Preoperative risk factor analysis in orthotopic liver transplantation with pretransplant artificial liver support therapy

AIM： To assess the value of pre-transplant artificial liver support in reducing the pre-operative risk factors relating to early mortality after orthotopic liver transplantation （OLT）. METHODS： Fifty adult patients with various stages and various etiologies undergoing OLT procedures were treated with molecular adsorbent recycling system （MARS） as preoperative liver support therapy. The study included two parts, the first one is to evaluate the medical effectiveness of single MARS treatment with some clinical and laboratory parameters, which were supposed to be the therapeutical pre-transplant risk factors, the second part is to study the patients undergoing OLT using the regression analysis on preoperative risk factors relating to early mortality （30 d） after OLT. RESULTS： In the 50 patients, the statistically significant improvement in the biochemical parameters was observed （pre-treatment and post-treatment）. Eight patients avoided the scheduled Ltx due to significant relief of clinical condition or recovery of failing liver function, 8 patients died, 34 patients were successfully bridged to Ltx, the immediate outcome of this 34 patients within 30d observation was： 28 kept alive and 6 patients died. CONCLUSION： Pre-operative SOFA, level of creatinine, INR, TNF-α, IL-10 are the main preoperative risk factors that cause early death after operation, MARS treatment before transplantion can relieve these factors significantly.

Liver transplantation and artificial liver support in fulminant hepatic failure

INTRODUCTIONFulminant hepatic failure(FHF)is a severe disease with devastating consequences;the incidence is high in China.Before the availability of liver transplantation,the mortality rate was more than 80%[1,2].The advent of liver transplantation revolutionized the outcome of FHF[3,4].However,many patients were unwilling to accept liver transplantation until very late,hence most of them died because of donor shortage and urgency of the disease[5-7],To overcome he problems,we performed orthotopic liver transplantation(OLT)in combination with artificial liver support(ALS) in the treatment of FHF in the past 2 years with satisfactory results.Our experience was reported below.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

Improving the radiological diagnosis of hepatic artery thrombosis after liver transplantation: Current approaches and future challenges

Hepatic artery thrombosis(HAT)is a devastating vascular complication following liver transplantation,requiring prompt diagnosis and rapid revascularization treatment to prevent graft loss.At present,imaging modalities such as ultrasound,computed tomography,and magnetic resonance play crucial roles in diagnosing HAT.Although imaging techniques have improved sensitivity and specificity for HAT diagnosis,they have limitations that hinder the timely diagnosis of this complication.In this sense,the emergence of artificial intelligence(AI)presents a transformative opportunity to address these diagnostic limitations.The development of machine learning algorithms and deep neural networks has demonstrated the potential to enhance the precision diagnosis of liver transplant complications,enabling quicker and more accurate detection of HAT.This article examines the current landscape of imaging diagnostic techniques for HAT and explores the emerging role of AI in addressing future challenges in the diagnosis of HAT after liver transplant.

Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence

The shortage of deceased donor organs has prompted the development of alternative liver grafts for transplantation.Living-donor liver transplantation(LDLT)has emerged as a viable option,expanding the donor pool and enabling timely transplantation with favorable graft function and improved long-term outcomes.An accurate evaluation of the donor liver’s volumetry(LV)and anatomical study is crucial to ensure adequate future liver remnant,graft volume and precise liver resection.Thus,ensuring donor safety and an appropriate graftto-recipient weight ratio.Manual LV(MLV)using computed tomography has traditionally been considered the gold standard for assessing liver volume.However,the method has been limited by cost,subjectivity,and variability.Automated LV techniques employing advanced segmentation algorithms offer improved reproducibility,reduced variability,and enhanced efficiency compared to manual measurements.However,the accuracy of automated LV requires further investigation.The study provides a comprehensive review of traditional and emerging LV methods,including semi-automated image processing,automated LV techniques,and machine learning-based approaches.Additionally,the study discusses the respective strengths and weaknesses of each of the aforementioned techniques.The use of artificial intelligence(AI)technologies,including machine learning and deep learning,is expected to become a routine part of surgical planning in the near future.The implementation of AI is expected to enable faster and more accurate image study interpretations,improve workflow efficiency,and enhance the safety,speed,and cost-effectiveness of the procedures.Accurate preoperative assessment of the liver plays a crucial role in ensuring safe donor selection and improved outcomes in LDLT.MLV has inherent limitations that have led to the adoption of semi-automated and automated software solutions.Moreover,AI has tremendous potential for LV and segmentation;however,its widespread use is hindered by cost and availability.Therefore,the integration of multiple specialties is necessary to embrace technology and explore its possibilities,ranging from patient counseling to intraoperative decision-making through automation and AI.

Artificial liver support molecular adsorbents recirculating system therapy as a bridge to re-transplantation in two cases of long anhepatic duration

BACKGROUND: Molecular adsorbents recirculating sys- tem (MARS) liver support therapy is the development of albumin dialysis. This study was to assess the successful ap- plication of MARS artificial liver support therapy as a bridge to re-transplantation in two cases of long anhepatic duration. METHODS: MARS therapy was given after failure plasma- exchange ( PE) treatment, which resulted in circulatory de- rangement and acute renal dysfunction in a 36-year-old male patient. Finally his uncontrolled anhepatic condition led to a successful re-transplantation. In another 48-year- old man who was diagnosed as having primary nonfunction (PNF) during the liver transplantation, 10-hour MARS treatment contributed to smooth bridging of his anhepatic phase. RESULTS: The two anhepatic patients were bridged for 26 and 17 hours respectively to re-transplantation with MARS therapy. CONCLUSION: Our experience proves that MARS artifi- cial liver can be an effective support for long time bridging PNF until re-transplantation is available.

Artificial intelligence and liver transplantation:Looking for the best donor-recipient pairing

Decision-making based on artificial intelligence(AI)methodology is increasingly present in all areas of modern medicine.In recent years,models based on deep-learning have begun to be used in organ transplantation.Taking into account the huge number of factors and variables involved in donor-recipient(DR)matching,AI models may be well suited to improve organ allocation.AI-based models should provide two solutions:complement decision-making with current metrics based on logistic regression and improve their predictability.Hundreds of classifiers could be used to address this problem.However,not all of them are really useful for D-R pairing.Basically,in the decision to assign a given donor to a candidate in waiting list,a multitude of variables are handled,including donor,recipient,logistic and perioperative variables.Of these last two,some of them can be inferred indirectly from the team’s previous experience.Two groups of AI models have been used in the D-R matching:artificial neural networks(ANN)and random forest(RF).The former mimics the functional architecture of neurons,with input layers and output layers.The algorithms can be uni-or multi-objective.In general,ANNs can be used with large databases,where their generalizability is improved.However,they are models that are very sensitive to the quality of the databases and,in essence,they are black-box models in which all variables are important.Unfortunately,these models do not allow to know safely the weight of each variable.On the other hand,RF builds decision trees and works well with small cohorts.In addition,they can select top variables as with logistic regression.However,they are not useful with large databases,due to the extreme number of decision trees that they would generate,making them impractical.Both ANN and RF allow a successful donor allocation in over 80%of D-R pairing,a number much higher than that obtained with the best statistical metrics such as model for end-stage liver disease,balance of risk score,and survival outcomes following liver transplantation scores.Many barriers need to be overcome before these deeplearning-based models can be included for D-R matching.The main one of them is the resistance of the clinicians to leave their own decision to autonomous computational models.

Risk factors and prediction of acute kidney injury after liver transplantation:Logistic regression and artificial neural network approaches

BACKGROUND Acute kidney injury(AKI)has serious consequences on the prognosis of patients undergoing liver transplantation.Recently,artificial neural network(ANN)was reported to have better predictive ability than the classical logistic regression(LR)for this postoperative outcome.AIM To identify the risk factors of AKI after deceased-donor liver transplantation(DDLT)and compare the prediction performance of ANN with that of LR for this complication.METHODS Adult patients with no evidence of end-stage kidney dysfunction(KD)who underwent the first DDLT according to model for end-stage liver disease(MELD)score allocation system was evaluated.AKI was defined according to the International Club of Ascites criteria,and potential predictors of postoperative AKI were identified by LR.The prediction performance of both ANN and LR was tested.RESULTS The incidence of AKI was 60.6%(n=88/145)and the following predictors were identified by LR:MELD score>25(odds ratio[OR]=1.999),preoperative kidney dysfunction(OR=1.279),extended criteria donors(OR=1.191),intraoperative arterial hypotension(OR=1.935),intraoperative massive blood transfusion(MBT)(OR=1.830),and postoperative serum lactate(SL)(OR=2.001).The area under the receiver-operating characteristic curve was best for ANN(0.81,95%confidence interval[CI]:0.75-0.83)than for LR(0.71,95%CI:0.67-0.76).The root-mean-square error and mean absolute error in the ANN model were 0.47 and 0.38,respectively.CONCLUSION The severity of liver disease,pre-existing kidney dysfunction,marginal grafts,hemodynamic instability,MBT,and SL are predictors of postoperative AKI,and ANN has better prediction performance than LR in this scenario.

Artificial neural network for prediction of acute kidney injury after liver transplantation for cirrhosis and hepatocellular carcinoma

Acute kidney injury(AKI)has serious consequences on the prognosis of patients undergoing liver transplantation(LT)for liver cancer and cirrhosis.Artificial neural network(ANN)has recently been proposed as a useful tool in many fields in the setting of solid organ transplantation and surgical oncology,where patient prognosis depends on a multidimensional and nonlinear relationship between variables pertaining to the surgical procedure,the donor(graft characteristics),and the recipient comorbidities.In the specific case of LT,ANN models have been developed mainly to predict survival in patients with cirrhosis,to assess the best donor-to-recipient match during allocation processes,and to foresee postoperative complications and outcomes.This is a specific opinion review on the role of ANN in the prediction of AKI after LT for liver cancer and cirrhosis,highlighting potential strengths of the method to forecast this serious postoperative complication.

Liver transplantation in China: problems and their solutions

BACKGROUND: The past decade has witnessed the rapid development of liver transplantation in China. The 1-year survival of liver transplant patients comes to 80% in many leading medical centers and the number of liver transplanta- tion is increasing. However, liver transplantation in China is facing several challenges including recipient with hepato- cellular carcinoma (HCC), recurrence of HCC and hepati- tis B, long-term postoperative care, the bridge to liver transplantation, and shortage of liver donor. This review was to understand the status of and problems in liver trans- plantation in China. DATA RESOURCES: An English-language literature search using MEDLINE (1990-2003) on liver transplantation and other related reports and review articles in Chinese from major transplant centers in China. RESULTS: HCC is one of the main indications for liver transplantation in China but different centers adopted dif- ferent criteria for selection of patients. Hepatitis B virus re- infection is a vital problem after liver transplantation in HBV-related patients. More and more attention was fo- cused on long-term postoperative care and donor shortage. Artificial liver support system has been applied in patients waiting for a graft in many centers. CONCLUSIONS: HCC remains to be one of the main indi- cations for liver transplantation in China; combined hepati- tis B immune globulin and lamivudine is considered effec- tive to prevent hepatitis B virus reinfection. Apart from long-term postoperative care for the improvement of the survival rate, early steroid withdrawal is feasible in liver transplantation. Living donor liver transplantation, split liv- er transplantation, and marginal donor transplantation can deal with donor shortage to some extent. Artificial liver as- sist system serves as a bridge to liver transplantation.

Molecular adsorbent recycling system in treat-ing patients with acute liver failure: a bridge to liver transplantation

BACKGROUND:Acute liver failure is still a life-threaten- ing disease although it can be treated by liver transplanta- tion. This study was conducted to assess the molecular ad- sorbent recycling system (MARS), which may bridge acute liver failure patients to liver transplantation. METHODS: Biochemical indexes and other clinical data were analyzed of 8 patients with acute liver failure, who had been treated by MARS for 34 times and subsequent Piggyback liver transplantation. RESULTS: After treatment with MARS, the levels of tran- saminase and total bilirubin decreased markedly, but coagu- lation function remained unimproved. All patients survived and discharged from the hospital. CONCLUSION: MARS is effective in bridging patients with acute liver failure to liver transplantation.

Efficacy of liver transplantation for acute hepatic failure:asingle-center experience

BACKGROUND:Acute hepatic failure (AHF) is a devastating clinical syndrome with a high mortality rate.The outcome of AHF varies with etiology,but liver transplantation (LT) can significantly improve the prognosis and survival rate of such patients.This study aimed to detect the role of LT and artificial liver support systems (ALSS) for AHF patients and to analyze the etiology and outcome of patients with this disease.METHODS:A retrospective analysis was made of 48 consecutive patients with AHF who fulfilled the Kings College Criteria for LT at our center.We analyzed and compared the etiology,outcome,prognosis,and survival rates of patients between the transplantation (LT) group and the non-transplantation (N-LT) group.RESULTS:AHF was due to viral hepatitis in 25 patients (52.1%;hepatitis B virus in 22),drug or toxic reactions in 14 (29.2%;acetaminophen in 6),Wilson disease in 4 (8.3%),unknown reasons in 3 (6.3%),and miscellaneous conditions in 2 (4.2%).In the LT group,36 patients (7 underwent living donor LT,and 29 cadaveric LT) had an average model for endstage liver disease score (MELD) of 35.7.Twenty-eight patients survived with good graft function after a follow-up of 27.3± 4.5 months.During the waiting time,6 patients were treated with ALSS and 2 of them died during hospitalization.The 30-day,12-month,and 18-month survival rates were 77.8%,72.2%,and 66.7%,respectively.In the N-LT group,12 patients had an average MELD score of 34.5.Four patients were treated with ALSS and all died during hospitalization.The 90-day and 1-year survival rates were only 16.7% and 8.3%,respectively.CONCLUSIONS:Hepatitis is the most prominent cause of AHF at our center.Most patients with AHF,who fulfill the Kings College Criteria for LT,did not survive longer without LT.ALSS did not improve the prognosis of AHF patients,but may extend the waiting time for a donor.Currently,LT is still the most effective way to improve the prognosis of AHF patients.

A Molecular Adsorbent Recycling System in Treating Posthepatectomy Acute Hepatic Failure Patients with Hepatocellular Carcinoma:a Bridge to Liver Transplantation

OBJECTIVE To evaluate the effect and safety of a Molecular Adsorbent Recycling System （MARS） in treating posthepatoectomy hepatic failure （AHF） patients surgically treated for primary hepatocellular carcinoma （HCC）. METHODS 12 AHF patients induced by resection of HCC were treated with MARS before orthotopic liver transplantation （OLT）. Their vital signs, urine volume, APACHElll and Glasgow scores were monitored. Routine laboratory blood tests, measurements of coagulatory function, liver and kidney function, serum ammonia, lactic acid and blood gas were conducted before and after treatment with MARS. All of the patients were followed up for a period of 6 months after OLT for prognosis and complication assessment. RESULTS Each patient was treated with MARS for 2-5 times （average of 3.6） with a length of 8-24 h each time. Their mean arterial blood pressure and urine volume were improved, APACHE III and Glasgow scores were better. Liver function was improved with the following alterations before and after treatment with MARS： serum ammonia （127.1±21.4 umol/L vs. 77.4±19.7 umol/L, P〈0.05）, lactic acid （6.53±0.45 mmol/L vs. 3.75± 0.40 mmol/L, P〈0.05） and total bilirubin （452.3±153.7 umol/L vs. 230.9± 115.2 umol/L, P〈0.05）. However, there was no significant change in platelet count （44.25±3.60×10^9/L vs. 43.19±8.26×10^9/L, P〉0.05） on international normalized ratio （INR） （2.74±0.50 vs. 2.82±0.60, P〉0.05）, which showed the safety of MARS. For all patients no serious adverse effects occurred during the treatment with MARS. CONCLUSION MARS is effective and safe for treatment of AHF patients with HCC, especially as a bridge to OLT when a donor organ is not available.

Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation

Background: Acute kidney injury(AKI) is a common complication after liver transplantation(LT) and is an indicator of poor prognosis. The establishment of a more accurate preoperative prediction model of AKI could help to improve the prognosis of LT. Machine learning algorithms provide a potentially effective approach. Methods: A total of 493 patients with donation after cardiac death LT(DCDLT) were enrolled. AKI was defined according to the clinical practice guidelines of kidney disease: improving global outcomes(KDIGO). The clinical data of patients with AKI(AKI group) and without AKI(non-AKI group) were compared. With logistic regression analysis as a conventional model, four predictive machine learning models were developed using the following algorithms: random forest, support vector machine, classical decision tree, and conditional inference tree. The predictive power of these models was then evaluated using the area under the receiver operating characteristic curve(AUC). Results: The incidence of AKI was 35.7%(176/493) during the follow-up period. Compared with the nonAKI group, the AKI group showed a remarkably lower survival rate( P<0.001). The random forest model demonstrated the highest prediction accuracy of 0.79 with AUC of 0.850 [95% confidence interval(CI): 0.794–0.905], which was significantly higher than the AUCs of the other machine learning algorithms and logistic regression models( P<0.001). Conclusions: The random forest model based on machine learning algorithms for predicting AKI occurring after DCDLT demonstrated stronger predictive power than other models in our study. This suggests that machine learning methods may provide feasible tools for forecasting AKI after DCDLT.

Strategies to optimize the use of marginal donors in liver transplantation

Liver transplantation is the treatment of choice for end stage liver disease, but availability of liver grafts is still the main limitation to its wider use. Extended criteria donors(ECD) are considered not ideal for several reasons but their use has dramatically grown in the last decades in order to augment the donor liver pool. Due to improvement in surgical and medical strategies, results using grafts from these donors have become acceptable in terms of survival and complications; nevertheless a big debate still exists regarding their selection, discharge criteria and allocation policies. Many studies analyzed the use of these grafts from many points of view producing different or contradictory results so that accepted guidelines do not exist and the use of these grafts is still related to non-standardized policies changing from center to center. The aim of this review is to analyze every step of the donationtransplantation process emphasizing all those strategies, both clinical and experimental, that can optimize results using ECD.

MARS人工肝在肝移植围手术期中的应用

目的 总结肝移植围手术期应用分子吸附循环系统(MARS)人工肝的经验。方法 回顾分析 19 例48次MARS人工肝治疗的疗效。结果 单次或多次 MARS人工肝治疗能显著降低血清总胆红素、间接胆红素、血氨、尿素氮、肌酐、尿酸等水平。19例患者中15例成功过渡到肝移植治疗; 1 例因免疫抑制剂中毒经 MARS人工肝治疗症状改善; 1例移植肝功能好转; 1例晚期肝硬变患者治疗后肝性脑病症状得到缓解,因各种原因不愿再行肝移植,出院后2周死亡; 1例患者因上消化道大出血未能等到肝移植而死亡。治疗成功率为 89.5%。结论 MARS人工肝是治疗肝功能衰竭安全而有效的辅助方法,为使患者能过渡到肝移植创造了机会。

MARS人工肝治疗仪的临床使用和护理体会

近年人工肝脏技术发展迅速 ,有望成为重型肝炎肝衰竭及其它一些肝病最常用和最有效的治疗方法之一。分子吸附循环系统(MARS)人工肝治疗仪是新近用于临床的人工肝支持系统 ,在国内尚未普及。本文着重介绍了MARS人工肝治疗仪的工作原理及使用中的护理体会。

MARS人工肝治疗重型肝炎的操作注意事项

2005年3月至2010年3月在本科行MARS人工肝治疗的并且采用了MARS人工肝治疗的严重肝功能不全或肝衰竭患者,诊断符合《病毒性肝炎防治方案》诊断标准[1],在这几年,我科室共做260台MARS人工肝,在这几年工作经验的积累,操作技术及护理水平不断进步,治疗的效果也更加显著,现报道如下。

MARS人工肝治疗重型肝炎的观察和护理

目的：探讨MARS人工肝治疗重型肝炎的观察及护理措施。方法：26例重型肝炎患者,每例患者进行MARS人工肝治疗2~3次,每次治疗时间6 h,加强管道、观察、心理和并发症的护理。结果：通过MARS人工肝治疗和精心护理,治愈好转16例,无护理并发症发生。结论：重型肝炎早期采用MARS人工肝技术治疗,并加强相应的临床护理,提高了抢救成功率及安全性。

分子吸附循环系统(MARS人工肝)在肝移植围手术期应用的临床研究

目的 总结肝移植围手术期应用分子吸附循环系统 (MARS人工肝 )的经验。方法 回顾分析 19例 4 8次MARS人工肝治疗的疗效。结果 单次 6h(或多次 )MARS人工肝治疗能显著降低血清总胆红素、间接胆红素、血氨、尿素氮、肌苷、尿酸等水平 ,其中 15例病人成功过渡到肝移植 ,l例晚期肝硬化病人经人工肝治疗后肝性脑病症状得到缓解 ,因各种原因不愿再行肝移植 ,出院后 2周死亡 ,另 1例患者因上消化道大出血未能等到肝移植而死亡。 2例肝移植术后患者 ,治疗后好转出院。治疗成功率为 88.9%。结论 MARS人工肝分子吸附循环系统是治疗肝功能衰竭安全而有效的辅助方法 ,为过渡到肝移植创造了机会。