Journal of clinical monitoring and computing
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J Clin Monit Comput · Apr 2024
Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning.
This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. ⋯ The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.
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J Clin Monit Comput · Apr 2024
Impact of clinicians' behavior, an educational intervention with mandated blood pressure and the hypotension prediction index software on intraoperative hypotension: a mixed methods study.
Intraoperative hypotension (IOH) is associated with adverse outcomes. We therefore explored beliefs regarding IOH and barriers to its treatment. Secondarily, we assessed if an educational intervention and mandated mean arterial pressure (MAP), or the implementation of the Hypotension Prediction Index-software (HPI) were associated with a reduction in IOH. ⋯ Clinicians believed they had sufficient knowledge and skills, which could explain why no difference was found after the educational intervention. In the HPI cohort, IOH was significantly reduced compared to baseline, therefore HPI-software may help prevent IOH.
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J Clin Monit Comput · Apr 2024
A non-invasive continuous and real-time volumetric monitoring in spontaneous breathing subjects based on bioimpedance-ExSpiron®Xi: a validation study in healthy volunteers.
Tidal volume (TV) monitoring breath-by-breath is not available at bedside in non-intubated patients. However, TV monitoring may be useful to evaluate the work of breathing. A non-invasive device based on bioimpedance provides continuous and real-time volumetric tidal estimation during spontaneous breathing. ⋯ The calibration of the device did not improve its performance. Although the accuracy of ExSpiron®Xi was mild and the precision was limited for TV, TV/IBW and MV, the trending ability of the device was strong specifically for TV, TV/IBW and RR. This makes ExSpiron®Xi a non-invasive monitoring system that may detect real-time tidal volume ventilation changes and then suggest the need to better optimize the patient ventilatory support.
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J Clin Monit Comput · Feb 2024
Review Meta AnalysisPerformance of closed-loop systems for intravenous drug administration: a systematic review and meta-analysis of randomised controlled trials.
Closed-loop drug delivery systems are autonomous computers able to administer medication in response to changes in physiological parameters (controlled variables). While limited evidence suggested that closed-loop systems can perform better than manual drug administration in certain settings, this technology remains a research tool with an uncertain risk/benefit profile. Our aim was comparing the performance of closed-loop systems with manual intravenous drug administration in adults. ⋯ The certainty of the evidence was low or very low for most outcomes. Automatic technology may be used to improve the hemodynamic profile during noradrenaline and vasodilators administration and reduce the duration of postanaesthetic recovery. Registration: This systematic review was registered with PROSPERO (CRD42022336950) on the 7th of June 2022.