Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning
Introduction Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study’s aim was t...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | COPD |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15412555.2024.2321379 |
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| author | Leeran Talker Cihan Dogan Daniel Neville Rui Hen Lim Henry Broomfield Gabriel Lambert Ahmed Selim Thomas Brown Laura Wiffen Julian Carter Helen F. Ashdown Gail Hayward Elango Vijaykumar Scott T. Weiss Anoop Chauhan Ameera X. Patel |
| author_facet | Leeran Talker Cihan Dogan Daniel Neville Rui Hen Lim Henry Broomfield Gabriel Lambert Ahmed Selim Thomas Brown Laura Wiffen Julian Carter Helen F. Ashdown Gail Hayward Elango Vijaykumar Scott T. Weiss Anoop Chauhan Ameera X. Patel |
| author_sort | Leeran Talker |
| collection | DOAJ |
| description | Introduction Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study’s aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2) breath records captured with TidalSense’s N-TidalTM capnometer.Method For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1–4) and 705 non-COPD participants. A logistic regression model was also trained to distinguish GOLD 1 from GOLD 4 COPD with the output probability used as an index of severity.Results The best diagnostic model achieved an AUROC of 0.890, sensitivity of 0.771, specificity of 0.850 and positive predictive value (PPV) of 0.834. Evaluating performance on all test capnograms that were confidently ruled in or out yielded PPV of 0.930 and NPV of 0.890. The severity determination model yielded an AUROC of 0.980, sensitivity of 0.958, specificity of 0.961 and PPV of 0.958 in distinguishing GOLD 1 from GOLD 4. Output probabilities from the severity determination model produced a correlation of 0.71 with percentage predicted FEV1.Conclusion The N-TidalTM device could be used alongside interpretable machine learning as an accurate, point-of-care diagnostic test for COPD, particularly in primary care as a rapid rule-in or rule-out test. N-TidalTM also could be effective in monitoring disease progression, providing a possible alternative to spirometry for disease monitoring. |
| format | Article |
| id | doaj-art-be0a1094073f45a89b833fd1e0528dff |
| institution | Kabale University |
| issn | 1541-2555 1541-2563 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | COPD |
| spelling | doaj-art-be0a1094073f45a89b833fd1e0528dff2024-12-26T08:50:44ZengTaylor & Francis GroupCOPD1541-25551541-25632024-12-0121110.1080/15412555.2024.2321379Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine LearningLeeran Talker0Cihan Dogan1Daniel Neville2Rui Hen Lim3Henry Broomfield4Gabriel Lambert5Ahmed Selim6Thomas Brown7Laura Wiffen8Julian Carter9Helen F. Ashdown10Gail Hayward11Elango Vijaykumar12Scott T. Weiss13Anoop Chauhan14Ameera X. Patel15Department of Machine Learning, TidalSense, Cambridge, UKDepartment of Machine Learning, TidalSense, Cambridge, UKRespiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, UKDepartment of Machine Learning, TidalSense, Cambridge, UKDepartment of Machine Learning, TidalSense, Cambridge, UKDepartment of Clinical Operations, TidalSense, Cambridge, UKDepartment of Machine Learning, TidalSense, Cambridge, UKRespiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, UKRespiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, UKDepartment of Engineering, TidalSense, Cambridge, UKDepartment of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, UKDepartment of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, UKDepartment of Research, Modality GP Partnership, UKDepartment of Medicine, Channing Division of Network Medicine, Harvard Medical School, Boston, MA, USARespiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, UKExecutive Department, TidalSense, Cambridge, UKIntroduction Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study’s aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2) breath records captured with TidalSense’s N-TidalTM capnometer.Method For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1–4) and 705 non-COPD participants. A logistic regression model was also trained to distinguish GOLD 1 from GOLD 4 COPD with the output probability used as an index of severity.Results The best diagnostic model achieved an AUROC of 0.890, sensitivity of 0.771, specificity of 0.850 and positive predictive value (PPV) of 0.834. Evaluating performance on all test capnograms that were confidently ruled in or out yielded PPV of 0.930 and NPV of 0.890. The severity determination model yielded an AUROC of 0.980, sensitivity of 0.958, specificity of 0.961 and PPV of 0.958 in distinguishing GOLD 1 from GOLD 4. Output probabilities from the severity determination model produced a correlation of 0.71 with percentage predicted FEV1.Conclusion The N-TidalTM device could be used alongside interpretable machine learning as an accurate, point-of-care diagnostic test for COPD, particularly in primary care as a rapid rule-in or rule-out test. N-TidalTM also could be effective in monitoring disease progression, providing a possible alternative to spirometry for disease monitoring.https://www.tandfonline.com/doi/10.1080/15412555.2024.2321379Pulmonary Diseasechronic obstructivemachine learningdiagnosiscapnometryseverity assessment |
| spellingShingle | Leeran Talker Cihan Dogan Daniel Neville Rui Hen Lim Henry Broomfield Gabriel Lambert Ahmed Selim Thomas Brown Laura Wiffen Julian Carter Helen F. Ashdown Gail Hayward Elango Vijaykumar Scott T. Weiss Anoop Chauhan Ameera X. Patel Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning COPD Pulmonary Disease chronic obstructive machine learning diagnosis capnometry severity assessment |
| title | Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning |
| title_full | Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning |
| title_fullStr | Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning |
| title_full_unstemmed | Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning |
| title_short | Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning |
| title_sort | diagnosis and severity assessment of copd using a novel fast response capnometer and interpretable machine learning |
| topic | Pulmonary Disease chronic obstructive machine learning diagnosis capnometry severity assessment |
| url | https://www.tandfonline.com/doi/10.1080/15412555.2024.2321379 |
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