Miho Akabane , Jun Kawashima , Abdullah Altaf , Selamawit Woldesenbet , François Cauchy , Federico Aucejo , Irinel Popescu , Minoru Kitago , Guillaume Martel , Francesca Ratti , Luca Aldrighetti , George A. Poultsides , Yuki Imaoka , Andrea Ruzzenente , Itaru Endo , Ana Gleisner , Hugo P. Marques , Vincent Lam , Tom Hugh , Nazim Bhimani , Timothy M. Pawlik
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We sought to evaluate the combined impact of pre- and postoperative albumin-bilirubin (ALBI) score and Fibrosis-4 (FIB-4) index trends to predict severe complications after HCC resection.</div></div><div><h3>Method</h3><div>Patients with HCC undergoing curative-intent hepatectomy (2000–2023) were identified from an international, multi-institutional database. The cohort was divided into training (n = 439) and testing (n = 651) sets. ALBI score and FIB-4 index trends from preoperative to postoperative days 1, 3, and 5 were used for K-means clustering (K = 3). A logistic regression model was developed using the training set, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC) in both cohorts.</div></div><div><h3>Results</h3><div>Severe complications (Clavien-Dindo Grade ≥ IIIa) occurred in 118 patients (10.8 %); 43 (9.8 %) in training and 75 (11.5 %) in testing set (p = 0.42). K-means clustering identified three groups: Cluster1 (low), Cluster2 (intermediate), and Cluster3 (high), which was associated with a progressively increasing risk of complications (p < 0.01). On multivariable logistic regression, patients in ALBI Cluster1 had 76 % decreased odds (odds ratio[OR] 0.24, 95 % CI 0.07–0.83, p = 0.02) of postoperative complications relative to Cluster3 patients. Individuals categorized into FIB-4 Cluster1 had 85 % decreased odds (OR 0.15, 95 % CI 0.02–1.24, p = 0.07) versus patients in FIB-4 Cluster3. A new prediction model incorporating ALBI and FIB-4 index clusters achieved an AUC of 0.71, outperforming models based on preoperative data. A tool was made available at <span><span>https://nm49jf-miho-akabane.shinyapps.io/HCC_ALBI/</span><svg><path></path></svg></span>.</div></div><div><h3>Conclusion</h3><div>A dynamic ALBI score and FIB-4 index trend tool improved risk stratification of patients undergoing resection of HCC relative to severe complications.</div></div>","PeriodicalId":11522,"journal":{"name":"Ejso","volume":"51 6","pages":"Article 109723"},"PeriodicalIF":3.5000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic ALBI score and FIB-4 index trends to predict complications after resection of hepatocellular carcinoma: A K-means clustering approach\",\"authors\":\"Miho Akabane , Jun Kawashima , Abdullah Altaf , Selamawit Woldesenbet , François Cauchy , Federico Aucejo , Irinel Popescu , Minoru Kitago , Guillaume Martel , Francesca Ratti , Luca Aldrighetti , George A. 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引用次数: 0
摘要
背景:肝细胞癌(HCC)患者在肝切除术后仍会出现严重的术后并发症。有必要确定严重并发症的高危患者,以加强患者安全。我们试图评估术前和术后白蛋白-胆红素(ALBI)评分和纤维化-4 (FIB-4)指数趋势对预测HCC切除术后严重并发症的综合影响。方法从国际多机构数据库中确定2000-2023年接受治愈性肝切除术的HCC患者。该队列被分为训练组(n = 439)和测试组(n = 651)。ALBI评分和FIB-4指数从术前到术后第1、3和5天的趋势用于K-means聚类(K = 3)。使用训练集建立逻辑回归模型,并使用两个队列的受试者工作特征曲线下面积(AUC)评估其性能。结果严重并发症(Clavien-Dindo Grade≥IIIa) 118例(10.8%);训练组有43人(9.8%),测试组有75人(11.5%)(p = 0.42)。K-means聚类确定了三组:Cluster1(低)、Cluster2(中)和Cluster3(高),这与并发症的风险逐渐增加有关(p <;0.01)。多变量logistic回归分析显示,与Cluster3患者相比,Cluster1患者术后并发症发生率降低76%(比值比[OR] 0.24, 95% CI 0.07-0.83, p = 0.02)。与FIB-4 Cluster3患者相比,FIB-4 Cluster1患者的赔率降低了85% (OR 0.15, 95% CI 0.02-1.24, p = 0.07)。结合ALBI和FIB-4指数簇的新预测模型的AUC为0.71,优于基于术前数据的模型。在https://nm49jf-miho-akabane.shinyapps.io/HCC_ALBI/.ConclusionA上提供了一个工具,动态ALBI评分和FIB-4指数趋势工具可改善HCC切除术患者相对于严重并发症的风险分层。
Dynamic ALBI score and FIB-4 index trends to predict complications after resection of hepatocellular carcinoma: A K-means clustering approach
Background
Severe postoperative complications still occur following hepatectomy among patients with hepatocellular carcinoma (HCC). There is a need to identify high-risk patients for severe complications to enhance patient safety. We sought to evaluate the combined impact of pre- and postoperative albumin-bilirubin (ALBI) score and Fibrosis-4 (FIB-4) index trends to predict severe complications after HCC resection.
Method
Patients with HCC undergoing curative-intent hepatectomy (2000–2023) were identified from an international, multi-institutional database. The cohort was divided into training (n = 439) and testing (n = 651) sets. ALBI score and FIB-4 index trends from preoperative to postoperative days 1, 3, and 5 were used for K-means clustering (K = 3). A logistic regression model was developed using the training set, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC) in both cohorts.
Results
Severe complications (Clavien-Dindo Grade ≥ IIIa) occurred in 118 patients (10.8 %); 43 (9.8 %) in training and 75 (11.5 %) in testing set (p = 0.42). K-means clustering identified three groups: Cluster1 (low), Cluster2 (intermediate), and Cluster3 (high), which was associated with a progressively increasing risk of complications (p < 0.01). On multivariable logistic regression, patients in ALBI Cluster1 had 76 % decreased odds (odds ratio[OR] 0.24, 95 % CI 0.07–0.83, p = 0.02) of postoperative complications relative to Cluster3 patients. Individuals categorized into FIB-4 Cluster1 had 85 % decreased odds (OR 0.15, 95 % CI 0.02–1.24, p = 0.07) versus patients in FIB-4 Cluster3. A new prediction model incorporating ALBI and FIB-4 index clusters achieved an AUC of 0.71, outperforming models based on preoperative data. A tool was made available at https://nm49jf-miho-akabane.shinyapps.io/HCC_ALBI/.
Conclusion
A dynamic ALBI score and FIB-4 index trend tool improved risk stratification of patients undergoing resection of HCC relative to severe complications.
期刊介绍:
JSO - European Journal of Surgical Oncology ("the Journal of Cancer Surgery") is the Official Journal of the European Society of Surgical Oncology and BASO ~ the Association for Cancer Surgery.
The EJSO aims to advance surgical oncology research and practice through the publication of original research articles, review articles, editorials, debates and correspondence.