Forecasting respiratory collapse: theory and practice for averting life-threatening infant apneas

Apnea of prematurity is a common disorder of respiratory control among preterm infants, with potentially serious adverse consequences on infant development. We review the capability for automatically assessing apnea risk and predicting apnea episodes from multimodal physiological measurements, and for using this knowledge to provide timely therapeutic intervention. We also review other, similar clinical domains of respiratory distress assessment and prediction in the hope of gaining useful insights. We propose an algorithmic framework for constructing discriminative feature vectors from physiological measurements, and for building robust and effective statistical models for apnea assessment and prediction.

Analysis of roots in ARMA model for the classification of patients on weaning trials

One objective of mechanical ventilation is the recovery of spontaneous breathing as soon as possible. Remove the mechanical ventilation is sometimes more difficult that maintain it. This paper proposes the study of respiratory flow signal of patients on weaning trials process by autoregressive moving average model (ARMA), through the location of poles and zeros of the model. A total of 151 patients under extubation process (T-tube test) were analyzed: 91 patients with successful weaning (GS), 39 patients that failed to maintain spontaneous breathing and were reconnected (GF), and 21 patients extubated after the test but before 48 hours were reintubated (GR). The optimal model was obtained with order 8, and statistical significant differences were obtained considering the values of angles of the first four poles and the first zero. The best classification was obtained between GF and GR, with an accuracy of 75.3% on the mean value of the angle of the first pole.

Comparisons of predictive performance of breathing pattern variability measured during T-piece, automatic tube compensation, and pressure support ventilation for weaning intensive care unit patients from mechanical ventilation

OBJECTIVE

To investigate the influence of different ventilatory supports on the predictive performance of breathing pattern variability for extubation outcomes in intensive care unit patients.

DESIGN AND SETTING

A prospective measurement of retrospectively analyzed breathing pattern variability in a medical center.

PATIENTS

Sixty-eight consecutive and ready-for-weaning patients were divided into success (n=45) and failure (n=23) groups based on their extubation outcomes.

MEASUREMENTS

Breath-to-breath analyses of peak inspiratory flow, total breath duration, tidal volume, and rapid shallow breathing index were performed for three 30-min periods while patients randomly received T-piece, 100% inspiratory automatic tube compensation with 5 cm H2O positive end-expiratory pressure, and 5 cm H2O pressure support ventilation with 5 cm H2O positive end-expiratory pressure trials. Coefficient of variations and data dispersion (standard descriptor values SD1 and SD2 of the Poincaré plot) were analyzed to serve as breathing pattern variability indices.

MAIN RESULTS

Under all three trials, breathing pattern variability in extubation failure patients was smaller than in extubation success patients. Compared to the T-piece trial, 100% inspiratory automatic tube compensation with 5 cm H2O positive end-expiratory pressure and 5 cm H2O pressure support ventilation with 5 cm H2O positive end-expiratory pressure decreased the ability of certain breathing pattern variability indices to discriminate extubation success from extubation failure. The areas under the receiver operating characteristic curve of these breathing pattern variability indices were: T-piece (0.73-0.87)>100% inspiratory automatic tube compensation with 5 cm H2O positive end-expiratory pressure (0.60-0.79)>5 cm H2O pressure support ventilation with 5 cm H2O positive end-expiratory pressure (0.53-0.76). Analysis of the classification and regression tree indicated that during the T-piece trial, a SD1 of peak inspiratory flow>3.36 L/min defined a group including all extubation success patients. Conversely, the combination of a SD1 of peak inspiratory flow ≤3.36 L/min and a coefficient of variations of rapid shallow breathing index ≤0.23 defined a group of all extubation failure patients. The decision strategies using SD1 of peak inspiratory flow and coefficient of variations of rapid shallow breathing index measured during 100% inspiratory automatic tube compensation with 5 cm H2O positive end-expiratory pressure and 5 cm H2O pressure support ventilation with 5 cm H2O positive end-expiratory pressure trials achieved a less clear separation of extubation failure from extubation success.

CONCLUSIONS

Since 100% inspiratory automatic tube compensation with 5 cm H2O positive end-expiratory pressure and 5 cm H2O pressure support ventilation with 5 cm H2O positive end-expiratory pressure reduce the predictive performance of breathing pattern variability, breathing pattern variability measurement during the T-piece trial is the best choice for predicting extubation outcome in intensive care unit patients patients.