Role of continuous pulse oximetry and capnography monitoring in the prevention of postoperative respiratory failure, postoperative opioid-induced respiratory depression and adverse outcomes on hospital wards: A systematic review and meta-analysis

Khanna, Ashish K.; Banga, Akshat; Rigdon, Joseph; White, Brian N.; Cuvillier, Christian; Ferraz, Joao; Olsen, Fredrik; Hackett, Loren; Bansal, Vikas; Kaw, Roop

doi:10.1016/j.jclinane.2024.111374

Journal of Clinical Anesthesia

2024

DOI: 10.1016/j.jclinane.2024.111374

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Role of continuous pulse oximetry and capnography monitoring in the prevention of postoperative respiratory failure, postoperative opioid-induced respiratory depression and adverse outcomes on hospital wards: A systematic review and meta-analysis

Ashish K. Khanna,

Akshat Banga,

Joseph Rigdon

et al.

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2024

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Cited by 2 publications

References 32 publications

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Evaluation and Management of Perioperative Pulmonary Complications

Junaidi,

Hawrylak,

Kaw

2024

Medical Clinics of North America

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Evaluation and Management of Perioperative Pulmonary Complications

Junaidi,

Hawrylak,

Kaw

2024

Medical Clinics of North America

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Evaluating AI Methods for Pulse Oximetry: Performance, Clinical Accuracy, and Comprehensive Bias Analysis

Cabanas,

Sáez,

Collao-Caiconte

et al. 2024

Bioengineering

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Blood oxygen saturation (SpO2) is vital for patient monitoring, particularly in clinical settings. Traditional SpO2 estimation methods have limitations, which can be addressed by analyzing photoplethysmography (PPG) signals with artificial intelligence (AI) techniques. This systematic review, following PRISMA guidelines, analyzed 183 unique references from WOS, PubMed, and Scopus, with 26 studies meeting the inclusion criteria. The review examined AI models, key features, oximeters used, datasets, tested saturation intervals, and performance metrics while also assessing bias through the QUADAS-2 criteria. Linear regression models and deep neural networks (DNNs) emerged as the leading AI methodologies, utilizing features such as statistical metrics, signal-to-noise ratios, and intricate waveform morphology to enhance accuracy. Gaussian Process models, in particular, exhibited superior performance, achieving Mean Absolute Error (MAE) values as low as 0.57% and Root Mean Square Error (RMSE) as low as 0.69%. The bias analysis highlighted the need for better patient selection, reliable reference standards, and comprehensive SpO2 intervals to improve model generalizability. A persistent challenge is the reliance on non-invasive methods over the more accurate arterial blood gas analysis and the limited datasets representing diverse physiological conditions. Future research must focus on improving reference standards, test protocols, and addressing ethical considerations in clinical trials. Integrating AI with traditional physiological models can further enhance SpO2 estimation accuracy and robustness, offering significant advancements in patient care.

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Role of continuous pulse oximetry and capnography monitoring in the prevention of postoperative respiratory failure, postoperative opioid-induced respiratory depression and adverse outcomes on hospital wards: A systematic review and meta-analysis

Cited by 2 publications

References 32 publications

Evaluation and Management of Perioperative Pulmonary Complications

Evaluation and Management of Perioperative Pulmonary Complications

Evaluating AI Methods for Pulse Oximetry: Performance, Clinical Accuracy, and Comprehensive Bias Analysis

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