ABSTRACT
This retrospective observational study evaluated the safety and efficacy of the ketamine and dexmedetomidine combination (keta-dex) compared to ketamine or dexmedetomidine alone for sedation of patients with acute respiratory distress due to COVID-19 pneumonia who require non-invasive ventilation. The following factors were assessed: tolerance to the ventilation, sedation level on the Richmond Agitation-Sedation Scale (RASS), hemodynamic and saturation profile, adverse effects, and discontinuation or mortality during ventilation. The study included 66 patients who underwent sedation for non-invasive ventilation using keta-dex (KETA-DEX group, n = 22), ketamine (KET group, n = 22), or dexmedetomidine (DEX group, n = 22). The DEX group showed a slower sedation rate and a significant reduction in blood pressure compared to the KETA-DEX group (p < 0.05). An increase in blood pressure was recorded more frequently in the KET group. No reduction in oxygen saturation and no deaths were observed in any of the groups. None of the patients discontinued ventilation due to intolerance. The mean duration of sedation was 28.12 h. No cases of delirium were observed in any of the groups. Overall, keta-dex was associated with faster sedation rates and better hemodynamic profiles compared to dexmedetomidine alone. Keta-dex is effective and safe for sedation of uncooperative patients undergoing non-invasive ventilation.
ABSTRACT
BACKGROUND: Shortage of human resources, increasing educational costs, and the need to keep social distances in response to the COVID-19 worldwide outbreak have prompted the necessity of clinical training methods designed for distance learning. Virtual patient simulators (VPSs) may partially meet these needs. Natural language processing (NLP) and intelligent tutoring systems (ITSs) may further enhance the educational impact of these simulators. OBJECTIVE: The goal of this study was to develop a VPS for clinical diagnostic reasoning that integrates interaction in natural language and an ITS. We also aimed to provide preliminary results of a short-term learning test administered on undergraduate students after use of the simulator. METHODS: We trained a Siamese long short-term memory network for anamnesis and NLP algorithms combined with Systematized Nomenclature of Medicine (SNOMED) ontology for diagnostic hypothesis generation. The ITS was structured on the concepts of knowledge, assessment, and learner models. To assess short-term learning changes, 15 undergraduate medical students underwent two identical tests, composed of multiple-choice questions, before and after performing a simulation by the virtual simulator. The test was made up of 22 questions; 11 of these were core questions that were specifically designed to evaluate clinical knowledge related to the simulated case. RESULTS: We developed a VPS called Hepius that allows students to gather clinical information from the patient's medical history, physical exam, and investigations and allows them to formulate a differential diagnosis by using natural language. Hepius is also an ITS that provides real-time step-by-step feedback to the student and suggests specific topics the student has to review to fill in potential knowledge gaps. Results from the short-term learning test showed an increase in both mean test score (P<.001) and mean score for core questions (P<.001) when comparing presimulation and postsimulation performance. CONCLUSIONS: By combining ITS and NLP technologies, Hepius may provide medical undergraduate students with a learning tool for training them in diagnostic reasoning. This may be particularly useful in a setting where students have restricted access to clinical wards, as is happening during the COVID-19 pandemic in many countries worldwide.