Analysis of the Clinical Value of Laboratory Testing in the Diagnosis of Respiratory Diseases

Objective To investigate the differences in laboratory indicators for diagnosing respiratory diseases and evaluate their clinical utility in differential diagnosis and disease severity assessment. Methods A retrospective analysis was conducted on laboratory data from 377 patients with respiratory diseases treated at Anhui Chest hospital in 2024. Parameters included T lymphocyte subsets, inflammatory markers, and nutritional indicators. Analysis of variance (ANOVA) compared inter-group differences. K-means clustering and principal component analysis (PCA) assessed the diagnostic value of these indicators for disease classification. Results Patients with severe pneumonia exhibited significantly lower levels of total T lymphocytes and CD4 + T cells compared to those with pulmonary tuberculosis (TB) and other lung infections (P < 0.05). CD8 + T cell levels were significantly reduced in non-tuberculous mycobacterial (NTM) infection versus lung cancer and severe pneumonia (P < 0.05). Markedly elevated levels of procalcitonin (PCT), white blood cell count (WBC), absolute neutrophil count (ANC), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were observed in severe pneumonia compared to other groups (P < 0.05), indicating a severe acute inflammatory response. Absolute monocyte count was significantly lower in TB than in other diseases (P < 0.05). Significant reductions in red blood cells (RBC), hemoglobin (Hb), total protein (TP), and albumin (ALB) were found in severe pneumonia patients (P < 0.05), reflecting a state of severe metabolic consumption. Declines in nutritional indicators were less pronounced in chronic conditions like lung cancer and TB. Clustering analysis effectively differentiated the seven disease types. PCA extracted eight components explaining 75.91% of the variance, identifying WBC, ANC, and RBC as core discriminatory indicators. Conclusion Laboratory indicators hold significant value for pathogen differentiation, assessment of infection severity, and nutritional status monitoring in respiratory diseases. Multi-indicator analysis can aid clinical diagnosis and provide data support for developing precise diagnostic models.