Accuracy and interobserver-agreement of respiratory rate measurements by healthcare professionals, and its effect on the outcomes of clinical prediction/diagnostic rules

Abstract

Objective In clinical prediction/diagnostic rules aimed at early detection of critically ill patients, the respiratory rate plays an important role. We investigated the accuracy and interobserver-agreement of respiratory rate measurements by healthcare professionals, and the potential effect of incorrect measurements on the scores of 4 common clinical prediction/diagnostic rules: Systemic Inflammatory Response Syndrome (SIRS) criteria, quick Sepsis-related Organ Failure Assessment (qSOFA), National Early Warning Score (NEWS), and Modified Early Warning Score (MEWS). Methods Using an online questionnaire, we showed 5 videos with a healthy volunteer, breathing at a fixed (true) rate (13–28 breaths/minute). Respondents measured the respiratory rate, and categorized it as low, normal, or high. We analysed how accurate the measurements were using descriptive statistics, and calculated interobserver-agreement using the intraclass correlation coefficient (ICC), and agreement between measurements and categorical judgments using Cohen’s Kappa. Finally, we analysed how often incorrect measurements led to under/overestimation in the selected clinical rules. Results In total, 448 healthcare professionals participated. Median measurements were slightly higher (1-3/min) than the true respiratory rate, and 78.2% of measurements were within 4/min of the true rate. ICC was moderate (0.64, 95% CI 0.39–0.94). When comparing the measured respiratory rates with the categorical judgments, 14.5% were inconsistent. Incorrect measurements influenced the 4 rules in 8.8% (SIRS) to 37.1% (NEWS). Both underestimation (4.5–7.1%) and overestimation (3.9–32.2%) occurred. Conclusions The accuracy and interobserver-agreement of respiratory rate measurements by healthcare professionals are suboptimal. This leads to both over- and underestimation of scores of four clinical prediction/diagnostic rules. The clinically most important effect could be a delay in diagnosis and treatment of (critically) ill patients.