Journal of clinical monitoring and computing
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J Clin Monit Comput · Oct 2024
Impact of positive end-expiratory pressure on renal resistive index in mechanical ventilated patients.
Growing evidence shows the complex interaction between lung and kidney in critically ill patients. The renal resistive index (RRI) is a bedside measurement of the resistance of the renal blood flow and it is correlated with kidney injury. The positive end-expiratory pressure (PEEP) level could affect the resistance of renal blood flow, so we assumed that RRI could help to monitoring the changes in renal hemodynamics at different PEEP levels. Our hypothesis was that the RRI at ICU admission could predict the risk of acute kidney injury in mechanical ventilated critically ill patients. ⋯ RRI seems able to predict the risk of AKI in mechanical ventilated patients; further, RRI values are influenced by the PEEP level applied.
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J Clin Monit Comput · Oct 2024
Observational StudyMachine learning based analysis and detection of trend outliers for electromyographic neuromuscular monitoring.
Neuromuscular monitoring is frequently plagued by artefacts, which along with the frequent unawareness of the principles of this subtype of monitoring by many clinicians, tends to lead to a cynical attitute by clinicians towards these monitors. As such, the present study aims to derive a feature set and evaluate its discriminative performance for the purpose of Train-of-Four Ratio (TOF-R) outlier analysis during continuous intraoperative EMG-based neuromuscular monitoring. ⋯ Engineered TOF-R trend features and the resulting Cost-Sensitive Logistic Regression (CSLR) models provide useful insights and serve as a potential first step towards the automated removal of outliers for neuromuscular monitoring devices.
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J Clin Monit Comput · Oct 2024
Measurement of transcranial Doppler insonation angles from three-dimensional reconstructions of CT angiography scans.
Blood velocities measured by Transcranial Doppler (TCD) are dependent on the angle between the incident ultrasound beam and the direction of blood flow (known as the Doppler angle). However, when TCD examinations are performed without imaging the Doppler angle for each vessel segment is not known. We have measured Doppler angles in the basal cerebral arteries examined with TCD using three-dimensional (3D) vessel models generated from computed tomography angiography (CTA) scans. ⋯ Doppler angles were smallest for the middle cerebral artery M1 segment (median 24.6°) and ophthalmic artery (median 25.0°), and largest for the anterior cerebral artery A2 segment (median 76.4°) and posterior cerebral artery P2 segment (median 75.8°). The ophthalmic artery had the highest proportion of Doppler angles that were less than 60° (99%) while the anterior cerebral artery A2 segment had the lowest proportion of Doppler angles that were less than 60° (10%). These angle measurements indicate the expected deviation between measured and true velocities in the cerebral arteries, highlighting specific segments that may be prone to underestimation of velocity.
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This study retrospectively examined the hemodynamic effects of passive leg raising (PLR) in mechanically ventilated patients during fluid removal before spontaneous breathing trials. In previous studies, we noticed varying cardiac responses after PLR completion, particularly in positive tests. Using a bioreactance monitor, we recorded and analyzed hemodynamic parameters, including stroke volume and cardiac index (CI), before and after PLR in post-acute ICU patients. ⋯ This effect could be due to a combination of autotransfusion and sympathetic activation affecting venous return and vascular tone. Further research in larger cohorts and more comprehensive hemodynamic assessments are warranted to validate these observations and elucidate the possible underlying mechanisms. The Fluid unLoading On Weaning (FLOW) study was prospectively registered under the ID NCT04496583 on 2020-07-29 at ClinicalTrials.gov.
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J Clin Monit Comput · Oct 2024
Early prediction of ventricular peritoneal shunt dependency in aneurysmal subarachnoid haemorrhage patients by recurrent neural network-based machine learning using routine intensive care unit data.
Aneurysmal subarachnoid haemorrhage (aSAH) can lead to complications such as acute hydrocephalic congestion. Treatment of this acute condition often includes establishing an external ventricular drainage (EVD). However, chronic hydrocephalus develops in some patients, who then require placement of a permanent ventriculoperitoneal (VP) shunt. ⋯ At that point, the accuracy of the prediction was 76% (CI: 75.98-83.09%), with a sensitivity of 85% (CI: 83-88%) and a specificity of 74% (CI: 71-78%). RNN-based machine learning has the potential to predict VP shunt dependency on Day 4 after ictus in aSAH patients using routine data collected in the ICU. The use of machine learning may allow early identification of patients with specific therapeutic needs and accelerate the execution of required procedures.