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3. Spirometric indices of early airflow impairment in individuals at risk of developing COPD: Spirometry beyond FEV1/FVC

Author

Hoesterey, Daniel, Das, Nilakash, Janssens, Wim, Buhr, Russell G, Martinez, Fernando J, Cooper, Christopher B, Tashkin, Donald P, and Barjaktarevic, Igor

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Research, Chronic Obstructive Pulmonary Disease, Lung, 4.2 Evaluation of markers and technologies, 7.3 Management and decision making, 2.1 Biological and endogenous factors, Respiratory, Forced Expiratory Volume, Pulmonary Disease, Chronic Obstructive, Risk, Spirometry, Vital Capacity, Early COPD, Obstruction, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Respiratory System, Cardiovascular medicine and haematology
Abstract

Spirometry is the current gold standard for diagnosing and monitoring the progression of Chronic Obstructive Pulmonary Disease (COPD). However, many current and former smokers who do not meet established spirometric criteria for the diagnosis of this disease have symptoms and clinical courses similar to those with diagnosed COPD. Large longitudinal observational studies following individuals at risk of developing COPD offer us additional insight into spirometric patterns of disease development and progression. Analysis of forced expiratory maneuver changes over time may allow us to better understand early changes predictive of progressive disease. This review discusses the theoretical ability of spirometry to capture fine pathophysiologic changes in early airway disease, highlights the shortcomings of current diagnostic criteria, and reviews existing evidence for spirometric measures which may be used to better detect early airflow impairment.

Published
2019

5. Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation

Author

Das, Nilakash, primary, Happaerts, Sofie, additional, Gyselinck, Iwein, additional, Staes, Michael, additional, Derom, Eric, additional, Brusselle, Guy, additional, Burgos, Felip, additional, Contoli, Marco, additional, Dinh-Xuan, Anh Tuan, additional, Franssen, Frits M.E., additional, Gonem, Sherif, additional, Greening, Neil, additional, Haenebalcke, Christel, additional, Man, William D-C., additional, Moisés, Jorge, additional, Peché, Rudi, additional, Poberezhets, Vitalii, additional, Quint, Jennifer K., additional, Steiner, Michael C., additional, Vanderhelst, Eef, additional, Abdo, Mustafa, additional, Topalovic, Marko, additional, and Janssens, Wim, additional

Published
2023
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9. ERS International Congress 2021: highlights from the Respiratory Clinical Care and Physiology Assembly

Author

Gille, Thomas, Sivapalan, Pradeesh, Kaltsakas, Georgios, Kolekar, Shailesh B, Armstrong, Matthew, Tuffnell, Rachel, Evans, Rachael A, Vagheggini, Guido, Degani-Costa, Luiza Helena, Vicente, Cláudia, Das, Nilakash, Poberezhets, Vitalii, Rolland-Debord, Camille, Bayat, Sam, Vogiatzis, Ioannis, Franssen, Frits ME, Pinnock, Hilary, Vanfleteren, Lowie EGW, Gille, Thomas [0000-0002-1577-6595], Sivapalan, Pradeesh [0000-0002-8620-3655], Kolekar, Shailesh B [0000-0002-7375-4618], Tuffnell, Rachel [0000-0001-9415-4745], Evans, Rachael A [0000-0002-1667-868X], Vagheggini, Guido [0000-0001-6733-2809], Degani-Costa, Luiza Helena [0000-0001-5968-7853], Vicente, Cláudia [0000-0003-0133-3408], Poberezhets, Vitalii [0000-0003-2581-824X], Bayat, Sam [0000-0002-8565-0293], Franssen, Frits ME [0000-0002-1633-6356], Pinnock, Hilary [0000-0002-5976-8386], Vanfleteren, Lowie EGW [0000-0002-4387-4096], and Apollo - University of Cambridge Repository

Subjects
3201 Cardiovascular Medicine and Haematology, Clinical Research, Clinical Trials and Supportive Activities, Respiratory, 32 Biomedical and Clinical Sciences, 3 Good Health and Well Being, Health Services, 3211 Oncology and Carcinogenesis, Lung
Abstract

It is a challenge to keep abreast of all the clinical and scientific advances in the field of respiratory medicine. This article contains an overview of laboratory-based science, randomised controlled trials and qualitative research that were presented during the 2021 European Respiratory Society International Congress within the sessions from the five groups of the Assembly 1 - Respiratory clinical care and physiology. Selected presentations are summarised from a wide range of topics: clinical problems, rehabilitation and chronic care, general practice and primary care, electronic/mobile health (e-health/m-health), clinical respiratory physiology, exercise and functional imaging.

Published
2022
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10. ERS International Congress 2021:highlights from the Respiratory Clinical Care and Physiology Assembly

Author

Gille, Thomas, Sivapalan, Pradeesh, Kaltsakas, Georgios, Kolekar, Shailesh B, Armstrong, Matthew, Tuffnell, Rachel, Evans, Rachael A, Vagheggini, Guido, Degani-Costa, Luiza Helena, Vicente, Cláudia, Das, Nilakash, Poberezhets, Vitalii, Rolland-Debord, Camille, Bayat, Sam, Vogiatzis, Ioannis, Franssen, Frits M E, Pinnock, Hilary, Vanfleteren, Lowie E G W, Gille, Thomas, Sivapalan, Pradeesh, Kaltsakas, Georgios, Kolekar, Shailesh B, Armstrong, Matthew, Tuffnell, Rachel, Evans, Rachael A, Vagheggini, Guido, Degani-Costa, Luiza Helena, Vicente, Cláudia, Das, Nilakash, Poberezhets, Vitalii, Rolland-Debord, Camille, Bayat, Sam, Vogiatzis, Ioannis, Franssen, Frits M E, Pinnock, Hilary, and Vanfleteren, Lowie E G W

Abstract

It is a challenge to keep abreast of all the clinical and scientific advances in the field of respiratory medicine. This article contains an overview of laboratory-based science, randomised controlled trials and qualitative research that were presented during the 2021 European Respiratory Society International Congress within the sessions from the five groups of the Assembly 1 - Respiratory clinical care and physiology. Selected presentations are summarised from a wide range of topics: clinical problems, rehabilitation and chronic care, general practice and primary care, electronic/mobile health (e-health/m-health), clinical respiratory physiology, exercise and functional imaging.

Published
2022

11. European Respiratory Society International Congress 2021: Highlights from the Respiratory clinical care and physiology assembly

Author

Gille, Thomas, primary, Sivapalan, Pradeesh, additional, Kaltsakas, Georgios, additional, Kolekar, Shailesh B., additional, Armstrong, Matthew, additional, Tuffnell, Rachel, additional, Evans, Rachael A., additional, Vagheggini, Guido, additional, Degani-Costa, Luiza Helena, additional, Vicente, Cláudia, additional, Das, Nilakash, additional, Poberezhets, Vitalii, additional, Rolland-Debord, Camille, additional, Bayat, Sam, additional, Vogiatzis, Ioannis, additional, Franssen, Frits M.E., additional, Pinnock, Hilary, additional, and Vanfleteren, Lowie E.G.W., additional

Published
2022
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16. Real-world application of Spirometry Quality Control Deep-Learning Algorithm

Author

Stanojevic, Sanja, primary, Powell, Pippa, additional, Hall, Graham, additional, Topalovic, Marko, additional, Das, Nilakash, additional, Ray, Kevin, additional, Ratjen, Felix, additional, Jensen, Renee, additional, Jimenez-Ruiz, Carlos, additional, Hernandez, Carmen, additional, Burgos, Felip, additional, and Janssens, Wim, additional

Published
2021
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23. Area under the forced expiratory flow-volume loop in spirometry indicates severe hyperinflation in COPD patients

Author

Das, Nilakash, Topalovic, Marko, Aerts, Jean-Marie, and Janssens, Wim

Subjects
severe hyperinflation, Male, spirometry, Vital Capacity, Maximal Midexpiratory Flow Rate, International Journal of Chronic Obstructive Pulmonary Disease, Severity of Illness Index, Pulmonary Disease, Chronic Obstructive, area under flow-volume loop, Predictive Value of Tests, Forced Expiratory Volume, COPD, Humans, air trapping, Lung, Original Research, Aged, Plethysmography, Whole Body, Total Lung Capacity, flow-volume loop, Middle Aged, respiratory tract diseases, Residual Volume, ROC Curve, Area Under Curve, Female
Abstract

Nilakash Das,1 Marko Topalovic,1 Jean-Marie Aerts,2 Wim Janssens1 1Laboratory of Respiratory Diseases, Department of Chronic Diseases, Metabolism and Ageing, Katholieke Universiteit Leuven, Leuven, Belgium; 2Division of Animal and Human Health Engineering, Department of Biosystems, Katholieke Universiteit Leuven, Leuven, Belgium Background: Severe hyperinflation causes detrimental effects such as dyspnea and reduced exercise capacity and is an independent predictor of mortality in COPD patients. Static lung volumes are required to diagnose severe hyperinflation, which are not always accessible in primary care. Several studies have shown that the area under the forced expiratory flow-volume loop (AreaFE) is highly sensitive to bronchodilator response and is correlated with residual volume/total lung capacity (RV/TLC), a common index of air trapping. In this study, we investigate the role of AreaFE% (AreaFE expressed as a percentage of reference value) and conventional spirometry parameters in indicating severe hyperinflation. Materials and methods: We used a cohort of 215 individuals with COPD. The presence of severe hyperinflation was defined as elevated air trapping (RV/TLC >60%) or reduced inspiratory fraction (inspiratory capacity [IC]/TLC 60% and IC/TLC

Published
2019

24. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

Author

Topalovic, Marko, Das, Nilakash, Burgel, Pierre-Regis, Daenen, Marc, Derom, Eric, Haenebalcke, Christel, Janssen, Rob, Kerstjens, Huib AM, Liistro, Giuseppe, Louis, Renaud, Ninane, Vincent, Pison, Christophe, Schlesser, Marc, Vercauter, Piet, Vogelmeier, Claus F, Wouters, Emiel, Wynants, Jokke, Janssens, Wim, De Pauw, R, Depuydt, C, Haenebalcke, C, Muyldermans, S, Ringoet, V, Stevens, D, Bayat, S, Benet, J, Catho, E, Claustre, J, Fedi, A, Ferjani, MA, Guzun, R, Isnard, M, Nicolas, S, Pierret, T, Pison, C, Rouches, S, Wuyam, B, Corhay, JL, Guiot, J, Ghysen, K, Renaud, L, Sibille, A, De La Barriere, H, Charpentier, C, Corhut, S, Hamdan, KA, Schlesser, M, Wirtz, G, Alabadan, E, Birsen, G, Burgel, PR, Chohra, A, Hamard, C, Lemarie, B, Lothe, MN, Martin, C, Sainte-Marie, AC, Sebane, L, Berk, Y, de Brouwer, B, Janssen, R, Kerkhoff, J, Spaanderman, A, Stegers, M, Termeer, A, van Grimbergen, I, van Veen, A, van Ruitenbeek, L, Vermeer, L, Zaal, R, Zijlker, M, Aumann, J, Cuppens, K, Degraeve, D, Demuynck, K, Dieriks, B, Pat, K, Spaas, L, Van Puijenbroek, R, Weytjens, K, Wynants, J, Adam, V, Berendes, BJ, Hardeman, E, Jordens, P, Munghen, E, Tournoy, K, Vercauter, P, Alame, T, Bruyneel, M, Gabrovska, M, Muylle, I, Ninane, V, Rozen, D, Rummens, P, Van den Broecke, S, Froidure, A, Gohy, S, Liistro, G, Pieters, T, Pilette, C, Pirson, F, Kerstjens, H, Van den Berge, M, Ten Hacken, N, Duiverman, M, Koster, D, Vosse, B, Conemans, L, Maus, M, Bischoff, M, Rutten, M, Agterhuis, D, Sprooten, R, Beutel, B, Jerrentrup, A, Klemmer, A, Viniol, C, Vogelmeier, C, Bode, H, Dooms, C, Gullentops, D, Janssens, W, Nackaerts, K, Rutens, D, Wauters, E, Wuyts, W, Derom, E, Dobbelaere, S, Loof, S, Serry, G, Putman, B, Van Acker, L, Vandeweygaerde, Y, Criel, M, Daenen, M, Gubbelmans, R, Klerkx, S, Michiels, E, Thomeer, M, Vanhauwaert, A, UCL - (SLuc) Service de pneumologie, Groningen Research Institute for Asthma and COPD (GRIAC), Lifestyle Medicine (LM), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Hospitalier Universitaire [Grenoble] (CHU), Laboratory of Fundamental and Applied Bioenergetics = Laboratoire de bioénergétique fondamentale et appliquée (LBFA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), RS: NUTRIM - R3 - Respiratory & Age-related Health, MUMC+: MA Longziekten (3), Pulmonologie, MUMC+: MA Med Staf Spec Longziekten (9), and MUMC+: MA Med Staf Artsass Longziekten (9)

Subjects
Pulmonary and Respiratory Medicine, Adult, Male, Pulmonary function, STRATEGIES, Pulmonary Function Study Investigators, Context (language use), DIAGNOSIS, GUIDELINES, [SDV.MHEP.PSR]Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract, Pulmonary function testing, 03 medical and health sciences, 0302 clinical medicine, Clinical history, Artificial Intelligence, Pulmonary Medicine, Medicine, Humans, 030212 general & internal medicine, Prospective Studies, Medical diagnosis, Pulmonologists, Aged, Aged, 80 and over, Interpretation (logic), business.industry, Gold standard (test), STANDARDIZATION, PERFORMANCE, Middle Aged, 3. Good health, Respiratory Function Tests, Clinical Practice, 030228 respiratory system, Female, Artificial intelligence, business, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Software
Abstract

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases.120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests.The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4±5.9% of the cases (range 56-88%). The interrater variability of κ=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6±8.7% of the cases (range 24-62%) with a large interrater variability (κ=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p

Published
2018
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29. Area under the forced expiratory flow-volume loop in spirometry indicates severe hyperinflation in COPD patients

Author

Das,Nilakash, Topalovic,Marko, Aerts,Jean-Marie, Janssens,Wim, Das,Nilakash, Topalovic,Marko, Aerts,Jean-Marie, and Janssens,Wim

Abstract

Nilakash Das,1 Marko Topalovic,1 Jean-Marie Aerts,2 Wim Janssens1 1Laboratory of Respiratory Diseases, Department of Chronic Diseases, Metabolism and Ageing, Katholieke Universiteit Leuven, Leuven, Belgium; 2Division of Animal and Human Health Engineering, Department of Biosystems, Katholieke Universiteit Leuven, Leuven, Belgium Background: Severe hyperinflation causes detrimental effects such as dyspnea and reduced exercise capacity and is an independent predictor of mortality in COPD patients. Static lung volumes are required to diagnose severe hyperinflation, which are not always accessible in primary care. Several studies have shown that the area under the forced expiratory flow-volume loop (AreaFE) is highly sensitive to bronchodilator response and is correlated with residual volume/total lung capacity (RV/TLC), a common index of air trapping. In this study, we investigate the role of AreaFE% (AreaFE expressed as a percentage of reference value) and conventional spirometry parameters in indicating severe hyperinflation. Materials and methods: We used a cohort of 215 individuals with COPD. The presence of severe hyperinflation was defined as elevated air trapping (RV/TLC >60%) or reduced inspiratory fraction (inspiratory capacity [IC]/TLC <25%) measured using body plethysmography. AreaFE% was calculated by integrating the maximal expiratory flow-volume loop with the trapezoidal rule and expressing it as a percentage of the reference value estimated using predicted values of FVC, peak expiratory flow and forced expiratory flow at 25%, 50% and 75% of FVC. Receiver operating characteristics (ROC) curve analysis was used to identify cut-offs that were used to indicate severe hyperinflation, which were then validated in a separate group of 104 COPD subjects. Results: ROC analysis identified cut-offs of 15% and 20% for AreaFE% in indicating RV/TLC >60% and IC/TLC <25%, respectively (N=215). On validation (N=104), these cut-offs consistently

Published
2019

30. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

Author

Topalovic, Marko, primary, Das, Nilakash, additional, Burgel, Pierre-Régis, additional, Daenen, Marc, additional, Derom, Eric, additional, Haenebalcke, Christel, additional, Janssen, Rob, additional, Kerstjens, Huib A.M., additional, Liistro, Giuseppe, additional, Louis, Renaud, additional, Ninane, Vincent, additional, Pison, Christophe, additional, Schlesser, Marc, additional, Vercauter, Piet, additional, Vogelmeier, Claus F., additional, Wouters, Emiel, additional, Wynants, Jokke, additional, and Janssens, Wim, additional

Published
2019
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31. Artificial intelligence improves experts in reading pulmonary function tests

Author

Topalovic, Marko, primary, Das, Nilakash, additional, Burgel, Pierre-Regis, additional, Daenen, Marc, additional, Derom, Eric, additional, Haenebalcke, Christel, additional, Janssen, Rob, additional, Kerstjens, Huib, additional, Liistro, Giuseppe, additional, Louis, Renaud, additional, Ninane, Vincent, additional, Pison, Christophe, additional, Schlesser, Marc, additional, Vercauter, Piet, additional, Vogelmeier, Claus, additional, Wouters, Emiel, additional, Wynants, Jokke, additional, and Janssens, Wim, additional

Published
2018
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33. ERS International Congress 2021: highlights from the Respiratory Clinical Care and Physiology Assembly

Author

Gille, Thomas, Sivapalan, Pradeesh, Kaltsakas, Georgios, Kolekar, Shailesh B., Armstrong, Matthew, Tuffnell, Rachel, Evans, Rachael A., Vagheggini, Guido, Degani-Costa, Luiza, Vicente, Cláudia, Das, Nilakash, Poberezhets, Vitalii, Rolland-Debord, Camille, Bayat, Sam, Vogiatzis, Ioannis, Franssen, Frits ME, Pinnock, Hilary, Vanfleteren, Lowie E. G. W., Gille, Thomas, Sivapalan, Pradeesh, Kaltsakas, Georgios, Kolekar, Shailesh B., Armstrong, Matthew, Tuffnell, Rachel, Evans, Rachael A., Vagheggini, Guido, Degani-Costa, Luiza, Vicente, Cláudia, Das, Nilakash, Poberezhets, Vitalii, Rolland-Debord, Camille, Bayat, Sam, Vogiatzis, Ioannis, Franssen, Frits ME, Pinnock, Hilary, and Vanfleteren, Lowie E. G. W.

Abstract

It is a challenge to keep abreast of all the clinical and scientific advances in the field of respiratory medicine. This article contains an overview of laboratory-based science, randomised controlled trials and qualitative research that were presented during the 2021 European Respiratory Society International Congress within the sessions from the five groups of the Assembly 1 – Respiratory clinical care and physiology. Selected presentations are summarised from a wide range of topics: clinical problems, rehabilitation and chronic care, general practice and primary care, electronic/mobile health (e-health/m-health), clinical respiratory physiology, exercise and functional imaging.

34. Artificial intelligence based software facilitates spirometry quality control in asthma and COPD clinical trials.

Author

Topole E, Biondaro S, Montagna I, Corre S, Corradi M, Stanojevic S, Graham B, Das N, Ray K, and Topalovic M

Abstract

Rationale: Acquiring high-quality spirometry data in clinical trials is important, particularly when using forced expiratory volume in 1 s or forced vital capacity as primary end-points. In addition to quantitative criteria, the American Thoracic Society (ATS)/European Respiratory Society (ERS) standards include subjective evaluation which introduces inter-rater variability and potential mistakes. We explored the value of artificial intelligence (AI)-based software (ArtiQ.QC) to assess spirometry quality and compared it to traditional over-reading control., Methods: A random sample of 2000 sessions (8258 curves) was selected from Chiesi COPD and asthma trials (n=1000 per disease). Acceptability using the 2005 ATS/ERS standards was determined by over-reader review and by ArtiQ.QC. Additionally, three respiratory physicians jointly reviewed a subset of curves (n=150)., Results: The majority of curves (n=7267, 88%) were of good quality. The AI agreed with over-readers in 91% of cases, with 97% sensitivity and 93% positive predictive value. Performance was significantly better in the asthma group. In the revised subset, n=50 curves were repeated to assess intra-rater reliability (κ=0.83, 0.86 and 0.80 for each of the three reviewers). All reviewers agreed on 63% of 100 unique tests (κ=0.5). When reviewers set the consensus (gold standard), individual agreement with it was 88%, 94% and 70%. The agreement between AI and "gold-standard" was 73%; over-reader agreement was 46%., Conclusion: AI-based software can be used to measure spirometry data quality with comparable accuracy as experts. The assessment is a subjective exercise, with intra- and inter-rater variability even when the criteria are defined very precisely and objectively. By providing consistent results and immediate feedback to the sites, AI may benefit clinical trial conduct and variability reduction., Competing Interests: Conflict of interest: E. Topole, S. Biondaro, I. Montagna and S. Corre are all employees of Chiesi Farmaceutici S.p.A. K. Ray was an employee of ArtiQ NV, Belgium at the time the study was performed. M. Topalovic is a founder of ArtiQ NV, Belgium. M. Corradi received research funds and professional fees from Chiesi Farmaceutici S.p.A. B. Graham received professional fees from Chiesi Farmaceutici S.p.A., MGC Diagnostics, Vyaire Medical and the Lung Association of Saskatchewan; and has a patent application underway related to pulmonary function testing., (Copyright ©The authors 2023.)

Published
2023
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