the steps of receiving diagnostic results of patient samples calculated by the analyzer using pre-set parameters of the analyzer, and calculating the centroid of the analyzer's diagnostic results. The centroid of the analyzer's diagnostic results is subtracted from a centroid of diagnostic results of a field population of comparable analyzers to obtain a bias in the centroid of the analyzer's diagnostic results. The bias is compared to predetermined limits. Should the bias fall outside the predetermined limits, an optimization factor is derived and applied to the analyzer's diagnostic results to obtain optimized results from the analyzer without changing the pre-set parameters of the analyzer.