Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program

Abstract

Background/aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy.

Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment.

Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset.

Conclusion: Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Keywords: Algorithms; Antiviral agents; Artificial intelligence; Hepatitis C virus; Machine learning.

Figure 1.

Performance of the predictive models. Figure 1 shows the ROC curves and precision-recall curves of the eXtreme Gradient Boosting (XGBoost), random forest (RF), decision tree (DT), artificial neural network (ANN) algorithms, and logistic regression (LR) models in the training dataset (A, B) and validation dataset (C, D). A precision-recall curve closer to the upper-right corner indicates better performance.

Figure 2.

DAA treatment failure rate by decile risk subgroups assessed using the XGBoost model among overall cases. The overall HCV patients were further divided into ten subgroups using the deciles of risk coefficients obtained from the XGBoost model. Patients with risk coefficients ranging from 0 to 0.1 belong to decile 1, from 0.1 to 0.2 were decile 2, …, and so on. The bars represent the predictive non-SVR accuracy in each subgroup. The red line represents the accumulated non-SVR rate. DAA, direct-acting antivirals; XGBoost, eXtreme Gradient Boosting; HCV, hepatitis C virus; SVR, sustained virological response.

Figure 3.

SHAP summary plot. The SHAP summary plot combined the feature importance and effects on DAA efficacy in all cases. The x-axis represents the SHAP value of the feature. A SHAP value >0 represents a positive correlation with SVR, and a SHAP value <0 represents a negative correlation with SVR. The overlapping points jittered along the x-axis represent the samples; the colors represent feature values ranging from low (yellow) to high (purple). SHAP, Shapley additive explanations; DAA, direct-acting antivirals; SVR, sustained virological response; BMI, body mass index; AFP, α-fetoprotein; PLT, platelets; FIB-4, fibrosis-4 index; AST, aspartate aminotransferase; INR, international normalized ratio; APRI, aminotransferase to platelet ratio index.

Figure 4.

SHAP dependence plot. SHAP dependence plot revealed that global model interpretations depend on the given features. SHAP, Shapley additive explanations; HCV, hepatitis C virus; BMI, body mass index; AFP, α-fetoprotein; PLT, platelets; FIB-4, fibrosis-4 index; AST, aspartate aminotransferase; INR, international normalized ratio; APRI, aminotransferase to platelet ratio index.