Abstract
Electrocardiogram (EKG) monitoring is a common standard of care across all operating rooms and intensive care units. Studies have suggested that respiratory variations in the EKG R wave amplitude (EKGv) can be used as an indicator of fluid responsiveness in mechanically ventilated patients under general anesthesia, but to date all calculations of variation have been done by hand. The aim of this study was to assess if a computer-automated algorithm could compute and monitor EKGv with the same precision as manual measurement. Batches of 30 s each of EKG lead II waveforms were recorded during surgical procedures with mechanical ventilation. R wave amplitude variability was assessed both manually and by automated algorithm. For both calculations, wave height was defined as R wave peak minus preceding Q wave trough, and the minimum and maximum amplitudes determined for each respiratory cycle. EKGv was calculated as 100 × [(RDIImax − RDIImin)/(RDIImax + RDIImin)/2]. Fifty-seven batches of waveforms were calculated. We found that our computer-automated algorithm calculation of EKGv was significantly correlated to manual measurements (r = 0.968, P < 0.001). Bland-Altman analysis also showed a strong agreement between automated and manual EKGv measurements (bias 0.13% ± 3.06%). The observed correlations between the manually and automatically calculated EKGv suggest that our current computer-automated algorithm is a reliable method for calculating EKGv. Validation in prospective volume expansion studies will be needed to assess the true clinical utility of this automated measurement.
Highlights
The first line of therapy for hypotensive patients in critical care settings is usually intravenous fluid infusion
Existing static predictors of fluid responsiveness such as central venous pressure (CVP), pulmonary capillary wedge pressure (PCWP), and left ventricular end diastolic area have been shown to be nonpredictive of fluid responsiveness [2,3]; while respiratory induced variation in circulatory-based variables such as pulse pressure (PP), stroke volume (SV), and plethysmographic waveform (PV) have been shown to be predictive [4]
In response to the need for an accurate and dynamic calculation of EKG R wave amplitude (EKGv) in the clinical environment, the aim of this study was to develop a computer-automated algorithm and assess its accuracy compared to manual measurements in correctly identifying R wave amplitude variability
Summary
The first line of therapy for hypotensive patients in critical care settings is usually intravenous fluid infusion. Suggest that more than 50% of the time fluid therapy does not result in the expected volume expansion response: an increase in stroke volume [1]. Pulse pressure variation (PPV) and stroke volume variation (SVV) are already widely accepted and used in clinical settings for fluid optimization [5]. Both predictors rely on invasive blood pressure measurements and are not available for many low-to-moderate-risk surgical patients. There is an opportunity to improve currently available predictors of fluid responsiveness by providing a noninvasive dynamic predictor that is cheap, easy to use, effective, and a part of the standard care in all surgical patients
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