Abstract
Background
Chronic obstructive pulmonary disease (COPD) exacerbations cause considerable morbidity and mortality. We sought to identify a panel of urine biomarkers that can distinguish between stable and exacerbation states and predict risk of future exacerbations.
Methods
A retrospective discovery study was done measuring 35 biomarkers implicated in COPD pathogenesis in paired urine samples from 55 COPD subjects during stable and exacerbation states. A logistic regression model combining the 10 most discriminatory biomarkers in distinguishing between stable and exacerbation states was developed as a near-patient dipstick test with an opto-electronic reader. This biomarker panel was tested in a prospective study of 105 COPD subjects who undertook daily home urine testing over 6 months. The regression model was validated in paired samples from 26 individuals out of 105. An artificial neural network (ANN) using the urine biomarkers from 85 out of 105 subjects was developed and tested as a clinical decision tool to predict risk of an exacerbation.
Results
The 10-biomarker panel (NGAL, TIMP1, CRP, fibrinogen, CC16, fMLP, TIMP2, A1AT, B2M, and MMP8) was able to distinguish exacerbation versus stable state in the discovery study (ROC with an AUC 0.84 [95% CI, 0.76 to 0.92; p <0.01]), and validation study (AUC 0.81 [95% CI; 0.70 to 0.92, p<0.01]). The ANN model predicted an exacerbation within a 13-day window frame with an AUC 0.89 (95% CI 0.89–0.90) and identified an exacerbation median (interquartile range) 7 (5 to 9) days prior to clinical diagnosis.
Conclusion
We identified a panel of biomarkers that can distinguish between stable and exacerbation state and using an artificial neural network model, it can predict exacerbations before symptoms occur.
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Footnotes
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Conflict of Interest: A. J. Yousuf has nothing to disclose.
Conflict of Interest: G. Parekh reports support for the present study from GADx, SBRI/Innovate UK.
Conflict of Interest: M. Farrow reports support for the present study from SBRI/Innovate UK.
Conflict of Interest: G. Ball has nothing to disclose.
Conflict of Interest: S. Graziadio reports support for the present study from NIHR MIC.
Conflict of Interest: K. Wilson has nothing to disclose.
Conflict of Interest: C. Lendrem has nothing to disclose.
Conflict of Interest: L. Carr has nothing to disclose.
Conflict of Interest: L. Watson reports support for the present study from GADx, SBRI/Innovate UK.
Conflict of Interest: S. Parker has nothing to disclose.
Conflict of Interest: J. Finch has nothing to disclose.
Conflict of Interest: S. Glover has nothing to disclose.
Conflict of Interest: V. Mistry has nothing to disclose.
Conflict of Interest: K. Porter has nothing to disclose.
Conflict of Interest: A. Duvoix reports support for the present study from GADx, SBRI/Innovate UK.
Conflict of Interest: L. O'Brien has nothing to disclose.
Conflict of Interest: S. Rees has nothing to disclose.
Conflict of Interest: K. E. Lewis reports support for the present study from Mologic/NIHR, payment or honoraria for lectures, presentations, manuscript writing, or educational events from AstraZeneca, GSK, and Chiesi, support for attending meetings from AstraZeneca, GSK, and Chiesi, and receives payment as Medical Director of Respiratory Innovation Wales, a not-for-profit company set up by the Welsh Government.
Conflict of Interest: P. Davis reports support for the present study from GADx, SBRI/Innovate UK.
Conflict of Interest: C. E. Brightling reports support for the present study from Mologic Ltd, SBRI/Innovate UK and NIHR BRC, grants from GSK, AstraZeneca, Boehringer Ingelheim, Chiesi, Regeneron, Sanofi, Genentech, Roche, Novartis, and Mologic, consultancy fees from GSK, AstraZeneca, Boehringer Ingelheim, Chiesi, Regeneron, Sanofi, Genentech, Roche, Novartis, Mologic and Areteia.