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Volume 42 Issue 5
May  2026
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Article Navigation >  Journal of Clinical Hepatology  > 2026  >  42(5): 1109-1118

Establishment of a predictive model for the risk of hypoalbuminemia after partial hepatectomy based on machine learning methods

DOI: 10.12449/JCH260516

  • Department of Hepatobiliary Surgery，Nanfang Hospital，Southern Medical University，Guangzhou 510445，China

Research funding:

General Project of National Natural Science Foundation of China (82270661);

General Project of National Natural Science Foundation of China (82570721);

Natural Science Foundation of Guangdong Province (2024A1515013204)

More Information
  • Corresponding author: ZHOU Jie， jacky@smu.cn （ORCID： 0009-0009-2722-4489）
  • Received Date: 2025-11-30
  • Accepted Date: 2026-02-09
  • Published Date: 2026-05-25
    Abstract
  •   Objective  To investigate the application value of a machine learning model based on preoperative clinical indicators in predicting the risk of hypoalbuminemia after partial hepatectomy.  Methods  A retrospective analysis was performed for the clinical data of 700 patients who underwent partial hepatectomy in Nanfang Hospital， Southern Medical University， from January 2018 to January 2023， including demographic data， history of underlying diseases， tumor characteristics， preoperative laboratory markers， and perioperative indicators. The research data were divided into a training set and a test set at a ratio of 7∶3. The two-independent-samples t test was used for comparison of normally distributed continuous data between two groups； the two-independent-samples Wilcoxon rank-sum test was used for comparison of continuous data with skewed distribution between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. The least absolute shrinkage and selection operator （LASSO） regression analysis was used to identify characteristic variables， and 7 machine learning algorithms were used to construct predictive models， i.e.， logistic regression， decision tree， artificial neural network， K-nearest neighbors （KNN）， support vector machine， eXtreme gradient boosting， and light gradient boosting machine. The receiver operating characteristic （ROC） curve and the area under the ROC curve （AUC） were used to assess the discriminatory ability of models， and the DeLong test was used for comparison of AUC. The calibration curve and decision curve analysis were used to assess the calibration and clinical practicability of models， and the models were compared with albumin-bilirubin （ALBI） score and Model for End-Stage Liver Disease （MELD） score. SHapley Additive exPlanations （SHAP） were used to interpret the key influencing factors for the optimal model.  Results  A total of 700 patients were finally enrolled， 283 （40.42%） developed hypoalbuminemia after surgery. The LASSO regression analysis identified 8 predictive factors of age， hepatitis B， fatty liver， blockade time， preoperative albumin （Alb）， time of operation， intraoperative blood loss， and preoperative aspartate aminotransferase （AST）. Among the 7 machine learning models， the KNN model showed the best overall predictive performance， with an AUC of 0.835 （95% confidence interval： 0.781 — 0.889）， a sensitivity of 84.0%， and a specificity of 65.5% in the test set. ALBI and MELD scores had an AUC of 0.652 and 0.524， respectively， and the KNN model had a better predictive performance than these two scores （Z=5.309 and 8.945， both P <0.001）. The calibration curve showed good consistency between predicted probabilities and actual incidence rates， and the decision curve analysis showed that the KNN model had net clinical benefit across a wide threshold range. The SHAP analysis showed that preoperative Alb， hepatitis B， time of operation， and age were the most significant influencing factors， and a synergistic effect was observed between hepatitis B and age/time of operation.  Conclusion  The KNN machine learning model constructed based on preoperative clinical indicators can effectively predict the risk of hypoalbuminemia after partial hepatectomy and has a better performance than traditional scoring models， which provides a reference for the early identification of high-risk patients in clinical practice.

     

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    • References(26)
  • [1]
    LIU R, HU SY. Clinical practice guidelines for laparoscopic hepatopancreatobiliary surgery[J]. J Clin Hepatol, 2019, 35( 7): 1450- 1458. DOI: 10.3969/j.issn.1001-5256.2019.07.008.

    刘荣, 胡三元. 腹腔镜肝胆胰手术操作指南[J]. 临床肝胆病杂志, 2019, 35( 7): 1450- 1458. DOI: 10.3969/j.issn.1001-5256.2019.07.008.
    [2]
    GATTA A, VERARDO A, BOLOGNESI M. Hypoalbuminemia[J]. Intern Emerg Med, 2012, 7( 3): 193- 199. DOI: 10.1007/s11739-012-0802-0.
    [3]
    LI MX, QIN L, QIU ZC, et al. Influence of platelet-albumin-bilirubin score on textbook outcome of patients with hepatocellular carcinoma after hepatectomy[J]. J Clin Hepatol, 2025, 41( 5): 927- 933. DOI: 10.12449/JCH250519.

    李梅霞, 覃莉, 邱占成, 等. 血小板-白蛋白-胆红素评分对肝细胞癌患者肝切除术后教科书式结局的影响[J]. 临床肝胆病杂志, 2025, 41( 5): 927- 933. DOI: 10.12449/JCH250519.
    [4]
    ROTHSCHILD MA, ORATZ M, SCHREIBER SS. Serum albumin[J]. Hepatology, 1988, 8( 2): 385- 401. DOI: 10.1002/hep.1840080234.
    [5]
    FANALI G, di MASI A, TREZZA V, et al. Human serum albumin: From bench to bedside[J]. Mol Aspects Med, 2012, 33( 3): 209- 290. DOI: 10.1016/j.mam.2011.12.002.
    [6]
    WEIMANN A, BRAGA M, CARLI F, et al. ESPEN practical guideline: Clinical nutrition in surgery[J]. Clin Nutr, 2021, 40( 7): 4745- 4761. DOI: 10.1016/j.clnu.2021.03.031.
    [7]
    SOETERS PB, WOLFE RR, SHENKIN A. Hypoalbuminemia: Pathogenesis and clinical significance[J]. JPEN J Parenter Enteral Nutr, 2019, 43( 2): 181- 193. DOI: 10.1002/jpen.1451.
    [8]
    GARCIA-MARTINEZ R, CARACENI P, BERNARDI M, et al. Albumin: Pathophysiologic basis of its role in the treatment of cirrhosis and its complications[J]. Hepatology, 2013, 58( 5): 1836- 1846. DOI: 10.1002/hep.26338.
    [9]
    WIEDERMANN CJ. Hypoalbuminemia as surrogate and culprit of infections[J]. Int J Mol Sci, 2021, 22( 9): 4496. DOI: 10.3390/ijms22094496.
    [10]
    BOHL DD, SHEN MR, KAYUPOV E, et al. Hypoalbuminemia independently predicts surgical site infection, pneumonia, length of stay, and readmission after total joint arthroplasty[J]. J Arthroplasty, 2016, 31( 1): 15- 21. DOI: 10.1016/j.arth.2015.08.028.
    [11]
    BOHL DD, SHEN MR, HANNON CP, et al. Serum albumin predicts survival and postoperative course following surgery for geriatric hip fracture[J]. J Bone Joint Surg Am, 2017, 99( 24): 2110- 2118. DOI: 10.2106/JBJS.16.01620.
    [12]
    SHMATKO A, JUNG AW, GAURAV K, et al. Learning the natural history of human disease with generative transformers[J]. Nature, 2025, 647( 8088): 248- 256. DOI: 10.1038/s41586-025-09529-3.
    [13]
    CHEN M, DECARY M. Artificial intelligence in healthcare: An essential guide for health leaders[J]. Healthc Manage Forum, 2020, 33( 1): 10- 18. DOI: 10.1177/0840470419873123.
    [14]
    PLAUTH M, BERNAL W, DASARATHY S, et al. ESPEN guideline on clinical nutrition in liver disease[J]. Clin Nutr, 2019, 38( 2): 485- 521. DOI: 10.1016/j.clnu.2018.12.022.
    [15]
    CHEN GH, JIA WD, YANG Y, et al. Chinese expert consensus on enhanced recovery after hepatectomy(2017)[J]. J Clin Hepatol, 2017, 33( 10): 1876- 1882. DOI: 10.3969/j.issn.1001-5256.2017.10.006.

    陈规划, 荚卫东, 杨扬, 等. 肝切除术后加速康复中国专家共识(2017版)[J]. 临床肝胆病杂志, 2017, 33( 10): 1876- 1882. DOI: 10.3969/j.issn.1001-5256.2017.10.006.
    [16]
    AHN J, SUNDARAM V, AYOUB WS, et al. Hypoalbuminemia is associated with significantly higher liver transplant waitlist mortality and lower probability of receiving liver transplant[J]. J Clin Gastroenterol, 2018, 52( 10): 913- 917. DOI: 10.1097/MCG.0000000000000984.
    [17]
    FANG XY, WANG SJ, XU HW, et al. Research progress on the correlation between perioperative hypoalbuminemia and adverse events after spinal surgery[J]. Chin J Spine Spinal Cord, 2025, 35( 1): 91- 95. DOI: 10.3969/j.issn.1004-406X.2025.01.13.

    房心月, 王善金, 徐浩伟, 等. 围手术期低白蛋白血症与脊柱术后不良事件的相关性研究进展[J]. 中国脊柱脊髓杂志, 2025, 35( 1): 91- 95. DOI: 10.3969/j.issn.1004-406X.2025.01.13.
    [18]
    SU ZR, JIA WD, XU GL, et al. Liver functional reserve estimation in hepatitis B virus-related hepatocellular carcinoma: A preliminary study of three methods[J]. Chin J Gen Surg, 2012, 21( 7): 783- 786.

    苏昭然, 荚卫东, 许戈良, 等. 乙肝相关性肝癌肝脏储备功能评估方案的初步探讨[J]. 中国普通外科杂志, 2012, 21( 7): 783- 786.
    [19]
    YU BR, CHEN YW. Basic research on chronic hepatitis B virus infection and metabolic dysfunction: Advances and controversies[J]. J Clin Hepatol, 2024, 40( 3): 446- 452. DOI: 10.12449/JCH240302.

    余博容, 陈源文. 慢性 HBV 感染与代谢功能障碍基础研究: 当前进展与争议[J]. 临床肝胆病杂志, 2024, 40( 3): 446- 452. DOI: 10.12449/JCH240302.
    [20]
    WONG GL, CHAN HL, WONG CK, et al. Liver stiffness-based optimization of hepatocellular carcinoma risk score in patients with chronic hepatitis B[J]. J Hepatol, 2014, 60( 2): 339- 345. DOI: 10.1016/j.jhep.2013.09.029.
    [21]
    ALLAN K, GRIMBLE G, WHELAN K, et al. Metabolic response, energy expenditure and nutritional status of patients undergoing coronary artery bypass surgery[J]. Clin Nutr ESPEN, 2025, 68: 869. DOI: 10.1016/j.clnesp.2025.03.155.
    [22]
    TØNNESEN E, BACH V, ENGQVIST A, et al. Perioperative electrolyte and fluid management. A survey about knowledge, teaching, routines and placing of responsibility among Danish anaesthetists and surgeons: A-836[J]. Eur J Anaesthesiol, 2004, 21( Supplement 32): 206. DOI: 10.1097/00003643-200406002-00746.
    [23]
    HAJIBANDEH S. Comments on“sarcopenia and postoperative complication risk in gastrointestinal surgical oncology: A meta-analysis”[J]. Ann Surg, 2019, 270( 2): e24. DOI: 10.1097/SLA.0000000000003011.
    [24]
    QIU JJ, MO XS, TENG YJ, et al. Establishment and evaluation of a nomogram risk prediction model for severe complications in patients after hepatectomy for hepatocellular carcinoma[J]. Chin J Gen Surg, 2021, 30( 1): 24- 31. DOI: 10.7659/j.issn.1005-6947.2021.01.004.

    邱洁净, 莫新少, 滕艳娟, 等. 肝细胞癌患者肝切除术后严重并发症列线图风险预测模型的建立与评价[J]. 中国普通外科杂志, 2021, 30( 1): 24- 31. DOI: 10.7659/j.issn.1005-6947.2021.01.004.
    [25]
    ZHANG SC, ZHOU DC, HOU H, et al. ICG-R15-based clinical prediction model of liver failure after hepatectomy for hepatocellular carcinoma[J]. Acta Univ Med Anhui, 2023, 58( 9): 1593- 1598. DOI: 10.19405/j.cnki.issn1000-1492.2023.09.026.

    张思成, 周大臣, 侯辉, 等. 基于ICG-R15的肝细胞型肝癌肝切除术后肝衰竭的列线图临床预测模型[J]. 安徽医科大学学报, 2023, 58( 9): 1593- 1598. DOI: 10.19405/j.cnki.issn1000-1492.2023.09.026.
    [26]
    JOLIAT GR, KOBAYASHI K, HASEGAWA K, et al. Guidelines for perioperative care for liver surgery: Enhanced recovery after surgery(ERAS) society recommendations 2022[J]. World J Surg, 2023, 47( 1): 11- 34. DOI: 10.1007/s00268-022-06732-5.
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