Journal of clinical monitoring and computing
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J Clin Monit Comput · Apr 2020
Non-invasive continuous respiratory monitoring using temperature-based sensors.
Respiratory rate (RR) is a key vital sign that has been traditionally employed in the clinical assessment of patients and in the prevention of respiratory compromise. Despite its relevance, current practice for monitoring RR in non-intubated patients strongly relies on visual counting, which delivers an intermittent and error-prone assessment of the respiratory status. Here, we present a novel non-invasive respiratory monitor that continuously measures the RR in human subjects. ⋯ The performance of the respiratory monitor is assessed through respiratory experiments performed on healthy subjects. Under spontaneous breathing, the mean RR difference between our respiratory monitor and visual counting was 0.4 breaths per minute (BPM), with a 95% confidence interval equal to [- 0.5, 1.3] BPM. The robustness of the respiratory sensor to the position is assessed by studying the signal-to-noise ratio in different locations on the upper lip, displaying a markedly better performance than traditional thermal sensors used for respiratory airflow measurements.
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J Clin Monit Comput · Apr 2020
Correction to: Mathematical arterialisation of peripheral venous blood gas for obtainment of arterial blood gas values: a methodological validation study in the clinical setting.
The corresponding author has identified a calculation mistake in the original publication of the article. The corrected value is given in this Correction. Under the Results section, the median (range) age of the patients in the methodological study should read 76 (26-86) years instead of 56 (26-86) years.
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J Clin Monit Comput · Apr 2020
Clinical TrialThe response of a standardized fluid challenge during cardiac surgery on cerebral oxygen saturation measured with near-infrared spectroscopy.
Near infrared spectroscopy (NIRS) has been used to evaluate regional cerebral tissue oxygen saturation (ScO2) during the last decades. Perioperative management algorithms advocate to maintain ScO2, by maintaining or increasing cardiac output (CO), e.g. with fluid infusion. We hypothesized that ScO2 would increase in responders to a standardized fluid challenge (FC) and that the relative changes in CO and ScO2 would correlate. ⋯ Despite this, relative changes in CO correlated to relative changes in ScO2. However, the clinical impact of the present observations is unclear, and the results must be interpreted with caution. Trial registration:http://ClinicalTrial.gov identifier for main study (FLuid Responsiveness Prediction Using Extra Systoles-FLEX): NCT03002129.
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J Clin Monit Comput · Apr 2020
Observational StudyValidation of electrical velocimetry in resuscitation of patients undergoing liver transplantation. Observational study.
Major hemodynamic changes are frequently noted during liver transplantation (LT). We evaluated the performance of electrical velocimetry (EV) as compared to that of TEE in SV optimization during liver transplantation. This was an observational study in 32 patients undergoing LT. ⋯ The absolute values of SV derived from EV did not agree with SV derived from TEE. However, EV was able to track the direction of changes in SV during hemodynamic management of patients undergoing liver transplantation. Clinical trial registration: Clinicaltrials.gov Identifier: NCT03228329 prospectively Registered on 13-July-2017.
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J Clin Monit Comput · Apr 2020
Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU.
Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing patient condition. We hypothesized that applying this data-driven approach to medical monitors will help reduce the FAR even when data from sensors are missing. ⋯ While the FAR for PER with missing parameters was 0.17-0.39, it was only 0.01-0.02 for RF. When scenarios were examined separately, RF showed clear superiority in almost all combinations of scenarios and numbers of missing parameters. When sensor data are missing, specialist performance worsens with the number of missing parameters, whereas the RF model attains high accuracy and low FAR due to its ability to fuse information from available sensors, compensating for missing parameters.