Arterial Waveform Sepsis Screening for Faster Early Diagnosis
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Solution Overview
Problem
Current methods for diagnosing sepsis rely on systemic inflammatory response syndrome (SIRS) criteria, which are not always readily available, leading to delays in diagnosis and treatment, especially in emergency settings.
Innovation Solution
Utilizing hemodynamic data features extracted from arterial blood pressure waveforms through sensors like intra-arterial catheters, pressurized finger cuffs, or applanation tonometers, and applying these features to computational models such as regression-based or classification-based models to screen for or predict sepsis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SIRS criteria are used to diagnose sepsis, then diagnosis accuracy can be maintained, but diagnosis time increases and delays treatment
Solution Approach 1:
The patent applies preliminary action by continuously monitoring hemodynamic parameters (such as arterial pressure waveform features, heart rate variability, and systemic vascular resistance) before SIRS criteria fully develop. The computational model analyzes these parameters in real-time to detect early sepsis signs, enabling diagnosis before traditional criteria are met, thus reducing diagnosis time while maintaining accuracy through predictive analytics
2Reliability
If traditional SIRS criteria assessment is performed, then sepsis can be diagnosed, but the process becomes complex and difficult to implement rapidly in emergency settings
Solution Approach 1:
The patent extracts the essential diagnostic function from the complex SIRS criteria by isolating and monitoring specific hemodynamic parameters that are most indicative of sepsis. The computational model focuses on key features like arterial pressure waveform analysis, heart rate variability, and systemic vascular resistance changes, extracting only the critical information needed for rapid diagnosis while eliminating unnecessary complexity of full SIRS assessment
Solution Approach 2:
The patent introduces a computational model as an intermediary between raw hemodynamic data and clinical diagnosis. This intermediary automatically processes and analyzes multiple physiological parameters, translating complex waveforms and vital signs into a simplified sepsis risk assessment, thereby reducing the complexity burden on clinicians while maintaining diagnostic reliability
3Productivity
If hemodynamic monitoring is implemented for early sepsis detection, then diagnosis speed improves, but device complexity and cost increase
Solution Approach 1:
The patent applies universality by designing a monitoring system that uses existing multi-functional hemodynamic monitoring devices already present in clinical settings. The same arterial pressure monitoring equipment used for routine cardiovascular assessment is leveraged to extract additional sepsis-specific parameters, eliminating the need for dedicated complex devices while maintaining rapid diagnosis capability
Solution Approach 2:
The patent implements self-service by enabling the monitoring system to automatically analyze its own data without requiring additional manual intervention. The computational model processes hemodynamic parameters autonomously, generating sepsis risk assessments directly from routine monitoring data, thus improving diagnosis speed without proportionally increasing operational complexity or requiring specialized equipment
Data Source
AI summary
Systems and methods for assessment of sepsis using a waveform data and/or other patient information are provided. The waveform data corresponds to a signal, for example, from an arterial blood pressure, or any signal proportional to, or derived from the arterial pressure signal. These systems and methods involve extracting hemodynamic data features from the waveform data and entering the hemodynamic data features into predictive computational models, to yield scores that can be utilized to screen for an early indication of sepsis or can be utilized to predict a probability that an individual is experiencing sepsis.