Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation

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.

Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters

“…According to Bien et al, patients who required noninvasive or invasive mechanical ventilation within 48 h had a considerably reduced quantitative variability of tidal volume during a 30 min SBT ( 38 ). Similarly, Sarlabous et al developed an entropy-based technique to accurately detect patient–ventilator asynchronies ( 39 ). Correspondingly, we used a machine-learning approach with ventilatory parameters derived from a time-series dataset to predict extubation success with an accuracy of 94.0% (95% CI, 93.8–94.3%).…”

A machine learning model for prediction of successful extubation in patients admitted to the intensive care unit

“…Patients who required noninvasive or invasive mechanical ventilation within 48 h had a considerably reduced quantitative variability of tidal volume during a 30-min SBT, according to Bien et al [39]. Similarly, Sarlabous et al have developed an entropy-based technique to accurately detect patient-ventilator asynchronies [40]. Correspondingly, we used a machine learning approach using ventilatory parameters derived from time series dataset to predict extubation success with an accuracy of 94.0% (95% CI, 93.8-94.3%).…”

Weaning from Mechanical Ventilation: On the Improvement of the Prediction of Patients' Readiness with Cardiopulmonary Coupling Indices

“…This is why clinicians take the criteria for weaning readiness and SBT performance as one among several considerations rather than rigid requirements. Such uncertainty can be reduced by implementing new research and technologies to daily clinical practice 70,71 , and this study is other step forward in the field of predictive precision medicine, that exploits the capabilities of the CPC estimates.…”