Diagnostic Challenges in the Differential Diagnosis of Chronic Inflammatory Respiratory Diseases: Asthma versus Chronic Obstructive Pulmonary Disease. A literature review

Abstract

Chronic inflammatory respiratory diseases—particularly asthma and chronic obstructive pulmonary disease (COPD)—represent a significant burden on healthcare systems worldwide. More than 350 million people are affected by asthma, while COPD is the third leading cause of death globally. Despite distinct pathophysiological mechanisms, the clinical manifestations of these diseases often overlap in everyday practice, making accurate differential diagnosis challenging and predisposing to therapeutic errors. Differential diagnosis is crucial due to important differences in treatment strategies, disease progression, and clinical course. Misdiagnosis may result in inappropriate therapeutic choices, which can worsen disease outcomes and increase the risk of exacerbations and hospitalizations, whereas early detection may prevent such consequences. Spirometry remains the cornerstone of diagnosis for both asthma and COPD. However, each patient requires an individualized approach that takes into account current medications, age, comorbidities, disease duration, and smoking history. Specialists have access to a broad range of diagnostic tools for differentiation—such as FeNO, HRCT, sputum eosinophil count, questionnaire and PEF, IA but the most fundamental and valuable source of diagnostic information remains continuous patient observation, assessment of treatment response, symptom progression, and a detailed medical history obtained from the patient and their close contacts.

Conclusion

Effective differential diagnosis of asthma and COPD requires a combination of functional testing, inflammatory biomarkers, and a thorough patient history. Implementation of spirometry in primary care and broader use of biomarkers (e.g., FeNO, eosinophils) in clinical practice are recommended. In the future, prospective studies on novel biomarkers and the application of artificial intelligence may improve diagnostic accuracy and enable more personalized therapy.