Automated Quantification of QT-Intervals by an Algorithm: A Validation Study in Patients with Chronic Obstructive Pulmonary Disease.

Dario KohlbrennerMaya BisangSayaka S AeschbacherEmanuel HeusserStéphanie Saxer Konrad E BlochMichael Furian

Published in: International journal of chronic obstructive pulmonary disease (2024)

Overall, 8% of nocturnal 1-min periods showed clinically relevant QTc prolongations in patients with stable COPD. The automated QTc-algorithm accurately identified clinically relevant QTc-prolongations with a very high sensitivity and specificity. Using this tool, hospital sleep laboratories may identify asymptomatic patients with QTc-prolongations at risk for malignant arrhythmia, allowing them to consult a cardiologist before an eventual cardiac event.

Keyphrases

deep learning
machine learning
high throughput
sleep quality
chronic obstructive pulmonary disease
healthcare
left ventricular
obstructive sleep apnea
physical activity
lung function
emergency department
heart failure
air pollution
cystic fibrosis
drug induced
adverse drug