Predicting the Risk of Incorrect Inhalation Technique in Patients with Chronic Airway Diseases by a New Predictive Nomogram

Abstract

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.

Real-world data: A relevant component in the framework of scientific evidence

“…Opposite data are usually detected in RCTs, where low adherence is a criterion for excluding patients from the study evaluation. Some tools, such as the one (a Predictive Nomogram) described recently by Chen [ 5 ], are able to predict the patient risk of incorrect inhalation technique. Adherence to treatment and new technologies has been and still is a hot topic [ 6 ]; in a validation trial, they described a new tool for inhalers’ use and adherence monitoring, suggesting that a new technology tool allows identifying, storing, and communicating data for adherence improvement and misuse correction.…”

“… Illustration of a digital health solution that could support optimal medication use and therefore disease control in patients with asthma. Modified from Rumi et al [ 5 ]. …”