Imperial College London
Alternate

Dr Melody Zhifang Ni

Faculty of Medicine, Department of Surgery & Cancer

Senior Research Fellow
 
 
 
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Contact

 

+44 (0)20 3312 7657z.ni

 
 
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Location

 

Queen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Shah:2017:10.1136/thoraxjnl-2017-210983.23,
author = {Shah, A and Abdolrasouli, A and Schelenz, S and Thornton, C and Ni, MZ and Devaraj, A and Devic, N and Ward, L and Carby, M and Reed, A and Costelloe, C and Armstrong-James, D},
doi = {10.1136/thoraxjnl-2017-210983.23},
pages = {A13--A14},
publisher = {BMJ PUBLISHING GROUP},
title = {Latent class modelling for pulmonary aspergillosis diagnosis in lung transplant recipients},
url = {http://dx.doi.org/10.1136/thoraxjnl-2017-210983.23},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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 =<0.001) and composite biomarker positive Results (P =<0.001) had significantly increased 12 month mortality. There was poor correlation between clinical diagnosis, EORTC-based IA diagnosis and composite biomarker positivity. Tracheobronchitis was positively predictive of a clinical and composite biomarker positive diagnosis of IA (p=0.004;95% CI–1.79–21.28 and p=0.03;95% CI–0.85–15.62 respectively). Latent class modelling resulted in the formation of 3 groups: Class 1: likely fungal infection; Class 2: unlikely fungal infection; Class 3: unclassifiable. A. fumigatus PCR was positive in ∼90% of class 1 LTRs compared to only 1% in class 2. Analysis of mortality showed a trend towards significance comparing class 1 with class 2 (p=0.06;HR–4.7;95% CI(0.91–24)) (figure 1).
AU  - Shah,A
AU  - Abdolrasouli,A
AU  - Schelenz,S
AU  - Thornton,C
AU  - Ni,MZ
AU  - Devaraj,A
AU  - Devic,N
AU  - Ward,L
AU  - Carby,M
AU  - Reed,A
AU  - Costelloe,C
AU  - Armstrong-James,D
DO  - 10.1136/thoraxjnl-2017-210983.23
EP  - 14
PB  - BMJ PUBLISHING GROUP
PY  - 2017///
SN  - 0040-6376
SP  - 13
TI  - Latent class modelling for pulmonary aspergillosis diagnosis in lung transplant recipients
UR  - http://dx.doi.org/10.1136/thoraxjnl-2017-210983.23
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000432666600024&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/63539
ER  -
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