Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2022
Observational StudyFocus on renal blood flow in mechanically ventilated patients with SARS-CoV-2: a prospective pilot study.
Mechanically ventilated patients with ARDS due to the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) seem particularly susceptible to AKI. Our hypothesis was that the renal blood flow could be more compromised in SARS-CoV-2 patients than in patients with "classical" ARDS. We compared the renal resistivity index (RRI) and the renal venous flow (RVF) in ARDS patients with SARS-CoV-2 and in ARDS patients due to other etiologies. ⋯ A linear correlation was found between PEEP and RRI in patients with SARS-COV-2 ARDS (r2 = 0.31; p = 0.03) but not in patients with ARDS. Occurrence of AKI was 53% in patients with SARS-COV-2 ARDS and 33% in patients with ARDS (p = 0.46). We found a more pronounced impairment in renal blood flow in mechanically ventilated patients with SARS-COV-2 ARDS, compared with patients with "classical" ARDS.
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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
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.
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J Clin Monit Comput · Feb 2022
Observational StudyClinical validation of a computerized algorithm to determine mean systemic filling pressure.
Mean systemic filling pressure (Pms) is a promising parameter in determining intravascular fluid status. Pms derived from venous return curves during inspiratory holds with incremental airway pressures (Pms-Insp) estimates Pms reliably but is labor-intensive. A computerized algorithm to calculate Pms (Pmsa) at the bedside has been proposed. ⋯ Further studies should be performed to determine the place of Pmsa in the circulatory management of critically ill patients. ( www.clinicaltrials.gov ; TRN NCT04202432, release date 16-12-2019; retrospectively registered). Clinical Trial Registration www. ClinicalTrials.gov , TRN: NCT04202432, initial release date 16-12-2019 (retrospectively registered).