Inter- and intra-observer reliability and agreement of O2Pulse inflection during cardiopulmonary exercise testing: A comparison of subjective and novel objective methodology.
Thomas NickolayGordon McGregorRichard PowellBrian BeggStefan BirkettSimon NicholsStuart EnnisPrithwish BanerjeeRob ShaveJames MetcalfeAngela HoyeLee InglePublished in: PloS one (2024)
Cardiopulmonary exercise testing (CPET) is the 'gold standard' method for evaluating functional capacity, with oxygen pulse (O2Pulse) inflections serving as a potential indicator of myocardial ischaemia. However, the reliability and agreement of identifying these inflections have not been thoroughly investigated. This study aimed to assess the inter- and intra-observer reliability and agreement of a subjective quantification method for identifying O2Pulse inflections during CPET, and to propose a more robust and objective novel algorithm as an alternative methodology. A retrospective analysis was conducted using baseline data from the HIIT or MISS UK trial. The O2Pulse curves were visually inspected by two independent examiners, and compared against an objective algorithm. Fleiss' Kappa was used to determine the reliability of agreement between the three groups of observations. The results showed almost perfect agreement between the algorithm and both examiners, with a Fleiss' Kappa statistic of 0.89. The algorithm also demonstrated excellent inter-rater reliability (ICC) when compared to both examiners (0.92-0.98). However, a significant level (P ≤0.05) of systematic bias was observed in Bland-Altman analysis for comparisons involving the novice examiner. In conclusion, this study provides evidence for the reliability of both subjective and novel objective methods for identifying inflections in O2Pulse during CPET. These findings suggest that further research into the clinical significance of O2Pulse inflections is warranted, and that the adoption of a novel objective means of quantification may be preferable to ensure equality of outcome for patients.
Keyphrases
- blood pressure
- machine learning
- deep learning
- high intensity
- nuclear factor
- end stage renal disease
- newly diagnosed
- physical activity
- clinical trial
- electronic health record
- sleep quality
- neural network
- heart failure
- ejection fraction
- peritoneal dialysis
- artificial intelligence
- risk assessment
- cross sectional
- depressive symptoms