Journal of clinical monitoring and computing
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J Clin Monit Comput · Oct 2024
Comparative Study Observational StudyThe Optimal pressure reactivity index range is disease-specific: A comparison between aneurysmal subarachnoid hemorrhage and traumatic brain injury.
Impaired cerebral pressure autoregulation is common and detrimental after acute brain injuries. Based on the prevalence of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage (aSAH) patients compared to traumatic brain injury (TBI), we hypothesized that the type of autoregulatory disturbance and the optimal PRx range may differ between these two conditions. The aim of this study was to determine the optimal PRx ranges in relation to functional outcome following aSAH and TBI, respectively. ⋯ Extreme PRx values in both directions were unfavorable in aSAH, possibly as high PRx could indicate proximal vasospasm with exhausted distal vasodilatory reserve, while very negative PRx could reflect myogenic hyperreactivity with suppressed cerebral blood flow. Only elevated PRx was unfavorable in TBI, possibly as pressure passive vessels may be a more predominant pathomechanism in this disease.
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J Clin Monit Comput · Oct 2024
Observational StudyInferior vena cava distensibility during pressure support ventilation: a prospective study evaluating interchangeability of subcostal and trans‑hepatic views, with both M‑mode and automatic border tracing.
The Inferior Vena Cava (IVC) is commonly utilized to evaluate fluid status in the Intensive Care Unit (ICU),with more recent emphasis on the study of venous congestion. It is predominantly measured via subcostal approach (SC) or trans-hepatic (TH) views, and automated border tracking (ABT) software has been introduced to facilitate its assessment. Prospective observational study on patients ventilated in pressure support ventilation (PSV) with 2 × 2 factorial design. ⋯ Correlation was generally suboptimal (r = 0.43 to 0.60). In PSV ventilated patients, we found that IVC-DI calculated with M-mode is not interchangeable with ABT measurements. Moreover, the IVC-DI gathered from SC or TH view produces not comparable results, mainly in terms of precision.
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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
Bayesian networks for Risk Assessment and postoperative deficit prediction in intraoperative neurophysiology for brain surgery.
To this day there is no consensus regarding evidence of usefulness of Intraoperative Neurophysiological Monitoring (IONM). Randomized controlled trials have not been performed in the past mainly because of difficulties in recruitment control subjects. In this study, we propose the use of Bayesian Networks to assess evidence in IONM. ⋯ Bayesian Networks are an effective way to audit clinical practice within IONM. We have found that IONM warnings can serve to prevent neurological deficits in patients, especially when corrective surgical action is taken to attempt to revert signals changes back to baseline properties. We show that Bayesian Networks could be used as a mathematical tool to calculate the utility of conducting IONM, which could save costs in healthcare when performed.
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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.