Validating Respiratory Rate Measurements in Patients Receiving High Flow Nasal Cannula: A Comparative Study of Nellcor PM1000N and visual inspection

Validating Respiratory Rate Measurements in Patients Receiving High Flow Nasal Cannula: A Comparative Study of Nellcor PM1000N and visual inspection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validating Respiratory Rate Measurements in Patients Receiving High Flow Nasal Cannula: A Comparative Study of Nellcor PM1000N and visual inspection Takuma Iwaya, Junpei Haruna, Aki Sasaki, Sayaka Nakano, Hiroomi Tatsumi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4043306/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose : Recently, the Nellcor PM1000N was developed for the concurrent assessment of respiratory rate and percutaneous oxygen saturation. However, the validation of respiratory rate measurements in patients receiving a high-flow nasal cannula (HFNC) using the PM1000N remains unestablished. Therefore, this study aimed to assess the validity of respiratory rate measurements obtained using PM1000N in patients receiving HFNC. Methods: A retrospective assessment was conducted on the respiratory rate measurements obtained using the PM1000N and electrocardiogram (ECG) impedance methods in comparison to those visually assessed by nurses. This evaluation included patients admitted to the Intensive Care Unit (ICU) of Sapporo Medical University Hospital who received HFNC between June 2022 and December 2022. Correlation coefficients, intraclass correlation coefficients (ICCs), Bland-Altman plots, and t-tests were employed to assess the concordance between the visually observed respiratory rates by nurses and those recorded by the PM1000N and ECG impedance. Results: Twenty patients were enrolled in this study. Among them, 119 instances of respiratory rate were recorded. The ICCs for the PM1000N and impedance methods were 0.918 and 0.846, respectively, compared with the rates visually assessed by nurses. The mean differences were p=0.947 (95% CI: -3.186 – 0.2987) and p < .001 (95% CI: 16.4609–17.9532), respectively. Conclusion: The PM1000N demonstrated superiority over ECG impedance in measuring respiratory rate in patients with HFNC. Furthermore, PM1000N shows promise for effective application in patients receiving HFNC. Figures Figure 1 Introduction Among the pivotal vital signs measured in daily patient care, respiratory rate is reportedly correlated with in-hospital mortality and serious adverse events (1,2). Therefore, respiratory rate has emerged as a more discerning prognostic indicator, such as mortality, than traditional metrics, such as blood pressure or pulse rate (3,4). However, many acute care hospitals report that blood pressure, pulse rate, temperature, and SpO 2 are recorded, instead of the respiratory rate (5,6). The rationale for not measuring respiratory rate may be the lack of awareness among nurses and junior healthcare professionals (7) and the complexity of the method of measurement (8); because several measurement methods are available. Capnography is the standard method for accurately measuring respiratory rate (9). Other methods include electrocardiogram (ECG) - derived respiration rate (10), radar-based respiration rate monitoring (11), and optical-based respiration rate monitoring (12). However, in general ward settings, the enumeration of respiratory rate typically relies on ECG impedance, a method susceptible to inaccuracies (13). Visual observation of respiratory rate by healthcare professionals is also widely used in several patient care settings; however, this approach is labor-intensive and often results in low adherence to respiratory rate measurements (14). Recently, high-flow nasal cannulas (HFNC) have been widely used in patients with acute respiratory failure (15). HFNC slightly increases positive airway and intrathoracic pressures (16), manifesting distinct effects on work breathing compared with conventional low-flow oxygen delivery systems. Although HFNC is often used in patients with respiratory failure in ICUs and general wards, capnography cannot concurrently correctly measure respiratory rate because there is no position to attach the capnograph. In addition, the accuracy of ECG impedance is reduced by patient movement, physiological movements of the chest wall, such as coughing, and placement of ECG electrodes (17,18). However, a new device (Nellcor TM PM1000N, Medtronic Japan, Co. Ltd., Tokyo, Japan) that facilitates the concurrent measurement of the respiratory rate and percutaneous oxygen saturation is now available. The veracity of this device has been substantiated in cohorts undergoing low-flow oxygen therapy and in healthy individuals (3,19–21). However, the efficacy of Nellcor PM1000N (PM1000N) in gauging the respiratory rate in HFNC-utilizing patients remains unclear. PM1000N, which is intended for error-free application, holds promise for accurately ascertaining the respiratory rate of patients receiving HFNC therapy in general ward settings. Therefore, we hypothesized that there would be no difference between the number of breaths per minute measured visually by nurses (hereafter referred to as "visual inspection") and the number of breaths per minute measured by the PM1000N in patients receiving HFNC therapy and aimed to investigate this hypothesis. Methods Design and Setting This single-center, retrospective, observational study was conducted at a university hospital. The study design and protocol were approved by the Institutional Review Board (IRB) of Sapporo Medical University (IRB-authorized number: 342-242, December 12th, 2022). Participants Patients who received HFNC therapy in the intensive care unit (ICU) of Sapporo Medical University Hospital between June 2022 and December 2022 were selected based on their electronic health records (EHR).Patients who were under 18 years of age, frequently removed their own cannula, unable to wear the SpO 2 probe on their finger, or whose respiratory rate was not recorded in the EHR were excluded from this study. Data collection Patient data including age, sex, and primary pathology were extracted from the hospital’s EHR. Additionally, parameters including respiratory rate, heart rate, arrhythmia presence, systolic and diastolic blood pressure, and SpO 2 during HFNC use were recorded. The respiratory rate was visually inspected at 3:00, 7:00, 11:00, 15:00, 19:00, and 23:00 and manually recorded in the EHR. Furthermore, the measured respiratory rates obtained by the impedance method from a bedside monitor (BSM-1763 Lifescope PT, Nihon Kohden Co. Ltd., Tokyo, Japan: bedside monitor) and PM1000N were automatically recorded in the EHR. Respiratory rate recordings from the three modalities were retrospectively compiled from the EHR. Three investigators procured data from records between August 2023 and December 2023. Respiratory rate measurement procedure PM1000N An adhesive SpO2 measurement sensor (Nellcor TM OxySensor III), utilized in conjunction with the PM1000N, was affixed to the patient’s digit. A Nellcor OxySensor III was applied according to the instructions, positioning the sensor window adjacent to the terminal joint on the nail side and orienting the sensor cable to the dorsal aspect of the hand. Bedside monitor The electrodes were affixed to the patient’s skin at the anterior and lateral chest walls using bipolar leads for all inductions. The impedance method was used for these measurements. The operational concept involves the application of a respiratory measurement current via an electrode designed for ECG assessment. The resultant impedance alteration in the thoracic region induced by respiratory activity manifests as a modification of the respiratory measurement current. The subsequent amplification and computation of this signal yielded a visually represented respiratory curve. Visual observation by the nurse (visual inspection) During each designated observation interval, nursing practitioners positioned themselves proximal to the patient at the bedside, undertaking direct scrutiny of the vertical excursion of the thoracic region. Each observation spanned one minute, and the documented respiratory was derived from the observed findings inscribed in the patient's EHR. Outcomes The primary outcome was to validate the concordance rate of the respiratory rate measured using the PM1000N, with the visual inspection respiratory rate as the reference. Statistical Analysis Data were assessed for Gaussian distribution using the Shapiro–Wilk normality test. Normally distributed data were presented as the mean ± standard deviation (SD), and non-normally distributed data were presented as the median and interquartile range (IQR). Correlation coefficients and intraclass correlation coefficients (ICC) were calculated for each measurement method based on visual inspection to validate the accuracy of the PM1000N and bedside monitors. We also calculated the standard deviation of the respiratory rate measured by the three methods, created a Bland–Altman plot, and performed a t -test on the mean difference. To evaluate the background of the inability to measure respiratory rate with the PM1000N, the patients were divided into two groups: a measurement group and a non-measurement group, and the flow of HFNC and the presence of arrhythmia were compared. Statistical significance was set at p < 0.05. Statistical analyses were performed using the SPSS software version 27 (IBM Corp., Armonk, NY, USA). Results Patient Characteristics Of the 354 individuals admitted to our ICU during the study period, 334 were ineligible based on the prespecified exclusion criteria. Patient characteristics are shown in Table 1. The resulting cohort consisted of 20 participants (5.6%), characterized by seven (35%) males and a median age of 75.6 years (interquartile range: 69.8–80.4). Cardiovascular disease was the predominant disease affecting 11 (55%) patients, representing approximately 50% of all cases. The respiratory rate recorded by each measurement method is shown in Table 2. During the observation period, the respiratory rate was recorded 119 times. Visual inspection and bedside monitoring were used to record respiratory rate at all time points. However, PM1000N did not measure the respiratory rate at 20 time points. The time periods for which the respiratory rate could not be measured with PM1000N were treated as missing values and excluded from the primary analysis. The mean respiratory rate observed by visual inspection, bedside monitor, and PM1000N were 17.7 ± 3.9, 18.3 ± 3.7, and 17.2 ± 3.9, respectively. The intra-class correlation coefficients between the bedside monitor and PM1000N, based on visual inspection, are listed in Table 3. The correlation of respiratory rate by each measurement method based on visual inspection and the Bland–Altman plot and linear intubation plot are shown in Figure 1. The correlation coefficients between the bedside monitor and PM1000N based on visual inspection were 0.85, p < .001 and 0.84, p < .001, respectively. The respiratory rate could not be measured at 40 points using PM1000N. A comparison of the measurable and non-measurable points for the respiratory rate using PM1000N is shown in Table 4. Arrhythmias were significantly more common at non-measurable points than at measurable points (54 (27.3%) vs. 36 (90), P < 0.01). Discussion This study aimed to validate the automated respiratory rate measurement of PM1000N in patients using HFNC. Respiratory rate measurements with the PM1000N showed a better correlation with visual inspection than with the impedance method by bedside monitors, indicating that the PM1000N can provide favorable outcomes in determining respiratory rate comparable to visual respiratory rate measurements in patients with HFNC. However, there are some concerns about accurately measuring the respiratory rate using the PM1000N in patients with HFNC. First, the PM1000N measures the respiratory rate by analyzing three types of respiratory pulse wave variations: respiratory sinus arrhythmia, pulse wave baseline variations, and pulse wave amplitude variations, sensed by a sensor attached to the finger. Among these, respiratory sinus arrhythmia captures changes in the heart rate associated with the respiratory cycle (22). Therefore, as a precaution when using the PM1000N, it is indicated that if the pulse wave becomes small owing to peripheral circulatory failure or pressure on the arm on the side where the sensor is worn by the blood pressure cuff, the measured value will be unstable (23).However, in this study, we believe that this effect was avoided by attaching the sensor to the finger opposite the arm where the blood pressure was measured. The second concern was the baseline and pulse wave amplitude variations. To measure the respiratory rate with the PM1000N, it is necessary to capture the variation in the venous annulus caused by changes in the intrathoracic pressure during the respiratory cycle. Airway pressure in patients using HFNC is higher in the closed state than in the open state, and increases with an increase in flow (24). Flow settings of 30 and 50 L/min with the mouth closed reportedly produce average airway pressures of approximately 3 cmH 2 O and 5 cmH 2 O, respectively (16). In contrast, a study investigating the effects of ventilator-assisted PEEP on circulatory dynamics reported no significant changes in circulatory dynamics with the addition of 0 cmH 2 O and 5 cmH 2 O PEEP levels (25). Because ventilator-induced PEEP occurs continuously in a closed respiratory circuit, and does not affect circulatory dynamics, the intermittent changes in airway pressure produced by HFNC should have little effect on circulatory dynamics. The HFNC flow in this study was treated over a wide range of 30 L–60 L. The comparison of the flow of HFNC between the measurable and non-measurable groups also showed no significant difference, suggesting that changes in intrathoracic pressure due to HFNC are unlikely to affect respiratory rate measurements with the PM1000N. Therefore, we believe that even if HFNC caused changes in intrathoracic pressure, the baseline and pulse wave amplitude fluctuations would be less affected, and an accurate respiratory rate could be measured even with the PM1000N using HFNC. Lynn et al. (26) found that an increased respiratory rate was a more important predictor of sudden deterioration than decreased SpO 2 . Moreover, the respiratory rate and oxygenation (ROX) index used to predict the progression to tracheal intubation in patients on HFNC includes the respiratory rate as a scoring component (27,28). Moreover, continuous, rather than cross-sectional, physiological monitoring based on these scores and indicators is useful for the early detection of patient deterioration. Therefore, continuous monitoring of the respiratory rate is important, regardless of the section. Further, although several conventional methods for measuring the respiratory rate exist, visual measurement by nurses is time-consuming and labor-intensive. Moreover, the impedance method allows continuous measurement with equipment, however, it is prone to errors due to the position of the electrode and patient's body movements. Conversely. in this study, the PM1000N was found to measure the respiratory rate with high accuracy, even in patients using HFNC. Thus, the PM1000N solves the problems of conventional measurement methods because it is easy to install and minimizes the burden on patients. In addition, the previously uncertain validity and reliability of HFNC use were also assessed in this study, indicating that respiratory rate measurement with the PM1000N is also useful for monitoring the respiratory rate in patients using HFNC. In contrast, 16.8% of the patients in the PM1000N group were not measurable. Additionally, a higher percentageof patients at the non-measurable points had arrhythmia than those at the measured points. Moreover, the safety and efficacy of measuring respiratory rate with the PM1000N in patients with arrhythmia, such as three or more irregular events within 30 s, have not been established because they may result in inaccurate respiratory rate values and loss of displayed respiratory rate information. Consequently, respiratory rate monitoring in combination with visual or other reliable measurement methods is necessary for patients with detectable arrhythmias or an unstable cardiovascular status. Strengths and Limitations To our knowledge, this is the first study to validate respiratory rate measurement with PM1000N in patients using HFNC; however, there are some limitations. First, the number of patients was limited because this was a retrospective observational study. Additionally, the patients' diseases and other backgrounds varied, which may have resulted in different patient conditions. Therefore, further studies with larger sample sizes are warranted. Second, the data used in this study analyzed respiratory rates recorded every four hours, which suggests accuracy in intermittent measurements but did not verify whether respiratory rates were measured with high accuracy on a continuous basis. Consequently, Further validation is required to clarify whether a continuously accurate respiratory rate can be measured. Third, this study was conducted in an ICU setting. The same study should be conducted in patients treated in general wards. Implications for Clinical Practice This study found that the PM1000N can easily and continuously measure the respiratory rate with the same accuracy as visual inspection, even in patients with changes in intrathoracic pressure caused by HFNC. Therefore, the PM1000N can be used in general hospital beds for patients with HFNC to accurately measure their respiratory rate and detect changes in their condition. Conclusion In this study, we evaluated the validity of measurements using the PM1000N in patients receiving HFNC therapy. The PM1000N was able to measure the respiratory rate in patients with HFNC with the same accuracy as visual inspection. Thus, the PM1000N may be used to accurately measure the respiratory rates of patients in general wards. Declarations Funding: The authors have not received any funding. Conflicts of interest/ Competing Interest: The authors declare that they have no competing interests. Data availability statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Code availability (software application or custom code): None. Author Contributions: TI, JH, AS, SN, and HT designed the work, collected and analyzed the data. JH and TI wrote the initial draft of the manuscript. TI, JH, AS and MY contributed to the analysis and interpretation of the data and assisted in the preparation of the manuscript. All authors critically revised the manuscript and approved the final version for publication. Acknowledgements We would like to thank the patients who participated in this study. Ethics Approval: This single-center, retrospective, observational study was conducted at a university hospital. The study design and protocol were approved by the Institutional Review Board (IRB) of Sapporo Medical University (IRB-authorized number: 342-242, December 12th, 2022). This study does not constitute research using human samples or tissues. Consent to participate: Owing to the retrospective observational nature of this study, the information was released on an opt-out basis. The need for informed consent was waived by the IRB of Sapporo Medical University Consent to publish: Owing to the retrospective observational nature of this study, consent for publish was released on an opt-out basis. References Goldhill DR, McNarry AF, Mandersloot G, McGinley A. A physiologically-based early warning score for ward patients: the association between score and outcome. Anaesthesia 2005;60:547–553. https://doi.org/10.1111/j.1365-2044.2005.04186.x Marik PE, Taeb AM. SIRS, qSOFA and new sepsis definition. J Thorac Dis 2017;9:943–945. https://doi.org/10.21037/jtd.2017.03.125 Subbe CP, Davies RG, Williams E, Rutherford P, Gemmell L. 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Patient characteristics Variables (n = 20) Age (years), Median [IQR] 75.6 [69.8 – 80.4] Males, n (%) 7 (35.0) Reasons for ICU admission Cardiovascular surgery, n (%) 11 (55.0) Sepsis, n (%) 2 (10.0) Respiratory failure, n (%) 3 (15.0) Cerebrovascular disease, n (%) 2 (10.0) Other 2 (10.0) Abbreviations: IQR, interquartile range Table 2. Respiration rate for each measurement method Variables Respiratory rate (n = 99) Visual inspection, mean ± SD 17.4 ± 3.9 Bedside monitor, mean ± SD 18.3 ± 3.7 PM1000N, mean ± SD 17.2 ± 3.9 Abbreviations: SD, standard deviation: PM1000N, Nellcor TM PM1000N: ICC, intraclass correlation coefficients Table 3. Intra-class correlation coefficient for each measurement method Variables ICC (95% confidence interval) F test with true value 0 Value df1 df2 p-value Bedside monitor 0.85 (0.79 – 0.89) 12.012 118 118 < .001 PM1000N 0.92 (0.88 – 0.94) 23.346 98 98 < .001 Abbreviations: IQR, interquartile range: PM1000N, Nellcor TM PM1000N: ICC, intraclass correlation coefficients Table 4. Comparison of the measurable and non-measurable points for respiratory rate using PM1000N the measurable points (n= 198) the non-measurable points (n = 40) p - value Vital signs Body temperature (°C), [Median, IQR] 37.3 [36.7–37.6] 37.4 [37.2–37.6] 0.77 Pulse rate (/min), [Median, IQR] 85 [75–98.8] 84 [79.5–92] 0.49 Systolic arterial pressure (mmHg),[Median, IQR] 122 [113–133.8] 133.5 [115.5–151.8] 0.02 SpO2 (%), [Median, IQR] 96 [95–97] 96.5 [95–98] 0.12 Arrhythmia, n (%) 54 (27.3) 36 (90) < 0.01 HFNC setting FIO2 0.5 [0.4–0.5] 0.5 [0.4–0.6] 0.03 Flow 40 [40–50] 40 [40–50] 0.93 Abbreviations: IQR, interquartile range Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4043306","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293748025,"identity":"39773186-1cdc-467c-9e48-ae0c9657d4f4","order_by":0,"name":"Takuma Iwaya","email":"","orcid":"","institution":"Sapporo Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Takuma","middleName":"","lastName":"Iwaya","suffix":""},{"id":293748026,"identity":"e7eb8bbe-c477-460d-a42d-14d07b5b5c33","order_by":1,"name":"Junpei Haruna","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACxgYGNgiLvQdM8fBBKGK08JxhYDgApNgIaQECqBaJHLAWGBc3YJ7dwPbg557Defwz3x58/DHHToaNgfnhAwaZO7gdNucAu2HPs8PFErfzkg0ObksGOozN2ICB5xluLTMS2CR4DhxObLidYyZxcBszUAsPmwQDz2G8WiT/ALXMv3kGpKWeOC3SIFs23OABaTlMhJY5B9ukZQ6kJ248k2NscHbbcR42ZqBfEvD4xXB28zHJNwesE+cdP2P4oHJbtT0/e/PDBx97cIeY4QxgZKICZiBO7DmAU4u8BHbxH7i1jIJRMApGwYgDAEgZUYUAsCYNAAAAAElFTkSuQmCC","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":true,"prefix":"","firstName":"Junpei","middleName":"","lastName":"Haruna","suffix":""},{"id":293748027,"identity":"cc04e2b2-6d71-4f16-a040-d8a83f6e9958","order_by":2,"name":"Aki Sasaki","email":"","orcid":"","institution":"Sapporo Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Aki","middleName":"","lastName":"Sasaki","suffix":""},{"id":293748028,"identity":"1cc0446b-ecf6-44bc-9c78-cc5f2c440dc7","order_by":3,"name":"Sayaka Nakano","email":"","orcid":"","institution":"Sapporo Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sayaka","middleName":"","lastName":"Nakano","suffix":""},{"id":293748029,"identity":"4e1b558a-995c-410a-a42a-c85e0f2f8b02","order_by":4,"name":"Hiroomi Tatsumi","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hiroomi","middleName":"","lastName":"Tatsumi","suffix":""},{"id":293748030,"identity":"0fd5df09-76cb-4136-89ab-d941fac0d62c","order_by":5,"name":"Yoshiki Masuda","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yoshiki","middleName":"","lastName":"Masuda","suffix":""}],"badges":[],"createdAt":"2024-03-08 12:17:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4043306/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4043306/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55517191,"identity":"4ef38770-52ec-4c01-b6c9-9b1da1522a7c","added_by":"auto","created_at":"2024-04-29 13:21:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101779,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4043306/v1/b0af57db75614bbce74fb6b1.png"},{"id":55517865,"identity":"4e405589-09c2-4159-bdcc-7c71583f6f21","added_by":"auto","created_at":"2024-04-29 13:29:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":445434,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4043306/v1/62dac77c-d9e9-48de-a660-a87d50392ca8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validating Respiratory Rate Measurements in Patients Receiving High Flow Nasal Cannula: A Comparative Study of Nellcor PM1000N and visual inspection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAmong the pivotal vital signs measured in daily patient care, respiratory rate is reportedly correlated with in-hospital mortality and serious adverse events\u0026nbsp;(1,2). Therefore, respiratory rate has emerged as a more discerning prognostic indicator, such as mortality, than traditional metrics, such as blood pressure or pulse rate\u0026nbsp;(3,4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, many acute care hospitals report that blood pressure, pulse rate, temperature, and SpO\u003csub\u003e2\u003c/sub\u003e are recorded, instead of the respiratory rate\u0026nbsp;(5,6). The rationale for not measuring respiratory rate may be the lack of awareness among nurses and junior healthcare professionals\u0026nbsp;(7)\u0026nbsp;and the complexity of the method of measurement\u0026nbsp;(8); because several measurement methods are available. Capnography is the standard method for accurately measuring respiratory rate\u0026nbsp;(9). Other methods include electrocardiogram (ECG) - derived respiration rate\u0026nbsp;(10), radar-based respiration rate monitoring\u0026nbsp;(11), and optical-based respiration rate monitoring\u0026nbsp;(12). However, in general ward settings, the enumeration of respiratory rate typically relies on ECG impedance, a method susceptible to inaccuracies\u0026nbsp;(13). Visual observation of respiratory rate by healthcare professionals is also widely used in several patient care settings; however, this approach is labor-intensive and often results in low adherence to respiratory rate measurements\u0026nbsp;(14).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecently, high-flow nasal cannulas (HFNC) have been widely used in patients with acute respiratory failure\u0026nbsp;(15). HFNC slightly increases positive airway and intrathoracic pressures\u0026nbsp;(16), manifesting distinct effects on work breathing compared with conventional low-flow oxygen delivery systems. Although HFNC is often used in patients with respiratory failure in ICUs and general wards, capnography cannot concurrently correctly measure respiratory rate because there is no position to attach the capnograph. In addition, the accuracy of ECG impedance is reduced by patient movement, physiological movements of the chest wall, such as coughing, and placement of ECG electrodes\u0026nbsp;(17,18).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, a new device (Nellcor\u003csup\u003eTM\u003c/sup\u003e PM1000N, Medtronic Japan, Co. Ltd., Tokyo, Japan) that facilitates the concurrent measurement of the respiratory rate and percutaneous oxygen saturation is now available. The veracity of this device has been substantiated in cohorts undergoing low-flow oxygen therapy and in healthy individuals\u0026nbsp;(3,19\u0026ndash;21). However, the efficacy of Nellcor PM1000N (PM1000N) in gauging the respiratory rate in HFNC-utilizing patients remains unclear. PM1000N, which is intended for error-free application, holds promise for accurately ascertaining the respiratory rate of patients receiving HFNC therapy in general ward settings. Therefore, we hypothesized that there would be no difference between the number of breaths per minute measured visually by nurses (hereafter referred to as \u0026quot;visual inspection\u0026quot;) and the number of breaths per minute measured by the PM1000N in patients receiving HFNC therapy and aimed to investigate this hypothesis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDesign and Setting\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center, retrospective, observational study was conducted at a university hospital. The study design and protocol were approved by the Institutional Review Board (IRB) of Sapporo Medical University (IRB-authorized number: 342-242, December 12th, 2022). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients who received HFNC therapy in the intensive care unit (ICU) of Sapporo Medical University Hospital between June 2022 and December 2022 were selected based on their electronic health records (EHR).Patients who were under 18 years of age, frequently removed their own cannula, unable to wear the SpO\u003csub\u003e2\u003c/sub\u003e probe on their finger, or whose respiratory rate was not recorded in the EHR were excluded from this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient data including age, sex, and primary pathology were extracted from the hospital’s EHR. Additionally, parameters including respiratory rate, heart rate, arrhythmia presence, systolic and diastolic blood pressure, and SpO\u003csub\u003e2\u003c/sub\u003e during HFNC use were recorded. The respiratory rate was visually inspected at 3:00, 7:00, 11:00, 15:00, 19:00, and 23:00 and manually recorded in the EHR. Furthermore, the measured respiratory rates obtained by the impedance method from a bedside monitor (BSM-1763 Lifescope PT, Nihon Kohden Co. Ltd., Tokyo, Japan: bedside monitor) and PM1000N were automatically recorded in the EHR. Respiratory rate recordings from the three modalities were retrospectively compiled from the EHR. Three investigators procured data from records between August 2023 and December 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRespiratory rate measurement procedure\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePM1000N\u003c/p\u003e\n\u003cp\u003eAn adhesive SpO2 measurement sensor (Nellcor\u003csup\u003eTM\u003c/sup\u003e OxySensor III), utilized in conjunction with the PM1000N, was affixed to the patient’s digit. A Nellcor OxySensor III was applied according to the instructions, positioning the sensor window adjacent to the terminal joint on the nail side and orienting the sensor cable to the dorsal aspect of the hand.\u003c/p\u003e\n\u003cp\u003eBedside monitor\u003c/p\u003e\n\u003cp\u003eThe electrodes were affixed to the patient’s skin at the anterior and lateral chest walls using bipolar leads for all inductions. The impedance method was used for these measurements. The operational concept involves the application of a respiratory measurement current via an electrode designed for ECG assessment. The resultant impedance alteration in the thoracic region induced by respiratory activity manifests as a modification of the respiratory measurement current. The subsequent amplification and computation of this signal yielded a visually represented respiratory curve.\u003c/p\u003e\n\u003cp\u003eVisual observation by the nurse (visual inspection)\u003c/p\u003e\n\u003cp\u003eDuring each designated observation interval, nursing practitioners positioned themselves proximal to the patient at the bedside, undertaking direct scrutiny of the vertical excursion of the thoracic region. Each observation spanned one minute, and the documented respiratory was derived from the observed findings inscribed in the patient's EHR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOutcomes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was to validate the concordance rate of the respiratory rate measured using the PM1000N, with the visual inspection respiratory rate as the reference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were assessed for Gaussian distribution using the Shapiro–Wilk normality test. Normally distributed data were presented as the mean ± standard deviation (SD), and non-normally distributed data were presented as the median and interquartile range (IQR). Correlation coefficients and intraclass correlation coefficients (ICC) were calculated for each measurement method based on visual inspection to validate the accuracy of the PM1000N and bedside monitors. We also calculated the standard deviation of the respiratory rate measured by the three methods, created a Bland–Altman plot, and performed a\u003cem\u003e\u0026nbsp;t\u003c/em\u003e-test on the mean difference. To evaluate the background of the inability to measure respiratory rate with the PM1000N, the patients were divided into two groups: a measurement group and a non-measurement group, and the flow of HFNC and the presence of arrhythmia were compared. Statistical significance was set at p \u0026lt; 0.05. Statistical analyses were performed using the SPSS software version 27 (IBM Corp., Armonk, NY, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatient Characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 354 individuals admitted to our ICU during the study period, 334 were ineligible based on the prespecified exclusion criteria. Patient characteristics are shown in Table 1. The resulting cohort consisted of 20 participants (5.6%), characterized by seven (35%) males and a median age of 75.6 years (interquartile range: 69.8\u0026ndash;80.4). Cardiovascular disease was the predominant disease affecting 11 (55%) patients, representing approximately 50% of all cases.\u003c/p\u003e\n\u003cp\u003eThe respiratory rate recorded by each measurement method is shown in Table 2. During the observation period, the respiratory rate was recorded 119 times. Visual inspection and bedside monitoring were used to record respiratory rate at all time points. However, PM1000N did not measure the respiratory rate at 20 time points. The time periods for which the respiratory rate could not be measured with PM1000N were treated as missing values and excluded from the primary analysis. The mean respiratory rate observed by visual inspection, bedside monitor, and PM1000N were 17.7 \u0026plusmn; 3.9, 18.3 \u0026plusmn; 3.7, and 17.2 \u0026plusmn; 3.9, respectively.\u003c/p\u003e\n\u003cp\u003eThe intra-class correlation coefficients between the bedside monitor and PM1000N, based on visual inspection, are listed in Table 3.\u003c/p\u003e\n\u003cp\u003eThe correlation of respiratory rate by each measurement method based on visual inspection and the Bland\u0026ndash;Altman plot and linear intubation plot are shown in Figure 1. The correlation coefficients between the bedside monitor and PM1000N based on visual inspection were 0.85, p \u0026lt; .001 and 0.84, p \u0026lt; .001, respectively.\u003c/p\u003e\n\u003cp\u003eThe respiratory rate could not be measured at 40 points using PM1000N. A comparison of the measurable and non-measurable points for the respiratory rate using PM1000N is shown in Table 4.\u003c/p\u003e\n\u003cp\u003eArrhythmias were significantly more common at non-measurable points than at measurable points (54 (27.3%) vs. 36 (90), P \u0026lt; 0.01).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to validate the automated respiratory rate measurement of PM1000N in patients using HFNC. Respiratory rate measurements with the PM1000N showed a better correlation with visual inspection than with the impedance method by bedside monitors, indicating that the PM1000N can provide favorable outcomes in determining respiratory rate comparable to visual respiratory rate measurements in patients with HFNC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, there are some concerns about accurately measuring the respiratory rate using the PM1000N in patients with HFNC. First, the PM1000N measures the respiratory rate by analyzing three types of respiratory pulse wave variations: respiratory sinus arrhythmia, pulse wave baseline variations, and pulse wave amplitude variations, sensed by a sensor attached to the finger. Among these, respiratory sinus arrhythmia captures changes in the heart rate associated with the respiratory cycle\u0026nbsp;(22). Therefore, as a precaution when using the PM1000N, it is indicated that if the pulse wave becomes small owing to peripheral circulatory failure or pressure on the arm on the side where the sensor is worn by the blood pressure cuff, the measured value will be unstable\u0026nbsp;(23).However, in this study, we believe that this effect was avoided by attaching the sensor to the finger opposite the arm where the blood pressure was measured.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe second concern was the baseline and pulse wave amplitude variations. To measure the respiratory rate with the PM1000N, it is necessary to capture the variation in the venous annulus caused by changes in the intrathoracic pressure during the respiratory cycle. Airway pressure in patients using HFNC is higher in the closed state than in the open state, and increases with an increase in flow\u0026nbsp;(24). Flow settings of 30 and 50 L/min with the mouth closed reportedly produce average airway pressures of approximately 3 cmH\u003csub\u003e2\u003c/sub\u003eO and 5 cmH\u003csub\u003e2\u003c/sub\u003eO, respectively\u0026nbsp;(16). In contrast, a study investigating the effects of ventilator-assisted PEEP on circulatory dynamics reported no significant changes in circulatory dynamics with the addition of 0 cmH\u003csub\u003e2\u003c/sub\u003eO and 5 cmH\u003csub\u003e2\u003c/sub\u003eO PEEP levels\u0026nbsp;(25). Because ventilator-induced PEEP occurs continuously in a closed respiratory circuit, and does not affect circulatory dynamics, the intermittent changes in airway pressure produced by HFNC should have little effect on circulatory dynamics. The HFNC flow in this study was treated over a wide range of 30 L–60 L. The comparison of the flow of HFNC between the measurable and non-measurable groups also showed no significant difference, suggesting that changes in intrathoracic pressure due to HFNC are unlikely to affect respiratory rate measurements with the PM1000N. Therefore, we believe that even if HFNC caused changes in intrathoracic pressure, the baseline and pulse wave amplitude fluctuations would be less affected, and an accurate respiratory rate could be measured even with the PM1000N using HFNC. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLynn et al.\u0026nbsp;(26)\u0026nbsp;found that an increased respiratory rate was a more important predictor of sudden deterioration than decreased SpO\u003csub\u003e2\u003c/sub\u003e. Moreover, the respiratory rate and oxygenation (ROX) index used to predict the progression to tracheal intubation in patients on HFNC includes the respiratory rate as a scoring component\u0026nbsp;(27,28). Moreover, continuous, rather than cross-sectional, physiological monitoring based on these scores and indicators is useful for the early detection of patient deterioration. Therefore, continuous monitoring of the respiratory rate is important, regardless of the section. Further, although several conventional methods for measuring the respiratory rate exist, visual measurement by nurses is time-consuming and labor-intensive. Moreover, the impedance method allows continuous measurement with equipment, however, it is prone to errors due to the position of the electrode and patient's body movements. Conversely. in this study, the PM1000N was found to measure the respiratory rate with high accuracy, even in patients using HFNC. Thus, the PM1000N solves the problems of conventional measurement methods because it is easy to install and minimizes the burden on patients. In addition, the previously uncertain validity and reliability of HFNC use were also assessed in this study, indicating that respiratory rate measurement with the PM1000N is also useful for monitoring the respiratory rate in patients using HFNC.\u003c/p\u003e\n\u003cp\u003eIn contrast, 16.8% of the patients in the PM1000N group were not measurable. Additionally, a higher percentageof patients at the non-measurable points had arrhythmia than those at the measured points. Moreover, the safety and efficacy of measuring respiratory rate with the PM1000N in patients with arrhythmia, such as three or more irregular events within 30 s, have not been established because they may result in inaccurate respiratory rate values and loss of displayed respiratory rate information. Consequently, respiratory rate monitoring in combination with visual or other reliable measurement methods is necessary for patients with detectable arrhythmias or an unstable cardiovascular status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStrengths and Limitations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo our knowledge, this is the first study to validate respiratory rate measurement with PM1000N in patients using HFNC; however, there are some limitations. First, the number of patients was limited because this was a retrospective observational study. Additionally, the patients' diseases and other backgrounds varied, which may have resulted in different patient conditions. Therefore, further studies with larger sample sizes are warranted. Second, the data used in this study analyzed respiratory rates recorded every four hours, which suggests accuracy in intermittent measurements but did not verify whether respiratory rates were measured with high accuracy on a continuous basis. Consequently, Further validation is required to clarify whether a continuously accurate respiratory rate can be measured. Third, this study was conducted in an ICU setting. The same study should be conducted in patients treated in general wards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eImplications for Clinical Practice\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study found that the PM1000N can easily and continuously measure the respiratory rate with the same accuracy as visual inspection, even in patients with changes in intrathoracic pressure caused by HFNC. Therefore, the PM1000N can be used in general hospital beds for patients with HFNC to accurately measure their respiratory rate and detect changes in their condition.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we evaluated the validity of measurements using the PM1000N in patients receiving HFNC therapy. The PM1000N was able to measure the respiratory rate in patients with HFNC with the same accuracy as visual inspection. Thus, the PM1000N may be used to accurately measure the respiratory rates of patients in general wards.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe authors have not received any funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflicts of interest/\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interest:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability statement:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCode availability\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;(software application or custom code):\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eTI, JH, AS, SN, and HT designed the work, collected and analyzed the data. JH and TI wrote the initial draft of the manuscript. TI, JH, AS and MY contributed to the analysis and interpretation of the data and assisted in the preparation of the manuscript. All authors critically revised the manuscript and approved the final version for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the patients who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics Approval:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center, retrospective, observational study was conducted at a university hospital. The study design and protocol were approved by the Institutional Review Board (IRB) of Sapporo Medical University (IRB-authorized number: 342-242, December 12th, 2022). \u0026nbsp;This study does not constitute research using human samples or tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to participate:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOwing to the retrospective observational nature of this study, the information was released on an opt-out basis.\u0026nbsp;The need for informed consent was waived by\u0026nbsp;the IRB of Sapporo Medical University\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to publish:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOwing to the retrospective observational nature of this study,\u0026nbsp;consent for publish was\u0026nbsp;released\u0026nbsp;on an opt-out basis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGoldhill DR, McNarry AF, Mandersloot G, McGinley A. A physiologically-based early warning score for ward patients: the association between score and outcome. Anaesthesia 2005;60:547\u0026ndash;553. https://doi.org/10.1111/j.1365-2044.2005.04186.x\u003c/li\u003e\n\u003cli\u003eMarik PE, Taeb AM. SIRS, qSOFA and new sepsis definition. J Thorac Dis 2017;9:943\u0026ndash;945. https://doi.org/10.21037/jtd.2017.03.125\u003c/li\u003e\n\u003cli\u003eSubbe CP, Davies RG, Williams E, Rutherford P, Gemmell L. Effect of introducing the Modified Early Warning score on clinical outcomes, cardio-pulmonary arrests and intensive care utilisation in acute medical admissions. Anaesthesia 2003;58:797\u0026ndash;802. https://doi.org/10.1046/j.1365-2044.2003.03258.x\u003c/li\u003e\n\u003cli\u003eFieselmann JF, Hendryx MS, Helms CM, Wakefield DS. Respiratory rate predicts cardiopulmonary arrest for internal medicine inpatients. J Gen Intern Med 1993;8:354\u0026ndash;360. https://doi.org/10.1007/BF02600071\u003c/li\u003e\n\u003cli\u003eHodgetts TJ, Kenward G, Vlachonikolis IG, Payne S, Castle N. The identification of risk factors for cardiac arrest and formulation of activation criteria to alert a medical emergency team. Resuscitation 2002;54:125\u0026ndash;131. https://doi.org/10.1016/s0300-9572(02)00100-4\u003c/li\u003e\n\u003cli\u003eMcBride J, Knight D, Piper J, Smith GB. Long-term effect of introducing an early warning score on respiratory rate charting on general wards. Resuscitation 2005;65:41\u0026ndash;44. https://doi.org/10.1016/j.resuscitation.2004.10.015\u003c/li\u003e\n\u003cli\u003eCretikos MA, Bellomo R, Hillman K, Chen J, Finfer S, Flabouris A. Respiratory rate: the neglected vital sign. Med J Aust 2008;188:657\u0026ndash;659. https://doi.org/10.5694/j.1326-5377.2008.tb01825.x\u003c/li\u003e\n\u003cli\u003eNicol\u0026ograve; A, Massaroni C, Schena E, Sacchetti M. The importance of respiratory rate monitoring: from healthcare to sport and exercise. Sensors (Basel). 2020;20:6396. http://doi.org/10.3390/s20216396\u003c/li\u003e\n\u003cli\u003eKodali BS. Capnography outside the operating rooms. Anesthesiology 2013;118:192\u0026ndash;201. https://doi.org/10.1097/ALN.0b013e318278c8b6\u003c/li\u003e\n\u003cli\u003eMazzanti B, Lamberti C, de Bie J. Validation of an ECG-derived respiration monitoring method. In: Comput Cardiol. 2003. IEEE Publications 2003:613\u0026ndash;616. https://doi.org/10.1109/CIC.2003.1291230\u003c/li\u003e\n\u003cli\u003eGreneker EF. Radar sensing of heartbeat and respiration at a distance with applications of the technology. In: Radar 97 (Conf Publ No 449). Institution of Engineering and Technology 1997:150\u0026ndash;14. https://doi.org/10.1049/cp:19971650\u003c/li\u003e\n\u003cli\u003eAoki H, Takemura Y, Mimura K, Nakajima M. Development of non-restrictive sensing system for sleeping person using fiber grating vision sensor. In: MHS2001 proceedings of 2001 International Symposium on Micromechatronics and Human Science (Cat No01TH8583). IEEE Publications; 2001:155\u0026ndash;160. https://doi.org/10.1109/MHS.2001.965238\u003c/li\u003e\n\u003cli\u003evan Loon K, van Zaane B, Bosch EJ, Kalkman CJ, Peelen LM. Non-invasive continuous respiratory monitoring on general hospital wards: A systematic review. PLOS ONE. 2015;10:e0144626. https://doi.org/10.1371/journal.pone.0144626\u003c/li\u003e\n\u003cli\u003eVan Leuvan CH, Mitchell I. Missed opportunities? An observational study of vital sign measurements. Crit Care Resusc 2008;10:111\u0026ndash;115. https://doi.org/10.1016/S1441-2772(23)01322-4\u003c/li\u003e\n\u003cli\u003eCrimi C, Pierucci P, Renda T, Pisani L, Carlucci A. High-flow nasal cannula and COVID-19: A clinical review. Respir Care 2022;67:227\u0026ndash;240. https://doi.org/10.4187/respcare.09056\u003c/li\u003e\n\u003cli\u003eRitchie JE, Williams AB, Gerard C, Hockey H. Evaluation of a humidified nasal high-flow oxygen system, using oxygraphy, capnography and measurement of upper airway pressures. Anaesth Intensive Care 2011;39:1103\u0026ndash;1110. https://doi.org/10.1177/0310057X1103900620\u003c/li\u003e\n\u003cli\u003eWilkinson JN, Thanawala VU. Thoracic impedance monitoring of respiratory rate during sedation--is it safe? Anaesthesia 2009;64:455\u0026ndash;456. https://doi.org/10.1111/j.1365-2044.2009.05908.x\u003c/li\u003e\n\u003cli\u003eDrummond GB, Nimmo AF, Elton RA. Thoracic impedance used for measuring chest wall movement in postoperative patients. Br J Anaesth 1996;77:327\u0026ndash;332. https://doi.org/10.1093/bja/77.3.327\u003c/li\u003e\n\u003cli\u003eAddison PS, Watson JN, Mestek ML, Ochs JP, Uribe AA, Bergese SD. Pulse oximetry-derived respiratory rate in general care floor patients. J Clin Monit Comput 2015;29:113\u0026ndash;120. https://doi.org/10.1007/s10877-014-9575-5\u003c/li\u003e\n\u003cli\u003eBergese SD, Mestek ML, Kelley SD, McIntyre R Jr, Uribe AA, Sethi R, et al. Multicenter study validating accuracy of a continuous respiratory rate measurement derived from pulse oximetry: A comparison with capnography. Anesth Analg 2017;124:1153\u0026ndash;1159. https://doi.org/10.1213/ANE.0000000000001852\u003c/li\u003e\n\u003cli\u003eFukada T, Tsuchiya Y, Iwakiri H, Ozaki M, Nomura M. Comparisons of the efficiency of respiratory rate monitoring devices and acoustic respiratory sound during endoscopic submucosal dissection. J Clin Monit Comput 2022;36:1013\u0026ndash;1019. https://doi.org/10.1007/s10877-021-00727-8\u003c/li\u003e\n\u003cli\u003eAddison PS, Watson JN, Mestek ML, Mecca RS. Developing an algorithm for pulse oximetry derived respiratory rate (RR(oxi)): a healthy volunteer study. J Clin Monit Comput 2012;26:45\u0026ndash;51. https://doi.org/10.1007/s10877-011-9332-y\u003c/li\u003e\n\u003cli\u003ePM1000N_OperatorsManual_EN_10104146E00.pdf. https://asiapac.medtronic.com/content/dam/covidien/library/us/en/product/pulse-oximetry/PM1000N_OperatorsManual_EN_10104146E00.pdf\u003c/li\u003e\n\u003cli\u003eParke RL, Eccleston ML, McGuinness SP. The effects of flow on airway pressure during nasal high-flow oxygen therapy. Respir Care 2011;56:1151\u0026ndash;1155. https://doi.org/10.4187/respcare.01106\u003c/li\u003e\n\u003cli\u003eSaner FH, Olde Damink SWM, Pavlaković G, van den Broek MAJ, Sotiropoulos GC, Radtke A, et al. Positive end-expiratory pressure induces liver congestion in living donor liver transplant patients: myth or fact. Transplantation 2008;85:1863\u0026ndash;1866. https://doi.org/10.1097/TP.0b013e31817754dc\u003c/li\u003e\n\u003cli\u003eLynn LA, Curry JP. Patterns of unexpected in-hospital deaths: a root cause analysis. Patient Saf Surg. 2011;5:3. https://doi.org/10.1186/1754-9493-5-3\u003c/li\u003e\n\u003cli\u003eLjunggren M, Castr\u0026eacute;n M, Nordberg M, Kurland L. The association between vital signs and mortality in a retrospective cohort study of an unselected emergency department population. Scand J Trauma Resusc Emerg Med 2016;24:21. https://doi.org/10.1186/s13049-016-0213-8\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Patient characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"359\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.73816155988858%\" valign=\"top\" style=\"width: 17.5478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.26183844011142%\" valign=\"top\" style=\"width: 30.4409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.73816155988858%\" valign=\"top\" style=\"width: 17.5478%;\"\u003e\n \u003cp\u003eAge (years), Median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.26183844011142%\" valign=\"top\" style=\"width: 30.4409%;\"\u003e\n \u003cp\u003e75.6 [69.8 \u0026ndash; 80.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.73816155988858%\" valign=\"top\" style=\"width: 17.5478%;\"\u003e\n \u003cp\u003eMales, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.26183844011142%\" valign=\"top\" style=\"width: 30.4409%;\"\u003e\n \u003cp\u003e7 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.73816155988858%\" valign=\"top\" style=\"width: 17.5478%;\"\u003e\n \u003cp\u003eReasons for ICU admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.26183844011142%\" valign=\"top\" style=\"width: 30.4409%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.231197771587745%\" valign=\"top\" style=\"width: 39.1345%;\"\u003e\n \u003cp\u003eCardiovascular surgery, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.26183844011142%\" valign=\"top\" style=\"width: 30.4409%;\"\u003e\n \u003cp\u003e11 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.231197771587745%\" style=\"width: 39.1345%;\"\u003e\n \u003cp\u003eSepsis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.26183844011142%\" valign=\"top\" style=\"width: 30.4409%;\"\u003e\n \u003cp\u003e2 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.231197771587745%\" style=\"width: 39.1345%;\"\u003e\n \u003cp\u003eRespiratory failure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.26183844011142%\" valign=\"top\" style=\"width: 30.4409%;\"\u003e\n \u003cp\u003e3 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.231197771587745%\" style=\"width: 39.1345%;\"\u003e\n \u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.26183844011142%\" valign=\"top\" style=\"width: 30.4409%;\"\u003e\n \u003cp\u003e2 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.674094707520894%\" valign=\"top\" style=\"width: 57.7861%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.26183844011142%\" valign=\"top\" style=\"width: 30.4409%;\"\u003e\n \u003cp\u003e2 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\" style=\"width: 92.3546%;\"\u003e\n \u003cp\u003eAbbreviations: IQR, interquartile range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2. Respiration rate for each measurement method\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"494\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.550607287449395%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.449392712550605%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespiratory rate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.550607287449395%\" valign=\"top\"\u003e\n \u003cp\u003eVisual inspection, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.449392712550605%\" valign=\"top\"\u003e\n \u003cp\u003e17.4 \u0026plusmn; 3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.550607287449395%\" valign=\"top\"\u003e\n \u003cp\u003eBedside monitor, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.449392712550605%\" valign=\"top\"\u003e\n \u003cp\u003e18.3 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.550607287449395%\" valign=\"top\"\u003e\n \u003cp\u003ePM1000N, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.449392712550605%\" valign=\"top\"\u003e\n \u003cp\u003e17.2 \u0026plusmn; 3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SD,\u0026nbsp;standard deviation: PM1000N, Nellcor\u003csup\u003eTM\u003c/sup\u003e PM1000N: ICC, intraclass correlation coefficients\u003c/p\u003e\n\u003cp\u003eTable 3. Intra-class correlation coefficient for each measurement method\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"520\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.26923076923077%\" rowspan=\"2\" valign=\"top\" style=\"width: 12.0603%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.384615384615383%\" rowspan=\"2\" valign=\"top\" style=\"width: 16.0804%;\"\u003e\n \u003cp\u003eICC (95% confidence interval)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.34615384615385%\" colspan=\"4\" valign=\"top\" style=\"width: 37.2261%;\"\u003e\n \u003cp\u003eF test with true value 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.91156462585034%\" valign=\"top\" style=\"width: 17.4204%;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\" valign=\"top\" style=\"width: 13.8401%;\"\u003e\n \u003cp\u003e\u003cem\u003edf1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986394557823129%\" valign=\"top\" style=\"width: 13.083%;\"\u003e\n \u003cp\u003e\u003cem\u003edf2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\" style=\"width: 4.8241%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.234165067178502%\" valign=\"top\" style=\"width: 12.0603%;\"\u003e\n \u003cp\u003eBedside monitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.335892514395393%\" valign=\"top\" style=\"width: 16.0804%;\"\u003e\n \u003cp\u003e0.85 (0.79 \u0026ndash; 0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.314779270633398%\" valign=\"top\" style=\"width: 17.4204%;\"\u003e\n \u003cp\u003e12.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.587332053742802%\" valign=\"top\" style=\"width: 13.8401%;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.021113243761997%\" valign=\"top\" style=\"width: 13.083%;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.940499040307103%\" valign=\"top\" style=\"width: 4.8241%;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.234165067178502%\" valign=\"top\" style=\"width: 12.0603%;\"\u003e\n \u003cp\u003ePM1000N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.335892514395393%\" valign=\"top\" style=\"width: 16.0804%;\"\u003e\n \u003cp\u003e0.92 (0.88 \u0026ndash; 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.314779270633398%\" valign=\"top\" style=\"width: 17.4204%;\"\u003e\n \u003cp\u003e23.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.587332053742802%\" valign=\"top\" style=\"width: 13.8401%;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.021113243761997%\" valign=\"top\" style=\"width: 13.083%;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.940499040307103%\" valign=\"top\" style=\"width: 4.8241%;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: IQR, interquartile range: PM1000N, Nellcor\u003csup\u003eTM\u003c/sup\u003e PM1000N: ICC, intraclass correlation coefficients\u003c/p\u003e\n\u003cp\u003eTable 4. Comparison of the measurable and non-measurable points for respiratory rate using PM1000N\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.848101265822784%\" valign=\"top\" style=\"width: 22.2602%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\" style=\"width: 19.0802%;\"\u003e\n \u003cp\u003ethe measurable points\u003cbr\u003e\u0026nbsp;(n= 198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" style=\"width: 20.5256%;\"\u003e\n \u003cp\u003ethe non-measurable points\u003cbr\u003e\u0026nbsp;(n = 40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.710669077757686%\" style=\"width: 15.7556%;\"\u003e\n \u003cp\u003ep - value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.741410488245933%\" style=\"width: 24.5729%;\"\u003e\n \u003cp\u003eVital signs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\" style=\"width: 19.0802%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" valign=\"top\" style=\"width: 20.5256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.710669077757686%\" valign=\"top\" style=\"width: 15.7556%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.848101265822784%\" style=\"width: 22.2602%;\"\u003e\n \u003cp\u003eBody temperature (\u0026deg;C), [Median, IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\" style=\"width: 19.0802%;\"\u003e\n \u003cp\u003e37.3 [36.7\u0026ndash;37.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" style=\"width: 20.5256%;\"\u003e\n \u003cp\u003e37.4 [37.2\u0026ndash;37.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.710669077757686%\" style=\"width: 15.7556%;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.848101265822784%\" style=\"width: 22.2602%;\"\u003e\n \u003cp\u003ePulse rate (/min), [Median, IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\" style=\"width: 19.0802%;\"\u003e\n \u003cp\u003e85 [75\u0026ndash;98.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" style=\"width: 20.5256%;\"\u003e\n \u003cp\u003e84 [79.5\u0026ndash;92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.710669077757686%\" style=\"width: 15.7556%;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.848101265822784%\" style=\"width: 22.2602%;\"\u003e\n \u003cp\u003eSystolic arterial pressure (mmHg),[Median, IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\" style=\"width: 19.0802%;\"\u003e\n \u003cp\u003e122 [113\u0026ndash;133.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" style=\"width: 20.5256%;\"\u003e\n \u003cp\u003e133.5 [115.5\u0026ndash;151.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.710669077757686%\" style=\"width: 15.7556%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.848101265822784%\" style=\"width: 22.2602%;\"\u003e\n \u003cp\u003eSpO2 (%), [Median, IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\" style=\"width: 19.0802%;\"\u003e\n \u003cp\u003e96 [95\u0026ndash;97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" style=\"width: 20.5256%;\"\u003e\n \u003cp\u003e96.5 [95\u0026ndash;98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.710669077757686%\" style=\"width: 15.7556%;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.848101265822784%\" style=\"width: 22.2602%;\"\u003e\n \u003cp\u003eArrhythmia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\" style=\"width: 19.0802%;\"\u003e\n \u003cp\u003e54 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" style=\"width: 20.5256%;\"\u003e\n \u003cp\u003e36 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.710669077757686%\" style=\"width: 15.7556%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.741410488245933%\" style=\"width: 24.5729%;\"\u003e\n \u003cp\u003eHFNC setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\" style=\"width: 19.0802%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" valign=\"top\" style=\"width: 20.5256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.710669077757686%\" valign=\"top\" style=\"width: 15.7556%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.848101265822784%\" style=\"width: 22.2602%;\"\u003e\n \u003cp\u003eFIO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\" style=\"width: 19.0802%;\"\u003e\n \u003cp\u003e0.5 [0.4\u0026ndash;0.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" style=\"width: 20.5256%;\"\u003e\n \u003cp\u003e0.5 [0.4\u0026ndash;0.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.710669077757686%\" style=\"width: 15.7556%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.848101265822784%\" style=\"width: 22.2602%;\"\u003e\n \u003cp\u003eFlow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\" style=\"width: 19.0802%;\"\u003e\n \u003cp\u003e40 [40\u0026ndash;50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" style=\"width: 20.5256%;\"\u003e\n \u003cp\u003e40 [40\u0026ndash;50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.710669077757686%\" style=\"width: 15.7556%;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" style=\"width: 79.7898%;\"\u003e\n \u003cp\u003eAbbreviations: IQR, interquartile range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Medicine](https://link.springer.com/journal/44337)","snPcode":"44337","submissionUrl":"https://submission.springernature.com/new-submission/44337/3","title":"Discover Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4043306/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4043306/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e: \u003c/em\u003eRecently, the Nellcor PM1000N was developed for the concurrent assessment of respiratory rate and percutaneous oxygen saturation. However, the validation of respiratory rate measurements in patients receiving a high-flow nasal cannula (HFNC) using the PM1000N remains unestablished. Therefore, this study aimed to assess the validity of respiratory rate measurements obtained using PM1000N in patients receiving HFNC.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods: \u003c/strong\u003e\u003c/em\u003eA retrospective assessment was conducted on the respiratory rate measurements obtained using the PM1000N and electrocardiogram (ECG) impedance methods in comparison to those visually assessed by nurses. This evaluation included patients admitted to the Intensive Care Unit (ICU) of Sapporo Medical University Hospital who received HFNC between June 2022 and December 2022. Correlation coefficients, intraclass correlation coefficients (ICCs), Bland-Altman plots, and t-tests were employed to assess the concordance between the visually observed respiratory rates by nurses and those recorded by the PM1000N and ECG impedance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults: \u003c/strong\u003e\u003c/em\u003eTwenty patients were enrolled in this study. Among them, 119 instances of respiratory rate were recorded. The ICCs for the PM1000N and impedance methods were 0.918 and 0.846, respectively, compared with the rates visually assessed by nurses. The mean differences were p=0.947 (95% CI: -3.186 – 0.2987) and p \u0026lt; .001 (95% CI: 16.4609–17.9532), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eThe PM1000N demonstrated superiority over ECG impedance in measuring respiratory rate in patients with HFNC. Furthermore, PM1000N shows promise for effective application in patients receiving HFNC.\u003c/p\u003e","manuscriptTitle":"Validating Respiratory Rate Measurements in Patients Receiving High Flow Nasal Cannula: A Comparative Study of Nellcor PM1000N and visual inspection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 13:21:48","doi":"10.21203/rs.3.rs-4043306/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-21T06:48:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-21T01:45:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59493244699902080700757430392855241135","date":"2024-05-17T02:43:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-13T23:45:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213246570783051060495947474426393843963","date":"2024-05-11T22:10:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-19T18:22:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-19T18:04:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-19T18:03:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Medicine","date":"2024-03-08T11:25:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Medicine](https://link.springer.com/journal/44337)","snPcode":"44337","submissionUrl":"https://submission.springernature.com/new-submission/44337/3","title":"Discover Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49daa43b-7120-4603-9b18-13065b04b90f","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T05:08:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-29 13:21:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4043306","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4043306","identity":"rs-4043306","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}