Background: Despite the ongoing innovations and the availability of numerous effective inhaled treatment options, achieving optimal disease control in most patients frequently remains disappointing. Unfortunately, although inhaled therapy is the cornerstone of respiratory disease management, the selection of the most appropriate inhaler is still overlooked or underestimated by some healthcare professionals, and inhaler misuse remains a significant challenge in managing chronic respiratory diseases which directly influences patients’ quality of life, clinical outcomes, and risk of disease progression. Materials and Methods: This is a unicentric, observational, cross-sectional study designed to evaluate the inhaled therapy prescribed in hospitalized patients and to analyze device changes after hospitalization, as well as the factors associated with these changes. A single face-to-face visit was performed during the patient’s hospitalization, where the inhaled therapy used prior to hospitalization was evaluated: technique (critical errors), compliance (TAI questionnaire), maximum peak inspiratory flow [PIF (L/min)], and level of inhaler handling-related knowledge. A binary logistic regression model was used to explore the association between changing device at discharge and the other independent variables Results: The inhaler most used during hospitalization was the metered-dose inhaler (MDI) with a chamber (51.9% of patients), with the dry powdered inhalers (DPI) being the inhalers used in 43% of maintenance inhaled therapies in the community setting prior to hospitalization. In addition, 90% of patients showed a maximum PIF ≥ 30 L/min, and 35.6% performed critical inhaler errors. These patients had statistically significantly lower maximum PIF values (52.1 L/min in patients with critical inhaler errors vs. 60.8 L/min without critical inhaler errors; p > 0.001) and were more likely to exhibit poor inhaler compliance compared to those without critical errors (50.5% vs. 31.0%, respectively). More than half of the patients who used MDI with spacer chamber made critical inhaler errors; 69.9% showed regular or poor treatment adherence, although 75.6% demonstrated good knowledge about inhaler handling. Only in 27% of the patients did the healthcare professional change the type of inhaler after hospitalization within clinical practice. The medical and nursing staff responsible for the patient’s hospitalization were not informed of the assessment carried out in the study. The probability of not performing a device change at discharge was lower in patients with previous at-home treatment with combined inhaled therapy with LABA + ICS (OR 0.3 [0.18–0.83], p = 0.016) and in patients under triple inhaled therapy (OR 0.3 [0.17–0.76], p = 0.007). No significant differences were observed in inhaler changes when considering the frequency of critical inhaler errors, inhaler handling-related knowledge or maximum PIF values. Conclusions: Our study highlights the urgent need for a more personalized inhaler selection and consistent monitoring by healthcare professionals to minimize inhaler misuse, increase treatment compliance and adherence, and improve disease management outcomes. It is essential to provide training and promote the role of nursing in the evaluation and education of inhaled therapy. Additionally, the use of standardized approaches and tools, such as the CHECK DIAL, is crucial to facilitate the adaptation of devices to patients’ needs.

Background: Hospitalized patients on chronic inhaled therapy suffer high rates of therapeutic failure. Current approaches often overlook patient heterogeneity, treating failure as a uniform problem. We hypothesized that clinical inertia, a key driver of failure, is not a monolithic entity but is governed by specific, non-overlapping factors. Methods: In this unicentric, observational cohort study of 499 hospitalized adults on chronic inhaled therapy, we used unsupervised clustering to identify patient phenotypes. Multivariable logistic regression was used to model predictors of critical inhaler errors and three distinct forms of clinical inertia: Therapeutic Class (TCI), Device-Level (DLI), and Adherence-Related (ARI). Results: Inhaler misuse was driven by objective capability—deficient knowledge (aOR 6.03, 95% CI 2.88–12.64) and low inspiratory flow (aOR 3.11, 95% CI 1.06–9.12)—while patient-reported adherence was not a significant independent predictor. Crucially, the three forms of clinical inertia were governed by distinct, non-overlapping predictors: TCI was predicted by high therapeutic potency (aOR 7.80, 95% CI 3.65–16.64), DLI by a failure in the clinical process (lack of patient training, aOR 3.49, 95% CI 1.21–10.03), and ARI by regimen complexity (aOR 0.06, 95% CI 0.02–0.25). Post-discharge mortality (21.6% overall; 25.8% in Cluster 1 vs. 18.3% in Cluster 2) was independently predicted by objective risk factors, including older age (aOR 1.51, 95% CI 1.20–1.89) and an unassessed inspiratory flow (aOR 2.44, 95% CI 1.19–5.03). Two underlying patient phenotypes were identified—an “Unassessed/Older” (n = 225) and an “Assessed/Younger” (n = 274)—which represented distinct in-hospital care pathways but did not independently predict mortality after multivariate adjustment. Conclusions: Therapeutic failure in hospitalized patients is a predictable outcome driven by distinct, non-overlapping factors. This study deconstructs this failure by identifying the specific, actionable drivers of inhaler misuse (patient capability) and the three forms of clinical inertia (therapeutic potency, failures in the care process, and regimen complexity). These processes occur within two distinct patient phenotypes that represent different in-hospital care pathways. Our findings provide a new framework to move beyond generic interventions toward a more precise, evidence-based approach to inhaled therapy.

Background: Inhaler device changes at hospital discharge should address patient capacity yet often reflect routine. We evaluated the appropriateness of these decisions and their impact on clinical outcomes. Methods: In this prospective observational study (N = 480), we assessed patient technical capacity using a composite of critical errors, inspiratory flow, adherence, and knowledge. We stratified patients into ‘Need-Positive’ and ‘Need-Negative’ cohorts to quantify patterns of clinical inertia and over-adjustment. Multivariable models identified predictors of decision-making and associations with 30-day outcomes. Results: Device changes were primarily determined by the pre-admission device class (spacers: aOR 0.52; 95% CI 0.28–0.96; p = 0.037) and by the patient’s treatment pathway rather than by clinical need. This disconnect generated two types of errors: 38.3% of Need-Positive patients (n = 214) experienced clinical inertia (no corrective action), while 36.8% of Need-Negative patients (n = 266) underwent over-adjustment (unnecessary switching). Inertia perpetuated errors in patients with need, whereas over-adjustment was associated with the emergence of new errors in patients without need. Successful mismatch resolution was associated with a significantly lower 30-day readmission rate (12.1% vs. 32.5%; OR 0.48; 95% CI 0.26–0.88; p = 0.017). Conclusions: Discharge prescribing is driven more by habit than by objective assessment, leading to widespread missed opportunities for correction. Implementing evidence-based protocols to identify and resolve patient–device mismatches may represent a high-impact strategy to reduce readmissions and associated healthcare use.

Introduction: Despite the ongoing innovations and the availability of numerous effective inhaled treatment options, achieving optimal disease control in most patients remains frequently disappointing. Unfortunately, although inhaled therapy is the cornerstone of respiratory disease management, the selection of the most appropriate inhaler is still overlooked or underestimated by some healthcare professionals, and inhaler misuse remains a significant challenge in managing chronic respiratory diseases, which impacts directly disease outcomes. Objectives/methods: In this real-world study, we investigate the most prescribed inhalers for both ambulatory and hospitalized patients, their associated critical inhaler use and treatment adherence, the frequency of inhaler switches after hospitalization, and the factors associated with these changes. Results: In our cohort of patients, we observed a high prevalence of critical inhaler errors, independently of good inhaler-handling knowledge. Most patients with a history of at-home inhaler use showed a maximum peak inspiratory flow (PIF)≥30 L/min, suggesting they were more likely to benefit from using dry powdered inhalers (DPIs). Despite the high rate of critical inhaler errors, only a small proportion of patients switched their inhaler type after hospitalization, being these changes predominantly associated to the type of therapy prescribed, regardless of patient misuse, compliance, or adherence to treatment. Conclusions: Our study highlights the urgent need for a more personalized inhaler selection and consistent monitoring by healthcare professionals to minimize inhaler misuse, increase treatment compliance and adherence, and improve disease management outcomes.