New Applications of Pulse Oximetry
New Applications of Pulse Oximetry
Quantifying respiratory efforts
The physiologic response to hypoxaemia is an increase in respiratory frequency and tidal volume. Work of breathing increases, resulting in large changes in pleural pressure which may be responsible for self-inflicted acute lung injury. This phenomenon has been advocated to explain, at least in part, the rapid deterioration of lung function in severe COVID-19 patients. A recent study (Tonelli et al. 2020) suggests that patients with acute respiratory failure in whom respiratory efforts do not quickly decrease after initiating non-invasive ventilation, ultimately require tracheal intubation. In research studies, respiratory efforts are quantified by monitoring the respiratory swings in oesophageal pressure. But in clinical practice, oesophageal probes are not used. In spontaneously breathing patients, PVI depends mainly on the magnitude of changes in pleural pressure and could therefore be used to approximate respiratory efforts (Michard and Shelley 2020). Thus, when initiating oxygen therapy or non-invasive ventilation, monitoring changes in PVI may help to assess the impact on respiratory efforts and has potential to prevent intubation delays. Clinical studies are currently ongoing to confirm these hypotheses.
Tracking changes in blood pressure
Pulse oximeters can be used to calculate the pulse wave transit time (PWTT), which is the time difference between cardiac contraction and the peripheral pulse arrival. The PWTT depends on blood flow and the mechanical properties of the arterial bed. Both a decrease in cardiac output and in vascular tone (the two determinants of blood pressure) induce an increase in PWTT. Thus, tracking changes in PWTT has been proposed to predict changes in blood pressure. For instance, continuous monitoring of PWTT has been shown to be useful to detect beat-to-beat changes in systolic arterial pressure during anaesthesia induction (Kim et al. 2013). It could therefore be used to predict hypotension and automatically trigger oscillometric brachial cuff measurements. In ward patients wearing wireless electrodes (to detect cardiac contraction) and a finger pulse oximeter, tracking changes in PWTT has been used to unmask hypotensive events that, otherwise, would have been missed by nurses who were spot-checking blood pressure only every 4h (Turan et al. 2019).
More recently, a machine learning algorithm has been developed to predict blood pressure from photoplethysmographic waveforms (Ghamri et al. 2020). A large number of pulse oximetry waveforms and corresponding invasive blood pressure numbers were used to “teach” the algorithm how to recognise specific patterns or signatures of blood pressure changes (learning phase). During the validation phase, the algorithm demonstrated an excellent performance to detect changes in arterial pressure during anaesthesia induction.
The surge of patients with COVID-19 has been a catalyst for the adoption of SpO2 monitoring from home, remote and continuous monitoring of vital signs on hospital wards and closed-loop administration of oxygen (Figure 3). In patients with acute respiratory failure, tracking changes in PI during a PLR manoeuvre may help to identify fluid non-responders and hence prevent unjustified fluid administration. In spontaneously breathing patients, a decrease in PVI may reflect a decrease in respiratory efforts. Therefore, monitoring PVI may help to assess the efficacy of oxygen therapy, CPAP or non-invasive ventilation. Changes in PWTT predict changes in blood pressure and could be used to trigger upper-arm cuff measurements (automatic smart triggering). Machine learning systems have potential to extract more information from pulse oximetry waveforms, and such waveforms can now be recorded by smartwatches or adhesive patches. These innovations should further push the envelope of photoplethysmography and create new opportunities for physiologic monitoring beyond the operating room and the ICU.
Dr. Frederic Michard writing
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