Abstract
Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm’s performance was compared versus the gold standard (the ventilator’s waveform recordings for CP-VI were scored visually by three experts; Fleiss’ kappa = 0.90 (0.87–0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient’s own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78–0.86) and 0.78 (0.78–0.85), and accuracies of 0.93 (0.89–0.93) and 0.89 (0.89–0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.
Highlights
Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms
The findings presented in this paper represents an ancillary analysis on an ongoing clinical study (ENTROPY-ICU, ClincalTrials.gov NCT04128124) designed to assess the feasibility of using Sample Entropy (SE) to identify complex patient-ventilator interactions (CP-VI) during mechanical ventilation (MV)
The experts visually assessed a total of 92 periods: 45 periods of PSV
Summary
Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. It would be extremely useful to have access to a method for assessing irregularity and complexity which could detect Complex Patient-Ventilator interactions (CP-VI), including not just asynchronies of any kind and changes in the respiratory rate, in an automated, non-invasive and personalized fashion. The complex behavior of a non-linear system cannot be characterized by the sum of its inputs, and the study of these systems requires methods that take into account the non-linear physiological response to a given stimulus. These methods could provide insights into organ-system interconnectivity, regulatory control, and complexity in time series during disease[17,18,19]
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