ArticleExperimental and therapeutic medicine2022
Application of alkaline phosphatase‑to‑platelet ratio as a novel noninvasive index predicts liver fibrosis in patients with chronic hepatitis B.
Article in Experimental and therapeutic medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Development and validation of a nomogram for predicting advanced liver fibrosis in patients with chronic hepatitis B.Frontiers in molecular biosciences · 2024Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Assessment of the extent of liver fibrosis is a crucial requirement for the design of antiviral treatments for patients with chronic hepatitis B (CHB). Several non-invasive predictive indices have been developed as potential alternatives to liver biopsy for fibrosis assessment. The present study aimed to establish a novel non-invasive method for predicting liver fibrosis in patients with CHB. A total of 382 patients with CHB who underwent liver biopsy and pathological examination at The Second Hospital of Anhui Medical University (Hefei, China) were enrolled into the present study. Liver fibrosis was assessed according to the meta-analysis of histological data in viral hepatitis scoring system. Logistic regression analyses were performed to explore possibly significant characteristics associated with liver fibrosis. In addition, potential correlations between the alkaline phosphatase (AKP)-to-platelet count (PLT) ratio (APPR) and the aspartate transaminase-to-platelet ratio index (APRI), fibrosis index based on four factors (FIB-4) and γ-glutamyl transpeptidase-to-platelet ratio (GPR) were assessed using Spearman's correlation analysis. Subsequently, the performance of APPR was compared with APRI, FIB-4 and GPR using receiver operating characteristic (ROC) analysis. Logistic regression analysis identified AKP and PLT to be significant independent predictors of fibrosis. Therefore, an index was then constructed for predicting the degree of fibrosis, which was expressed using the formula APPR=AKP (IU/ml)/PLT (1x10
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