COPD is one of the leading causes of death worldwide, so it represents a significant public health challenge. Over the years, new effective therapies have been proposed. However, the burden of COPD is still conditioned by frequent acute events defined as exacerbations (exacerbation of COPD-ECOPD), which have a significant impact not only on the patient’s quality of life but also on the progression of the disease, morbidity, and mortality. Related to the severity of the condition, ECOPD may require hospital admission and often repeatedly more admissions (readmission). The phenomenon of readmissions is a significant problem, contributing substantially to the utilisation of healthcare resources and the economic burden of COPD. Related contributing factors are still poorly understood, and managing the patients readmitted to the hospital with ECOPD may be challenging. Hospital readmissions should be optimally managed, including supporting and preventive strategies. Although early readmissions (30 days from discharge) are a marker of the quality of the patient’s care, we need to consider COPD patients globally. It is not sufficient to address just the acute events, so multidimensional management is necessary, able to follow the patient over time to identify, by a personalised approach, the correct treatment during and post hospitalisation and intercept any factor affecting the natural history of the disease, comprising the risk of hospital readmissions. In the context of the literature concerning respiratory medicine, particularly COPD patients, our narrative review analyses recent evidence regarding the current management of COPD hospital readmissions, aiming to propose preventive strategies helpful in clinical practice. The proposed strategies can potentially improve clinical outcomes and reduce healthcare costs when effectively implemented in practice.

Background/Objectives: Daytime sleepiness in obstructive sleep apnea (OSA) shows substantial variability and is not fully explained by disease severity. This study aimed to evaluate whether a history of cancer is associated with subjective daytime sleepiness independently of respiratory burden. Methods: In this observational cohort study, 402 untreated patients with OSA were included. Cancer history was defined as a documented diagnosis of malignancy prior to baseline polygraphy. Daytime sleepiness was assessed using the Epworth Sleepiness Scale (ESS). Multivariable linear and logistic regression models were used to evaluate the association between cancer history and ESS (continuous and dichotomized as >10), adjusting for age, sex, body mass index, smoking status, chronic obstructive pulmonary disease, and apnea–hypopnea index (AHI). Sensitivity analyses additionally adjusted for heart disease and nocturnal hypoxic burden. Results: Sixty-two patients (15%) had a history of cancer. OSA severity and hypoxemia indices were comparable between groups. In multivariable analysis, cancer history was independently associated with modestly lower ESS scores (B = −1.66, 95% confidence interval [CI] −2.96 to −0.37; p = 0.012) and a reduced likelihood of excessive daytime sleepiness (ESS > 10) (odds ratio [OR] = 0.25, 95% CI 0.07 to 0.85; p = 0.027). Conclusions: In this real-world cohort of untreated patients with OSA, cancer history is associated with modestly lower subjective daytime sleepiness despite comparable disease severity, supporting a potential dissociation between physiological burden and symptom perception. These findings indicate that reliance on subjective sleepiness alone may contribute to under-recognition of clinically relevant OSA in patients with a cancer history.

Chronic obstructive pulmonary disease (COPD) represents an escalating global health challenge characterized by profound clinical and biological heterogeneity. Conventional diagnostic paradigms, primarily reliant on spirometric criteria and broad phenotypic labels, often fail to capture the complex molecular mechanisms underlying effective precision medicine. This narrative review synthesizes the evolving landscape of COPD characterization, analyzing the integration of biomarkers, advanced quantitative imaging, and multi-omics technologies. Key developments highlighted include the clinical validation of biologics targeting type 2 inflammation, which reinforce the paradigm shift from generic symptomatic management toward the identification of specific treatable traits. We further explore the role of artificial intelligence and deep learning in enhancing radiological precision and body composition analysis. Ultimately, this work proposes a transition toward a GETomics (Genetics, Environment, and Time) framework as a fundamental prerequisite for transcending the limitations of traditional classification systems and delivering truly personalized care in the 21st century.