Anesthesia and analgesia
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Anesthesia and analgesia · May 2020
Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm.
Acute hypotensive episodes (AHE), defined as a drop in the mean arterial pressure (MAP) <65 mm Hg lasting at least 5 consecutive minutes, are among the most critical events in the intensive care unit (ICU). They are known to be associated with adverse outcome in critically ill patients. AHE prediction is of prime interest because it could allow for treatment adjustment to predict or shorten AHE. ⋯ The SL algorithm exhibits good performance for the prediction of an AHE 10 minutes ahead of time. It allows an efficient, robust, and rapid evaluation of the risk of hypotension that opens the way to routine use.
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Anesthesia and analgesia · May 2020
ReviewRegional Anesthesia for Pediatric Ophthalmic Surgery: A Review of the Literature.
Ophthalmic pediatric regional anesthesia has been widely described, but infrequently used. This review summarizes the available evidence supporting the use of conduction anesthesia in pediatric ophthalmic surgery. Key anatomic differences in axial length, intraocular pressure, and available orbital space between young children and adults impact conduct of ophthalmic regional anesthesia. ⋯ Intraconal blockade is a relative contraindication in neonates and infants, retinoblastoma surgery, and in the presence of posterior staphylomas and buphthalmos. Specific considerations include pertinent pediatric ophthalmologic topics, block placement in the syndromic child, and potential adverse effects associated with each technique. Recommendations based on our experience at a busy academic ophthalmologic tertiary referral center are provided.
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Anesthesia and analgesia · May 2020
Observational StudyUsing the Knowledge to Action Framework to Describe a Nationwide Implementation of the WHO Surgical Safety Checklist in Cameroon.
Surgical safety has advanced rapidly with evidence of improved patient outcomes through structural and process interventions. However, knowledge of how to apply these interventions successfully and sustainably at scale is often lacking. The 2019 Global Ministerial Patient Safety Summit called for a focus on implementation strategies to maintain momentum in patient safety improvements, especially in low- and middle-income settings. This study uses an implementation framework, knowledge to action, to examine a model of nationwide World Health Organization (WHO) Surgical Safety Checklist implementation in Cameroon. Cameroon is a lower-middle-income country, and based on data from high- and low-income countries, we hypothesized that more than 50% of participants would be using the checklist (penetration) in the correct manner (fidelity) 4 months postintervention. ⋯ This study shows that a multifaceted implementation strategy is associated with successful checklist implementation in a lower-middle-income country such as Cameroon, and suggests that a theoretical framework can be used to practically drive nationwide scale-up of checklist use.
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Anesthesia and analgesia · May 2020
Forecasting a Crisis: Machine-Learning Models Predict Occurrence of Intraoperative Bradycardia Associated With Hypotension.
Predictive analytics systems may improve perioperative care by enhancing preparation for, recognition of, and response to high-risk clinical events. Bradycardia is a fairly common and unpredictable clinical event with many causes; it may be benign or become associated with hypotension requiring aggressive treatment. Our aim was to build models to predict the occurrence of clinically significant intraoperative bradycardia at 3 time points during an operative course by utilizing available preoperative electronic medical record and intraoperative anesthesia information management system data. ⋯ We developed models to predict unstable bradycardia leveraging preoperative and real-time intraoperative data. Our study demonstrates how predictive models may be utilized to predict clinical events across multiple time intervals, with a future goal of developing real-time, intraoperative, decision support.