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      Multicenter Study Validating Accuracy of a Continuous Respiratory Rate Measurement Derived From Pulse Oximetry: A Comparison With Capnography

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          Abstract

          Published ahead of print January 17, 2017.

          Abstract

          BACKGROUND:

          Intermittent measurement of respiratory rate via observation is routine in many patient care settings. This approach has several inherent limitations that diminish the clinical utility of these measurements because it is intermittent, susceptible to human error, and requires clinical resources. As an alternative, a software application that derives continuous respiratory rate measurement from a standard pulse oximeter has been developed. We sought to determine the performance characteristics of this new technology by comparison with clinician-reviewed capnography waveforms in both healthy subjects and hospitalized patients in a low-acuity care setting.

          METHODS:

          Two independent observational studies were conducted to validate the performance of the Medtronic Nellcor TM Respiration Rate Software application. One study enrolled 26 healthy volunteer subjects in a clinical laboratory, and a second multicenter study enrolled 53 hospitalized patients. During a 30-minute study period taking place while participants were breathing spontaneously, pulse oximeter and nasal/oral capnography waveforms were collected. Pulse oximeter waveforms were processed to determine respiratory rate via the Medtronic Nellcor Respiration Rate Software. Capnography waveforms reviewed by a clinician were used to determine the reference respiratory rate.

          RESULTS:

          A total of 23,243 paired observations between the pulse oximeter-derived respiratory rate and the capnography reference method were collected and examined. The mean reference-based respiratory rate was 15.3 ± 4.3 breaths per minute with a range of 4 to 34 breaths per minute. The Pearson correlation coefficient between the Medtronic Nellcor Respiration Rate Software values and the capnography reference respiratory rate is reported as a linear correlation, R, as 0.92 ± 0.02 ( P < .001), whereas Lin’s concordance correlation coefficient indicates an overall agreement of 0.85 ± 0.04 (95% confidence interval [CI] +0.76; +0.93) (healthy volunteers: 0.94 ± 0.02 [95% CI +0.91; +0.97]; hospitalized patients: 0.80 ± 0.06 [95% CI +0.68; +0.92]). The mean bias of the Medtronic Nellcor Respiration Rate Software was 0.18 breaths per minute with a precision (SD) of 1.65 breaths per minute (healthy volunteers: 0.37 ± 0.78 [95% limits of agreement: –1.16; +1.90] breaths per minute; hospitalized patients: 0.07 ± 1.99 [95% limits of agreement: –3.84; +3.97] breaths per minute). The root mean square deviation was 1.35 breaths per minute (healthy volunteers: 0.81; hospitalized patients: 1.60).

          CONCLUSIONS:

          These data demonstrate the performance of the Medtronic Nellcor Respiration Rate Software in healthy subjects and patients hospitalized in a low-acuity care setting when compared with clinician-reviewed capnography. The observed performance of this technology suggests that it may be a useful adjunct to continuous pulse oximetry monitoring by providing continuous respiratory rate measurements. The potential patient safety benefit of using combined continuous pulse oximetry and respiratory rate monitoring warrants assessment.

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          Most cited references22

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          Association between clinically abnormal observations and subsequent in-hospital mortality: a prospective study.

          Patients with unexpected in-hospital cardiac arrest often have an abnormal clinical observation prior to the arrest. Previous studies have suggested that a medical emergency team responding to such patients may decrease in-hospital mortality from cardiac arrest, but the association between any abnormal clinical observation and subsequent increased mortality has not been studied prospectively. The aim of this study was to determine the predictive value of selected abnormal clinical observations in a ward population for subsequent in-hospital mortality. Prospective data collection in five general hospital ward areas at Dandenong Hospital, Victoria, Australia. None. During the study period, 6303 patients were admitted to the study areas. Of those, 564 (8.9%) experienced 1598 pre-determined clinically abnormal events and 146 of these patients (26%) died. The two commonest abnormal clinical events were arterial oxygen desaturation (51% of all events), and hypotension (17.3% of all events). Using a multiple linear logistic regression model, there were six clinical observations which were significant predictors of mortality. These were: a decrease in Glasgow Coma Score by two points, onset of coma, hypotension ( 30 min(-1). The presence of any one of the six events was associated with a 6.8-fold (95% CI: 2.7-17.1) increase in the risk of mortality. Six abnormal clinical observations are associated with a high risk of mortality for in-hospital patients. These observations should be included as criteria for the early identification of patients at higher risk of unexpected in-hospital cardiac arrest.
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            Photoplethysmographic derivation of respiratory rate: a review of relevant physiology.

            An abnormal respiratory rate is often the earliest sign of critical illness. A reliable estimate of respiratory rate is vital in the application of remote telemonitoring systems, which may facilitate early supported discharge from hospital or prompt recognition of physiological deterioration in high-risk patient groups. Traditional approaches use analysis of respiratory sinus arrhythmia from the electrocardiogram (ECG), but this phenomenon is predominantly limited to the young and healthy. Analysis of the photoplethysmogram (PPG) waveform offers an alternative means of non-invasive respiratory rate monitoring, but further development is required to enable reliable estimates. This review conceptualizes the challenge by discussing the effect of respiration on the PPG waveform and the key physiological mechanisms that underpin the derivation of respiratory rate from the PPG. Copyright © 2012 Informa UK, Ltd.
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              Predicting cardiac arrest on the wards: a nested case-control study.

              Current rapid response team activation criteria were not statistically derived using ward vital signs, and the best vital sign predictors of cardiac arrest (CA) have not been determined. In addition, it is unknown when vital signs begin to accurately detect this event prior to CA.
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                Author and article information

                Journal
                Anesth Analg
                Anesth. Analg
                ANE
                Anesthesia and Analgesia
                Lippincott Williams & Wilkins
                0003-2999
                1526-7598
                April 2017
                17 January 2017
                : 124
                : 4
                : 1153-1159
                Affiliations
                From the Departments of [* ]Anesthesiology and []Neurological Surgery, The Ohio State University Wexner Medical Center, Columbus, Ohio; []Respiratory & Monitoring Solutions, Medtronic, Boulder, Colorado; [§ ]Department of Surgery, University of Colorado Hospital, Aurora, Colorado; and []Respiratory & Monitoring Solutions, Medtronic, Edinburgh, United Kingdom.
                Author notes
                Address correspondence to Michael L. Mestek, PhD, Medical Affairs, Respiratory & Monitoring Solutions, Medtronic, 6135 Gunbarrel Ave, Boulder, CO 80301. Address e-mail to michael.l.mestek@ 123456medtronic.com .
                Article
                00022
                10.1213/ANE.0000000000001852
                5367492
                28099286
                d61f873f-25b4-439e-b4d3-ec18c8e38cb2
                Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Anesthesia Research Society.

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

                History
                : 16 November 2016
                Categories
                Technology, Computing, and Simulation
                Original Clinical Research Report
                Custom metadata
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