ERJ Open Research 2020 6: 00023-2020; DOI: 10.1183/23120541.00023-2020
Abstract
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 6 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 120 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 25 breaths·min−1, used by the UK National Early Warning Score, for possible mis-scoring.
Results When the sample duration was nominally 30 s, the mean interquartile range of repeated estimates was 3.4 breaths·min−1. For the 60 s samples, the mean interquartile range was 3 breaths·min−1, and for the 120 s samples it was 2.5 breaths·min−1. Thus, repeat clinical counts of respiratory rate often differ by >3 breaths·min−1. For 30 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.
Abstract
In acutely ill patients, the usual 30 s to count breathing are insufficient to give a reliable measurement //bit.ly/31DNhKU
Introduction
Respiratory rate is universally employed in the clinical assessment of ill patients and is now used widely in early warning scores to grade severe illness in acutely ill patients in an emergency setting [1]. Doubts have been raised about the reliability of such observations [2, 3], but measurement error is rarely considered [4].
Studies of respiratory rate tend to assume that breathing is stable and disregard breath-to-breath variation. In fact, variation from breath to breath can be substantial and appears random. When comparing alternative measurement methods, observations can differ unless exactly the same time periods are considered by each method. Thus, discrepancies between devices may not result from measurement error, as previous studies have assumed [5]. We could find no systematic studies of the repeatability of respiratory rate measurements, so we investigated the imprecision of clinical measurements of respiratory rate by simulating repeated measurements.
The obvious factor affecting repeatability is sample size. Random variation exerts a greater effect in small samples [the law of large numbers was first described by Gerolamo Cardano in the 16th century]. We studied observations of different durations [i.e. sample size] to assess how this affected repeatability.
Methods
We used records made in a previous study in which we assessed a new device to measure respiratory rate. For that study, we had recruited a convenience cohort of adult patients who were admitted to hospital with acute illness.
Patients were studied in the acute admission unit of a 570-bed teaching hospital. All were studied in the first 4–8 h after admission. Nasal cannula pressure was recorded in each patient. This continuous measure allows precise measurement of successive breath durations over a greater time period than would be clinically feasible [figure 1]. The sole criterion for admission to the study was that the patient accepted the nasal cannula placement. If possible, respiratory signals were recorded for 1 h, or until the patient was prepared for discharge or transfer from the ward. These data gave us a unique chance to simulate repeated clinical measurements of breathing rate. We designed the current study to present results in terms familiar to clinical workers.
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FIGURE 1
a] Example of a nasal pressure trace. Nasal pressure decrease at the onset of each inspiration is detected automatically. Breath duration is calculated from the time between each mark. b] Each patient record consists of successive breath durations. The distribution of breath duration is shown in the insert, with the quartiles shown. c] Random samples are taken from this series, using a whole number of breath cycles, as close to 30 s as possible.
For this previous study, we were granted ethical permission by the Scotland A Research Ethics committee [ref 12/SS/0054] subject to the provisions of section 51 of the Adults with Incapacity [Scotland] Act 2000. This legislation was relevant because we could not be certain that acutely ill patients were capable of giving full informed consent. We were allowed to retain only limited patient data [year of birth, height and weight] but were permitted to process anonymised data recordings and retain them for further use.
Patient recordings
To obtain a precise measure of breathing rate [intended for comparison with the new device] we recorded pressure at the nostrils with a nasal cannula [Sleep Sense 15802–2; Medes Ltd., Radlett, UK]. This is a well-established and reliable method often used in sleep studies [6]. A single-use set of nasal cannulae were placed below the nostrils. The cannulae were connected through a bacterial filter to a battery-powered pressure transducer [PTAF2; Philips Respironics, Chichester, UK; www.philips.com/respironics]. This was placed beside the patient and the pressure signal was transmitted wirelessly [Bluetooth LE] to an iPod receiver. The pressure signal was digitised at 12.5 Hz. After each study period, the patient recordings were transferred from the receiver to a secure computer for further analysis.
The records were analysed using proprietary software [Spike2, version 5.19; CED, Cambridge, UK]. Each breath onset time was identified and recorded automatically using a threshold detection facility in the display software, to give a sequence of times [accuracy >0.1 s] from the start of the record [figure 1a]. An overall respiratory rate was calculated for each patient, in breaths per minute, by dividing the total number of complete breath cycles in the record by the total duration of those cycles. This value represents the most exact measure of breathing rate for that patient [figure 1b].
Each patient record of breath times was then randomly sampled, on multiple occasions, to select time segments, each of specified duration [figure 1c]. The durations of these random samples were nominally 30, 60 and 120 s: the 30 s duration was chosen because it is common in clinical practice [most data sets show a substantial excess of even values [3]], 60 s because it is an ideal duration [7] and 120 s to assess the effect of a larger sample, although this is rarely used clinically. The respiratory rate was calculated from the whole number of breaths and the overall duration of those breaths, to the nearest 0.1 s, with the total duration as near as possible to the chosen value. The number of random sample periods taken from each patient record was related to the size of the recording: approximately one random sample was taken for each minute of each recording. Since the values were mostly not normally distributed, we expressed variation in repeated measures by the interquartile range of the observed rates. For each patient, and each sample duration, the median and interquartile range of all the rate estimates was calculated. Figure 2a shows an example of the rate values obtained, taken from the record of the patient shown in figure 1.
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FIGURE 2
a] The distribution of separate rate value measurements [using 30 s samples] for the patient shown in figure 1, with median and quartile values, in relation to overall respiratory rate [shown as ●]. b] Measurements from all subjects, with median and interquartile range, in relation to the overall respiratory rate for each subject, using a 30 s sample duration. c] The influence of a greater duration of sample on the interquartile range of the observations. Observations from each subject are linked.
We used the measurements from the entire cohort of patients as a model population. These data allowed us to derive the likelihood of a repeat observation taken from a single patient, drawn from a population of patients similar to those we had studied. The model population allows incorporation of variance within and between patients, and the procedure is described in supplementary appendix 1.
Finally, we estimated how variations in the observed rate might affect the respiratory element of the UK National Early Warning Score allocated to a particular patient. We took the overall respiratory rate, based on the entire record, as the true respiratory rate of the patient. We then estimated the proportion of observations made from the patient that would have resulted in the same score value.
Data were processed using Python scripts and GraphPad Prism version 6.05 for Windows [GraphPad Software, La Jolla, CA, USA; www.graphpad.com]. Unless otherwise stated, data are summarised as the median [quartiles]. The original breath time data used are available as a web appendix [appendix 2].
Results
We used recordings of >30 min duration obtained from 25 patients. These patients [11 female, 14 male] had a mean [sd] age of 66 [15] years, weight 81 [16] kg, height 1.67 [0.12] m and BMI 28 [8] kg·m−2. They were admitted to hospital with a variety of acute medical conditions, the most frequent being respiratory [7], cardiac [4], neurological [4] and urinary [4]. Most patients had intercurrent disease, predominantly cardiac and respiratory.
Plots of the breath durations against elapsed time [as in figure 1b] did not show a trend in any of the patients that could cause variation of the measured rates. The median number of rate estimates made from the patients was 62 [quartiles 54, 81]. The scatter of rate values around the median varied from patient to patient. As would be expected, the median of the rates from a patient was always close to the overall respiratory rate, measured from the entire sample from that patient. Each patient generated three sets of rate measurements, making 75 sets of rate measurements in total.
Figure 2b shows the median and interquartile range of all the rate measurements in each patient, based on samples of 30 s, plotted in relation to the overall respiratory rate. The distribution of rates was normal in only 35 of the 75 sets of rates considered [D'Agostino & Pearson omnibus test].
The interquartile ranges for samples of 30, 60 and 120 s are shown in figure 2c. For samples of 30 s, the mean interquartile range of the rate estimates was 3.4 breaths·min−1. For samples of 60 s, the mean interquartile range was 3.0 breaths·min−1 and for 120 s samples was 2·5 breaths·min−1.
Using the model described in appendix 1, we predicted respiratory rates if a specific rate observation, taken from our studied population, were to be repeated. We selected observation values of 12, 20 and 25 breaths·min−1, which are threshold values in the UK National Early Warning Score. The predictions are shown in figure 3. Particularly for the 30 s samples, the possibility that a repeat observation would be within 2 breaths·min−1 of the previous value is