Abstract Background In chronic respiratory diseases (CRD), current assessment tools for inhaler technique often fail to differentiate between critical and non-critical errors, leading to incorrect device use and suboptimal drug delivery. To address this gap, we developed an tool called Inhaler Technology Assessment Scale (ITAS) and implemented a weighted scoring system to better quantify and prioritize inhalation errors. Objectives The primary objective of this study is to validate whether the scale outperforms conventional isometric scoring in detecting critical errors; the secondary objective is to characterize current inhaler usage patterns among chronic respiratory disease patients in Southwestern China. Methods This study enrolled 405 patients with CRDs from Southwestern China. Descriptive statistics quantified error frequencies for each procedural step. Non-parametric tests compared ITAS and isometric scores in cohorts with versus without critical errors. Visual analysis contrasted score distributions between both scoring systems across incremental critical error counts. ITAS performance was compared against isometric scoring using ROC analysis and Youden-index optimization. Results The operational compliance rate was 18% in the study cohort. The top 3 critical errors were inadequate breath-holding, ineffective drug breath-in, and incomplete exhalation before inhalation. ITAS demonstrated progressively widening score differentials as critical errors accumulated, with significantly greater discrimination than isometric scoring (AUC=0.993, 95% CI: 0.986-0.999 vs. 0.879; p 0.001). At the Youden-optimized cut-off of 86.5 points, the ITAS demonstrated high sensitivity (93.2%, 95% CI: 84.7-97.7%) and absolute specificity (100%, 95% CI: 98.9-100.0%), enabling the definitive identification of incorrect inhaler technique. Conclusion In a cohort of 405 patients, the weighted scoring system of the ITAS demonstrated superior performance over traditional methods in detecting critical inhaler errors. By prioritizing key errors and incorporating an optimized threshold, ITAS enables rapid clinical assessment and effectively identifies individuals requiring re-training. Future studies are required to focus on validating device-specific benchmarks across broader and more diverse populations. This abstract is funded by: None
Wu et al. (Fri,) studied this question.