Development of a clinical applicable graphical user interface to automatically detect exercise oscillatory...

Area of Science:

• Cardiopulmonary physiology
• Medical diagnostics
• Data analysis

Background:

• Cardiopulmonary exercise testing (CPET) is crucial for prognostic assessment in heart failure (HF).
• Exercise oscillatory ventilation (EOV), characterized by abnormal breathing patterns during CPET, is a significant predictor of early mortality in HF.
• Current EOV assessment relies on subjective visual interpretation or manual calculations, limiting its widespread clinical use.

Purpose of the Study:

• To develop and validate an automated method for interpreting EOV during CPET.
• To facilitate the routine investigation of EOV as a prognostic parameter in HF patients.

Main Methods:

• Four definitions of EOV were integrated into the "Ventilatory Oscillations during Exercise-tool" (VOdEX-tool).
• A Discrete Meyer Level 2 wavelet transformation was employed to analyze breath-by-breath minute ventilation data from CPET.
• The VOdEX-tool calculates and visualizes oscillations based on cycle length, amplitude, regularity, and duration, with adjustable parameters.

Main Results:

• The VOdEX-tool successfully automates the calculation and visualization of EOV.
• The tool integrates various EOV characteristics and allows for adjustable filter methods and cut-off criteria.
• The VOdEX-tool has been connected to a database for efficient data management.

Conclusions:

• The VOdEX-tool enables automatic calculation and at-a-glance presentation of EOV for clinicians.
• Computerized EOV analysis can be readily integrated into clinical practice by embedding the VOdEX-tool into existing CPET software.
• This tool enhances EOV assessment, thereby improving prognostic estimations, particularly for patients with heart failure.