Journal of clinical monitoring and computing
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J Clin Monit Comput · Apr 2020
Optimizing B-lines on lung ultrasound: an in-vitro to in-vivo pilot study with clinical implications.
B-lines on lung ultrasound (US) are the hallmark of pulmonary edema. It is unknown if ultrasound machine settings or probe type matter. We created an in-vitro gelatin model. ⋯ The experiment was then repeated in-vivo in a patient with known pulmonary edema. Based on a multivariable regression LS-ratings were similar when comparing the in-vitro versus in-vivo experiment (P = 0.16; partial R2 = 0.2%) and when using the curvilinear versus linear probe (P = 0.69; partial R2 = 0.02%) but significantly different across machine settings (P < 0.0001; partial R2 = 34.4%). Limited by its pilot character, our study suggests that (1) certain US-machine settings heavily impact B-line visibility, with no clear difference between probes; (2) in-vitro models are a valid and practical alternative to more challenging patient-based research; (3) there is significant potential to improve B-line visibility and thus diagnostic yield in the clinical setting by using lung presets, centering the focal zone at the pleural line and increasing the distal time gain compensation, most of which are (in our experience) rarely done.
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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
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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Monitoring level of hypnosis is a major ongoing challenge for anesthetists to reduce anesthetic drug consumption, avoiding intraoperative awareness and prolonged recovery. This paper proposes a novel automated method for accurate assessing of the level of hypnosis with sevoflurane in 17 patients using the electroencephalogram signal. ⋯ Then deep state is identified by extracting the sample entropy feature; and finally light and general states are identified by extracting the three mentioned features of the first step. The accuracy of the proposed method of analyzing the brain activity during anesthesia is 94.11%; which was better than previous studies and also a commercial monitoring system (Response Entropy Index).