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

Rehm, Gregory B.; Kühn, Bärbel; Delplanque, Jean‐Pierre; Guo, Edward C.; Lieng, Monica K.; Nguyen, James; Anderson, Nick; Adams, Jason Y.

doi:10.1093/jamia/ocx116

Cited by 14 publications

(7 citation statements)

References 24 publications

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“…We used our ventMAP software platform ( 18 ) to extract nine physiologic features from raw VWD, representing pressure and flow, sampled at 50 Hz, obtained from Puritan-Bennett model 840 ventilators ( 25 ). Features were extracted and aimed to capture relevant respiratory pathophysiology, while avoiding features that might strongly correlate with ARDS management such as TV or positive end-expiratory pressure ( Supplemental Digital Content Table 3 , http://links.lww.com/CCX/A480 ).…”

Section: Methodsmentioning

confidence: 99%

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

Rehm

Cortés‐Puch

Kühn

et al. 2021

Critical Care Explorations

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Objectives: To develop and characterize a machine learning algorithm to discriminate acute respiratory distress syndrome from other causes of respiratory failure using only ventilator waveform data. Design: Retrospective, observational cohort study. Setting: Academic medical center ICU. Patients: Adults admitted to the ICU requiring invasive mechanical ventilation, including 50 patients with acute respiratory distress syndrome and 50 patients with primary indications for mechanical ventilation other than hypoxemic respiratory failure. Interventions: None. Measurements and Main Results: Pressure and flow time series data from mechanical ventilation during the first 24-hours after meeting acute respiratory distress syndrome criteria (or first 24-hr of mechanical ventilation for non-acute respiratory distress syndrome patients) were processed to extract nine physiologic features. A random forest machine learning algorithm was trained to discriminate between the patients with and without acute respiratory distress syndrome. Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Analyses examined performance when the model was trained using data from the first 24 hours and tested using withheld data from either the first 24 hours (24/24 model) or 6 hours (24/6 model). Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.88, 0.90, 0.71, 0.77, and 0.90 (24/24); and 0.89, 0.90, 0.75, 0.83, and 0.83 (24/6). Conclusions: Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings with limited availability of conventional diagnostic tests and electronic health records.

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Section: Methodsmentioning

confidence: 99%

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

Rehm

Cortés‐Puch

Kühn

et al. 2021

Critical Care Explorations

Self Cite

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“…Data were collected from Puritan Bennet Model 840 TM (PB-840) ventilators (Covidien, Dublin, Republic of Ireland) and stored in multiple sequential files up to two hours in length. (34,35) APL uses an open source stack including: ventMAP software for extraction of waveform metadata, the Python Flask web framework, the JavaScript library Dygraphs for graphing of VWD, Redis for storage, and Docker for APL deployment. (1,36,37) APL's source code and install instructions are available on GitHub for modification or use of APL.…”

Section: Methodsmentioning

confidence: 99%

An Analytic Platform for the Rapid and Reproducible Annotation of Ventilator Waveform Data

Rehm

Kühn

Lieng

et al. 2019

Preprint

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Algorithmic classifiers are crucial components of clinical decision support (CDS) systems needed to advance healthcare delivery. Robust CDS systems must be derived and validated via creation of multi-reviewer adjudicated gold standard datasets. Manual annotation of physiologic data such as mechanical ventilator waveform data (VWD) can be time-consuming, and lacks methodological consistency in dataset development. To address these issues, we have created a system for annotating and adjudicating VWD called the Annotation PipeLine (APL) to optimize VWD annotation by expert reviewers. APL combines visual assessment of waveform characteristics with metadata display, enabling inclusion of quantitative thresholds into annotation decisions by reviewers. APL also includes specific features for resolving multireviewer disagreements and generating gold standard data sets. APL's unique combination of methods and open source framework may accelerate the creation of CDS algorithms for ventilator management, and may serve as a model for future research into physiologic waveform annotation systems.

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“…Most studies have been relying on clinicians at the bedside to diagnose and manage MV treatment [25]. A number of monitoring systems in the literature [26], [27] provide data acquisition, but do not provide real-time analysis and feedback. In order to address the aforementioned drawbacks, there is a need to improve data acquisition of ventilator waveform data (VWD) which enhance the study of patient respiratory mechanics and PVI.…”

Section: Introductionmentioning

confidence: 99%

Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment

Chiew

Wang

et al. 2021

IEEE Access

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The rise of model-based and machine learning methods have created increasingly realistic opportunities to implement personalized, patient-specific mechanical ventilation (MV) in the ICU. These methods require monitoring of real-time patient ventilation waveform data (VWD) during MV treatment. However, there are relatively few non-invasive and/or non-proprietary systems to monitor and record patientspecific lung condition in real-time. In this paper, we present a CARE network data acquisition and monitoring system (CARENet) to automate data collection and to remotely monitor patient-specific lung condition and ventilation parameters. The automated system acquires VWD from a mechanical ventilator using a data acquisition device (DAQ), stores data in network-attached storage (NAS), and processes VWDs via a data management platform (DMP) web application. The web application enables real-time and retrospective modelbased monitoring and analysis of clinical MV data. In particular, CARENet provides detailed breath-by-breath patient-specific respiratory mechanics, as well as the overall trends assessing changes in patient condition. Validation testing with a retrospective data set illustrated how breath-to-breath and time-varying patientventilator interaction during MV can be monitored, and, in turn, can be used to guide MV treatment. The network data acquisition system design presented is low-cost, open, and enables continuous, automated, scalable, realtime collection and analysis of waveform data, to help improve decision making, care, and outcomes in MV.

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

Cited by 14 publications

References 24 publications

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

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

An Analytic Platform for the Rapid and Reproducible Annotation of Ventilator Waveform Data

Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment

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