Diagnostic significance of clinical and laboratory indices in predicting non-alcoholic fatty liver disease during screening studies
- 作者: Nosov A.E.1, Zenina M.T.1, Gorbushina O.Y.1, Baidina A.S.1, Vlasova E.M.1, Alekseev V.B.1
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隶属关系:
- Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
- 期: 卷 93, 编号 8 (2021)
- 页面: 883-889
- 栏目: Original articles
- URL: https://ogarev-online.ru/0040-3660/article/view/79422
- DOI: https://doi.org/10.26442/00403660.2021.08.200973
- ID: 79422
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Aim. To study the significance of clinical and laboratory non-invasive indexes along with the insulin resistance index when carrying out diagnostic assessment of non-alcoholic fatty liver disease (NAFLD) during screening examinations.
Materials and methods. The study involved 348 employees working at oil-production enterprises. An ultrasound scanning of the liver was carried out to assess the criteria of NAFLD. The following indexes were calculated: fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation products (LAP), and homeostasis model assessment of insulin resistance (HOMA1-IR). The prognostic significance of these indexes in relation to the probability of NAFLD diagnosis based on ultrasound data was studied using single-factor and multi-factor logistic regression models followed by ROC-analysis.
Results. The FLI, HSI, and HOMA1-IR indexes in single-factor logistic regression models showed a high statistical significance when carrying out diagnostic assessment the NAFLD with good model calibration capability. The percentage of correct binary classification regards the presence/absence of NAFLD amounted to 82.4% for FLI, 79.7% for HSI, and 72.7% for HOMA1-IR (p<0.001). According to the ROC-analysis, the area under the curve (AUC) by the NAFLD diagnostic assessment was 0.917 (95% CI 0.889–0.945); 0.880 (95% CI 0.846–0.915) and 0.849 (95% CI 0.764–0.934), respectively. The multi-factor logistic regression model with the inclusion of FLI and HOMA1-IR 72.7% enabled us to achieve the correct binary classification in terms of NAFLD in 84.2% of cases. When it comes to the ROC-analysis, considering the probabilities predicted in the multi-factor logistic model as the test variable and NAFLD in ultrasound examination as the state variable, it was possible to set the value of AUC 0.933 (95% CI 0.882–0.985).
Conclusion. The studied clinical and laboratory indexes (FLI, HSI, HOMA1-IR) have a high diagnostic significance regarding NAFLD diagnosed using ultrasonographic criteria. The application of the proposed two-factor logistics model makes it possible to predict the presence of NAFLD when examining a large number of patients, without involving additional ultrasound diagnostics specialists in order to use medical resources rationally.
作者简介
Aleksandr Nosov
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
编辑信件的主要联系方式.
Email: nosov@fcrisk.ru
ORCID iD: 0000-0003-0539-569X
кандидат медицинских наук, заведующий стационаром (отделение профпатологии терапевтического профиля)
俄罗斯联邦, PermMariia Zenina
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
Email: nosov@fcrisk.ru
ORCID iD: 0000-0001-6623-3075
врач ультразвуковой диагностики
俄罗斯联邦, PermOlga Gorbushina
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
Email: nosov@fcrisk.ru
ORCID iD: 0000-0002-7592-3219
врач-терапевт стационара (отделение профпатологии терапевтического профиля)
俄罗斯联邦, PermAnastasiia Baidina
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
Email: nosov@fcrisk.ru
ORCID iD: 0000-0003-3131-5868
кандидат медицинских наук, врач-кардиолог консультативно-поликлинического отделения
俄罗斯联邦, PermElena Vlasova
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
Email: nosov@fcrisk.ru
ORCID iD: 0000-0003-3344-3361
кандидат медицинских наук, заведующий центром профессиональной патологии
俄罗斯联邦, PermVadim Alekseev
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
Email: nosov@fcrisk.ru
доктор медицинских наук, директор
俄罗斯联邦, Perm参考
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