Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2023
Comparison between the Rotational Thromboelastometry (ROTEM) Delta device against the Cartridge-based Thromboelastography 6s and Quantra in a healthy third trimester pregnant cohort.
Rotational Thromboelastometry (ROTEM) Delta has been described in several postpartum hemorrhage algorithms, but this device requires pipetting and careful mixing of reagents to initiate the clotting reaction. In contrast, thromboelastography (TEG 6s) and the Quantra devices operate utilizing an automated pre-mixed cartridge that only requires a blood sample to start the clot strength analysis. We compared the correlation between 3 point of care viscoelastic testing (POCVT) devices to laboratory Clauss fibrinogen and platelets, their inter-device correlation, and the total running time difference between Quantra and ROTEM. ⋯ In contrast, a moderate correlation was noted between the platelet parameters of Quantra and ROTEM (r = 0.51, p = 0.0036). The Quantra device resulted 20.9 min (95% CI -0.2 to 4.7, P = 0.07) faster than the ROTEM if the warming and pipetting of reagents of the latter were considered. All the POCVT devices demonstrated a high correlation to laboratory Clauss fibrinogen, making each beneficial for the early recognition and management of hypofibrinogenemia.
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J Clin Monit Comput · Feb 2023
The impact of urine flow on urine oxygen partial pressure monitoring during cardiac surgery.
Urine oxygen partial pressure (PuO2) may be useful for assessing acute kidney injury (AKI) risk. The primary purpose of this study was to quantify the ability of a novel urinary oxygen monitoring system to make real-time PuO2 measurements intraoperatively which depends on adequate urine flow. We hypothesized that PuO2 data could be acquired with enough temporal resolution to provide real-time information in both AKI and non-AKI patients. ⋯ NCT03335865, First Posted Date: Nov. 8th, 2017.
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J Clin Monit Comput · Feb 2023
Randomized Controlled TrialComparing the effects of continuous positive airway pressure via mask or helmet interface on oxygenation and pulmonary complications after major abdominal surgery: a randomized trial.
The risk of pulmonary complications is high after major abdominal surgery but may be reduced by prophylactic postoperative noninvasive ventilation using continuous positive airway pressure (CPAP). This study compared the effects of intermittent mask CPAP (ICPAP) and continuous helmet CPAP (HCPAP) on oxygenation and the risk of pulmonary complications following major abdominal surgery. Patients undergoing open abdominal aortic aneurysm repair or pancreaticoduodenectomy were randomized (1:1) to either postoperative ICPAP or HCPAP. ⋯ Comfort scores were similar in both groups (p = 0.43), although a sensation of claustrophobia during treatment was only experienced in the HCPAP group (11% vs. 0%, p = 0.03). Compared with ICPAP, using HCPAP was associated with similar oxygenation (i.e., PaO2/FIO2 ratio) and a similar risk of pulmonary complications. However, HCPAP treatment was associated with a higher sensation of claustrophobia.
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J Clin Monit Comput · Feb 2023
Evaluation of machine learning models as decision aids for anesthesiologists.
Machine Learning (ML) models have been developed to predict perioperative clinical parameters. The objective of this study was to determine if ML models can serve as decision aids to improve anesthesiologists' prediction of peak intraoperative glucose values and postoperative opioid requirements. A web-based tool was used to present actual surgical case and patient information to 10 practicing anesthesiologists. ⋯ Feedback questionnaire responses revealed that the anesthesiologist primarily used the ML estimates as reference to modify their clinical judgement. ML models can improve anesthesiologists' estimation of clinical parameters. ML predictions primarily served as reference information that modified an anesthesiologist's clinical estimate.
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J Clin Monit Comput · Feb 2023
Pulmonary gas exchange evaluated by machine learning: a computer simulation.
Using computer simulation we investigated whether machine learning (ML) analysis of selected ICU monitoring data can quantify pulmonary gas exchange in multi-compartment format. A 21 compartment ventilation/perfusion (V/Q) model of pulmonary blood flow processed 34,551 combinations of cardiac output, hemoglobin concentration, standard P50, base excess, VO2 and VCO2 plus three model-defining parameters: shunt, log SD and mean V/Q. From these inputs the model produced paired arterial blood gases, first with the inspired O2 fraction (FiO2) adjusted to arterial saturation (SaO2) = 0.90, and second with FiO2 increased by 0.1. 'Stacked regressor' ML ensembles were trained/validated on 90% of this dataset. ⋯ Single-point estimates were less accurate: R2 = 0.77-0.89, slope = 0.991-0.993, intercept = 0.009-0.334. ML applications using blood gas, indirect calorimetry, and cardiac output data can quantify pulmonary gas exchange in terms describing a 20 compartment V/Q model of pulmonary blood flow. High fidelity reports require data from two FiO2 settings.