Literature DB >> 29092068

Development of a research-oriented system for collecting mechanical ventilator waveform data.

Gregory B Rehm1, Brooks T Kuhn2, Jean-Pierre Delplanque3, Edward C Guo1, Monica K Lieng4, Jimmy Nguyen5, Nicholas R Anderson6, Jason Y Adams2.   

Abstract

Lack of access to high-frequency, high-volume patient-derived data, such as mechanical ventilator waveform data, has limited the secondary use of these data for research, quality improvement, and decision support. Existing methods for collecting these data are obtrusive, require high levels of technical expertise, and are often cost-prohibitive, limiting their use and scalability for research applications. We describe here the development of an unobtrusive, open-source, scalable, and user-friendly architecture for collecting, transmitting, and storing mechanical ventilator waveform data that is generalizable to other patient care devices. The system implements a software framework that automates and enforces end-to-end data collection and transmission. A web-based data management application facilitates nontechnical end users' abilities to manage data acquisition devices, mitigates data loss and misattribution, and automates data storage. Using this integrated system, we have been able to collect ventilator waveform data from >450 patients as part of an ongoing clinical study.
© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  artificial; intensive care units; mechanical; monitoring; patient ventilator asynchrony; physiologic; respiration; translational medical research; ventilators

Year:  2018        PMID: 29092068     DOI: 10.1093/jamia/ocx116

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  5 in total

1.  ICU Cockpit: a platform for collecting multimodal waveform data, AI-based computational disease modeling and real-time decision support in the intensive care unit.

Authors:  Jens Michael Boss; Gagan Narula; Christian Straessle; Jan Willms; Jan Azzati; Dominique Brodbeck; Rahel Luethy; Susanne Suter; Christof Buehler; Carl Muroi; David Jule Mack; Marko Seric; Daniel Baumann; Emanuela Keller
Journal:  J Am Med Inform Assoc       Date:  2022-06-14       Impact factor: 7.942

2.  Leveraging IoTs and Machine Learning for Patient Diagnosis and Ventilation Management in the Intensive Care Unit.

Authors:  Gregory B Rehm; Sang Hoon Woo; Xin Luigi Chen; Brooks T Kuhn; Irene Cortes-Puch; Nicholas R Anderson; Jason Y Adams; Chen-Nee Chuah
Journal:  IEEE Pervasive Comput       Date:  2020-05-25       Impact factor: 1.603

3.  Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data.

Authors:  Gregory B Rehm; Irene Cortés-Puch; Brooks T Kuhn; Jimmy Nguyen; Sarina A Fazio; Michael A Johnson; Nicholas R Anderson; Chen-Nee Chuah; Jason Y Adams
Journal:  Crit Care Explor       Date:  2021-01-08

4.  VentMon: An open source inline ventilator tester and monitor.

Authors:  Robert L Read; Lauria Clarke; Geoff Mulligan
Journal:  HardwareX       Date:  2021-04-22

5.  CAREDAQ: Data acquisition device for mechanical ventilation waveform monitoring.

Authors:  Qing Arn Ng; Christopher Yew Shuen Ang; Yeong Shiong Chiew; Xin Wang; Chee Pin Tan; Mohd Basri Mat Nor; Nor Salwa Damanhuri; J Geoffrey Chase
Journal:  HardwareX       Date:  2022-09-06
  5 in total

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