Maarten Lanclus1, Johan Clukers2, Cedric Van Holsbeke3, Wim Vos3, Glenn Leemans3, Birgit Holbrechts2, Katherine Barboza3, Wilfried De Backer4, Jan De Backer3. 1. FluidDA nv, Groeningenlei 132, 2550 Kontich, Belgium. Electronic address: maarten.lanclus@fluidda.com. 2. Faculty of Medicine and Health Sciences, University of Antwerp (UAntwerpen), Antwerpen, Belgium. 3. FluidDA nv, Groeningenlei 132, 2550 Kontich, Belgium. 4. FluidDA nv, Groeningenlei 132, 2550 Kontich, Belgium; Faculty of Medicine and Health Sciences, University of Antwerp (UAntwerpen), Antwerpen, Belgium.
Abstract
RATIONALE AND OBJECTIVES: Acute chronic obstructive pulmonary disease exacerbations (AECOPD) have a significant negative impact on the quality of life and accelerate progression of the disease. Functional respiratory imaging (FRI) has the potential to better characterize this disease. The purpose of this study was to identify FRI parameters specific to AECOPD and assess their ability to predict future AECOPD, by use of machine learning algorithms, enabling a better understanding and quantification of disease manifestation and progression. MATERIALS AND METHODS: A multicenter cohort of 62 patients with COPD was analyzed. FRI obtained from baseline high resolution CT data (unenhanced and volume gated), clinical, and pulmonary function test were analyzed and incorporated into machine learning algorithms. RESULTS: A total of 11 baseline FRI parameters could significantly distinguish ( p < 0.05) the development of AECOPD from a stable period. In contrast, no baseline clinical or pulmonary function test parameters allowed significant classification. Furthermore, using Support Vector Machines, an accuracy of 80.65% and positive predictive value of 82.35% could be obtained by combining baseline FRI features such as total specific image-based airway volume and total specific image-based airway resistance, measured at functional residual capacity. Patients who developed an AECOPD, showed significantly smaller airway volumes and (hence) significantly higher airway resistances at baseline. CONCLUSION: This study indicates that FRI is a sensitive tool (PPV 82.35%) for predicting future AECOPD on a patient specific level in contrast to classical clinical parameters.
RATIONALE AND OBJECTIVES: Acute chronic obstructive pulmonary disease exacerbations (AECOPD) have a significant negative impact on the quality of life and accelerate progression of the disease. Functional respiratory imaging (FRI) has the potential to better characterize this disease. The purpose of this study was to identify FRI parameters specific to AECOPD and assess their ability to predict future AECOPD, by use of machine learning algorithms, enabling a better understanding and quantification of disease manifestation and progression. MATERIALS AND METHODS: A multicenter cohort of 62 patients with COPD was analyzed. FRI obtained from baseline high resolution CT data (unenhanced and volume gated), clinical, and pulmonary function test were analyzed and incorporated into machine learning algorithms. RESULTS: A total of 11 baseline FRI parameters could significantly distinguish ( p < 0.05) the development of AECOPD from a stable period. In contrast, no baseline clinical or pulmonary function test parameters allowed significant classification. Furthermore, using Support Vector Machines, an accuracy of 80.65% and positive predictive value of 82.35% could be obtained by combining baseline FRI features such as total specific image-based airway volume and total specific image-based airway resistance, measured at functional residual capacity. Patients who developed an AECOPD, showed significantly smaller airway volumes and (hence) significantly higher airway resistances at baseline. CONCLUSION: This study indicates that FRI is a sensitive tool (PPV 82.35%) for predicting future AECOPD on a patient specific level in contrast to classical clinical parameters.
Authors: Christopher B Cooper; Worawan Sirichana; Michael T Arnold; Eric V Neufeld; Michael Taylor; Xiaoyan Wang; Brett A Dolezal Journal: Int J Chron Obstruct Pulmon Dis Date: 2020-08-24