Transthoracic ultrasound features to differentiate tuberculous from malignant pleural effusion: development of a machine learning-based clinical prediction model

Résumé fourni par la source

Background: MPE and incorporates clinical and pleural fluid (Pf) parameters into a machine learning-based random forest (RF) model for differentiation. Methods: This prospective observational study was conducted over 6 months across six tertiary hospitals in Malaysia, involving patients undergoing diagnostic medical thoracoscopy (MT). Diagnostic confidence for TBE and MPE was determined based on final thoracoscopic diagnosis. Logistic regression and RF models were applied to predefined clinical, imaging, and laboratory variables. Results: 87.9% accuracy), even without Pf ADA. Conclusions: TUS parameters can aid in differentiating TBE from MPE. The RF model demonstrated superior diagnostic performance, offering a potential point-of-care tool, even in the absence of Pf ADA.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.