Serum Lactate Prediction via Cardiovascular Data Classifiers
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Solution Overview
Problem
Current methods for estimating serum lactate levels in patients require blood draws and laboratory tests, which are costly and time-consuming, hindering timely patient monitoring and management, especially in critical care situations.
Innovation Solution
A system and method that measure arterial blood pressure and heart rate to estimate serum lactate levels using trained classifiers, incorporating cardiovascular parameters and patient history to predict lactate levels and assess sepsis risk, allowing for real-time monitoring and alerting if thresholds are crossed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional laboratory testing methods are used to measure serum lactate levels, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The patent replaces the mechanical/chemical laboratory testing system with a computational system that uses machine learning classifiers. The system processes electronic health record data through trained classifiers to predict serum lactate levels, eliminating the need for physical blood draws and laboratory analysis while maintaining clinical utility for patient monitoring and sepsis risk assessment
Solution Approach 2:
The patent creates a computational model that copies the diagnostic function of laboratory testing. By training classifiers on historical data containing blood draw results and patient outcomes, the system learns to replicate the information value of actual serum lactate measurements using only electronic health record data, providing a virtual copy of the diagnostic capability
2Measurement precision
If traditional laboratory testing methods are used to measure serum lactate levels, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent replaces the resource-intensive laboratory testing process with a computational prediction system that operates instantly on existing electronic health record data. This substitution eliminates the time-consuming workflow of blood draws, sample processing, and laboratory analysis, dramatically improving patient monitoring efficiency and clinical productivity
Solution Approach 2:
The system performs preliminary risk assessment by continuously analyzing electronic health record data and predicting serum lactate levels in real-time. This preliminary action enables clinicians to identify at-risk patients before actual lactate elevation occurs, allowing early intervention and improving overall patient care productivity
3Measurement precision
If blood draws and laboratory tests are performed to estimate serum lactate, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the complex physical infrastructure of phlebotomy equipment, sample collection systems, and laboratory analyzers with a software-based classification system. The solution uses computational algorithms running on existing hospital information systems, eliminating the need for specialized medical equipment and reducing overall system complexity
Solution Approach 2:
The patent creates a universal prediction system that can estimate serum lactate levels for all patients using the same electronic health record data and classification algorithms. This multi-functional approach serves multiple purposes including sepsis risk assessment, patient monitoring, and clinical decision support without requiring patient-specific equipment or procedures
4Measurement precision
If blood draws and laboratory tests are performed to estimate serum lactate, then measurement precision is improved, but loss of substance increases
Solution Approach 1:
The patent replaces the invasive blood collection process with a non-invasive computational analysis of existing electronic health record data. This substitution eliminates the need to draw blood samples, thereby preventing loss of bodily substances while still providing clinically useful serum lactate level estimates for patient management
Data Source
AI summary
A method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient.