Risk factor analysis and prediction of multi-organ metastasis in hepatic alveolar echinococcosis.

Abstract

Background: Patients with multiple organ metastases from hepatic alveolar echinococcosis have a high mortality rate. However, predictors of multi-organ metastasis have not been identified. We aimed to develop a nomogram that predicts multi-organ metastasis in patients with hepatic alveolar echinococcosis.

Methods: We retrospectively screened patients with hepatic alveolar echinococcosis who were treated between January 2016 and December 2021 at Qinghai Provincial People's Hospital, China. The outcome of the nomogram was multi-organ metastasis of hepatic alveolar echinococcosis. We collected patients' basic characteristics, disease course, imaging, and blood laboratory results. The Least Absolute Shrinkage Selection Operator (LASSO) analysis selected the predictors preliminarily. A predictive model was constructed by multivariate logistic regression and presented as a nomogram. The performance of the nomogram was measured by the receiver operating characteristic (ROC) curve, calibration diagram, and decision curve analysis (DCA). The model was internally validated by calculating the performance of the validation cohort.

Results: A total of 353 patients were enrolled in this study. Ninety five (26.9%) patients presented with multi-organ metastases. All participants were randomized into a development cohort (n = 249) and a validation cohort (n = 104). Predictors in this nomogram were the course of the disease, the long diameter of the lesion, multiple intrahepatic lesions, and medication. The ROC curve of the training set was 0.907 (95% CI: 0.870, 0.943). A similar ROC curve was achieved at the validation set (0.927, 95% CI: 0.876, 0.979). The calibration curve demonstrated that the prediction outcome was correlated with the observed outcome.

Conclusion: The nomogram can predict the risk of multi-organ metastasis in patients with hepatic alveolar echinococcosis, and help clinicians develop or adjust a reasonable diagnosis and treatment plan in time.