Journal of clinical monitoring and computing
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J Clin Monit Comput · Oct 2022
Randomized Controlled TrialBlind vs. video-laryngoscope-guided laryngeal mask insertion: A prospective randomized comparison of oropharyngeal leak pressure and fiberoptic grading.
Laryngeal Mask Airway (LMA) insertion may not always be smooth without complications. Controversial results of several studies evaluating ideal insertion conditions have been published. This study compared the oropharyngeal leak pressure values and fiberoptic grading scores between blind and video-laryngoscope-guided LMA insertion. ⋯ The findings of our study suggest that the video-laryngoscope-guided LMA-Classic insertion with a standard blade technique may be a helpful alternative to blind insertion.
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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
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
Evaluation of a new smartphone optical blood pressure application (OptiBP™) in the post-anesthesia care unit: a method comparison study against the non-invasive automatic oscillometric brachial cuff as the reference method.
We compared blood pressure (BP) values obtained with a new optical smartphone application (OptiBP™) with BP values obtained using a non-invasive automatic oscillometric brachial cuff (reference method) during the first 2 h of surveillance in a post-anesthesia care unit in patients after non-cardiac surgery. Three simultaneous BP measurements of both methods were recorded every 30 min over a 2-h period. The agreement between measurements was investigated using Bland-Altman and error grid analyses. ⋯ We observed a good agreement between BP values obtained by the OptiBP™ system and BP values obtained with the reference method. The OptiBP™ system fulfilled the AAMI validation requirements for MAP and DAP and error grid analysis indicated that the vast majority of measurement pairs (≥ 99%) were in risk zones A and B. Trial Registration ClinicalTrials.gov Identifier: NCT04262323.
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J Clin Monit Comput · Oct 2022
In-silico analysis of closed-loop vasopressor control of phenylephrine versus norepinephrine.
We have previously demonstrated in in-silico, pre-clinical animal models, and finally human clinical studies the ability of a novel closed-loop vasopressor titration system to manage norepinephrine infusion rates to keep mean arterial blood pressure in a very tight range, reduce hypotension time and severity, and reduce overtreatment. We hypothesized that the same controller could, with modification for pharmacologic differences, suitably titrate a lower-potency longer duration of action agent like phenylephrine. Using the same physiologic simulation model as was used previously for in-silico testing of our controller for norepinephrine, we first updated the model to include a new vasopressor agent modeled after phenylephrine. ⋯ The controller kept the simulated patients' MAP in target for 94% of management time in the simple scenarios and more than 85% of time in the most challenging scenarios. Varvel criteria were all under 1% error for expected pharmacologic responses and were consistent with those established for norepinephrine in our previous studies. The controller was able to acceptably titrate phenylephrine in this simulated patient model consistent with performance previously seen for norepinephrine after adjusting for the anticipated differences between the two agents.