Literature DB >> 27270963

Acoustic sensor versus electrocardiographically derived respiratory rate in unstable trauma patients.

Shiming Yang1, Ashley Menne2, Peter Hu2, Lynn Stansbury2, Cheng Gao2, Nicolas Dorsey2, William Chiu2, Stacy Shackelford3, Colin Mackenzie2.   

Abstract

Respiratory rate (RR) is important in many patient care settings; however, direct observation of RR is cumbersome and often inaccurate, and electrocardiogram-derived RR (RRECG) is unreliable. We asked how data derived from the first 15 min of RR recording after trauma center admission using a novel acoustic sensor (RRa) would compare to RRECG and to end-tidal carbon dioxide-based RR ([Formula: see text]) from intubated patients, the "gold standard" in predicting life-saving interventions in unstable trauma patients. In a convenience sample subset of trauma patients admitted to our Level 1 trauma center, enrolled in the ONPOINT study, and monitored with RRECG, some of whom also had [Formula: see text] data, we collected RRa using an adhesive sensor with an integrated acoustic transducer (Masimo RRa™). Using Bland-Altman analysis of area under the receiver operating characteristic (AUROC) curves, we compared the first 15 min of continuous RRa and RRECG to [Formula: see text] and assessed the performance of these three parameters compared to the Revised Trauma Score (RTS) in predicting blood transfusion 3, 6, and 12 h after admission. Of the 1200 patients enrolled in ONPOINT from December 2011 to May 2013, 1191 had RRECG data recorded in the first 15 min, 358 had acoustic monitoring, and 14 of the latter also had [Formula: see text]. The three groups did not differ demographically or in mechanism of injury. RRa showed less bias (0.8 vs. 6.9) and better agreement than RRECG when compared to [Formula: see text]. At [Formula: see text] 10-29 breaths per minute, RRa was more likely to be the same as [Formula: see text] and assign the same RTS. In predicting transfusion, features derived from RRa and RRECG gave AUROCs 0.59-0.66 but with true positive rate 0.70-0.89. RRa monitoring is a non-invasive option to glean valid RR data to assist clinical decision making and could contribute to prediction models in non-intubated unstable trauma patients.

Entities:  

Keywords:  Acoustic sensor; Monitoring; Respiratory rate; Transfusion prediction; Trauma

Mesh:

Year:  2016        PMID: 27270963     DOI: 10.1007/s10877-016-9895-8

Source DB:  PubMed          Journal:  J Clin Monit Comput        ISSN: 1387-1307            Impact factor:   2.502


  25 in total

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Authors:  Jackie McBride; Debbie Knight; Jo Piper; Gary B Smith
Journal:  Resuscitation       Date:  2005-04       Impact factor: 5.262

2.  Prediction of mortality and of the need for massive transfusion in casualties arriving at combat support hospitals in Iraq.

Authors:  Leopoldo C Cancio; Charles E Wade; Susan A West; John B Holcomb
Journal:  J Trauma       Date:  2008-02

3.  Performance of Masimo rainbow acoustic monitoring for tracking changing respiratory rates under laryngeal mask airway general anesthesia for surgical procedures in the operating room: a prospective observational study.

Authors:  Joshua H Atkins; Jeff E Mandel
Journal:  Anesth Analg       Date:  2014-12       Impact factor: 5.108

Review 4.  Respiration rate monitoring methods: a review.

Authors:  F Q Al-Khalidi; R Saatchi; D Burke; H Elphick; S Tan
Journal:  Pediatr Pulmonol       Date:  2011-01-31

5.  Evaluation of acoustic respiration rate monitoring after extubation in intensive care unit patients.

Authors:  L M Autet; D Frasca; M Pinsard; A Cancel; L Rousseau; B Debaene; O Mimoz
Journal:  Br J Anaesth       Date:  2014-07       Impact factor: 9.166

6.  Accuracy of respiratory rate monitoring by capnometry using the Capnomask(R) in extubated patients receiving supplemental oxygen after surgery.

Authors:  A Gaucher; D Frasca; O Mimoz; B Debaene
Journal:  Br J Anaesth       Date:  2011-12-11       Impact factor: 9.166

7.  Developing an algorithm for pulse oximetry derived respiratory rate (RR(oxi)): a healthy volunteer study.

Authors:  Paul S Addison; James N Watson; Michael L Mestek; Roger S Mecca
Journal:  J Clin Monit Comput       Date:  2012-01-10       Impact factor: 2.502

8.  The identification of risk factors for cardiac arrest and formulation of activation criteria to alert a medical emergency team.

Authors:  Timothy J Hodgetts; Gary Kenward; Ioannis G Vlachonikolis; Susan Payne; Nicolas Castle
Journal:  Resuscitation       Date:  2002-08       Impact factor: 5.262

9.  Non-contact respiratory rate measurement validation for hospitalized patients.

Authors:  Amy D Droitcour; Todd B Seto; Byung-Kwon Park; Shuhei Yamada; Alex Vergara; Charles El Hourani; Tommy Shing; Andrea Yuen; Victor M Lubecke; Olga Boric-Lubecke
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

10.  The accuracy, precision and reliability of measuring ventilatory rate and detecting ventilatory pause by rainbow acoustic monitoring and capnometry.

Authors:  Michael A E Ramsay; Mohammad Usman; Elaine Lagow; Minerva Mendoza; Emylene Untalan; Edward De Vol
Journal:  Anesth Analg       Date:  2013-04-30       Impact factor: 5.108

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  2 in total

Review 1.  Journal of Clinical Monitoring and Computing 2017 end of year summary: respiration.

Authors:  D S Karbing; G Perchiazzi; S E Rees; M B Jaffe
Journal:  J Clin Monit Comput       Date:  2018-02-26       Impact factor: 2.502

2.  The effect of dental scaling noise during intravenous sedation on acoustic respiration rate (RRa™).

Authors:  Jung Ho Kim; Seong In Chi; Hyun Jeong Kim; Kwang-Suk Seo
Journal:  J Dent Anesth Pain Med       Date:  2018-04-27
  2 in total

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