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Method for detecting risk of torsades de pointes

A state-of-the-art technology for ventricular tachycardia, used in medical automation diagnosis, medical informatics, health index calculation, etc.

Pending Publication Date: 2022-01-28
ASSISTANCE PUBLIQUE HOPITAUX DE PARIS +4
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, since the test is not 100% specific, some patients may have the trait and not have TdP episodic symptoms (false positive); and since the test is not 100% sensitive, there may be patients who do not have the trait and will develop TdP Some patients with events (underreported)

Method used

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  • Method for detecting risk of torsades de pointes
  • Method for detecting risk of torsades de pointes
  • Method for detecting risk of torsades de pointes

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0169] experimental design

[0170] The training database included 9014 ECG signals from 792 healthy patients before (n=4014; Sot-) and after (n=5000) sotalol intake (80 mg orally, Sot+). ECGs were taken at different time points (1, 2, 3 and 5 hours after sotalol ingestion).

[0171] A subset of the dataset was split to test the trained model consisting of 198 healthy patients with ECGs before (n=999) and after (n=1238) sotalol ingestion.

[0172] In addition, a third dataset consisting of long congenital QT patients was used to test the trained model, specifically LQT-1 (n=266 patients and n=560 ECGs), LQT-2 ( n=188 patients and n=456 ECGs) and LQT-3 (n=33 patients and n=67 ECGs) constituted.

[0173] group description

[0174] Two cohorts of patients were utilized.

[0175] The first cohort contained ECGs from 990 healthy subjects before and after consumption of 80 mg of sotalol at different time periods (i.e., 2, 3, 4 and 5 days after sotalol ingestion). Hour). Each...

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Abstract

The invention relates to methods and devices for the detection and prediction of the risk for a patient to have a torsade de pointe event, and causes thereof, in particular via the use of neural networks.

Description

technical field [0001] The present invention relates to detecting a patient's risk of suffering a Torsades de pointes event and the mechanisms underlying such risk, in particular through the use of neural networks. Background technique [0002] Torsades de Pointes (TdP) is an arrhythmia that can progress to ventricular fibrillation and sudden death. It occurs essentially in congenital long QT syndrome or as a rare side effect of QT-prolonging myocardial and non-cardiac drugs. [0003] Current detection strategies are based on QT measurements (ie, the interval between the onset of the QRS complex and the end of the T wave, traditionally, usually using lead II of the electrocardiogram (ECG)). TdP risk is a major concern for the pharmaceutical industry and can be a reason for withdrawing a drug from the market. [0004] Another approach is described in US 20190059764 which discloses using the QT interval corrected for heart rate, the maximum amplitude of the first peak of the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20
CPCG16H50/20G16H50/30
Inventor J-E·萨朗E·普里夫蒂A·A·普利尼J-D·楚克尔C·丰克-布伦塔诺A·莱纳特I·当茹瓦F·克塞特尔默阿纳
Owner ASSISTANCE PUBLIQUE HOPITAUX DE PARIS
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