Surgical Risk Stratification in Patients with Cirrhosis
Hepatology International(2024)
University Hospital Center Zagreb | University of Pennsylvania
Abstract
Individuals with cirrhosis experience higher morbidity and mortality rates than the general population, irrespective of the type or scope of surgery. This increased risk is attributed to adverse effects of liver disease, encompassing coagulation dysfunction, altered metabolism of anesthesia and sedatives, immunologic dysfunction, hemorrhage related to varices, malnutrition and frailty, impaired wound healing, as well as diminished portal blood flow, overall hepatic circulation, and hepatic oxygen supply during surgical procedures. Therefore, a frequent clinical dilemma is whether surgical interventions should be pursued in patients with cirrhosis. Several risk scores are widely used to aid in the decision-making process, each with specific advantages and limitations. This review aims to discuss the preoperative risk factors in patients with cirrhosis, describe and compare surgical risk assessment models used in everyday practice, provide insights into the surgical risk according to the type of surgery and present recommendations for optimizing those with cirrhosis for surgical procedures. As the primary focus is on currently available risk models, the review describes the predictive value of each model, highlighting its specific advantages and limitations. Furthermore, for models that do not account for the type of surgical procedure to be performed, the review suggests incorporating both patient-related and surgery-related risks into the decision-making process. Finally, we provide an algorithm for the preoperative assessment of patients with cirrhosis before elective surgery as well as guidance perioperative management.
MoreTranslated text
Key words
Liver cirrhosis,Hepatic surgery,Non-hepatic surgery,Surgical risk,Surgical risk assessment,Child–Turcotte–Pugh classification,MELD,Mayo risk model,VOCAL-Penn score
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2011
被引用175 | 浏览
2000
被引用669 | 浏览
2005
被引用139 | 浏览
2004
被引用247 | 浏览
2011
被引用32 | 浏览
2011
被引用246 | 浏览
2012
被引用97 | 浏览
2015
被引用56 | 浏览
2019
被引用28 | 浏览
2019
被引用25 | 浏览
2019
被引用25 | 浏览
2020
被引用10 | 浏览
2021
被引用24 | 浏览
2022
被引用4 | 浏览
2022
被引用4 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest