Anesthesia & Analgesia最新文献

The Involvement of Anesthesiologists in Alternative Payment Models, Value-Based Care, and Care-Redesign: Myth or Reality 麻醉医师参与替代支付模式、基于价值的医疗服务和医疗服务重新设计：神话还是现实

Anesthesia & Analgesia Pub Date : 2024-12-16 DOI: 10.1213/ane.0000000000006980

Zeev N. Kain, Thomas R. Vetter

引用次数: 0

Magnesium and Its Emerging Role in Perioperative Pain Management 镁及其在围手术期疼痛管理中的新作用

Anesthesia & Analgesia Pub Date : 2024-12-16 DOI: 10.1213/ane.0000000000007121

Andrzej P. Kwater, Michael C. Grant, Tong J. Gan

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Preoperative Anemia and Patient Vulnerability 术前贫血与患者的脆弱性

Anesthesia & Analgesia Pub Date : 2024-12-16 DOI: 10.1213/ane.0000000000007335

Naveen Nathan

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Optimal Maternal Ventilation During Laparotomy with General Anesthesia in Pregnancy in the Ovine Model 在妊娠期全身麻醉下进行腹腔手术时雌性绵羊模型的最佳产妇通气量

Anesthesia & Analgesia Pub Date : 2024-12-16 DOI: 10.1213/ane.0000000000006872

Tom Bleeser, Luc Joyeux, Simen Vergote, David Basurto, Ignacio Valenzuela, Talia Rose Hubble, Yada Kunpalin, Doaa Emam, Marc Van de Velde, Sarah Devroe, Jan Deprest, Steffen Rex

{"title":"Optimal Maternal Ventilation During Laparotomy with General Anesthesia in Pregnancy in the Ovine Model","authors":"Tom Bleeser, Luc Joyeux, Simen Vergote, David Basurto, Ignacio Valenzuela, Talia Rose Hubble, Yada Kunpalin, Doaa Emam, Marc Van de Velde, Sarah Devroe, Jan Deprest, Steffen Rex","doi":"10.1213/ane.0000000000006872","DOIUrl":"https://doi.org/10.1213/ane.0000000000006872","url":null,"abstract":"rnal paCO2 in awake pregnant women. However, there is no evidence that this target, compared to other targets, would enable optimal conditions for the fetus during general anesthesia. Maternal paCO2 can affect uterine blood flow, affinity of hemoglobin for oxygen, and fetal CO2 elimination. In this study, a range of potential targets of maternal paCO2 was investigated in the ovine model, aiming to determine which target is most conducive to physiological fetal blood gas values during laparotomy with general anesthesia. METHODS: Ten time-mated pregnant Swifter ewes with a gestational age of 93 to 104 days were used. During the first phase of the experiment, anesthesia was induced, all ewes were ventilated to target a physiological maternal paCO2 of 30 mm Hg, a maternal laparotomy was performed, and a fetal microcatheter was inserted surgically to enable blood sampling from the fetal aorta. Thereafter, in the second phase of the experiment, the 10 pregnant ewes were randomized to 10 different targets of maternal paCO2 between 27 and 50 mm Hg (1 target for each ewe), and maternal ventilation was adjusted accordingly. Forty-five minutes later, maternal and fetal arterial blood gas samples were analyzed. Linear regression models were used to estimate maternal paCO2 enabling physiologic fetal parameters, including fetal paCO2 (primary outcome). RESULTS: A maternal paCO2 of 27.4 mm Hg (95% confidence interval, 23.1–30.3) enabled physiological fetal paCO2. Each increase in maternal paCO2 by 1 mm Hg, on average, increased fetal paCO2 by 0.94 mm Hg (0.69–1.19). This relationship had a strong correlation (r² = 0.906). No fetuses died during the experiment. CONCLUSIONS: This study provides experimental support for the clinical recommendation to maintain maternal paCO2 close to the physiologic value of 30 mm Hg during general anesthesia for maternal laparotomy in pregnancy as it is conducive to physiological fetal blood gas values. Given the lower bound of the 95% confidence interval, the possibility that a lower maternal paCO2 would improve fetal gas exchange cannot be excluded....","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"223 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wearable Diabetes Devices - Perioperative Implications 可穿戴糖尿病设备--围手术期的影响

Anesthesia & Analgesia Pub Date : 2024-12-16 DOI: 10.1213/ane.0000000000007334

Naveen Nathan

引用次数: 0

Blood Pressure Estimation Using Explainable Deep-Learning Models Based on Photoplethysmography 使用基于光敏血压计的可解释深度学习模型估测血压

Anesthesia & Analgesia Pub Date : 2024-12-16 DOI: 10.1213/ane.0000000000007295

Jade Perdereau, Thibaut Chamoux, Etienne Gayat, Arthur Le Gall, Fabrice Vallée, Jérôme Cartailler, Jona Joachim

{"title":"Blood Pressure Estimation Using Explainable Deep-Learning Models Based on Photoplethysmography","authors":"Jade Perdereau, Thibaut Chamoux, Etienne Gayat, Arthur Le Gall, Fabrice Vallée, Jérôme Cartailler, Jona Joachim","doi":"10.1213/ane.0000000000007295","DOIUrl":"https://doi.org/10.1213/ane.0000000000007295","url":null,"abstract":". We developed a deep-learning model that reconstructs continuous mean arterial pressure (MAP) using the photoplethysmograhy (PPG) signal and compared it to the arterial line gold standard. METHODS: We analyzed high-frequency PPG signals from 117 patients in neuroradiology and digestive surgery with a median of 2201 (interquartile range [IQR], 788–4775) measurements per patient. We compared models with different combinations of convolutional and recurrent layers using as inputs for our neural network high-frequency PPG and derived features including dicrotic notch relative amplitude, perfusion index, and heart rate. Mean absolute error (MAE) was used as performance metrics. Explainability of the deep-learning model was reconstructed with Grad-CAM, a visualization technique using saliency maps to highlight the parts of an input that are significant for a deep-learning model decision-making process. RESULTS: An MAP baseline model, which consisted only of standard cuff measures, reached an MAE of 6.1 (± 14.5) mm Hg. In contrast, the deep-learning model achieved an MAE of 3.5 (± 4.4) mm Hg on the external test set (a 42.6% improvement). This model also achieved the narrowest confidence intervals and met international standards used within the community (grade A). The saliency map revealed that the deep-learning model primarily extracts information near the dicrotic notch region. CONCLUSIONS: Our deep-learning model noninvasively estimates arterial pressure with high accuracy. This model may show potential as a decision-support tool in operating-room settings, particularly in scenarios where invasive blood pressure monitoring is unavailable....","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142832269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Additive and Multiplicative Interactions in Factorial Randomized Trials: What, Why, and How? 因子随机试验中的加法和乘法相互作用：是什么、为什么、怎么做？

Anesthesia & Analgesia Pub Date : 2024-12-16 DOI: 10.1213/ane.0000000000007175

Edward J. Mascha

引用次数: 0

Large Language Models and the American Board of Anesthesiology Examination 大语言模型和美国麻醉学委员会考试

Anesthesia & Analgesia Pub Date : 2024-12-16 DOI: 10.1213/ane.0000000000007322

Alex Macario, Mohammed M. Minhaj, Mark T. Keegan, Ann E. Harman

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Self-Citation in Anesthesiology Journals: Methodology Concerns 麻醉学期刊中的自我引用：方法论问题

Anesthesia & Analgesia Pub Date : 2024-12-16 DOI: 10.1213/ane.0000000000007162

Shu-Yueh Cheng, Syu-Han Ren, Ming-Hui Hung

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In Response 回应

Anesthesia & Analgesia Pub Date : 2024-12-16 DOI: 10.1213/ane.0000000000007163

Burhan Dost, Alessandro De Cassai

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