In-hospital Mortality in Hospitalized Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease and Type 2 Diabetes Mellitus: A Multicenter Validation Study of a Clinical Prediction Model

Background Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) frequently coexists with type 2 diabetes mellitus (T2DM) and is associated with substantial short-term mortality. However, patients with AECOPD and metabolic comorbidity represent a clinically heterogeneous subgroup, and admission-based, externally validated tools for early in-hospital mortality risk stratification in this population remain limited. Methods In this multicenter retrospective cohort study, hospitalized patients with AECOPD and concomitant T2DM were consecutively enrolled and divided into a training cohort and an independent validation cohort. Candidate predictors routinely available at admission were evaluated using multivariable logistic regression to develop a parsimonious prediction model for in-hospital mortality. Model performance was assessed in terms of discrimination, calibration, and clinical utility, and compared with established bedside scores, including the quick Sequential Organ Failure Assessment (qSOFA) and BAP-65. Results Elevated arterial carbon dioxide tension (PaCO₂), procalcitonin (PCT), and D-dimer measured early after admission were independently associated with in-hospital mortality. A simplified model incorporating these three variables demonstrated stable discrimination in both the training and validation cohorts, with an area under the receiver operating characteristic curve of approximately 0.79, and showed good calibration. Decision curve analysis indicated higher or non-inferior net clinical benefit across clinically relevant threshold probabilities compared with qSOFA and BAP-65. Conclusions In hospitalized patients with AECOPD and T2DM, PaCO₂, PCT, and D-dimer independently predict in-hospital mortality, collectively reflecting key pathophysiological pathways of ventilatory failure, infection-related inflammation, and coagulation activation. An early, admission-based model integrating these markers provides robust and clinically applicable risk stratification, outperforming commonly used bedside scores. This simplified tool may support early identification of high-risk patients and inform monitoring intensity and resource allocation in this high-risk population.