Pub Date : 2024-12-16DOI: 10.1213/ane.0000000000006980
Zeev N. Kain, Thomas R. Vetter
An abstract is unavailable.
{"title":"The Involvement of Anesthesiologists in Alternative Payment Models, Value-Based Care, and Care-Redesign: Myth or Reality","authors":"Zeev N. Kain, Thomas R. Vetter","doi":"10.1213/ane.0000000000006980","DOIUrl":"https://doi.org/10.1213/ane.0000000000006980","url":null,"abstract":"An abstract is unavailable.","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831974","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}
Pub Date : 2024-12-16DOI: 10.1213/ane.0000000000007121
Andrzej P. Kwater, Michael C. Grant, Tong J. Gan
An abstract is unavailable.
{"title":"Magnesium and Its Emerging Role in Perioperative Pain Management","authors":"Andrzej P. Kwater, Michael C. Grant, Tong J. Gan","doi":"10.1213/ane.0000000000007121","DOIUrl":"https://doi.org/10.1213/ane.0000000000007121","url":null,"abstract":"An abstract is unavailable.","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831972","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}
Pub Date : 2024-12-16DOI: 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
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....
{"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}
Pub Date : 2024-12-16DOI: 10.1213/ane.0000000000007295
Jade Perdereau, Thibaut Chamoux, Etienne Gayat, Arthur Le Gall, Fabrice Vallée, Jérôme Cartailler, Jona Joachim
. 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....
.我们开发了一种深度学习模型，该模型可使用光电血压计 (PPG) 信号重建连续平均动脉压 (MAP)，并将其与动脉管路金标准进行比较。方法：我们分析了神经放射科和消化外科 117 位患者的高频 PPG 信号，每位患者的中位测量值为 2201（四分位数间距 [IQR]，788-4775）。我们比较了不同卷积层和递归层组合的模型，这些模型使用高频 PPG 作为神经网络的输入，并衍生出包括微凹槽相对振幅、灌注指数和心率在内的特征。平均绝对误差（MAE）被用作性能指标。使用 Grad-CAM 重构了深度学习模型的可解释性，Grad-CAM 是一种可视化技术，使用显著性图突出显示输入中对深度学习模型决策过程具有重要意义的部分。结果：仅由标准袖带测量值组成的 MAP 基线模型的 MAE 为 6.1 (± 14.5) mm Hg。相比之下，深度学习模型在外部测试集上的 MAE 为 3.5 (± 4.4) mm Hg（提高了 42.6%）。该模型还达到了最窄的置信区间，符合国际通用标准（A 级）。突出图显示，深度学习模型主要提取了微凹口区域附近的信息。结论：我们的深度学习模型可以无创、高精度地估算动脉压。该模型有望成为手术室环境中的决策支持工具，尤其是在有创血压监测无法使用的情况下....。
{"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}
Pub Date : 2024-12-16DOI: 10.1213/ane.0000000000007175
Edward J. Mascha
An abstract is unavailable.
{"title":"Additive and Multiplicative Interactions in Factorial Randomized Trials: What, Why, and How?","authors":"Edward J. Mascha","doi":"10.1213/ane.0000000000007175","DOIUrl":"https://doi.org/10.1213/ane.0000000000007175","url":null,"abstract":"An abstract is unavailable.","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"110 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831971","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}
Pub Date : 2024-12-16DOI: 10.1213/ane.0000000000007322
Alex Macario, Mohammed M. Minhaj, Mark T. Keegan, Ann E. Harman
An abstract is unavailable.
{"title":"Large Language Models and the American Board of Anesthesiology Examination","authors":"Alex Macario, Mohammed M. Minhaj, Mark T. Keegan, Ann E. Harman","doi":"10.1213/ane.0000000000007322","DOIUrl":"https://doi.org/10.1213/ane.0000000000007322","url":null,"abstract":"An abstract is unavailable.","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831964","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：