Labored breathing pattern: an unmeasured dimension of respiratory pathophysiology

Labored breathing pattern: an unmeasured dimension of respiratory pathophysiology | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Labored breathing pattern: an unmeasured dimension of respiratory pathophysiology Valerie E. Cyphers , Swet M. Patel , Brendan D. McNamara , William B Ashe , Sarah J. Ratcliffe , J. Randall Moorman , Jessica Keim-Malpass , Shrirang M. Gadrey , Sherry L. Kausch doi: https://doi.org/10.1101/2024.01.27.24301872 Valerie E. Cyphers 1 School of Medicine, University of Virginia ; Charlottesville, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: VEC8UE{at}uvahealth.org Swet M. Patel 1 School of Medicine, University of Virginia ; Charlottesville, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brendan D. McNamara 1 School of Medicine, University of Virginia ; Charlottesville, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site William B Ashe 1 School of Medicine, University of Virginia ; Charlottesville, USA 2 Center for Advanced Medical Analytics, University of Virginia ; Charlottesville, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah J. Ratcliffe 1 School of Medicine, University of Virginia ; Charlottesville, USA 2 Center for Advanced Medical Analytics, University of Virginia ; Charlottesville, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site J. Randall Moorman 1 School of Medicine, University of Virginia ; Charlottesville, USA 2 Center for Advanced Medical Analytics, University of Virginia ; Charlottesville, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jessica Keim-Malpass 2 Center for Advanced Medical Analytics, University of Virginia ; Charlottesville, USA 3 School of Nursing, University of Virginia ; Charlottesville, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shrirang M. Gadrey 1 School of Medicine, University of Virginia ; Charlottesville, USA 2 Center for Advanced Medical Analytics, University of Virginia ; Charlottesville, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sherry L. Kausch 2 Center for Advanced Medical Analytics, University of Virginia ; Charlottesville, USA 3 School of Nursing, University of Virginia ; Charlottesville, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Introduction Respiratory failure is a common organ failure syndrome in hospitalized patients 1 . Vital sign monitoring (like respiratory rate & oximetry) is a necessary aspect of risk stratification, but it is not sufficient. In one study of hospitalized patients, 46% of the patients had no significant vital sign change in the 24 hours before an unplanned intubation 2 . Therefore, clinicians must also monitor for physical diagnostic signs that link the appearance of breaths to respiratory instability. Many pathognomonic patterns of high-risk labored breathing have been described. For example, when rib-dominant breaths alternate with abdomen-dominant ones, the patient is said to exhibit “respiratory alternans”, a sign of inspiratory muscle overload 3 . However, the manual assessment of such signs lacks sensitivity, inter-rater reliability, and scalability 4 . We sought to (a) identify technologies that can measure labored breathing and (b) assess their readiness for clinical adoption by hospitals. Methods We selected four well-established diagnostic signs of labored breathing: ( 1 ) respiratory rate variability, ( 2 ) recruitment of accessory muscles (upper-rib elevation by the scalene and sternocleidomastoid muscles), ( 3 ) Abdominal Paradox (rib-abdomen asynchrony), and ( 4 ) respiratory alternans (rib-dominant breaths alternate with abdomen-dominant ones). We systematically searched PubMed using pre- specified keywords corresponding to these four signs. We identified 2868 abstracts. Two reviewers independently screened each abstract to ensure that it reported on technology that quantified the diagnostic sign of interest. A third reviewer resolved any disagreements. We excluded 2423 articles with an abstract review and included 445 articles for full paper review. We excluded an additional 127 articles after full paper review, and we were unable to acquire 4 articles. We included the remaining 314 articles for analysis. Results Quantification of labored breathing has been attempted for over 50 years; the earliest study included in our analysis was published in 1975. Over 30 different hardware configurations have been tried, either alone or in combination; but none of them has been validated as a comprehensive solution to measure all the four diagnostic signs that we studied. Despite enormous improvements in sensor technologies and computing capacity, the scale of investigation has not meaningfully increased since 1975. In the first decade of kinematic measurements (1975–1984), there average annual number of studies was 2.7 and the median sample size was 19. In the decade prior to our study (2013–2022), the average annual number of studies was 11.3 and the median sample size was 20. To this day, a majority of the studies are conducted in a specialized laboratories (73% between 2013-2022) rather than clinical practice settings. Most studies aimed to measure the construct validity of a technology (19%) or to describe kinematic distributions in specific clinical scenarios (77%). Rarely did studies attempt to quantify the predictive validity for a clinical outcome (4%). We did not find any clinical trial where a kinematics-based early warning intervention was tested. Conclusions This study describes a major bottleneck in the translation of bedside diagnostic signs of high-risk labored breathing patterns into measurable physiomarkers of respiratory instability. Despite half a century of attempted measurement, the technology readiness level for clinical adoption remains low. Introduction Respiratory failure is the most common organ failure syndrome in US hospitals 1 . It necessitates endotracheal intubation for invasive mechanical ventilation in about 800,000 hospitalizations annually 5 . It’s estimated national costs were $27 billion per year, representing 12% of all hospital costs 5 . The COVID-19 pandemic further increased the incidence of severe respiratory failure and the nationwide demand for ventilators 6 . Respiratory failure requiring mechanical ventilation is associated with an in-hospital mortality rate of 35% 5 . Among emergency intubations for unanticipated deterioration, the mortality rate can approach 50% 2 . Up to 40% of all in-hospital cardiac arrests are linked to respiratory deterioration 7 . One half of all in-hospital intubations are performed during the initial presentation to the emergency room 8 . The other half are performed during the hospitalization. In these cases, the patient suffers a clinical deterioration while in the hospital which precipitates intubation. For early detection of respiratory deterioration, hospitalized patients are monitored by many physiological “track and trigger” warning systems, but the sensitivity of such systems is poor 9 . Too often, subtle pathophysiologic antecedents of instability fail to trigger early alerts 10 . Delayed calls to emergency response teams are common, and independently predict higher mortality 11 . Abrupt respiratory compromise causes 64,000 unplanned intubation events every year in US hospitals 2 . Predictive analytics that use conventional physiomarkers (respiratory rate, oximetry, heart rate, blood pressure etc.) have not revealed a reliable signature of respiratory instability. In one review of 448 unplanned intubation events, 46% of the events were not preceded by any significant change in any vital sign 2 . Even when vital signs did change significantly, their trajectory was complex and heterogeneous. For example, pulse oximetry saturation dropped significantly in 7% of patients, but rose significantly in 6% 2 . To improve advance warning systems for respiratory deterioration, researchers are applying increasingly sophisticated modelling techniques (regression, decision trees, clustering, and deep learning) to increasingly complex datasets 12–14 . Modern datasets include the basic vital signs like respiratory rate and oximetry, basic electronic health record (EHR) variables like laboratory tests and diagnosis codes, continuous monitoring signals like electrocardiography and impedance pneumography 15,16 , and advanced bioinformatic variables (like natural language processing of notes 17,18 . However, the real-time implementation of advanced (often proprietary) predictive models is significantly more costly than that of a simple, open-source early warning score (like NEWS). The costs include data storage, computation capacity, licensing fees, and human responders to alarms. If the clinical gains are incremental, or if they are offset by a manifold rise in false alarms, then cost-benefit metrics (e.g., number needed to monitor) do not justify the upgrade. The economic barriers to adoption are particularly strong in underprivileged health systems and the benefits of complex analytics are often inaccessible. In most settings, therefore, respiratory remains difficult to predict. One way in which clinicians manage this risk is to visually inspect their patients’ breathing motion patterns. This idea is rooted in the fundamental principles of bedside diagnosis that link the appearance of breaths (i.e., respiratory kinematics) to respiratory pathology 19 . For example, when rib-dominant breaths alternate with abdomen-dominant ones, the patient is said to exhibit “respiratory alternans” which is a manifestation of inspiratory muscle overload 3 . Many such pathognomonic visual signs are known to clinicians, but there has been no way to quantify them in routine practice. Clinicians use qualitative visual inspections to assess for abnormal breathing patterns. Such assessments lack sensitivity and inter-rater reliability 4 , and are manually effort-intensive. Therefore, we sought to (a) identify technologies that can measure labored breathing and (b) assess their readiness for clinical adoption by hospitals. Methods We reviewed textbooks of physical diagnosis 20 , pertinent expert commentaries 3 , research studies, and used our own clinical domain expertise to identify the breathing motion patterns that are most widely used as indicators of respiratory instability. We excluded respiratory rate because it is a conventional vital sign and it’s measurement is a well-addressed in the literature 21 . We identified four labored breathing patterns that adequately represent the type of measurements of interest to clinicians: ( 1 ) irregular respiratory rhythm or elevated respiratory rate variability where one sees short breath intervals alternate with long ones, ( 2 ) recruitment of accessory muscles of respiration where one can observe increased contraction of neck muscles (like sternocleidomastoid and scalene) which elevate the upper ribs, ( 3 ) asynchronous abdominal motion, whereby synchrony between the chest and abdomen is lost (in the most severe form, known as abdominal paradox), and ( 4 ) respiratory alternans, during which breathing switches between being chest-predominant and abdomen-predominant phenotypes. We systematically searched PubMed using pre-specified keywords corresponding to these four breathing patterns. Al articles published before our search (June 2022) were included. Specific search terms are outlined in Table 1. We used the following inclusion criteria: (a) study describes a technology to measure one or more of the specified breathing patterns in human subjects, (b) the described technology is applicable in spontaneously breathing individuals, (c) study reports measurements made during an unmodified respiratory cycle. We excluded studies where a ventilator or other respiratory support device was used to collect respiratory data and/or subjects were only assessed during modified breathing patterns such that tidal breaths were not captured (like coughing, singing, or forced expiration with breath hold). We used the systematic review program Rayyan for screening and decision tracking. In a blinded screening process, two reviewers (VEC, SMP, BDM) independently assessed each abstract to identify studies that met the pre-specified eligibility criteria. A third reviewer (SMG) resolved any disagreements. Reviewers cross-screened for the other respiratory signs of interest when reviewing abstracts for any given keyword search. For example, if the search for respiratory rate variability returned an abstract that met eligibility for respiratory alternans, then this was included in final analysis. Following the abstract screening phase, one reviewer (VEC) performed full text analysis of the papers that met inclusion criteria to confirm eligibility and extract data. The datapoints recorded during full-text review were (a) year of publication, (b) study size (number of subjects, not number of measurements), (c) study setting (a clinical practice setting versus specialized laboratory; type of practice setting or laboratory); (d) stage of technology validation (construct validity, descriptive study, predictive validity, clinical trial). Results The results of the outlined selection process are shown in Figure 1 . The electronic search resulted in 2,868 abstracts. Based on title and abstract, 2,407 articles were eliminated because they did not meet eligibility criteria. The remaining 461 abstracts corresponded to 445 separate articles. We excluded an additional 127 articles after a review of the full article, and we were unable to acquire 4 articles. We included the remaining 314 articles for data abstraction (a full list of studies is presented in the supplemental file). Download figure Open in new tab Figure 1: Screening and full article reviews. This figure shows the number of studies that were included in the abstract screening (2868), full article review (445), and data collection (314) stages of our study. We note that the study numbers in the bottom four boxes adds up to 331 instead of 314 because 17 studies measured 2 signs of labored breathing and were counted in two boxes. The earliest study included in our analysis was published in 1975. We identified over 30 types of devices that have been applied for the quantification of labored breathing, either alone or in combination. We categorized them, as shown in Figure 2 , based on the sensor type. The most frequently used sensors measured inductance / strain (e.g., respiratory inductance plethysmography), followed optical (e.g., optoelectronic plethysmography), electromyography, and inertial (e.g., accelerometer) sensors. No system has been validated as a measurement for all four signs of labored breathing that we studied. Download figure Open in new tab Figure 2: Technologies for labored breathing measurement Some sensor-metric combinations were seen frequently. For example, inductance plethysmography for the measurement of abdominal paradox (rib-abdomen asynchrony) is the single most frequent type of labored breathing measurement. However, no technology had been validated as a method for comprehensive assessment of labored breathing. (Of note, 17 studies measured two diagnostic signs. These were counted twice in this tabulation, making the total 331 instead of 314.) Despite major advances in sensors and computers between 1975 and 2022, the scale of studies quantifying breathing patterns had not increased ( Figure 3 ). We identified only 314 studies that had ever quantified the signs of labored breathing that we studied (6-7/year). To this day, most studies are conducted in specialized laboratories. Download figure Open in new tab Figure 3: Technology readiness for clinical adoption remains low. This figure depicts the magnitude and type of research that has focused on quantification of labored breathing patterns. Panels A & B show that the scale of investigation has not meaningfully increased since 1975. In the first decade of kinematic measurements (1975–1984), there average annual number of studies was 2.7 and the median sample size was 19. In the decade prior to our study (2013–2022), the average annual number of studies was 11.3 and the median sample size was 20. To this day, a majority of the studies are conducted in a specialized laboratory (73% between 2013-2022). Panel C shows a bottleneck in the bench to bedside spectrum. Most studies aim to measure the construct validity of a technology (19%) or to describe kinematic distributions in specific clinical scenarios (77%). Rarely do studies attempt to quantify the predictive validity for a clinical outcome (4%). We did not find any clinical trial where a kinematics-based early warning intervention was tested. Therefore, we conclude that the technology readiness level for clinical adoption remains low. Discussion We conducted a scoping review of technologies that can quantify four major breathing motion abnormalities. We found a critical bottleneck in the translation of bedside diagnostic signs of labored breathing into measurable physiomarkers of respiratory instability. Despite multiple potential technologies being tried since 1975, their readiness for clinical adoption remains low. Physician ratings of labored breathing patterns have been shown to improve predictions of intubation beyond the conventional predictors 22 . In the absence of quantitative measurements, such insights are confined to conventional clinical wisdom; they have not been integrated into predictive analytics. For an informative clinical sign to be integrated into predictive analytics, it has to be measurable with ease, accuracy, and reproducibility. For example, the respiratory rate was included in the NEWS (National Early Warning Score) model after an analysis of 200,000 respiratory rate values across 35,585 hospitalizations 23 . Similarly, it was included in the qSOFA (quick Sequential Organ Failure Assessment) model after an analysis of respiratory rates across 2 million encounters in 175 world-wide hospitals 24 . Our study revealed that labored breathing patterns had never been quantified on a scale needed for predictive modelling. Most prevailing ideas about the significance of breathing patterns are based on experts’ opinions or small studies in research settings. Little is known about the real-world incidence of any breathing motion abnormality, or its sensitivity, specificity, or predictive value for respiratory failure. Crucially, breathing pattern abnormalities report a unique dimension of physiology that is not captured by the conventional vital signs. In a prospective study of 1,134 subjects, only a small portion of the variance in physicians’ rating of labored breathing was explained by vital signs and laboratory data 22 This is important because the performance of a predictive algorithm or early warning score is fundamentally linked to the quality of data on which it is trained and applied. If the conventional data sources fail to capture a certain portion of pathophysiologic information, then that missing information restricts the performance of any model trained on the data. As such, simpler models based on complete physiological information might to outperform complex models based on partial information. Improvements in technologies that quantify labored breathing can greatly improve the early detection of respiratory deterioration. Therefore, a solution for the problem identified by our study is urgently needed. Data Availability All data produced in the present study are available upon reasonable request to the authors References (1). ↵ Cartin-Ceba , R. ; Kojicic , M. ; Li , G. ; Kor , D. J. ; Poulose , J. ; Herasevich , V. ; Kashyap , R. ; Trillo-Alvarez , C. ; Cabello-Garza , J. ; Hubmayr , R. ; Seferian , E. G. ; Gajic , O . Epidemiology of Critical Care Syndromes, Organ Failures, and Life-Support Interventions in a Suburban US Community . Chest 2011 , 140 ( 6 ), 1447 – 1455 . doi: 10.1378/chest.11-1197 . OpenUrl CrossRef PubMed Web of Science (2). ↵ Bedoya , A. D. ; Bhavsar , N. A. ; Adagarla , B. ; Page , C. B. ; Goldstein , B. A. ; MacIntyre , N. R . Unanticipated Respiratory Compromise and Unplanned Intubations on General Medical and Surgical Floors . Respir Care 2020 , 65 ( 9 ), 1233 – 1240 . doi: 10.4187/respcare.07438 . OpenUrl Abstract / FREE Full Text (3). ↵ American Thoracic Society/European Respiratory Society . ATS/ERS Statement on Respiratory Muscle Testing. Am . J. Respir. Crit. Care Med . 2002 , 166 ( 4 ), 518 – 624 . doi: 10.1164/rccm.166.4.518 . OpenUrl CrossRef PubMed (4). ↵ Tulaimat , A. ; Patel , A. ; Wisniewski , M. ; Gueret , R . The Validity and Reliability of the Clinical Assessment of Increased Work of Breathing in Acutely Ill Patients . Journal of Critical Care 2016 , 34 , 111 – 115 . doi: 10.1016/j.jcrc.2016.04.013 . OpenUrl CrossRef (5). Wunsch , H. ; Linde-Zwirble , W. T. ; Angus , D. C. ; Hartman , M. E. ; Milbrandt , E. B. ; Kahn , J. M . The Epidemiology of Mechanical Ventilation Use in the United States . Crit Care Med 2010 , 38 ( 10 ), 1947 – 1953 . doi: 10.1097/CCM.0b013e3181ef4460 . OpenUrl CrossRef PubMed Web of Science (6). Tsai , T. C. ; Orav , E. J. ; Jha , A. K. ; Figueroa , J. F . National Estimates of Increase in US Mechanical Ventilator Supply During the COVID-19 Pandemic . JAMA Network Open 2022 , 5 ( 8 ), e2224853 . doi: 10.1001/jamanetworkopen.2022.24853 . OpenUrl CrossRef (7). Andersen , L. W. ; Holmberg , M. J. ; Berg , K. M. ; Donnino , M. W. ; Granfeldt , A . In-Hospital Cardiac Arrest: A Review . JAMA 2019 , 321 ( 12 ), 1200 – 1210 . doi: 10.1001/jama.2019.1696 . OpenUrl CrossRef PubMed (8). Turner , J. S. ; Bucca , A. W. ; Propst , S. L. ; Ellender , T. J. ; Sarmiento , E. J. ; Menard , L. M. ; Hunter , B. R . Association of Checklist Use in Endotracheal Intubation With Clinically Important Outcomes: A Systematic Review and Meta-Analysis . JAMA Network Open 2020 , 3 ( 7 ), e209278 . doi: 10.1001/jamanetworkopen.2020.9278 . OpenUrl CrossRef (9). Gao , H. ; McDonnell , A. ; Harrison , D. A. ; Moore , T. ; Adam , S. ; Daly , K. ; Esmonde , L. ; Goldhill , D. R. ; Parry , G. J. ; Rashidian , A. ; Subbe , C. P. ; Harvey , S . Systematic Review and Evaluation of Physiological Track and Trigger Warning Systems for Identifying At-Risk Patients on the Ward . Intensive Care Med 2007 , 33 ( 4 ), 667 – 679 . doi: 10.1007/s00134-007-0532-3 . OpenUrl CrossRef PubMed Web of Science (10). Hillman , K. M. ; Bristow , P. J. ; Chey , T. ; Daffurn , K. ; Jacques , T. ; Norman , S. L. ; Bishop , G. F. ; Simmons , G . Duration of Life-Threatening Antecedents Prior to Intensive Care Admission . Intensive Care Med 2002 , 28 ( 11 ), 1629 – 1634 . doi: 10.1007/s00134-002-1496-y . OpenUrl CrossRef PubMed Web of Science (11). Boniatti , M. M. ; Azzolini , N. ; Viana , M. V. ; Ribeiro , B. S. P. ; Coelho , R. S. ; Castilho , R. K. ; Guimarães , M. R. ; Zorzi , L. ; Schulz , L. F. ; Filho , E. M. R . Delayed Medical Emergency Team Calls and Associated Outcomes* . Critical Care Medicine 2014 , 42 ( 1 ), 26 . doi: 10.1097/CCM.0b013e31829e53b9 . OpenUrl CrossRef (12). Wong , A.-K. I. ; Cheung , P. C. ; Kamaleswaran , R. ; Martin , G. S. ; Holder , A. L . Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome . Frontiers in Big Data 2020 , 3 . (13). Dziadzko , M. A. ; Novotny , P. J. ; Sloan , J. ; Gajic , O. ; Herasevich , V. ; Mirhaji , P. ; Wu , Y. ; Gong , M. N . Multicenter Derivation and Validation of an Early Warning Score for Acute Respiratory Failure or Death in the Hospital . Critical Care 2018 , 22 ( 1 ), 286 . doi: 10.1186/s13054-018-2194-7 . OpenUrl CrossRef PubMed (14). Wong , A.-K. I. ; Kamaleswaran , R. ; Tabaie , A. ; Reyna , M. A. ; Josef , C. ; Robichaux , C. ; de Hond , A. A. H. ; Steyerberg , E. W. ; Holder , A. L. ; Nemati , S. ; Buchman , T. G. ; Blum , J. M . Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data . Crit Care Explor 2021 , 3 ( 5 ), e0402 . doi: 10.1097/CCE.0000000000000402 . OpenUrl CrossRef (15). Clark , M. T. ; Vergales , B. D. ; Paget-Brown , A. O. ; Smoot , T. J. ; Lake , D. E. ; Hudson , J. L. ; Delos , J. B. ; Kattwinkel , J. ; Moorman , J. R . Predictive Monitoring for Respiratory Decompensation Leading to Urgent Unplanned Intubation in the Neonatal Intensive Care Unit . Pediatr Res 2013 , 73 ( 1 ), 104 – 110 . doi: 10.1038/pr.2012.155 . OpenUrl CrossRef PubMed (16). Moss , T. J. ; Clark , M. T. ; Calland , J. F. ; Enfield , K. B. ; Voss , J. D. ; Lake , D. E. ; Moorman , J. R . Cardiorespiratory Dynamics Measured from Continuous ECG Monitoring Improves Detection of Deterioration in Acute Care Patients: A Retrospective Cohort Study . PLOS ONE 2017 , 12 ( 8 ), e0181448 . doi: 10.1371/journal.pone.0181448 . OpenUrl CrossRef (17). Afshar , M. ; Joyce , C. ; Oakey , A. ; Formanek , P. ; Yang , P. ; Churpek , M. M. ; Cooper , R. S. ; Zelisko , S. ; Price , R. ; Dligach , D . A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning . AMIA Annu Symp Proc 2018 , 2018, 157 – 165 . (18). Fernandes , M. ; Mendes , R. ; Vieira , S. M. ; Leite , F. ; Palos , C. ; Johnson , A. ; Finkelstein , S. ; Horng , S. ; Celi , L. A . Predicting Intensive Care Unit Admission among Patients Presenting to the Emergency Department Using Machine Learning and Natural Language Processing . PLOS ONE 2020 , 15 ( 3 ), e0229331 . doi: 10.1371/journal.pone.0229331 . OpenUrl CrossRef (19). Tobin , M. J . Why Physiology Is Critical to the Practice of Medicine: A 40-Year Personal Perspective . Clinics in Chest Medicine 2019 , 40 ( 2 ), 243 – 257 . doi: 10.1016/j.ccm.2019.02.012 . OpenUrl CrossRef (20). Evidence-Based Physical Diagnosis; Elsevier, 2007. doi: 10.1016/B978-1-4160-2898-7.X5001-1 . (21). Liu , H. ; Allen , J. ; Zheng , D. ; Chen , F . Recent Development of Respiratory Rate Measurement Technologies . Physiol Meas 2019 , 40 ( 7 ), 07T R01. doi: 10.1088/1361-6579/ab299e . OpenUrl CrossRef (22). Tulaimat , A. ; Gueret , R. M. ; Wisniewski , M. F. ; Samuel , J . Association Between Rating of Respiratory Distress and Vital Signs, Severity of Illness, Intubation, and Mortality in Acutely Ill Subjects . Respiratory Care 2014 , 59 ( 9 ), 1338 – 1344 . doi: 10.4187/respcare.02650 . OpenUrl Abstract / FREE Full Text (23). Smith , G. B. ; Prytherch , D. R. ; Meredith , P. ; Schmidt , P. E. ; Featherstone , P. I . The Ability of the National Early Warning Score (NEWS) to Discriminate Patients at Risk of Early Cardiac Arrest, Unanticipated Intensive Care Unit Admission, and Death . Resuscitation 2013 , 84 ( 4 ), 465 – 470 . doi: 10.1016/j.resuscitation.2012.12.016 . OpenUrl CrossRef PubMed (24). Singer , M. ; Deutschman , C. S. ; Seymour , C. W. ; Shankar-Hari , M. ; Annane , D. ; Bauer , M. ; Bellomo , R. ; Bernard , G. R. ; Chiche , J.-D. ; Coopersmith , C. M. ; Hotchkiss , R. S. ; Levy , M. M. ; Marshall , J. C. ; Martin , G. S. ; Opal , S. M. ; Rubenfeld , G. D. ; Poll , T. van der ; Vincent , J.-L .; Angus , D. C. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) . JAMA 2016 , 315 ( 8 ), 801 – 810 . doi: 10.1001/jama.2016.0287 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted January 28, 2024. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Labored breathing pattern: an unmeasured dimension of respiratory pathophysiology Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Labored breathing pattern: an unmeasured dimension of respiratory pathophysiology Valerie E. Cyphers , Swet M. Patel , Brendan D. McNamara , William B Ashe , Sarah J. Ratcliffe , J. Randall Moorman , Jessica Keim-Malpass , Shrirang M. Gadrey , Sherry L. Kausch medRxiv 2024.01.27.24301872; doi: https://doi.org/10.1101/2024.01.27.24301872 Share This Article: Copy Citation Tools Labored breathing pattern: an unmeasured dimension of respiratory pathophysiology Valerie E. Cyphers , Swet M. Patel , Brendan D. McNamara , William B Ashe , Sarah J. Ratcliffe , J. Randall Moorman , Jessica Keim-Malpass , Shrirang M. Gadrey , Sherry L. Kausch medRxiv 2024.01.27.24301872; doi: https://doi.org/10.1101/2024.01.27.24301872 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Respiratory Medicine Subject Areas All Articles Addiction Medicine (573) Allergy and Immunology (865) Anesthesia (303) Cardiovascular Medicine (4457) Dentistry and Oral Medicine (445) Dermatology (383) Emergency Medicine (610) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1517) Epidemiology (15244) Forensic Medicine (30) Gastroenterology (1132) Genetic and Genomic Medicine (6619) Geriatric Medicine (669) Health Economics (1002) Health Informatics (4556) Health Policy (1372) Health Systems and Quality Improvement (1614) Hematology (543) HIV/AIDS (1271) Infectious Diseases (except HIV/AIDS) (15934) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6634) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3348) Ophthalmology (980) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1696) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5463) Public and Global Health (9255) Radiology and Imaging (2210) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1197) Rheumatology (598) Sexual and Reproductive Health (716) Sports Medicine (532) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a030d779795c09d6',t:'MTc4MDAwODg3MQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();