Literature DB >> 19008777

Can we improve the clinical utility of respiratory rate as a monitored vital sign?

Liangyou Chen1, Andrew T Reisner, Andrei Gribok, Thomas M McKenna, Jaques Reifman.   

Abstract

Respiratory rate (RR) is a basic vital sign, measured and monitored throughout a wide spectrum of health care settings, although RR is historically difficult to measure in a reliable fashion. We explore an automated method that computes RR only during intervals of clean, regular, and consistent respiration and investigate its diagnostic use in a retrospective analysis of prehospital trauma casualties. At least 5 s of basic vital signs, including heart rate, RR, and systolic, diastolic, and mean arterial blood pressures, were continuously collected from 326 spontaneously breathing trauma casualties during helicopter transport to a level I trauma center. "Reliable" RR data were identified retrospectively using automated algorithms. The diagnostic performances of reliable versus standard RR were evaluated by calculation of the receiver operating characteristic curves using the maximum-likelihood method and comparison of the summary areas under the receiver operating characteristic curves (AUCs). Respiratory rate shows significant data-reliability differences. For identifying prehospital casualties who subsequently receive a respiratory intervention (hospital intubation or tube thoracotomy), standard RR yields an AUC of 0.59 (95% confidence interval, 0.48-0.69), whereas reliable RR yields an AUC of 0.67 (0.57-0.77), P < 0.05. For identifying casualties subsequently diagnosed with a major hemorrhagic injury and requiring blood transfusion, standard RR yields an AUC of 0.60 (0.49-0.70), whereas reliable RR yields 0.77 (0.67-0.85), P < 0.001. Reliable RR, as determined by an automated algorithm, is a useful parameter for the diagnosis of respiratory pathology and major hemorrhage in a trauma population. It may be a useful input to a wide variety of clinical scores and automated decision-support algorithms.

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Year:  2009        PMID: 19008777     DOI: 10.1097/SHK.0b013e318193e885

Source DB:  PubMed          Journal:  Shock        ISSN: 1073-2322            Impact factor:   3.454


  6 in total

1.  Development and validation of a portable platform for deploying decision-support algorithms in prehospital settings.

Authors:  A T Reisner; M Y Khitrov; L Chen; A Blood; K Wilkins; W Doyle; S Wilcox; T Denison; J Reifman
Journal:  Appl Clin Inform       Date:  2013-08-21       Impact factor: 2.342

2.  Flash mob research: a single-day, multicenter, resident-directed study of respiratory rate.

Authors:  Matthew W Semler; Daniel G Stover; Andrew P Copland; Gina Hong; Michael J Johnson; Michael S Kriss; Hannah Otepka; Li Wang; Brian W Christman; Todd W Rice
Journal:  Chest       Date:  2013-06       Impact factor: 9.410

3.  Clinician blood pressure documentation of stable intensive care patients: an intelligent archiving agent has a higher association with future hypotension.

Authors:  Caleb W Hug; Gari D Clifford; Andrew T Reisner
Journal:  Crit Care Med       Date:  2011-05       Impact factor: 7.598

4.  Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians.

Authors:  Alasdair J Mort; David Fitzpatrick; Philip M J Wilson; Chris Mellish; Anne Schneider
Journal:  J Clin Monit Comput       Date:  2015-03-25       Impact factor: 2.502

5.  Smartphone movement sensors for the remote monitoring of respiratory rates: Technical validation.

Authors:  Sophie Valentine; Adam C Cunningham; Benjamin Klasmer; Mohammad Dabbah; Marko Balabanovic; Mert Aral; Dan Vahdat; David Plans
Journal:  Digit Health       Date:  2022-04-25

6.  Classifying signals from a wearable accelerometer device to measure respiratory rate.

Authors:  Gordon B Drummond; Darius Fischer; Margaret Lees; Andrew Bates; Janek Mann; D K Arvind
Journal:  ERJ Open Res       Date:  2021-04-26
  6 in total

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