This study conducted at Leamna Pulmonology Hospital investigated the interrelations among cognitive, affective, and respiratory variables within a cohort of 100 patients diagnosed with chronic respiratory conditions, utilizing sophisticated machine learning-based clustering techniques. Spanning from October 2022 to February 2023, hospitalized individuals confirmed to have asthma or COPD underwent extensive evaluations using standardized instruments such as the mMRC scale, the CAT test, and spirometry. Complementary cognitive and affective assessments were performed employing the MMSE, MoCA, and the Hamilton Anxiety and Depression Scale, furnishing a holistic view of patient health statuses. The analysis delineated three distinct clusters: Moderate Cognitive Respiratory, Severe Cognitive Respiratory, and Stable Cognitive Respiratory, each characterized by unique profiles that underscore the necessity for tailored therapeutic strategies. These clusters exhibited significant correlations between the severity of respiratory symptoms and their effects on cognitive and affective conditions. The results highlight the benefits of an integrated treatment approach for COPD and asthma, which is personalized based on the intricate patterns identified through clustering. Such a strategy promises to enhance the management of these diseases, potentially elevating the quality of life and everyday functionality of the patients. These findings advocate for treatment customization according to the specific interplays among cognitive, affective, and respiratory dimensions, presenting substantial prospects for clinical advancement and pioneering new avenues for research in the domain of chronic respiratory disease management.

Background/Objectives: In chronic obstructive pulmonary disease (COPD), forced expiratory volume in one second (FEV₁) explains only part of the variability in symptoms and prognosis, while anxiety and depression are common but rarely quantified in composite indices. We aimed to develop and internally validate an Integrated Pulmonary Severity Score (IPSS) that combines respiratory function, symptom burden and affective status. Methods: In a prospective observational study, 390 adults with spirometry-confirmed COPD were consecutively enrolled at two tertiary Romanian centres (October 2022–September 2024). Within 48 h of admission, patients underwent spirometry (FEV₁% predicted), dyspnoea grading (mMRC), symptom assessment (CAT), affective evaluation (HADS-Anxiety/Depression) and cognitive screening (MoCA, MMSE). A PulmoScore was built from CAT, mMRC and ventilatory deficit (100 − FEV₁%) and extended with a HADS-based psychiatric multiplier to obtain IPSS. Spectral clustering, logistic regression, a multilayer perceptron (MLP) and LIME were used for phenotyping and validation. Results: Spectral clustering identified two phenotypes—psycho-respiratory and predominantly respiratory—with acceptable separation (silhouette coefficient 0.26). The psycho-respiratory group showed lower FEV₁%, higher CAT and mMRC scores, more severe anxiety–depression and markedly higher IPSS values. Logistic regression and the MLP achieved an accuracy of 0.89, an AUC of 0.95 and Cohen’s κ ≥ 0.75 for identifying this phenotype when using the same core clinical variables that informed phenotyping and IPSS construction. IPSS values were distributed across four strata (<30, 30–69, 70–119, ≥120 points), reflecting progressively worse respiratory and affective burden. Conclusions: In this cohort, IPSS captured a clinically meaningful psycho-respiratory phenotype and improved integrated severity assessment beyond spirometry alone, with potential utility for risk stratification. It can be computed from routine measures, is compatible with explainable AI workflows and warrants external, longitudinal validation before widespread implementation.