Anesthesia for Liver Transplantation

Adelmann, Dieter; Kronish, Kate; Ramsay, Michael A. E.

doi:10.1016/j.anclin.2017.04.006

Cited by 32 publications

(34 citation statements)

References 52 publications

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“…A low SvO 2 suggests poor oxygen delivery to the major organs including the kidney [ 36 ]. During liver transplantation, clamping of inferior vena cava and intraoperative bleeding decrease cardiac output and major organ perfusion, resulting in poor oxygen delivery, especially during graft reperfusion [ 37 ]. Therefore, SvO 2 may be an important hemodynamic goal.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

Lee

Yoon

Yang

et al. 2018

JCM

145

160

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Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86–0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56–0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.

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Section: Discussionmentioning

confidence: 99%

Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

Lee

Yoon

Yang

et al. 2018

JCM

145

160

View full text Add to dashboard Cite

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“…The resulting gap in care illustrates the poor relationship between quality and the cost of care . These findings may explain the paradox of why anesthesiologists strongly support full multidisciplinary team integration yet are unwilling to rank attendance at multidisciplinary meetings as a necessity …”

Section: Discussionmentioning

confidence: 99%

“…(24) These findings may explain the paradox of why anesthesiologists strongly support full multidisciplinary team integration yet are unwilling to rank attendance at multidisciplinary meetings as a necessity. (3,5,25,26) There are limitations to our study that may reduce the effectiveness of the recommendations. The Delphi method relies on expert opinion, (27) and we chose to use the OPTN/UNOS definition of the Director of Liver Transplant Anesthesia to define an expert in liver transplant anesthesia.…”

Section: Review Article | 589mentioning

confidence: 97%

Service Requirements of Liver Transplant Anesthesia Teams: Society for the Advancement of Transplant Anesthesia Recommendations

Hendrickse¹,

Crouch²,

Sakai³

et al. 2020

Liver Transpl

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There are disparities in liver transplant anesthesia team (LTAT) care across the United States. However, no policies address essential resources for liver transplant anesthesia services similar to other specialists. In response, the Society for the Advancement of Transplant Anesthesia appointed a task force to develop national recommendations. The Conditions of Transplant Center Participation were adapted to anesthesia team care and used to develop Delphi statements. A Delphi panel was put together by enlisting 21 experts from the fields of liver transplant anesthesiology and surgery, hepatology, critical care, and transplant nursing. Each panelist rated their agreement with and the importance of 17 statements. Strong support for the necessity and importance of 13 final items were as follows: resources, including preprocedure anesthesia assessment, advanced monitoring, immediate availability of consultants, and the presence of a documented expert in liver transplant anesthesia credentialed at the site of practice; call coverage, including schedules to assure uninterrupted coverage and methods to communicate availability; and characteristics of the team, including membership criteria, credentials at the site of practice, and identification of who supervises patient care. Unstructured comments identified competing time obligations for anesthesia and transplant services as the principle reason that the remaining recommendations to attend integrative patient selection and quality review committees were reduced to a suggestion rather than being a requirement. This has important consequences because deficits in team integration cause higher failure rates in service quality, timeliness, and efficiency. Solutions are needed that remove the time‐related financial constraints of competing service requirements for anesthesiologists. In conclusion, using a modified Delphi technique, 13 recommendations for the structure of LTATs were agreed upon by a multidisciplinary group of experts.

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“…Manipulation of the great vessels during the anhepatic phase could cause a decrease in the preload, and renal vein congestion may cause hemodynamic instability [ 33 ]. Reperfusion syndrome may also lead to low blood pressure during the reperfusion phase [ 34 , 35 ]. The reason why hemodynamic stability affected the correction coefficient was due to the measurement interval of those two techniques.…”

Section: Discussionmentioning

confidence: 99%

Non-invasive cardiac output measurement with electrical velocimetry in patients undergoing liver transplantation: comparison of an invasive method with pulmonary thermodilution

et al. 2018

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BackgroundThe goal of this study was to evaluate the accuracy and interchangeability between continuous cardiac output (CO) measured by electrical velocimetry (COEv) and continuous cardiac output obtained using the pulmonary thermodilution method (COPAC) during living donor liver transplantation (LDLT).MethodTwenty-three patients were enrolled in this prospective observational study. CO was recorded by both two methods and compared at nine specific time points. The data were analyzed using correlation coefficients, Bland-Altman analysis for the percentage errors, and the concordance rate for trend analysis using a four-quadrant plot.ResultsIn total, 207 paired datasets were recorded during LDLT. CO data were in the range of 2.8–12.7 L/min measured by PAC and 3.4–14.9 L/min derived from the EV machine. The correction coefficient between COPAC and COEv was 0.415 with p < 0.01. The 95% limitation agreement was − 5.9 to 3.4 L/min and the percentage error was 60%. The concordance rate was 56.5%.ConclusionsThe Aesculon™ monitor is not yet interchangeable with continuous thermodilution CO monitoring during LDLT.Trial registrationThe study was approved by the Institutional Review Board of Chang Gung Medical Foundation in Taiwan (registration number: 201600264B0).

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Anesthesia for Liver Transplantation

Cited by 32 publications

References 52 publications

Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

Service Requirements of Liver Transplant Anesthesia Teams: Society for the Advancement of Transplant Anesthesia Recommendations

Non-invasive cardiac output measurement with electrical velocimetry in patients undergoing liver transplantation: comparison of an invasive method with pulmonary thermodilution

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