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Chronic obstructive pulmonary disease (COPD) is characterized by chronic respiratory symptoms and airflow limitation, resulting from abnormalities in the airway and/or damage to the alveoli. Primary care physicians manage the healthcare of a large proportion of patients with COPD. In addition to determining the most appropriate medication regimen, which usually includes inhaled bronchodilators with or without inhaled corticosteroids, physicians are charged with optimizing inhalation device selection to facilitate effective drug delivery and patient adherence. The large variety of inhalation devices currently available present numerous challenges for physicians that include: (1) gaining knowledge of and proficiency with operating different device classes; (2) identifying the most appropriate inhalation device for the patient; and (3) providing the necessary education and training for patients on device use. This review provides an overview of the inhalation device types currently available in the United States for delivery of COPD medications, including information on their successful operation and respective advantages and disadvantages, factors to consider in matching a device to an individual patient, the need for device training for patients and physicians, and guidance for improving treatment adherence. Finally, the review will discuss established and novel tools and technology that may aid physicians in improving education and promoting better adherence to therapy.

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BackgroundAlthough asthma and chronic obstructive pulmonary disease (COPD) are clinically distinct diseases, they represent biologically diverse and overlapping clinical entities and it has been observed that they often co-occur. Some research and theorizing suggest there is a common comorbid condition termed asthma-chronic obstructive pulmonary disease overlap (ACO). However, the existence of ACO is controversial.ObjectiveThe objective of this study is to describe patient characteristics and estimate prevalence, health care utilization, and costs of ACO using claims-based diagnoses confirmed with medical record information.MethodsEligible patients were commercial US health plan enrollees; ≥40 years; had asthma, COPD, or ACO; ≥3 prescription fills for asthma/COPD medications; and ≥2 spirometry tests. Records for a random sample of 5000 patients with ACO were reviewed to validate claims-based diagnoses.ResultsThe estimated ACO prevalence was 6% (estimated 10,250/183,521) among 183,521 full study patients. In the claims-based cohorts, the comorbidity burden for ACO was greater versus asthma but similar to COPD cohorts. Medication utilization was higher in ACO versus asthma and COPD. Mean total health care costs were significantly higher for ACO versus asthma but similar to COPD. In confirmed diagnoses cohorts, mean total health care costs (medical plus pharmacy) were lower for ACO versus COPD but similar to asthma (US $20,035; P=.56). Among confirmed cases, where there was medical record evidence, smoking history was higher in ACO (300/343, 87.5%) versus asthma cohorts (100/181, 55.2%) but similar to COPD (68/84, 81%).ConclusionsACO had more comorbidities, medication utilization, and costs than patients with asthma or COPD but differences were not seen after confirmation with medical records.

Purpose Machine learning models informed by sensor data inputs have the potential to provide individualized predictions of asthma deterioration. This study aimed to determine if data from an integrated digital inhaler could be used to develop a machine learning model capable of predicting impending exacerbations. Patients and Methods Adult patients with poorly controlled asthma were enrolled in a 12-week, open-label study using ProAir ® Digihaler ® , an electronic multi-dose dry powder inhaler (eMDPI) with integrated sensors, as reliever medication (albuterol, 90 µg/dose; 1–2 inhalations every 4 hours, as needed). Throughout the study, the eMDPI recorded inhaler use, peak inspiratory flow (PIF), inhalation volume, inhalation duration, and time to PIF. A model predictive of impending exacerbations was generated by applying machine learning techniques to data downloaded from the inhalers, together with clinical and demographic information. The generated model was evaluated by receiver operating characteristic area under curve (ROC AUC) analysis. Results Of 360 patients included in the predictive analysis, 64 experienced a total of 78 exacerbations. Increased albuterol use preceded exacerbations; the mean number of inhalations in the 24-hours preceding an exacerbation was 7.3 (standard deviation 17.3). The machine learning model, using gradient-boosting trees with data from the eMDPI and baseline patient characteristics, predicted an impending exacerbation over the following 5 days with an ROC AUC of 0.83 (95% confidence interval: 0.77–0.90). The feature of the model with the highest weight was the mean number of daily inhalations during the 4 days prior to the day the prediction was made. Conclusion A machine learning model to predict impending asthma exacerbations using data from the eMDPI was successfully developed. This approach may support a shift from reactive care to proactive, preventative, and personalized management of chronic respiratory diseases.