Establishing a predictive model for liver fluke infection on the basis of early changes in laboratory indicators: a retrospective study
Abstract Background Hepatic clonorchiasis is one of the most prevalent foodborne parasitic diseases in China and is often overlooked because the initial symptoms are not obvious. In this study, a multivariate model for the early prediction of disease onset using laboratory test data from liver-fluke...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
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| Series: | Parasites & Vectors |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13071-025-06833-9 |
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| Summary: | Abstract Background Hepatic clonorchiasis is one of the most prevalent foodborne parasitic diseases in China and is often overlooked because the initial symptoms are not obvious. In this study, a multivariate model for the early prediction of disease onset using laboratory test data from liver-fluke-infected patients was developed and validated. Methods Laboratory data from 147 liver-fluke-infected patients and 151 healthy control subjects were collected. Univariate logistic regression, Spearman correlation analysis, and collinearity diagnosis were used to screen for independent factors. A multivariate model was then constructed using the backward likelihood ratio method. For external validation, an independent patient cohort from another hospital was analyzed. The discriminative performance of the combined model was compared with that of previously identified biomarkers (eosinophil count and γ-glutamyl transpeptidase). Results A 12-indicator prediction model for liver fluke infection was developed using traditional logistic regression (82.31% sensitivity and 88.08% specificity). The receiver operating characteristic curve, calibration curve, and decision curve analyses revealed that the model exhibited excellent discriminative ability (area under the curve [AUC]: training = 0.928, validation = 0.808), goodness of fit, and clinical practicability. The combined model showed superior discrimination compared with individual biomarkers, including eosinophil count (AUC = 0.577) and γ-glutamyl transpeptidase (AUC = 0.620). Conclusions This study developed an early risk prediction model for liver fluke infection using routine laboratory test data. Compared with previously reported biomarkers, the model demonstrated superior diagnostic performance and showed potential as a clinical tool for identifying early stage liver fluke infection in patients. Graphical Abstract |
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| ISSN: | 1756-3305 |