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The following is a summary of “Respiratory abnormalities in sarcoidosis: physiopathology and early diagnosis using oscillometry combined with respiratory modeling,” published in the February 2025 issue of the BMC Pulmonary Medicine by Oliveira et al.
Sarcoidosis is a complex multisystemic disorder of unknown etiology, with pulmonary involvement occurring in approximately 90% of cases. Spirometry remains the standard tool for assessing lung function in the diagnosis and monitoring of sarcoidosis; however, it has limitations in detecting subtle respiratory abnormalities. Respiratory oscillometry has emerged as a promising alternative, providing a more comprehensive evaluation of respiratory mechanics. Integer-order and fractional-order modeling techniques offer an advanced framework for interpreting oscillometric data, enabling a more detailed characterization of the pathophysiological changes associated with sarcoidosis. This study aimed to refine the understanding of these changes and evaluate the diagnostic accuracy of integer-order and fractional-order models in individuals with sarcoidosis.
A total of 75 participants were included: 25 healthy controls and 50 individuals with sarcoidosis, who were further categorized into those with normal spirometry (SNS) and those with abnormal spirometry (SAS). Diagnostic accuracy was assessed using receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) as a key metric. The integer-order model revealed significant increases in airway and total resistance in both SNS and SAS groups, with the SAS group additionally demonstrating reduced compliance and elevated peripheral resistance (p < 0.001). The fractional-order model indicated increased energy dissipation and hysteresivity in patients with sarcoidosis. Correlation analysis identified strong associations between model-derived parameters and spirometric measures, with total resistance showing a notable inverse correlation with forced expiratory volume in one second (FEV1) (r = -0.600, p = 0.0001).
The diagnostic performance analysis highlighted total resistance and hysteresivity as the most effective parameters for distinguishing patients with sarcoidosis, with AUC values of 0.986 in the SNS group and 0.938 in the SAS group. These findings suggest that integer-order and fractional-order models enhance the evaluation of pulmonary mechanics in sarcoidosis beyond conventional spirometry. The ability of total resistance and hysteresivity to detect sarcoidosis, even in patients with normal spirometry, highlights their potential as early diagnostic markers. Incorporating these modeling techniques into clinical practice may facilitate more precise disease assessment and improve patient management. Further validation through larger studies is warranted to establish their clinical utility and integration into routine diagnostic protocols.
Source: bmcpulmmed.biomedcentral.com/articles/10.1186/s12890-025-03510-6
