Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2022
ReviewThe contemporary pulmonary artery catheter. Part 1: placement and waveform analysis.
Nowadays, the classical pulmonary artery catheter (PAC) has an almost 50-year-old history of its clinical use for hemodynamic monitoring. In recent years, the PAC evolved from a device that enabled intermittent cardiac output measurements in combination with static pressures to a monitoring tool that provides continuous data on cardiac output, oxygen supply and-demand balance, as well as right ventricular (RV) performance. In this review, which consists of two parts, we will introduce the difference between intermittent pulmonary artery thermodilution using cold bolus injections, and the contemporary PAC enabling continuous measurements by using a thermal filament which at random heats up the blood. ⋯ The second part will cover the measurements of the contemporary PAC including measurement of continuous cardiac output, RV ejection fraction, end-diastolic volume index, and mixed venous oxygen saturation. Limitations of all of these measurements will be highlighted there as well. We conclude that thorough understanding of measurements obtained from the PAC are the first step in successful application of the PAC in daily clinical practice.
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J Clin Monit Comput · Feb 2022
Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema.
Discriminating acute respiratory distress syndrome (ARDS) from acute cardiogenic pulmonary edema (CPE) may be challenging in critically ill patients. Aim of this study was to investigate if gray-level co-occurrence matrix (GLCM) analysis of lung ultrasound (LUS) images can differentiate ARDS from CPE. The study population consisted of critically ill patients admitted to intensive care unit (ICU) with acute respiratory failure and submitted to LUS and extravascular lung water monitoring, and of a healthy control group (HCG). ⋯ HCG a statistical significance occurred only in two matrix features (correlation: P = 0.005; homogeneity: P = 0.048). The quantitative method proposed has shown high diagnostic accuracy in differentiating normal lung from ARDS or CPE, and good diagnostic accuracy in differentiating CPE and ARDS. Gray-level co-occurrence matrix analysis of LUS images has the potential to aid pulmonary edemas differential diagnosis.
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J Clin Monit Comput · Feb 2022
Prediction of reactivity during tracheal intubation by pre-laryngoscopy tetanus-induced ANI variation.
The ANI is a nociception monitor based on the high frequency parts of heart rate variability. Tracheal intubation may induce potentially deleterious hemodynamic disturbances or motor reactions if analgesia is inadequate. We investigated whether ANI modification generated by a standardized moderate short tetanic stimulation performed before laryngoscopy could predict hemodynamic or somatic reactions to subsequent intubation. ⋯ Regarding the ability of tetanus-induced ANI variation to predict hemodynamic or somatic reactions during subsequent intubation, the AUCROCs [95% CI] were 0.61 [0.41-0.81] and 0.52 [0.31-0.72] respectively. ANI varied after a short moderate tetanic stimulation performed before laryngoscopy but this variation was not predictive of a hemodynamic or somatic reaction during intubation. Trial registration NCT04354311, April 20th 2020, retrospectively registered.
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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
Frontal electroencephalogram based drug, sex, and age independent sedation level prediction using non-linear machine learning algorithms.
Brain monitors which track quantitative electroencephalogram (EEG) signatures to monitor sedation levels are drug and patient specific. There is a need for robust sedation level monitoring systems to accurately track sedation levels across all drug classes, sex and age groups. Forty-four quantitative features estimated from a pooled dataset of 204 EEG recordings from 66 healthy adult volunteers who received either propofol, dexmedetomidine, or sevoflurane (all with and without remifentanil) were used in a machine learning based automated system to estimate the depth of sedation. ⋯ Nonlinear machine-learning models using quantitative EEG features can accurately predict sedation levels. The results obtained in this study may provide a useful reference for developing next generation EEG based sedation level prediction systems using advanced machine learning algorithms. Clinical trial registration: NCT02043938 and NCT03143972.