ECG Screening Algorithm for Detecting Reduced LVEF
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Solution Overview
Problem
There is a significant underutilization of primary prevention implantable defibrillators due to the low prevalence of echocardiographic screening for reduced left ventricular ejection fraction (LVEF), and many patients with asymptomatic LV dysfunction remain undiagnosed, increasing the risk of sudden cardiac death.
Innovation Solution
A non-invasive ECG screening system and algorithm that identifies distinct ECG abnormalities, such as elevated resting heart rate, prolonged QRS duration, and QTc interval, to determine reduced LVEF, using a combination of ECG monitoring devices, servers, databases, and processors connected via communication links, applying algorithms like step-wise logistical regression to analyze ECG parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If echocardiographic screening is deployed to identify patients with reduced LVEF, then diagnostic accuracy is improved, but cost and complexity increase significantly
Solution Approach 1:
The patent creates a simplified copy of echocardiographic screening capability using ECG data. Instead of requiring actual echocardiography equipment and expertise, the system uses machine learning models trained on echocardiogram data to generate LVEF estimates from readily available ECG recordings, making the screening capability accessible through a different, simpler modality
Solution Approach 2:
The patent replaces the mechanical/physical echocardiography system with an computational/electrical system. Rather than using ultrasound waves and mechanical transducers, the system uses electrical ECG signals processed through machine learning algorithms to achieve the same diagnostic goal of estimating LVEF
2Productivity
If echocardiographic screening is broadly deployed in the community, then patient identification is improved, but cost-effectiveness deteriorates
Solution Approach 1:
The patent employs inexpensive ECG monitoring devices that can be widely distributed and used in community settings. These devices capture electrical heart signals that are then processed algorithmically to identify patients with reduced LVEF, providing a low-cost alternative to expensive echocardiography while maintaining adequate screening capability
Solution Approach 2:
The patent makes the ECG system perform multiple functions: it records standard ECG data for cardiac rhythm assessment, extracts features for machine learning-based LVEF estimation, and potentially monitors other cardiac parameters. This multi-functionality allows a single inexpensive device to replace multiple specialized screening tools
3Reliability
If asymptomatic patients are screened for LV dysfunction, then early treatment opportunity is improved, but false positive rate may increase
Solution Approach 1:
The patent performs preliminary screening using ECG-based LVEF estimation to identify asymptomatic patients who may have reduced ejection fraction. This early identification allows for further diagnostic evaluation and treatment initiation before symptoms develop, while the system is designed to flag only those with significant abnormalities for confirmatory testing
Solution Approach 2:
The patent implements a feedback mechanism where ECG-based LVEF estimates are used to identify patients needing further evaluation, and results can be refined through iterative processing. The system provides feedback to clinicians about which patients warrant additional testing, allowing for adjustment of screening thresholds and follow-up protocols to optimize the balance between sensitivity and specificity
Data Source
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AI summary
A system and a method for identifying a patient with a threshold number of distinct ECG abnormalities. The system and the method include an ECG monitoring device; a server; a database; a network; a memory containing machine readable medium comprising a machine executable code having stored thereon instructions for identifying patients with a threshold number of distinct ECG abnormalities; and a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to: receive an ECG data output from the ECG monitoring device; process the ECG data output to identify abnormalities in the ECG data; and analyze the abnormalities in the ECG data in order to output an indication of whether the patient has depressed LVEF, wherein the ECG monitoring device, the server, the database, the memory, and the processor are coupled to the network via communication links.