CLINICAL AND LABORATORY INFORMATION TO PR EDICT DIAGNOSIS AND SEVERITY OF FIBROSIS IN DIFFUSE INTERSTITIAL LUNG-DISEASES

Our objective was to assess the capacity of clinical and laboratory in formation to predict findings in the lung biopsy in interstitial lung diseases (ILD). We studied 121 patients with ILD as a cohort recruited in our institute from 1983 to 1987 with the diagnosis of hypersensiti vity pneumonitis (HP) and usual interstitial pneumonia (UIP). Hystolog ic diagnosis (HP vs UIP) and degree of fibrosis (<50% of the biopsy su rface vs greater than or equal to 50%) were used as the gold standard to compare a series of clinical and laboratory variables in the initia l assessment. We used a stepwise logistic regression model to predict the biopsy results. The model was calculated in half of the patients s elected by random sampling, and the calculated model was tested in the other half of the patients. Variables found to predict degree of fibr osis were (with relative risk RR and 95% confidence interval): a radio graphic pattern of honeycombing (RR 5.0 from 0.9-29), digital clubbing (RR 8 from 1.4-48) and gender (RR 2.9 from 0.4-20). This model classi fied correctly 72% of the biopsies, with a sensitivity of 0.38, a spec ificity of 0.85 and a kappa of 0.25 +/- 0.19 (p = 0.17 NS). For hystol ogic diagnosis (NIU vs NH), the model included gender (RR 6.6, 1.3-33) , honeycombing (RR 1.6, from 0.4-6.0), digital clubbing (RR 4.6, from 1.2-18), and vital capacity expressed as percent of predicted (RR 0.96 , from 0.92-1.00). Using this model, 72% of the sample was classified correctly, with a sensitivity of 0.56, a specificity of 0.82, and a ka ppa of 0.39 +/- 0.14 (p = 0.008). The model improved the prediction of results expected by chance in the biopsy but with a low effectivity. It was possible to separate groups with a very high or very low risks (extremes), but most of the information pf the lung biopsy cannot be o btained from the clinical and laboratory information we used in our st udy.