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 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.

Is stratification testing for treatment of chronic obstructive pulmonary disease exacerbations cost-effective in primary care? an early cost-utility analysis.

OBJECTIVES: Patients with chronic obstructive pulmonary disease (COPD) who experience acute exacerbations usually require treatment with oral steroids or antibiotics, depending on the etiology of the exacerbation. Current management is based on clinician's assessment and judgement, which lacks diagnostic accuracy and results in overtreatment. A test to guide these decisions in primary care is in development. We developed an early decision model to evaluate the cost-effectiveness of this treatment stratification test in the primary care setting in the United Kingdom. METHODS: A combined decision tree and Markov model was developed of COPD progression and the exacerbation care pathway. Sensitivity analysis was carried out to guide technology development and inform evidence generation requirements. RESULTS: The base case test strategy cost GBP 423 (USD 542) less and resulted in a health gain of 0.15 quality-adjusted life-years per patient compared with not testing. Testing reduced antibiotic prescriptions by 30 percent, potentially lowering the risk of antimicrobial resistance developing. In sensitivity analysis, the result depended on the clinical effects of treating patients according to the test result, as opposed to treating according to clinical judgement alone, for which there is limited evidence. The results were less sensitive to the accuracy of the test. CONCLUSIONS: Testing may be cost-saving in primary care, but this requires robust evidence on whether test-guided treatment is effective. High quality evidence on the clinical utility of testing is required for early modeling of diagnostic tests generally.

GPs' perceptions of workload in England: a qualitative interview study.

BACKGROUND: GPs report the lowest levels of morale among doctors, job satisfaction is low, and the GP workforce is diminishing. Workload is frequently cited as negatively impacting on commitment to a career in general practice, and many GPs report that their workload is unmanageable. AIM: To gather an in-depth understanding of GPs' perceptions and attitudes towards workload. DESIGN AND SETTING: All GPs working within NHS England were eligible. Advertisements were circulated via regional GP e-mail lists and national social media networks in June 2015. Of those GPs who responded, a maximum-variation sample was selected until data saturation was reached. METHOD: Semi-structured, qualitative interviews were conducted. Data were analysed thematically. RESULTS: In total, 171 GPs responded, and 34 were included in this study. GPs described an increase in workload over recent years, with current working days being long and intense, raising concerns over the wellbeing of GPs and patients. Full-time partnership was generally not considered to be possible, and many participants felt workload was unsustainable, particularly given the diminishing workforce. Four major themes emerged to explain increased workload: increased patient needs and expectations; a changing relationship between primary and secondary care; bureaucracy and resources; and the balance of workload within a practice. Continuity of care was perceived as being eroded by changes in contracts and working patterns to deal with workload. CONCLUSION: This study highlights the urgent need to address perceived lack of investment and clinical capacity in general practice, and suggests that managing patient expectations around what primary care can deliver, and reducing bureaucracy, have become key issues, at least until capacity issues are resolved.