Evidence map›Paper›PMID 36405384›Full record

ArticleWorld journal of gastroenterology2022

Clinical value of predictive models based on liver stiffness measurement in predicting liver reserve function of compensated chronic liver disease.

Rui-Min Lai, Miao-Miao Wang, Xiao-Yu Lin, Qi Zheng, Jing Chen

Abstract read
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Article in World journal of gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Rui-Min LaiDepartment of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian Province, China.
Miao-Miao WangDepartment of Endocrinology, The 910Hospital of The Joint Service Support Force, Quanzhou 362000, Fujian Province, China.
Xiao-Yu LinDepartment of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian Province, China.
Qi ZhengDepartment of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian Province, China.
Jing ChenDepartment of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian Province, China. mykelchen@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAssessment of liver reserve function (LRF) is essential for predicting the prognosis of patients with chronic liver disease (CLD) and determines the extent of liver resection in patients with hepatocellular carcinoma.

aimTo establish noninvasive models for LRF assessment based on liver stiffness measurement (LSM) and to evaluate their clinical performance.

methodsA total of 360 patients with compensated CLD were retrospectively analyzed as the training cohort. The new predictive models were established through logistic regression analysis and were validated internally in a prospective cohort (132 patients).

resultsOur study defined indocyanine green retention rate at 15 min (ICGR15) ≥ 10% as mildly impaired LRF and ICGR15 ≥ 20% as severely impaired LRF. We constructed predictive models of LRF, named the mLPaM and sLPaM, which involved only LSM, prothrombin time international normalized ratio to albumin ratio (PTAR), age and model for end-stage liver disease (MELD). The area under the curve of the mLPaM model (0.855, 0.872, respectively) and sLPaM model (0.869, 0.876, respectively) were higher than that of the methods for MELD, albumin-bilirubin grade and PTAR in the two cohorts, and their sensitivity and negative predictive value were the highest among these methods in the training cohort. In addition, the new models showed good sensitivity and accuracy for the diagnosis of LRF impairment in the validation cohort.

conclusionThe new models had a good predictive performance for LRF and could replace the indocyanine green (ICG) clearance test, especially in patients who are unable to undergo ICG testing.

Indexed as

End Stage Liver DiseaseLiver NeoplasmsAlbuminsHumansIndocyanine GreenProspective StudiesRetrospective StudiesSeverity of Illness IndexAlbuminsIndocyanine GreenChronic liver diseaseIndocyanine green clearance testLiver reserve functionLiver stiffness measurementPredictive model

Identifiers

PMID36405384
PMCPMC9669823

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