Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2022
ReviewAugmented reality in anesthesia, pain medicine and critical care: a narrative review.
Augmented reality (AR) is the integration of computer-generated information with the user's environment in real time. AR is used in many industries, including healthcare, where it has gained significant popularity. ⋯ AR has also been implemented in pediatric care to reduce periprocedural anxiety. In this narrative review, we summarize the current role of AR in anesthesiology, pain medicine, and critical care.
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J Clin Monit Comput · Feb 2022
ReviewQuestions about COVID-19 associated coagulopathy: possible answers from the viscoelastic tests.
Abnormal coagulation parameters are often observed in patients with coronavirus disease 2019 (COVID-19) and the severity of derangement has been associated with a poor prognosis. The COVID-19 associated coagulopathy (CAC) displays unique features that include a high risk of developing thromboembolic complications. ⋯ In the last year, many studies have proposed to explain the underlying characteristics of CAC; however, there remain many unanswered questions. We tried to address some of the important queries about CAC through VETs analysis.
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J Clin Monit Comput · Feb 2022
Detection of arterial pressure waveform error using machine learning trained algorithms.
In critically ill and high-risk surgical room patients, an invasive arterial catheter is often inserted to continuously measure arterial pressure (AP). The arterial waveform pressure measurement, however, may be compromised by damping or inappropriate reference placement of the pressure transducer. Clinicians, decision support systems, or closed-loop applications that rely on such information would benefit from the ability to detect error from the waveform alone. ⋯ A total of 40 h of monitoring time was recorded with approximately 120,000 heart beats featurized. For all error states, ROC AUCs for algorithm performance on classification of the state were greater than 0.9; when using patient-specific calibrated data AUCs were 0.94, 0.95, and 0.99 for the transducer low, transducer high, and damped conditions respectively. Machine-learning trained algorithms were able to discriminate arterial line transducer error states from the waveform alone with a high degree of accuracy.
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J Clin Monit Comput · Feb 2022
Observational StudyEvaluation of respiratory rate monitoring using a microwave Doppler sensor mounted on the ceiling of an intensive care unit: a prospective observational study.
Continuous monitoring of the respiratory rate is crucial in an acute care setting. Contact respiratory monitoring modalities such as capnography and thoracic impedance pneumography are prone to artifacts, causing false alarms. Moreover, their cables can restrict patient behavior or interrupt patient care. ⋯ Compared to visual counting, the microwave Doppler sensor showed small bias; however, the limits of agreement were similar to those observed in other conventional methods. Our monitor and the conventional ones are not interchangeable with visual counting. Trial registration number: UMIN000032021, March/30/2018.