Predict Arrhythmia Risk Using Intelligent Software

Primary objective:

Assessment of the performances of AI models in identifying patients at risk of sustained VT and VF from bedside monitor ECG in different timeframes:

• 30-minute prediction model
• 1-day prediction model

Secondary objectives:

• Assessment of potential healthcare savings if the AI model in would be used in clinical practice, such as CCU length-of-stay (CCU-LOS), hospital length-of-stay and associated costs

Exploratory objectives:

• Real time and continuous detection of events for alarming
• Prediction of other types of arrhythmias (e.g., Atrial fibrillation (AF), atrioventricular block, severe brady-arrhythmia) using in hospital ECG monitoring.
• Identification of clinical risk factors for sustained VT and/or VF
• Exploration of development and assessment of new AI models using new (clinical) input

Inclusion criteria

• Patients admitted from 1/1/2023*
• Patients aged 18 years or older
• Admitted for acute cardiac illness or after elective cardiac procedures
• Who are on ECG monitoring in the CCU, ICU or ward
• Patients for whom continuous waveform ECG data have been routinely stored.

• Continuous waveform ECG data has been routinely stored in the CZE since 1/1/2023 on the ICU, since 1/12/2025 on the CCU and on the ward it has yet to be implemented. As our project utilizes this continuous ECG data, it will only include patients for whom this data is available.

Exclusion criteria

• Patients who expressed their preference for not having their data used for scientific research or to improve quality of care in the opt-out program of the CZE.