Objective  To explore the value of constructing a predictive model using ultrasound controlled attenuation parameter (CAP) combined with clinical features in diagnosing fibrosis in patients with metabolic dysfunction-associated steatotic liver disease (MASLD).

Methods  This retrospective study analyzed adult samples from the National Health and Nutrition Examination Survey (NHANES) database between 2017 and 2020. MASLD was defined as CAP ≥ 248 dB/m, and fibrosis was defined as liver stiffness measured by transient elastography ≥ 8.2 kPa. Patients were divided into fibrosis and non-fibrosis groups. Features were selected using the Boruta algorithm, and a predictive model combining CAP and clinical features was constructed. The receiver operating characteristic curve and area under curve (AUC), sensitivity, specificity and accuracy were used to evaluate the model.

Results  A total of 1,472 MASLD patients were identified, with 213 patients in the fibrosis group and 1,259 in the non-fibrosis group. The features screened by the Boruta algorithm included waist circumference, body mass index, CAP, blood glucose, combined diabetes, ALT, AST, GGT, hs-CRP, age, ALB, ALP, STB and gender. AUC for CAP alone in predicting liver fibrosis was 0.727[95%CI(0.690, 0.765)] with a sensitivity of 62.4%, specificity of 70.2%, and accuracy of 69.1%. The AUC increased to 0.842[95%(0.813, 0.871)] when combining CAP with clinical features, with a sensitivity of 75.5%, specificity of 76.7%, and accuracy of 75.6%. Delong's test comparing the AUC values of CAP alone and CAP combined with clinical indicators indicated a statistically significant difference (Z=-6.877, P<0.001).

Conclusion  The prediction model constructed by CAP in combination with clinical features has good diagnostic efficacy in the diagnosis of MASLD fibrosis and provides a valuable reference tool for clinical practice.

1.Rinella ME, Lazarus JV, Ratziu V, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature[J]. Hepatology, 2023, 78(6): 1966-1986. DOI: 10.1097/HEP.0000000000000520.

2.Lazarus JV, Mark HE, Allen AM, et al. A global research priority agenda to advance public health responses to fatty liver disease[J]. J Hepatol, 2023, 79(3): 618-634. DOI: 10.1016/j.jhep.2023.04.035.

3.Yahoo N, Dudek M, Knolle P, et al. Role of immune responses in the development of NAFLD-associated liver cancer and prospects for therapeutic modulation[J]. J Hepatol, 2023, 79(2): 538-551. DOI: 10.1016/j.jhep. 2023.02.033.

4.Targher G, Byrne CD, Tilg H. MASLD: a systemic metabolic disorder with cardiovascular and malignant complications[J]. Gut, 2024, 73(4): 691-702. DOI: 10.1136/gutjnl-2023-330595.

5.Sanyal AJ, Castera L, Wong VW. Noninvasive assessment of liver fibrosis in NAFLD[J]. Clin Gastroenterol Hepatol, 2023, 21(8): 2026-2039. DOI: 10.1016/j.cgh.2023.03.042.

6.Xi WF, Yang AM. Association between cardiometabolic index and controlled attenuation parameter in U.S. adults with NAFLD: findings from NHANES (2017-2020)[J]. Lipids Health Dis, 2024, 23(1): 40. DOI: 10.1186/s12944-024-02027-x.

7.鲁景楠,王改莲,温雅,等. 受控衰减参数评估慢性乙肝患者肝脏脂肪变性的准确性及误诊分析[J]. 影像科学与光化学, 2023, 41(6): 290-295. [Lu JN, Wang GL, Wen Y, et al. Analysis of accuracy and misdiagnosis of controlled attenuation parameter in evaluating hepatic steatosis in patients with chronic hepatitis B[J]. Imaging Science and Photochemistry, 2023, 41(6): 290-295.] DOI: 10.7517/issn.1674-0475.230711.

8.景晓云,高岭,冯少阳. 瞬时弹性成像技术联合血清PTX3、YKL-40对乙型肝炎肝纤维化程度的诊断价值[J]. 中国实验诊断学, 2024, 28(6): 659-663. [Jing XY, Gao L, Feng SY. Diagnostic value of transient elastography combined with serum PTX3 and YKL-40 for the degree of liver fibrosis in hepatitis B[J]. Chinese Journal of Laboratory Diagnosis, 2024, 28(6): 659-663.] DOI: 10.3969/j.issn.1007-4287.2024.06.006.

9.刘继青,包剑锋,黄斌. 瞬时弹性成像技术联合血清学标志物评估慢性乙型肝炎早期肝纤维化的应用研究[J]. 中国现代医生, 2023, 61(34): 19-23. [Liu JQ, Bao JF, Huang B. Application of transient elastography combined with serological markers to distinguish early hepatic fibrosis of chronic hepatitis B[J]. China Modern Doctor, 2023, 61(34): 19-23.] DOI: 10.3969/j.issn.1673- 9701.2023.34.005.

10.王禹毅, 卜志军, 李元晞, 等. 临床预测模型变量筛选方法及比较[J]. 现代中医临床, 2024, 31(2): 6-12. [Wang YY, Bu ZJ, Li YX, et al. Variable selection methods and comparison in clinical prediction models[J]. Modern Chinese Clinical Medicine, 2024, 31(2): 6-12.] DOI: 10.3969/j.issn.2095-6606.2024.02.002.

11.Li Y, Lv X, Wang B, et al. Predicting EGFR T790M mutation in brain metastases using multisequence mri-based radiomics signature[J]. Acad Radiol, 2023, 30(9): 1887-1895. DOI: 10.1016/j.acra.2022.12.030.

12.Ye Z, An S, Gao Y, et al. Association between the triglyceride glucose index and in-hospital and 1-year mortality in patients with chronic kidney disease and coronary artery disease in the intensive care unit[J]. Cardiovasc Diabetol, 2023, 22(1): 110. DOI: 10.1186/s12933-023-01843-2.

13.Yan F, Chen X, Quan X, et al. Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning[J]. Cardiovasc Diabetol, 2024, 23(1): 163. DOI: 10.1186/s12933-024-02265-4.

14.Karlas T, Petroff D, Sasso M, et al. Individual patient data Meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis[J]. J Hepatol, 2017, 66(5): 1022-1030. DOI: 10.1016/j.jhep.2016.12.022.

15.Tian T, Zhang J, Xie W, et al. Dietary quality and relationships with metabolic dysfunction-associated fatty liver disease (MAFLD) among United States adults, results from NHANES 2017-2018[J]. Nutrients, 2022, 14(21): 4505. DOI: 10.3390/nu14214505.

16.Eddowes PJ, Sasso M, Allison M, et al. Accuracy of FibroScan controlled attenuation parameter and liver stiffness measurement in assessing steatosis and fibrosis in patients with nonalcoholic fatty liver disease[J]. Gastroenterology, 2019, 156(6): 1717-1730. DOI: 10.1053/j.gastro.2019.01.042.

17.Xiao G, Zhu S, Xiao X, et al. Comparison of laboratory tests, ultrasound, or magnetic resonance elastography to detect fibrosis in patients with nonalcoholic fatty liver disease: a Meta-analysis[J]. Hepatology, 2017, 66(5): 1486-1501. DOI: 10.1002/hep.29302.

18.Liu X, Shen H, Chen M, et al. Clinical relevance of vitamins and carotenoids with liver steatosis and fibrosis detected by transient elastography in adults[J]. Front Nutr, 2021, 8: 760985. DOI: 10.3389/fnut.2021.760985.

19.Liu J, Tan L, Liu Z, et al. The association between non-alcoholic fatty liver disease (NAFLD) and advanced fibrosis with blood selenium level based on the NHANES 2017-2018[J]. Ann Med, 2022, 54(1): 2259-2268. DOI: 10.1080/07853890.2022.2110277.

20.En Li Cho E, Ang CZ, Quek J, et al. Global prevalence of non-alcoholic fatty liver disease in type 2 diabetes mellitus: an updated systematic review and Meta-analysis[J]. Gut, 2023, 72(11): 2138-2148. DOI: 10.1136/gutjnl-2023-330110.

21.Kjaergaard M, Lindvig KP, Thorhauge KH, et al. Using the ELF test, FIB-4 and NAFLD fibrosis score to screen the population for liver disease[J]. J Hepatol, 2023, 79(2): 277-286. DOI: 10.1016/j.jhep.2023.04.002.

22.Lee DH, Sung SU, Lee YK, et al. A sequential approach using the age-adjusted fibrosis-4 index and vibration-controlled transient elastography to detect advanced fibrosis in Korean patients with non-alcoholic fatty liver disease[J]. Aliment Pharmacol Ther, 2022, 55(8): 994-1007. DOI: 10.1111/apt.16766.

23.Siddiqui MS, Vuppalanchi R, Van Natta ML, et al. Vibration-controlled transient elastography to assess fibrosis and steatosis in patients with nonalcoholic fatty liver disease[J]. Clin Gastroenterol Hepatol, 2019, 17(1): 156-163.e2. DOI: 10.1016/j.cgh.2018.04.043.

24.郭萌, 郭琦, 张峰. 超声瞬时弹性成像检测受控衰减参数评估非酒精性脂肪性肝病患者肝脂肪变程度价值研究[J]. 实用肝脏病杂志, 2024, 27(2): 189-192. [Guo M, Guo Q, Zhang F. Evaluation of hepatic steatosis by controlled attenuation parameter of ultrasonic transient elastography in patients with nonalcoholic fatty liver diseases[J]. Journal of Practical Hepatology, 2024, 27(2): 189-192.] DOI: 10.3969/j.issn.1672-5069.2024.02.008.

25.于一行, 牛雅宁, 张孟丽, 等. 基于超声影像组学的预测模型早期诊断甲状腺微小乳头状癌淋巴结转移的临床价值[J]. 临床超声医学杂志, 2024, 26(4): 279-284. [Yu YH, Niu YN, Zhang ML, et al. Clinical value of ultrasound imageomics-based predictive model for early diagnosis of lymph node metastasis in papillary thyroid microcarcinoma[J]. Journal of Clinical Ultrasound in Medicine, 2024, 26(4): 279-284.] DOI: 10.3969/j.issn.1008-6978.2024.04.004.

26.Zhang F, Han Y, Zheng L, et al. Association of non-invasive markers with significant fibrosis in patients with nonalcoholic fatty liver disease: a cross-sectional study[J]. Diabetes Metab Syndr Obes, 2023, 16: 2255-2268. DOI: 10.2147/DMSO.S417754.

27.Tahmasebi A, Wang S, Wessner CE. Ultrasound-based machine learning approach for detection of nonalcoholic fatty liver disease[J]. J Ultrasound Med, 2023, 42(8): 1747-1756. DOI: 10.1002/jum.16194.

28.陆晓玲, 陈冰. 机器学习在移植患者他克莫司个体化精准用药中的应用概述[J]. 药物流行病学杂志, 2023, 32(1): 82-88. [Lu XL, Chen B. Application of machine learning in the individualized therapy of tacrolimus in transplant patients[J]. Chinese Journal of Pharmacoepidemiology, 2023, 32(1): 82-88.] DOI: 10.19960/j.issn.1005-0698.202301011.

29.夏鑫,牟玮,李艳芬,等. 基于机器学习技术挖掘中医名家医案数据的方法探讨[J]. 医学新知, 2024, 34(4): 448-457. [Xia X, Mou W, Li YF, et al. Approaches to the mining of traditional Chinese medical experts'case histories using machine learning techniques[J]. Yixue Xinzhi Zazhi, 2024, 34(4): 448-457.] DOI: 10.12173/j.issn.1004-5511. 202312129.

30.Hussain FN, Rosenbluth E, Feldman KM. Transient elastography and controlled attenuation parameter to evaluate hepatic steatosis and liver stiffness in postpartum patients[J]. J Matern Fetal Neonatal Med, 2023, 36(1): 2190838. DOI: 10.1080/14767058.2023.2190838.

31.Ji Y, Wei CB, Gu W, et al. Relevance of vitamin D on NAFLD and liver fibrosis detected by vibration controlled transient elastography in US adults: a cross-sectional analysis of NHANES 2017-2018[J]. Ann Med, 2023, 55(1): 2209335. DOI: 10.1080/07853890.2023.2209335.