Dataset Information

Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

ABSTRACT: Feedback on chest compressions and ventilations during cardiopulmonary resuscitation (CPR) is important to improve survival from out-of-hospital cardiac arrest (OHCA). The thoracic impedance signal acquired by monitor-defibrillators during treatment can be used to provide feedback on ventilations, but chest compression components prevent accurate detection of ventilations. This study introduces the first method for accurate ventilation detection using the impedance while chest compressions are concurrently delivered by a mechanical CPR device. A total of 423 OHCA patients treated with mechanical CPR were included, 761 analysis intervals were selected which in total comprised 5 884 minutes and contained 34 864 ventilations. Ground truth ventilations were determined using the expired CO 2 channel. The method uses adaptive signal processing to obtain the impedance ventilation waveform. Then, 14 features were calculated from the ventilation waveform and fed to a random forest (RF) classifier to discriminate false positive detections from actual ventilations. The RF feature importance was used to determine the best feature subset for the classifier. The method was trained and tested using stratified 10-fold cross validation (CV) partitions. The training/test process was repeated 20 times to statistically characterize the results. The best ventilation detector had a median (interdecile range, IDR) F 1-score of 96.32 (96.26-96.37). When used to provide feedback in 1-min intervals, the median (IDR) error and relative error in ventilation rate were 0.002 (-0.334-0.572) min-1 and 0.05 (-3.71-9.08)%, respectively. An accurate ventilation detector during mechanical CPR was demonstrated. The algorithm could be introduced in current equipment for feedback on ventilation rate and quality, and it could contribute to improve OHCA survival rates.

SUBMITTER: Jaureguibeitia X

PROVIDER: S-EPMC7537815 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

Jaureguibeitia Xabier X Irusta Unai U Aramendi Elisabete E Owens Pamela C PC Wang Henry E HE Idris Ahamed H AH

IEEE journal of biomedical and health informatics 20200117 9

Feedback on chest compressions and ventilations during cardiopulmonary resuscitation (CPR) is important to improve survival from out-of-hospital cardiac arrest (OHCA). The thoracic impedance signal acquired by monitor-defibrillators during treatment can be used to provide feedback on ventilations, but chest compression components prevent accurate detection of ventilations. This study introduces the first method for accurate ventilation detection using the impedance while chest compressions are c ...[more]

PMID: 31976918

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Project description:Importance:Out-of-hospital cardiac arrest is a common scenario facing prehospital emergency medical services (EMS) professionals and nearly always involves either manual or mechanical cardiopulmonary resuscitation (CPR). Mechanical CPR devices are expensive and prior clinical trials have not provided evidence of benefit for patients when compared with manual CPR. Objectives:To investigate the use of mechanical CPR in the prehospital setting and determine whether patient demographic characteristics or geographical location is associated with its use. Design, Setting, and Participants:A retrospective cross-sectional study was performed using the 2010 through 2016 National Emergency Medical Services Information System data. Participants included all patients identified by EMS professionals as having out-of-hospital cardiac arrest. Main Outcomes and Measures:Use of CPR, categorized as manual or mechanical. Results:From 2010 to 2016, 892?022 patients (38.6% female, 60.4% male, missing for 1%; mean [SD] age, 61.1 [20.5] years) with out-of-hospital cardiac arrest were identified by EMS professionals. Overall, manual CPR was used for 618?171 patients (69.3%) and mechanical CPR was used for 45?493 patients (5.1%). The risk-standardized rate of mechanical CPR use, accounting for patient demographic and geographical characteristics, rose from 1.9% in 2010 to 8.0% in 2016 (P?<?.001). In multivariable analyses, use of mechanical CPR devices was increasingly likely over time among patients identified with out-of-hospital cardiac arrest treated by EMS professionals, increasing from an adjusted odds ratio of 1.58 (95% CI, 1.42-1.77; P?<?.001) when comparing 2011 with 2010, to an adjusted odds ratio of 11.32 (95% CI, 10.22-12.54; P?<?.001) when comparing 2016 with 2010. In addition, several other patient demographic and geographical characteristics were associated with a higher likelihood of receiving mechanical CPR, including being 65 years or older, being male, being Hispanic, as well as receiving treatment in the Northeast Census Region, in a suburban location, or in a zip code with a median annual income greater than $20?000. Conclusions and Relevance:Mechanical CPR device use increased more than 4-fold among patients with out-of-hospital cardiac arrest treated by EMS professionals. Given the high costs of mechanical CPR devices, better evidence is needed to determine whether these devices improve clinically meaningful outcomes for patients treated for out-of-hospital cardiac arrest by prehospital EMS professionals to justify the significant increase in their use.

Dataset Information

Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

Publications

Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

Similar Datasets

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