Journal of clinical monitoring and computing
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J Clin Monit Comput · Oct 2022
Continuous vital sign monitoring using a wearable patch sensor in obese patients: a validation study in a clinical setting.
Our aim was to determine the agreement of heart rate (HR) and respiratory rate (RR) measurements by the Philips Biosensor with a reference monitor (General Electric Carescape B650) in severely obese patients during and after bariatric surgery. Additionally, sensor reliability was assessed. Ninety-four severely obese patients were monitored with both the Biosensor and reference monitor during and after bariatric surgery. ⋯ No clear causes for data loss were found. The Biosensor is suitable for remote monitoring of HR, but not RR in morbidly obese patients. Future research should focus on improving RR measurements, the interpretation of continuous data, and development of smart alarm systems.
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J Clin Monit Comput · Oct 2022
How early warning with the Oxygen Reserve Index (ORi™) can improve the detection of desaturation during induction of general anesthesia?
The Oxygen Reserve Index (ORi™) is a dimensionless parameter with a value between 0 and 1. It is related to the real-time oxygenation status in the moderate hyperoxic range. The purpose of this study is to investigate the added warning time provided by different ORi alarm triggers and the continuous trends of ORi, SpO2, and PaO2. ⋯ The ORi enables the clinicians to monitor the patients' oxygen status during induction of general anesthesia and can improve the detection of impending desaturation. However, further studies are needed to assess its clinical potential in the high hyperoxic range. The protocol was retrospectively registered at ClinicalTrials.gov on July 21, 2021 (NCT04976504).
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J Clin Monit Comput · Oct 2022
Opal: an implementation science tool for machine learning clinical decision support in anesthesia.
Opal is the first published example of a full-stack platform infrastructure for an implementation science designed for ML in anesthesia that solves the problem of leveraging ML for clinical decision support. Users interact with a secure online Opal web application to select a desired operating room (OR) case cohort for data extraction, visualize datasets with built-in graphing techniques, and run in-client ML or extract data for external use. Opal was used to obtain data from 29,004 unique OR cases from a single academic institution for pre-operative prediction of post-operative acute kidney injury (AKI) based on creatinine KDIGO criteria using predictors which included pre-operative demographic, past medical history, medications, and flowsheet information. ⋯ At the default probability decision threshold of 0.5, the model sensitivity was 0.9 and the specificity was 0.8. K-means clustering was performed to partition the cases into two clusters and for hypothesis generation of potential groups of outcomes related to intraoperative vitals. Opal's design has created streamlined ML functionality for researchers and clinicians in the perioperative setting and opens the door for many future clinical applications, including data mining, clinical simulation, high-frequency prediction, and quality improvement.
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J Clin Monit Comput · Oct 2022
Current trends in anesthetic depth and antinociception monitoring: an international survey.
Current trends in anesthetic depth (i.e., hypnosis) and antinociception monitoring are unclear. We thus aimed to determine contemporary perspectives on monitoring these components of anesthesia during general anesthesia. Participants received and responded anonymously to an internet-based international survey supported by the European Society of Anaesthesiology and Intensive Care. ⋯ Nonetheless, a substantial number of participants were unsure if hypnotic (23%) or antinociception (32%) monitoring were recommended and there was a lack of knowledge (58%) of any published algorithms to titrate hypnotic and/or antinociceptive drugs based on the information provided by the monitors. In conclusion, current trends in European academic centers prioritize anesthesia depth over antinociception monitoring. Despite an agreement among respondents that applying strategies that optimize anesthetic depth and antinociception could improve outcome, there remains a lack of knowledge of appropriate algorithms. Future studies and recommendations should focus on clarifying goal-directed anesthetic strategies and determine their impact on perioperative patient outcome.
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J Clin Monit Comput · Oct 2022
Pressure-flow breath representation eases asynchrony identification in mechanically ventilated patients.
Breathing asynchronies are mismatches between the requests of mechanically ventilated subjects and the support provided by mechanical ventilators. The most widespread technique in identifying these pathological conditions is the visual analysis of the intra-tracheal pressure and flow time-trends. This work considers a recently introduced pressure-flow representation technique and investigates whether it can help nurses in the early detection of anomalies that can represent asynchronies. ⋯ The pressure-flow diagram significantly increases sensitivity and decreases the response time of early asynchrony detection performed by nurses. Moreover, the data suggest that operator experience does not affect the identification results. This outcome leads us to believe that, in emergency contexts with a shortage of nurses, intensive care nurses can be supplemented, for the sole identification of possible respiratory asynchronies, by inexperienced staff.