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External validation of non-invasive prediction models for identifying ultrasonography-diagnosed fatty liver disease in a Chinese population.

Abstract Several prediction models for fatty liver disease (FLD) are available with limited externally validation and less comprehensive evaluation. The aim was to perform external validation and direct comparison of 4 prediction models (the Fatty Liver Index, the Hepatic Steatosis Index, the ZJU index, and the Framingham Steatosis Index) for FLD both in the overall population and the obese subpopulation.This cross-sectional study included 4247 subjects aged 20 to 65 years recruited from the north of Shanxi Province in China. Anthropometric and biochemical features were collected using standard protocols. FLD was diagnosed by liver ultrasonography. We assessed all models in terms of discrimination, calibration, and decision curve analysis.The original models performed well in terms of discrimination for the overall population, with the area under the receiver operating characteristic curves (AUCs) around 0.85, while AUCs for obese individuals were around 0.68. Nevertheless, the predicted risks did not match well with the observed risks both in the overall population and the obese subpopulation. The FLI 2006 was 1 of the 2 best models in terms of discrimination (AUCs were 0.87 and 0.72 for the overall population and the obese subgroup, respectively) and had the best performance in terms of calibration, and attained the highest net benefit.The FLI 2006 is overall the best tool to identify high risk individuals and has great clinical utility. Nonetheless, it does not perform well enough to quantify the actual risk of FLD, which need to be (re)calibrated for clinical use.
PMID
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Authors

Mayor MeshTerms

Severity of Illness Index

Ultrasonography

Keywords
Journal Title medicine
Publication Year Start




PMID- 28746214
OWN - NLM
STAT- MEDLINE
DA  - 20170726
DCOM- 20170807
LR  - 20170807
IS  - 1536-5964 (Electronic)
IS  - 0025-7974 (Linking)
VI  - 96
IP  - 30
DP  - 2017 Jul
TI  - External validation of non-invasive prediction models for identifying
      ultrasonography-diagnosed fatty liver disease in a Chinese population.
PG  - e7610
LID - 10.1097/MD.0000000000007610 [doi]
AB  - Several prediction models for fatty liver disease (FLD) are available with
      limited externally validation and less comprehensive evaluation. The aim was to
      perform external validation and direct comparison of 4 prediction models (the
      Fatty Liver Index, the Hepatic Steatosis Index, the ZJU index, and the Framingham
      Steatosis Index) for FLD both in the overall population and the obese
      subpopulation.This cross-sectional study included 4247 subjects aged 20 to 65
      years recruited from the north of Shanxi Province in China. Anthropometric and
      biochemical features were collected using standard protocols. FLD was diagnosed
      by liver ultrasonography. We assessed all models in terms of discrimination,
      calibration, and decision curve analysis.The original models performed well in
      terms of discrimination for the overall population, with the area under the
      receiver operating characteristic curves (AUCs) around 0.85, while AUCs for obese
      individuals were around 0.68. Nevertheless, the predicted risks did not match
      well with the observed risks both in the overall population and the obese
      subpopulation. The FLI 2006 was 1 of the 2 best models in terms of discrimination
      (AUCs were 0.87 and 0.72 for the overall population and the obese subgroup,
      respectively) and had the best performance in terms of calibration, and attained 
      the highest net benefit.The FLI 2006 is overall the best tool to identify high
      risk individuals and has great clinical utility. Nonetheless, it does not perform
      well enough to quantify the actual risk of FLD, which need to be (re)calibrated
      for clinical use.
FAU - Shen, Ya-Nan
AU  - Shen YN
AD  - aDepartment of Health Statistics, School of Public Health, Shanxi Medical
      University, Taiyuan bDepartment of Neurosurgery, General Hospital of Datong Coal 
      Mining Group, Datong, China cDepartment of Epidemiology and Biostatistics,
      Michigan State University, East Lansing, Michigan.
FAU - Yu, Ming-Xing
AU  - Yu MX
FAU - Gao, Qian
AU  - Gao Q
FAU - Li, Yan-Yan
AU  - Li YY
FAU - Huang, Jian-Jun
AU  - Huang JJ
FAU - Sun, Chen-Ming
AU  - Sun CM
FAU - Qiao, Nan
AU  - Qiao N
FAU - Zhang, Hai-Xia
AU  - Zhang HX
FAU - Wang, Hui
AU  - Wang H
FAU - Lu, Qing
AU  - Lu Q
FAU - Wang, Tong
AU  - Wang T
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PT  - Validation Studies
PL  - United States
TA  - Medicine (Baltimore)
JT  - Medicine
JID - 2985248R
SB  - AIM
SB  - IM
MH  - Adult
MH  - Aged
MH  - Area Under Curve
MH  - Calibration
MH  - China
MH  - Coal Mining
MH  - Cross-Sectional Studies
MH  - Decision Support Techniques
MH  - Fatty Liver/complications/*diagnostic imaging
MH  - Female
MH  - Humans
MH  - Image Interpretation, Computer-Assisted
MH  - Liver/*diagnostic imaging
MH  - Male
MH  - Middle Aged
MH  - Models, Theoretical
MH  - Obesity/complications/diagnostic imaging
MH  - ROC Curve
MH  - Risk
MH  - *Severity of Illness Index
MH  - *Ultrasonography
MH  - Young Adult
EDAT- 2017/07/27 06:00
MHDA- 2017/08/08 06:00
CRDT- 2017/07/27 06:00
AID - 10.1097/MD.0000000000007610 [doi]
AID - 00005792-201707280-00045 [pii]
PST - ppublish
SO  - Medicine (Baltimore). 2017 Jul;96(30):e7610. doi: 10.1097/MD.0000000000007610.