Predictive Analysis Software for Successful Weaning From Ventilator of Patients
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|ClinicalTrials.gov Identifier: NCT02915458|
Recruitment Status : Recruiting
First Posted : September 27, 2016
Last Update Posted : July 24, 2018
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.
|Condition or disease|
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.
|Study Type :||Observational|
|Estimated Enrollment :||188 participants|
|Official Title:||Predictive Analysis Software for Successful Weaning From Ventilator of Patients in Critical Condition|
|Study Start Date :||January 2016|
|Estimated Primary Completion Date :||December 2018|
|Estimated Study Completion Date :||December 2018|
- Successful weaning from mechanical ventilation [ Time Frame: up to 72 hours ]The patients could breath by themselves after extubation without any ventilator assistance for 72 hours
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT02915458
|Contact: Ting-Yu Lin, MD||886-3-3281200 ext firstname.lastname@example.org|
|Contact: Yu-Lun Lo, MD||886-3-3281200 ext email@example.com|
|Department of Thoracic Medicine, Chang Gung Memorial Hospital||Recruiting|
|Taoyuan, Taiwan, 33305|
|Contact: Ting-Yu Lin, MD 886-3-3281200 ext 5108 firstname.lastname@example.org|
|Principal Investigator: Ting-Yu Lin, MD|
|Principal Investigator:||Yu-Lun Lo, MD||Chang Gung Memorial Hospital|