Journal of clinical monitoring and computing
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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
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.
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J Clin Monit Comput · Oct 2022
A capaciflector provides continuous and accurate respiratory rate monitoring for patients at rest and during exercise.
Respiratory rate (RR) is a marker of critical illness, but during hospital care, RR is often inaccurately measured. The capaciflector is a novel sensor that is small, inexpensive, and flexible, thus it has the potential to provide a single-use, real-time RR monitoring device. We evaluated the accuracy of continuous RR measurements by capaciflector hardware both at rest and during exercise. ⋯ Accuracy and continuity of monitoring were upheld even during vigorous CPET exercise, often with narrower limits of agreement than those reported for comparable technologies. We provide a unique clinical demonstration of the capaciflector as an accurate breathing monitor, which may have the potential to become a simple and affordable medical device. Clinical trial number: NCT03832205 https://clinicaltrials.gov/ct2/show/NCT03832205 registered February 6th, 2019.
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J Clin Monit Comput · Oct 2022
Positive end-expiratory pressure individualization guided by continuous end-expiratory lung volume monitoring during laparoscopic surgery.
To determine whether end-expiratory lung volume measured with volumetric capnography (EELVCO2) can individualize positive end-expiratory pressure (PEEP) setting during laparoscopic surgery. We studied patients undergoing laparoscopic surgery subjected to Fowler (F-group; n = 20) or Trendelenburg (T-group; n = 20) positions. EELVCO2 was measured at 0° supine (baseline), during capnoperitoneum (CP) at 0° supine, during CP with Fowler (head up + 20°) or Trendelenburg (head down - 30°) positions and after CP back to 0° supine. ⋯ Breath-by-breath noninvasive EELVCO2 detected changes in lung volume induced by capnoperitoneum and body position and was useful to individualize the level of PEEP during laparoscopy. Trial registry: Clinicaltrials.gov NCT03693352. Protocol started 1st October 2018.
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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.