Critical care medicine
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Critical care medicine · Feb 2024
Meta AnalysisSupraglottic Airway Versus Tracheal Intubation for Airway Management in Out-of-Hospital Cardiac Arrest: A Systematic Review, Meta-Analysis, and Trial Sequential Analysis of Randomized Controlled Trials.
Given the uncertainty regarding the optimal approach for airway management for adult patients with out-of-hospital cardiac arrest (OHCA), we conducted a systematic review and meta-analysis to compare the use of supraglottic airways (SGAs) with tracheal intubation for initial airway management in OHCA. ⋯ In adult patients with OHCA, compared with tracheal intubation, the use of SGA for initial airway management probably leads to more ROSC, and faster time to airway placement, but may have no effect on longer-term survival outcomes or aspiration events.
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Critical care medicine · Feb 2024
Multicenter StudyDeep Learning-Based Localization and Detection of Malpositioned Endotracheal Tube on Portable Supine Chest Radiographs in Intensive and Emergency Medicine: A Multicenter Retrospective Study.
We aimed to develop a computer-aided detection (CAD) system to localize and detect the malposition of endotracheal tubes (ETTs) on portable supine chest radiographs (CXRs). ⋯ The derived CAD system could localize ETT and detect ETT malposition with excellent performance, especially for endobronchial intubation, and with favorable potential for external generalizability.
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Critical care medicine · Feb 2024
Randomized Controlled TrialEffect of Automated Real-Time Feedback on Early-Sepsis Care: A Pragmatic Clinical Trial.
To determine if a real-time monitoring system with automated clinician alerts improves 3-hour sepsis bundle adherence. ⋯ Real-time monitoring and paging alerts significantly increased orders for and delivery of guideline-adherent care for suspected sepsis patients at risk of 3-hour bundle nonadherence. The trial was underpowered to determine whether adherence affected mortality. Despite enrolling patients with clinically suspected sepsis, early antibiotic discontinuation and pan-culture negativity were common, highlighting challenges in identifying appropriate patients for sepsis bundle application.
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Critical care medicine · Feb 2024
Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment.
Reinforcement learning (RL) is a machine learning technique uniquely effective at sequential decision-making, which makes it potentially relevant to ICU treatment challenges. We set out to systematically review, assess level-of-readiness and meta-analyze the effect of RL on outcomes for critically ill patients. ⋯ In this first systematic review on the application of RL in intensive care medicine we found no studies that demonstrated improved patient outcomes from RL-based technologies. All studies reported that RL-agent policies outperformed clinician policies, but such assessments were all based on retrospective off-policy evaluation.