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Background: Patients in acute care wards who deteriorate and are emergently transferred to intensive care units (ICUs) have poor outcomes. Early identification of patients who are decompensating might allow for earlier clinical intervention and reduced morbidity and mortality. Advances in bedside continuous predictive analytics monitoring (ie, artificial intelligence [AI]-based risk prediction) have made complex data easily available to health care providers and have provided early warning of potentially catastrophic clinical events. We present a dynamic, visual, predictive analytics monitoring tool that integrates real-time bedside telemetric physiologic data into robust clinical models to estimate and communicate risk of imminent events. This tool, Continuous Monitoring of Event Trajectories (CoMET), has been shown in retrospective observational studies to predict clinical decompensation on the acute care ward. There is a need to more definitively study this advanced predictive analytics or AI monitoring system in a prospective, randomized controlled, clinical trial.
Objective: The goal of this trial is to determine the impact of an AI-based visual risk analytic, CoMET, on improving patient outcomes related to clinical deterioration, response time to proactive clinical action, and costs to the health care system.
Methods: We propose a cluster randomized controlled trial to test the impact of using the CoMET display in an acute care cardiology and cardiothoracic surgery hospital floor. The number of admissions to a room undergoing cluster randomization was estimated to be 10,424 over the 20-month study period. Cluster randomization based on bed number will occur every 2 months. The intervention cluster will have the CoMET score displayed (along with standard of care), while the usual care group will receive standard of care only.
Results: The primary outcome will be hours free from events of clinical deterioration. Hours of acute clinical events are defined as time when one or more of the following occur: emergent ICU transfer, emergent surgery prior to ICU transfer, cardiac arrest prior to ICU transfer, emergent intubation, or death. The clinical trial began randomization in January 2021.
Conclusions: Very few AI-based health analytics have been translated from algorithm to real-world use. This study will use robust, prospective, randomized controlled, clinical trial methodology to assess the effectiveness of an advanced AI predictive analytics monitoring system in incorporating real-time telemetric data for identifying clinical deterioration on acute care wards. This analysis will strengthen the ability of health care organizations to evolve as learning health systems, in which bioinformatics data are applied to improve patient outcomes by incorporating AI into knowledge tools that are successfully integrated into clinical practice by health care providers.
Trial Registration: ClinicalTrials.gov NCT04359641; https://clinicaltrials.gov/ct2/show/NCT04359641.
International Registered Report Identifier (irrid): DERR1-10.2196/29631.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8285742 | PMC |
http://dx.doi.org/10.2196/29631 | DOI Listing |
Diabetol Metab Syndr
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Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, NO.1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
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View Article and Find Full Text PDFJ Headache Pain
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Pfizer SAS, Bogota, Colombia.
Background: To describe the epidemiology and clinical characteristics of migraine and the status of treatment in Colombia. Additionally, the use of health resources by patients was measured.
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Mol Med
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Department of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
Background: Acute myocardial infarction (AMI) remains a significant cause of global mortality, exacerbated by ischemia-reperfusion (IR) injury. Myocardial cell pyroptosis has emerged as a critical pathway influencing IR injury severity.
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Sci Rep
December 2024
Department of Critical Care Medicine, The Second Hospital of Dalian Medical University, Dalian Medical University, Dalian, 116000, Liaoning, China.
Acute myocardial infarction (AMI) and sepsis are the leading causes of high mortality rates in intensive care units. While sepsis frequently affects the cardiovascular system, distinguishing between sepsis-induced cardiomyopathy and AMI remains challenging due to overlapping biomarkers. Misdiagnosis can hinder timely treatment and increase risk of complications.
View Article and Find Full Text PDFInt J Stroke
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PN Sylaja, DM, Professor and Head of Neurology, SCTIMST, Thiruvananthapuram, Kerala, India.
Background: Stroke is a leading cause of global mortality and disability, with a disproportionately high burden in low- and middle-income countries. Access to intravenous thrombolysis (IVT) and endovascular treatment (EVT) remains extremely limited.
Aims: We evaluated the spatial distribution and geographic accessibility of stroke centers in India.
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