Journal of clinical monitoring and computing
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J Clin Monit Comput · Oct 2022
High-flow nasal cannula therapy, factors affecting effective inspired oxygen fraction: an experimental adult bench model.
Oxygenation through High Flow Delivery Systems (HFO) is described as capable of delivering accurate FiO2. Meanwhile, peak inspiratory flow [Formula: see text] ) of patients with acute hypoxemic respiratory failure can reach up to 120 L/min, largely exceeding HFO flow. Currently, very few data on the reliability of HFO devices at these high [Formula: see text] are available. ⋯ The present bench study did expose a weakness of HFO devices in reliability of delivering accurate FIO2 at high [Formula: see text] as well as, to a lesser extent, at [Formula: see text] below equivalent set HFO Flows. Moreover, set HFO flow and set FIO2 did influence the variability of effective inspired oxygen fraction. The adjunction of a dead space in the experimental set-up significantly amended this variability and should thus be further studied in order to improve success rate of HFO therapy.
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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
EditorialSample size determination in method comparison and observer variability studies.
The comparison of two quantitative measuring devices is often performed with the Limits of Agreement proposed by Bland and Altman in their seminal Lancet paper back in 1986. Sample size considerations were rare for such agreement analyses in the past, but recently several proposals have been made depending on how agreement is to be assessed and the number of replicates to be used. ⋯ These include current state-of-the-art analysis of and reporting guidelines for agreement studies. General recommendations close the paper.
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J Clin Monit Comput · Oct 2022
VACuum INtubation (VACcIN) box restricts the exhaled air dispersion generated by simulated cough: description and simulation-based tests of an innovative aerosolization protective prototype.
The COVID-19 pandemic has caused personal protective equipment shortages worldwide and required healthcare workers to develop novel ways of protecting themselves. Anesthesiologists in particular are exposed to increased risks of contamination when performing interventions such as airway manipulations. We developed and tested an aerosolization protective device which contains aerosols around the patient's airway and helps eliminate particles using negative pressure. ⋯ One minute following simulated cough, the mean number of particles per cubic foot in our box with suction on is around 45% that with the suction off (1,462,373 vs 3,272,080, P < 0.0001) in the negative pressure room, and four times lower than with the suction off (760,380 vs 3,088,700, P < 0.0001) in the positive pressure room. After a simulated cough inside the box, particles can be detected in front of the anesthesiologist's face with a non-airtight device, while none are detected when our box is sealed and its suction turned on. The use of our negative pressure intubation box prevents contamination of surroundings and increases particle elimination, regardless of room pressure.
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J Clin Monit Comput · Oct 2022
Multicenter StudyPredicting hypoglycemia in critically Ill patients using machine learning and electronic health records.
Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. ⋯ The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations.