Error in respiratory rate measurement by direct observation impacts on clinical early warning score algorithms

Respiratory rate(RR) is the only vital sign that is currently measured by direct observation in many highincome countries in many healthcare settings. However, when relying on direct observation by humans, values may be susceptible to measurement error. In particular, a continuous variable like RR may be inadvertently semicategorised due to rounding and/or multiplying up counts from shorter periods to give estimated counts for a full minute. This could have an important impact on early warning scores, as these scores rely on the aggregation of accurately measured physiological parameters to trigger specific warning thresholds. Systemic errors in the measurement of RR component could therefore negatively impact patient care if it meant a deteriorating patient did not meet the defined threshold. Seventysix per cent of UK hospital trusts receiving acute medical admissions use the National Early Warning Score—2 (NEWS2). 2 Introduced in 2017, it uses six simple physiological parameters which are respiration rate, oxygen saturation, systolic BP, pulse rate, level of consciousness and temperature. These are aggregated by a simple scoring system that uses predefined thresholds to give a composite NEWS2 score, which is then deployed to generate triggers for the review of clinical care. The threshold for RR in NEWS2 is the step between 20 and 21 breaths/min. We therefore examined the distribution of RR measurements around this clinically critical range for decisionmaking. Electronic data were available from all patients admitted to Nottingham University Hospital Trust with suspected or confirmed COVID19 infection from 2020 to 2021. To enable a statistical comparison to be performed, a normal distribution was imposed over the main dataset disregarding outlying values to permit estimation of the expected number of breaths per minute values at the trigger threshold of 21 breaths/min and compared with the observed value (χ test). A total of 843 006 RR measurements within a range of 10 to 40 breaths/min were observed from 9624 patients. There were more even integers of RR measurement than odd numbers (figure 1). This is likely to be an artefact of visually counting the RR for 30 s, and then doubling the count to give a rate per minute. Our data show the step from a RR of 20 breaths/min to 21 breaths/min is associated with a large drop in the frequency of readings, with a subsequent higher number of values for the 22 breaths/min category. The normal distribution of data (excluding the long tail to the right) shows a predicted number of readings at 20 breaths/min of 98 406 measurements as opposed to the observed number of 125 149 measurements. The comparable counts for the 21 breaths/min readings are 10 312 measurements (observed) and 32 395 (expected), respectively (p<0.0001, χ goodnessoffit test, χ test statistic=14 539), generating a measurement error of 214% (this value indicates the additional per cent of the observed 21 breaths/min readings that would be expected under the normal distribution and were therefore misclassified elsewhere). These numbers are clinically important, as RRs of 21 breaths/min or over cross the predefined threshold and change to the NEWS2 score which may generate a change in clinical response. The main limitation of this analysis is that the assumptions of the normal distribution required for the statistical analysis require estimates. Hence the findings should be regarded as statistically demonstrating the problem of measurement error of respiratory rate (as can be seen by observation in figure 1), as opposed to providing precise estimates of the scale of the issue. These obvious measurement errors in the recording of RR around the 20–21 breaths/min threshold have also been observed in the USA. This is likely to be a global issue that while readily apparent in clinical epidemiological datasets is less likely to be recognised at the level of the individual patient. This issue is another manifestation of the downside of developing composite outcome measures from healthcare system datasets that while aiming to simplify complex information and make it more useful to the end user, runs the risk of embedding and obscuring systemic errors and hence compromising patient care. Having demonstrated the issue of RR error in reducing the sensitivity of clinical early warning scores, the challenge now is to address this concern. While measuring RR manually for 60 s is one possibility, devices to automate the measurement of RR would be a better solution, in a similar manner to how pulse measurement has changed from a manual to a more automated process.