Latent class modelling for pulmonary aspergillosis diagnosis in lung transplant recipients
| Autor: | Shah, A, Abdolrasouli, A, Schelenz, S, Thornton, C, Ni, MZ, Devaraj, A, Devic, N, Ward, L, Carby, M, Reed, A, Costelloe, C, Armstrong-James, D |
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| Přispěvatelé: | National Institute for Health Research |
| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Winter Meeting of the British-Thoracic-Society A14 A13 |
| Popis: | Rationale Timely, accurate diagnosis of invasive aspergillosis (IA) is key to enable initiation of antifungal therapy in lung transplantation. Despite promising novel fungal biomarkers, the lack of a diagnostic gold-standard creates difficulty in determining utility. Objectives This study aimed to use latent class modelling of fungal diagnostics to classify lung transplant recipients (LTR) with IA in a large single centre. Methods Regression models were used to compare composite biomarker testing of bronchoalveolar lavage to clinical and EORTC-MSG guideline-based diagnosis of IA with mortality used as a surrogate primary outcome measure. Bootstrap analysis identified radiological features associated with IA. Bayesian latent class modelling was used to define IA. Measurements and Main Results A clinical diagnosis of fungal infection (P = |
| Databáze: | OpenAIRE |
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