RESUMEN
BACKGROUND: Automatic measurement of respiratory rate in general hospital patients is difficult. Patient movement degrades the signal and variation of the breathing cycle means that accurate observation for ≥60â s is needed for adequate precision. METHODS: We studied acutely ill patients recently admitted to a teaching hospital. Breath duration was measured from a triaxial accelerometer attached to the chest wall and compared with a signal from a nasal cannula. We randomly divided the patient records into a training (n=54) and a test set (n=7). We used machine learning to train a neural network to select reliable signals, automatically identifying signal features associated with accurate measurement of respiratory rate. We used the test records to assess the accuracy of the device, indicated by the median absolute difference between respiratory rates, provided by the accelerometer and by the nasal cannula. RESULTS: In the test set of patients, machine classification of the respiratory signal reduced the median absolute difference (interquartile range) from 1.25 (0.56-2.18) to 0.48 (0.30-0.78) breaths per min. 50% of the recording periods were rejected as unreliable and in one patient, only 10% of the signal time was classified as reliable. However, even only 10% of observation time would allow accurate measurement for 6â min in an hour of recording, giving greater reliability than nurse charting, which is based on much less observation time. CONCLUSION: Signals from a body-mounted accelerometer yield accurate measures of respiratory rate, which could improve automatic illness scoring in adult hospital patients.
RESUMEN
BACKGROUND: Respiratory rate is a basic clinical measurement used for illness assessment. Errors in measuring respiratory rate are attributed to observer and equipment problems. Previous studies commonly report rate differences ranging from 2 to 6â breaths·min-1 between observers. METHODS: To study why repeated observations should vary so much, we conducted a virtual experiment, using continuous recordings of breathing from acutely ill patients. These records allowed each breathing cycle to be precisely timed. We made repeated random measures of respiratory rate using different sample durations of 30, 60 and 120â s. We express the variation in these repeated rate measurements for the different sample durations as the interquartile range of the values obtained for each subject. We predicted what values would be found if a single measure, taken from any patient, were repeated and inspected boundary values of 12, 20 or 25â breaths·min-1, used by the UK National Early Warning Score, for possible mis-scoring. RESULTS: When the sample duration was nominally 30â s, the mean interquartile range of repeated estimates was 3.4â breaths·min-1. For the 60â s samples, the mean interquartile range was 3â breaths·min-1, and for the 120â s samples it was 2.5â breaths·min-1. Thus, repeat clinical counts of respiratory rate often differ by >3â breaths·min-1. For 30â s samples, up to 40% of National Early Warning Scores could be misclassified. CONCLUSIONS: Early warning scores will be unreliable when short sample durations are used to measure respiratory rate. Precision improves with longer sample duration, but this may be impractical unless better measurement methods are used.