Critical Care |

Automated Mechanical Ventilator Waveform Analysis of Patient-Ventilator Asynchrony FREE TO VIEW

Jason Adams, MD; Monica Lieng, BS; Brooks Kuhn, MD; Edward Guo; Edik Simonian; Sean Peisert, PhD; JP Delplanque, PhD; Nick Anderson, PhD
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UC Davis School of Medicine, Sacramento, CA

Chest. 2015;148(4_MeetingAbstracts):175A. doi:10.1378/chest.2281731
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SESSION TYPE: Original Investigation Poster

PRESENTED ON: Wednesday, October 28, 2015 at 01:30 PM - 02:30 PM

PURPOSE: Mechanical ventilation is a life-saving intervention but is associated with adverse effects including ventilator-induced lung injury (VILI). Patient-ventilator asynchrony (PVA) is thought to contribute to VILI, but the study of PVA has been hampered by limited access to the high frequency, large volume data streams produced by modern ventilators and a lack of robust analytics. To address these limitations, we developed an automated pipeline for breath-by-breath analysis of ventilator waveform data.

METHODS: Simulated pressure and flow time series data representing normal breaths and common forms of PVA were generated on PB840 ventilators, collected unobtrusively using small, customized wireless peripheral devices, and transmitted to a networked server for storage and analysis. Two critical care physicians reviewed all waveforms to generate gold standards. Rule-based algorithms were developed to quantify inspiratory and expiratory tidal volumes (TV) and identify PVA subtypes including double trigger and delayed termination asynchrony. Data were split randomly into derivation and validation sets. Algorithm performance was compared to ventilator reported values and clinician annotation.

RESULTS: The mean difference between algorithm-determined and ventilator-reported TVs was 3.1% (99% CI ± 1.36%). Algorithm agreement with clinician annotation was excellent for double trigger PVA and moderate for delayed termination PVA, with Kappa statistics of 0.85 and 0.58, respectively. In the validation data set (n = 492 breaths), double trigger asynchrony was detected with an overall accuracy of 94.1%, sensitivity of 100%, and specificity of 92.8%.

CONCLUSIONS: A pipeline combining wireless ventilator data acquisition and rule-based analytic algorithms informed by the principles of bedside ventilator waveform analysis allows for automated, quantitative breath-by-breath analysis of patient-ventilator interactions.

CLINICAL IMPLICATIONS: We have recently deployed this system in the medical intensive care unit of the UC Davis Medical Center, which will enable further development of mechanical ventilation analytics. We have begun to explore the use of supervised machine learning and dynamic time series modeling to improve the classification of other common types of PVA and of clinical phenotypes associated with respiratory failure. This system will help to better define the epidemiology and clinical impact of PVA and other forms of off-target mechanical ventilation, and may lead to improved decision support and patient outcomes.

DISCLOSURE: The following authors have nothing to disclose: Jason Adams, Monica Lieng, Brooks Kuhn, Edward Guo, Edik Simonian, Sean Peisert, JP Delplanque, Nick Anderson

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