Journal of clinical monitoring and computing
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J Clin Monit Comput · Jan 2025
ReviewAssessing fluid responsiveness by using functional hemodynamic tests in critically ill patients: a narrative review and a profile-based clinical guide.
Fluids are given with the purpose of increasing cardiac output (CO), but approximately only 50% of critically ill patients are fluid responders. Since the effect of a fluid bolus is time-sensitive, it diminuish within few hours, following the initial fluid resuscitation. Several functional hemodynamic tests (FHTs), consisting of maneuvers affecting heart-lung interactions, have been conceived to discriminate fluid responders from non-responders. ⋯ This is due to the random CO fluctuations. Finally, the presence of continuous CO monitoring in ICU patients is not standard and the assessment of fluid responsiveness with surrogate methods is clinically useful, but also challenging. In this review we provide an algorithm for the use of FHTs in different subgroups of ICU patients, according to ventilatory setting, cardiac rhythm and the availability of continuous hemodynamic monitoring.
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J Clin Monit Comput · Dec 2024
ReviewAutomated and reference methods for the calculation of left ventricular outflow tract velocity time integral or ejection fraction by non-cardiologists: a systematic review on the agreement of the two methods.
Echocardiography is crucial for evaluating patients at risk of clinical deterioration. Left ventricular ejection fraction (LVEF) and velocity time integral (VTI) aid in diagnosing shock, but bedside calculations can be time-consuming and prone to variability. Artificial intelligence technology shows promise in providing assistance to clinicians performing point-of-care echocardiography. ⋯ The accuracy and precision of these automated methods should be considered within the clinical context and decision-making. Such variability could be acceptable, especially in the hands of trained operators. PROSPERO number CRD42024564868.
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J Clin Monit Comput · Dec 2024
ReviewElectroencephalogram monitoring during anesthesia and critical care: a guide for the clinician.
Perioperative anesthetic, surgical and critical careinterventions can affect brain physiology and overall brain health. The clinical utility of electroencephalogram (EEG) monitoring in anesthesia and intensive care settings is multifaceted, offering critical insights into the level of consciousness and depth of anesthesia, facilitating the titration of anesthetic doses, and enabling the detection of ischemic events and epileptic activity. ⋯ This review provides a comprehensive overview of the fundamental principles of electroencephalography, including the foundations of processed and quantitative electroencephalography. It further explores the characteristic EEG signatures associated wtih anesthetic drugs, the interpretation of the EEG data during anesthesia, and the broader clinical benefits and applications of EEG monitoring in both anesthetic practice and intensive care environments.
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J Clin Monit Comput · Dec 2024
Review Meta AnalysisBeyond the debut: unpacking six years of Hypotension Prediction Index software in intraoperative hypotension prevention - a systematic review and meta-analysis.
Intraoperative hypotension (IOH) during general anesthesia is associated with higher morbidity and mortality, although randomized trials have not established a causal relation. Historically, our approach to IOH has been reactive. The Hypotension Prediction Index (HPI) is a machine learning software that predicts hypotension minutes in advance. This systematic review and meta-analysis explores whether using HPI alongside a personalized treatment protocol decreases intraoperative hypotension. ⋯ While the combination of HPI software with personalized treatment protocols may prevent intraoperative hypotension (IOH), the large heterogeneity among the studies and the lack of reliable data on its clinical significance necessitate further investigation.
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J Clin Monit Comput · Dec 2024
Review Meta AnalysisBeyond the debut: unpacking six years of Hypotension Prediction Index software in intraoperative hypotension prevention - a systematic review and meta-analysis.
Intraoperative hypotension (IOH) during general anesthesia is associated with higher morbidity and mortality, although randomized trials have not established a causal relation. Historically, our approach to IOH has been reactive. The Hypotension Prediction Index (HPI) is a machine learning software that predicts hypotension minutes in advance. This systematic review and meta-analysis explores whether using HPI alongside a personalized treatment protocol decreases intraoperative hypotension. ⋯ While the combination of HPI software with personalized treatment protocols may prevent intraoperative hypotension (IOH), the large heterogeneity among the studies and the lack of reliable data on its clinical significance necessitate further investigation.