• Liangyou Chen, Andrew T Reisner, Andrei Gribok, Thomas M McKenna, and Jaques Reifman.
• Bioinformatics Cell, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, Maryland 21702, USA.
• Shock. 2009 Jun 1;31(6):574-80.

AbstractRespiratory 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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