A clinical diagnostic model for the assessment of asbestosis: A new algorithmic approach

Abstract

Asbestosis, one of the pneumoconioses that is defined by a set of clinical, radiographic, and pathologic findings, occurs as a result of exposure to asbestos fibers. Several approaches have attempted to describe the presence, progression, or extent of asbestosis. However, these approaches have limitations or lack correlations with other diagnostic modalities. We propose a comprehensive clinical diagnostic model that uses the sensitivities and specificities of the various clinical, radiographic, and pathologic findings to generate a set of “likelihood numbers.” These likelihood numbers contribute to the calculation of a value that can indicate the probability of asbestosis. The clinical diagnostic model is heuristic in that a specific feature supportive of the diagnosis of asbestosis may be tested as to its sensitivity and specificity, and new features may be added to the model. The model also indicates how probabilistic a given set of findings is in the diagnosis of asbestosis and suggests what additional data may make the diagnosis more or less statistically probable. Regarding the radiologic considerations of asbestosis, the strength of the clinical diagnostic model is that it is capable of supporting a diagnosis of asbestosis in the presence of a normal chest radiograph and, conversely, may reject the diagnosis of asbestosis despite the radiographic finding of pulmonary fibrosis.