Purpose To develop and internally validate a nomogram for predicting the risk of incorrect inhalation techniques in patients with chronic airway diseases. Methods A total of 206 patients with chronic airway diseases treated with inhaled medications were recruited in this study. Patients were divided into correct (n=129) and incorrect (n=77) cohorts based on their mastery of inhalation devices, which were assessed by medical professionals. Data were collected on the basis of questionnaires and medical records. The least absolute shrinkage and selection operator method (LASSO) and multivariate logistic regression analyses were conducted to identify the risk factors of incorrect inhalation techniques. Then, calibration curve, Harrell’s C-index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and bootstrapping validation were applied to assess the apparent performance, clinical validity and internal validation of the predicting model, respectively. Results Seven risk factors including age, education level, drug cognition, self-evaluation of curative effect, inhalation device use instruction before treatment, post-instruction evaluation and evaluation at return visit were finally determined as the predictors of the nomogram prediction model. The ROC curve obtained by this model showed that the AUC was 0.814, with a sensitivity of 0.78 and specificity of 0.75. In addition, the C-index was 0.814, with a Z value of 10.31 (P<0.001). It was confirmed to be 0.783 by bootstrapping validation, indicating that the model had good discrimination and calibration. Furthermore, analysis of DCA showed that the nomogram had good clinical validity. Conclusion The application of the developed nomogram to predict the risk of incorrect inhalation techniques during follow-up visits is feasible.

BACKGROUND: At present, robust quality criteria and methods for the assessment of Peak inspiratory flow meter performance are lacking. OBJECTIVE: A standard flow-volume simulator for quality control analyses of an inhalation assessment device was utilized with different simulated resistance levels in order to propose a quality testing method and associated standard for this device type. METHODS: A standard flow-volume simulator was utilized to assess the performance of an In-Check DIAL® (Device I) and an intelligent inhalation assessment device (Device P) at a fixed volume and flow rate. Indices used to evaluate these two instruments included repeatability, accuracy, linearity, and impedance. RESULTS: Both devices exhibited good repeatability (<± 3 L/min). The difference between test results and standard simulator values for Device P was less than ± 5 L/min at resistance level R1 but higher than ± 5 L/min at resistance levels R2–5, while Device I were greater than 5 L/min at all resistance levels. The relative error for Device P was <± 10% at resistance levels R1, R2, and R4, but > 10% at resistance levels R3 and R5. The relative error values for Device I at all five resistance levels were > 10%. Device P passed the linearity test at the R2 resistance level, while Device I partially passed the linearity test at all five resistance levels. CONCLUSION: Standard monitoring methods and standards provide a valuable approach to the more reliable clinical assessment and application of these instruments.