Development and Validation of a Multi-Algorithm Analytic Platform to Detect Off-Target Mechanical Ventilation
| العنوان: | Development and Validation of a Multi-Algorithm Analytic Platform to Detect Off-Target Mechanical Ventilation |
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| المؤلفون: | Brooks Kuhn, Sandra L. Taylor, Jason Yeates Adams, Jean Pierre Delplanque, Nicholas R. Anderson, Gregory B. Rehm, Edward C. Guo, Monica K. Lieng |
| المصدر: | Scientific reports, vol 7, iss 1 Scientific Reports, Vol 7, Iss 1, Pp 1-11 (2017) Adams, JY; Lieng, MK; Kuhn, BT; Rehm, GB; Guo, EC; Taylor, SL; et al.(2017). Development and Validation of a Multi-Algorithm Analytic Platform to Detect Off-Target Mechanical Ventilation. Scientific Reports, 7(1). doi: 10.1038/s41598-017-15052-x. UC Davis: Retrieved from: http://www.escholarship.org/uc/item/8kz6m81j Scientific Reports |
| بيانات النشر: | eScholarship, University of California, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | Computer science, Critical Illness, Real-time computing, Ventilators, lcsh:Medicine, Bioengineering, Artifact (software development), Clinical decision support system, Article, 7.3 Management and decision making, 03 medical and health sciences, 0302 clinical medicine, Software, Rare Diseases, Clinical Research, Tidal Volume, Waveform, Humans, 030212 general & internal medicine, lcsh:Science, Simulation, screening and diagnosis, Assistive Technology, Ventilators, Mechanical, Multidisciplinary, Event (computing), Data stream mining, business.industry, Respiration, lcsh:R, Mechanical, Respiration, Artificial, 3. Good health, Asynchrony (computer programming), Detection, Intensive Care Units, 030228 respiratory system, Life support, Artificial, lcsh:Q, Management of diseases and conditions, business, Algorithms, 4.2 Evaluation of markers and technologies |
| الوصف: | Healthcare-specific analytic software is needed to process the large volumes of streaming physiologic waveform data increasingly available from life support devices such as mechanical ventilators. Detection of clinically relevant events from these data streams will advance understanding of critical illness, enable real-time clinical decision support, and improve both clinical outcomes and patient experience. We used mechanical ventilation waveform data (VWD) as a use case to address broader issues of data access and analysis including discrimination between true events and waveform artifacts. We developed an open source data acquisition platform to acquire VWD, and a modular, multi-algorithm analytic platform (ventMAP) to enable automated detection of off-target ventilation (OTV) delivery in critically-ill patients. We tested the hypothesis that use of artifact correction logic would improve the specificity of clinical event detection without compromising sensitivity. We showed that ventMAP could accurately detect harmful forms of OTV including excessive tidal volumes and common forms of patient-ventilator asynchrony, and that artifact correction significantly improved the specificity of event detection without decreasing sensitivity. Our multi-disciplinary approach has enabled automated analysis of high-volume streaming patient waveform data for clinical and translational research, and will advance the study and management of critically ill patients requiring mechanical ventilation. |
| وصف الملف: | application/pdf |
| DOI: | 10.1038/s41598-017-15052-x. |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e73e1fb45cff464d2797da08a438f40d https://escholarship.org/uc/item/8kz6m81j |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....e73e1fb45cff464d2797da08a438f40d |
| قاعدة البيانات: | OpenAIRE |
| DOI: | 10.1038/s41598-017-15052-x. |
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