Predictive Analysis Software for Successful Weaning From Ventilator of Patients

Overview

Making a weaning decision for a patient on a mechanical ventilator is an important clinical issue. The most common index to predict successful weaning is the rapid shallow breathing index (RSBI), however, the accuracy of RSBI to predict successful weaning have been questioned. The investigators proposed a new mathematical model and algorithm, called WIN, which capture the essential feature of the variability ruling the physiological dynamics to provides better perdition to wean than RSBI.

Making a weaning decision for a patient on a mechanical ventilator is an important clinical issue. It is thus important to decide accurately when patients can be weaned from the ventilator. To increase the weaning success, the present common practice is to conduct spontaneous breathing trials to get physiological signals that may provide the information about capacity of successful weaning. The most common index is the rapid shallow breathing index (RSBI), however, the accuracy of RSBI to predict successful weaning have been questioned. Weaning failure usually results from a complex interplay of multiple factors. Thus, predictors targeting a single pathophysiologic mechanism tend to be unreliable for heterogeneous abnormalities. The investigators proposed a new mathematical model and algorithm, which capture the essential feature of the variability ruling the physiological dynamics. Through the modern adaptive signal processing techniques, the investigators develop an index called WIN, which is evaluated from the 5 minutes continuous physiological signal and provides better perdition to wean than RSBI in a retrospective analysis. In this study, the investigators evaluate the predictive power of WIN and RSBI prospectively in patients undergoing weaning prospectively.

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