Machine learning algorithms for predicting direct-acting antiviral treatment failure in chronic hepatitis C: An HCV-TARGET analysis

Retreatment of Hepatitis C Virus Among People Who Inject Drugs

BACKGROUND

Hepatitis C virus (HCV) is a leading cause of infectious disease death in the United States. Although highly effective direct-acting antiviral (DAA) regimens are well established, retreatment among people who inject drugs (PWID) has not been sufficiently studied. This study assessed DAA retreatment outcomes and associated factors.

METHODS

We performed analyses of longitudinal data from the HERO Study, a US-based multi-site pragmatic randomized trial conducted in 8 states to evaluate effectiveness of 2 HCV care models among DAA treatment-naïve PWID in opioid treatment programs and community clinics. After initial HERO Study sofosbuvir/velpatasvir (SOF/VEL) treatment, participants eligible for retreatment were identified, from 15 September 2016 to 13 September 2021. This analysis characterizes participants who either did not achieve sustained virologic response (SVR) or were reinfected with HCV post-SVR. We compared categorical variables using Fisher exact test and continuous variables using the Welch 2 sample t test for means and an asymptotic 2-sample Mood median test.

RESULTS

One hundred four participants were identified as eligible for retreatment. Less than half, 43 (41.3%), initiated retreatment. Among the 25 who initiated retreatment and for whom SVR results were available, 24 achieved SVR (96%). Participants who did not achieve SVR initiated retreatment more promptly than participants reinfected post-SVR (respectively, 471 vs 784 days on average, P < .001).

CONCLUSIONS

After reinfection or not achieving SVR with the first DAA regimen, retreated PWID achieved higher SVR rates than with initial DAA treatment. To attain HCV elimination and benefit individual and public health, assisting PWID with accessing prompt retreatment is crucial.

Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.

The role of artificial intelligence in the management of liver diseases

Universal neonatal hepatitis B virus (HBV) vaccination and the advent of direct-acting antivirals (DAA) against hepatitis C virus (HCV) have reshaped the epidemiology of chronic liver diseases. However, some aspects of the management of chronic liver diseases remain unresolved. Nucleotide analogs can achieve sustained HBV DNA suppression but rarely lead to a functional cure. Despite the high efficacy of DAAs, successful antiviral therapy does not eliminate the risk of hepatocellular carcinoma (HCC), highlighted the need for cost-effective identification of high-risk populations for HCC surveillance and tailored HCC treatment strategies for these populations. The accessibility of high-throughput genomic data has accelerated the development of precision medicine, and the emergence of artificial intelligence (AI) has led to a new era of precision medicine. AI can learn from complex, non-linear data and identify hidden patterns within real-world datasets. The combination of AI and multi-omics approaches can facilitate disease diagnosis, biomarker discovery, and the prediction of treatment efficacy and prognosis. AI algorithms have been implemented in various aspects, including non-invasive tests, predictive models, image diagnosis, and the interpretation of histopathology findings. AI can support clinicians in decision-making, alleviate clinical burdens, and curtail healthcare expenses. In this review, we introduce the fundamental concepts of machine learning and review the role of AI in the management of chronic liver diseases.

Serum collagen IV as a predictor for response to direct-acting antivirals hepatitis C therapy

Althoughchronic hepatitis C (CHC) therapies based on direct-acting antiviral (DAA) agents safely improved treatment effectiveness, some cases do not obtain sustained virological response (SVR) and, thus, evaluating factors that may be related to treatment failure is very important. We aimed to evaluate the association of baseline serum collagen IV with DAA treatment failure in Egyptian patients with CHC. A total of 175 CHC patients (100 responders and 75non-responders tosofosbuvir/daclatasvir) were included. Collagen IV was assessed using sensitive chemiluminescent immunoassay. There was distinctly higher (P < 0.0001) collagen IV in non-responders compared to responder patients as the median (interquartile range) were 19.02 (13.4-25.2) vs.9.7 (7.2-12.3) µg/L, respectively. Collagen IV has a good ability for distinguishing nonresponders from responder patients (AUC = 0.890) with sensitivity of 92%, specificity 72%, PPV 71.1%, NPV 92.3% and accuracy of 80.6%. Collagen IV was correlated (p < 0.05) with decreased albumin (r=-0.266), elevated APRI (r = 0.288), and elevated FIB-4 (r = 0.281) scores. In conclusion,these findings suggested the remarkable role of baseline collagen IV in the prediction of HCV DAAs treatment response. Thus, however further studies are needed, its measurement may improve treatment duration and the disease control.

Development and validation of prediction models for nosocomial infection and prognosis in hospitalized patients with cirrhosis

BACKGROUND

Nosocomial infections (NIs) frequently occur and adversely impact prognosis for hospitalized patients with cirrhosis. This study aims to develop and validate two machine learning models for NIs and in-hospital mortality risk prediction.

METHODS

The Prediction of Nosocomial Infection and Prognosis in Cirrhotic patients (PIPC) study included hospitalized patients with cirrhosis at the Qingchun Campus of the First Affiliated Hospital of Zhejiang University. We then assessed several machine learning algorithms to construct predictive models for NIs and prognosis. We validated the best-performing models with bootstrapping techniques and an external validation dataset. The accuracy of the predictions was evaluated through sensitivity, specificity, predictive values, and likelihood ratios, while predictive robustness was examined through subgroup analyses and comparisons between models.

RESULTS

We enrolled 1,297 patients into derivation cohort and 496 patients into external validation cohort. Among the six algorithms assessed, the Random Forest algorithm performed best. For NIs, the PIPC-NI model achieved an area under the curve (AUC) of 0.784 (95% confidence interval [CI] 0.741-0.826), a sensitivity of 0.712, and a specificity of 0.702. For in-hospital mortality, the PIPC- mortality model achieved an AUC of 0.793 (95% CI 0.749-0.836), a sensitivity of 0.769, and a specificity of 0.701. Moreover, our PIPC models demonstrated superior predictive performance compared to the existing MELD, MELD-Na, and Child-Pugh scores.

CONCLUSIONS

The PIPC models showed good predictive power and may facilitate healthcare providers in easily assessing the risk of NIs and prognosis among hospitalized patients with cirrhosis.

Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions

A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.

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

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.

Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data

BACKGROUND

Most existing predictive models of hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) are built on data collected at baseline and therefore have limited accuracy. The current study aimed to construct an accurate predictive model incorporating longitudinal data using a novel modeling strategy. The predictive performance of the longitudinal model was also compared with a baseline model.

METHODS

A total of 400 patients with HCV-related cirrhosis who achieved SVR with direct-acting antivirals (DAA) were enrolled in the study. Patients were randomly divided into a training set (70%) and a validation set (30%). Informative features were extracted from the longitudinal variables and then put into the random survival forest (RSF) to develop the longitudinal model. A baseline model including the same variables was built for comparison.

RESULTS

During a median follow-up time of approximately 5 years, 25 patients (8.9%) in the training set and 11 patients (9.2%) in the validation set developed HCC. The areas under the receiver-operating characteristics curves (AUROC) for the longitudinal model were 0.9507 (0.8838-0.9997), 0.8767 (0.6972,0.9918), and 0.8307 (0.6941,0.9993) for 1-, 2- and 3-year risk prediction, respectively. The brier scores of the longitudinal model were also relatively low for the 1-, 2- and 3-year risk prediction (0.0283, 0.0561, and 0.0501, respectively). In contrast, the baseline model only achieved mediocre AUROCs of around 0.6 (0.6113, 0.6213, and 0.6480, respectively).

CONCLUSIONS

Our longitudinal model yielded accurate predictions of HCC risk in patients with HCV-relate cirrhosis, outperforming the baseline model. Our model can provide patients with valuable prognosis information and guide the intensity of surveillance in clinical practice.

Machine learning for prediction of viral hepatitis: A systematic review and meta-analysis

BACKGROUND

Lack of accurate and timely diagnosis of hepatitis poses obstacles to effective treatment, disease progression prevention, complication reduction, and life-saving interventions of patients. Utilizing machine learning can greatly enhance the achievement of timely and precise disease diagnosis. Therefore, we carried out this systematic review and meta-analysis to explore the performance of machine learning algorithms in predicting viral hepatitis.

METHODS

Using an extensive literature search in PubMed, Scopus, and Web of Science databases until June 15, 2023, English publications on hepatitis prediction using machine learning algorithms were included. Two authors independently extracted pertinent information from the selected studies. The PRISMA 2020 checklist was followed for study selection and result reporting. The risk of bias was checked using the International Journal of Medical Informatics (IJMEDI) checklist. Data were analyzed using the 'metandi' command in Stata 17.

RESULTS

Twenty-one original studies were included, covering 82 algorithms. Sixteen studies utilized five algorithms to predict hepatitis B. Ten studies used five algorithms for hepatitis C prediction. For hepatitis B prediction, the SVM algorithms demonstrated the highest sensitivity (90.0%; 95% confidence interval (CI): 77.0%-96.0%), specificity (94%; 95% CI: 90.0%-97.0%), and a diagnostic odds ratio (DOR) of 145 (95% CI: 37.0-559.0). In the case of hepatitis C, the KNN algorithms exhibited the highest sensitivity (80%; 95% CI:30.0%-97.0%), specificity (95%; 95% CI: 58.0%-99.0%), and DOR (72; 95% CI: 3.0-1644.0) for prediction.

CONCLUSION

SVM and KNN demonstrated superior performance in predicting hepatitis. The proper algorithm along with clinical practice could improve hepatitis prediction and management.

Machine learning prediction models for clinical management of blood-borne viral infections: a systematic review of current applications and future impact

BACKGROUND

Machine learning (ML) prediction models to support clinical management of blood-borne viral infections are becoming increasingly abundant in medical literature, with a number of competing models being developed for the same outcome or target population. However, evidence on the quality of these ML prediction models are limited.

OBJECTIVE

This study aimed to evaluate the development and quality of reporting of ML prediction models that could facilitate timely clinical management of blood-borne viral infections.

METHODS

We conducted narrative evidence synthesis following the synthesis without meta-analysis guidelines. We searched PubMed and Cochrane Central Register of Controlled Trials for all studies applying ML models for predicting clinical outcomes associated with hepatitis B virus (HBV), human immunodeficiency virus (HIV), or hepatitis C virus (HCV).

RESULTS

We found 33 unique ML prediction models aiming to support clinical decision making. Overall, 12 (36.4%) focused on HBV, 10 (30.3%) on HCV, 10 on HIV (30.3%) and two (6.1%) on co-infection. Among these, six (18.2%) addressed the diagnosis of infection, 16 (48.5%) the prognosis of infection, eight (24.2%) the prediction of treatment response, two (6.1%) progression through a cascade of care, and one (3.03%) focused on the choice of antiretroviral therapy (ART). Nineteen prediction models (57.6%) were developed using data from high-income countries. Evaluation of prediction models was limited to measures of performance. Detailed information on software code accessibility was often missing. Independent validation on new datasets and/or in other institutions was rarely done.

CONCLUSION

Promising approaches for ML prediction models in blood-borne viral infections were identified, but the lack of robust validation, interpretability/explainability, and poor quality of reporting hampered their clinical relevance. Our findings highlight important considerations that can inform standard reporting guidelines for ML prediction models in the future (e.g., TRIPOD-AI), and provides critical data to inform robust evaluation of the models.

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Hepatitis C Virus (HCV) is a viral infection that causes liver inflammation. Annually, approximately 3.4 million cases of HCV are reported worldwide. A diagnosis of HCV in earlier stages helps to save lives. In the HCV review, the authors used a single ML-based prediction model in the current research, which encounters several issues, i.e., poor accuracy, data imbalance, and overfitting. This research proposed a Hybrid Predictive Model (HPM) based on an improved random forest and support vector machine to overcome existing research limitations. The proposed model improves a random forest method by adding a bootstrapping approach. The existing RF method is enhanced by adding a bootstrapping process, which helps eliminate the tree's minor features iteratively to build a strong forest. It improves the performance of the HPM model. The proposed HPM model utilizes a 'Ranker method' to rank the dataset features and applies an IRF with SVM, selecting higher-ranked feature elements to build the prediction model. This research uses the online HCV dataset from UCI to measure the proposed model's performance. The dataset is highly imbalanced; to deal with this issue, we utilized the synthetic minority over-sampling technique (SMOTE). This research performs two experiments. The first experiment is based on data splitting methods, K-fold cross-validation, and training: testing-based splitting. The proposed method achieved an accuracy of 95.89% for k = 5 and 96.29% for k = 10; for the training and testing-based split, the proposed method achieved 91.24% for 80:20 and 92.39% for 70:30, which is the best compared to the existing SVM, MARS, RF, DT, and BGLM methods. In experiment 2, the analysis is performed using feature selection (with SMOTE and without SMOTE). The proposed method achieves an accuracy of 41.541% without SMOTE and 96.82% with SMOTE-based feature selection, which is better than existing ML methods. The experimental results prove the importance of feature selection to achieve higher accuracy in HCV research.

Perioperative predictors of outcome of hepatectomy for HBV-related hepatocellular carcinoma

Hepatitis B virus (HBV) is identified as a major risk factor for hepatocellular carcinoma (HCC), resulting in so-called hepatitis B virus-related hepatocellular carcinoma (HBV-related HCC). Hepatectomy for HCC is acknowledged as an efficient treatment strategy, especially for early HCC. Furthermore, patients with advanced HCC can still obtain survival benefits through surgical treatment combined with neoadjuvant therapy, adjuvant therapy, transcatheter arterial chemoembolization, and radiofrequency ablation. Therefore, preoperative and postoperative predictors of HBV-related HCC have crucial indicative functions for the follow-up treatment of patients with feasible hepatectomy. This review covers a variety of research results on preoperative and postoperative predictors of hepatectomy for HBV-related HCC over the past decade and in previous landmark studies. The relevant contents of Hepatitis C virus-related HCC, non-HBV non-HCV HCC, and the artificial intelligence application in this field are briefly addressed in the extended content. Through the integration of this review, a large number of preoperative and postoperative factors can predict the prognosis of HBV-related HCC, while most of the predictors have no standardized thresholds. According to the characteristics, detection methods, and application of predictors, the predictors can be divided into the following categories: 1. serological and hematological predictors, 2. genetic, pathological predictors, 3. imaging predictors, 4. other predictors, 5. analysis models and indexes. Similar results appear in HCV-related HCC, non-HBV non-HCV HCC. Predictions based on AI and big biological data are actively being applied. A reasonable prediction model should be established based on the economic, health, and other levels in specific countries and regions.

Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study

PURPOSE

Postoperative early recurrence (ER) leads to a poor prognosis for intrahepatic cholangiocarcinoma (ICC). We aimed to develop machine learning (ML) radiomics models to predict ER in ICC after curative resection.

METHODS

Patients with ICC undergoing curative surgery from three institutions were retrospectively recruited and assigned to training and external validation cohorts. Preoperative arterial and venous phase contrast-enhanced computed tomography (CECT) images were acquired and segmented. Radiomics features were extracted and ranked through their importance. Univariate and multivariate logistic regression analysis was used to identify clinical characteristics. Various ML algorithms were used to construct radiomics-based models, and the predictive performance was evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.

RESULTS

127 patients were included for analysis: 90 patients in the training set and 37 patients in the validation set. Ninety-two patients (72.4%) experienced recurrence, including 71 patients exhibiting ER. Male sex, microvascular invasion, TNM stage, and serum CA19-9 were identified as independent risk factors for ER, with the corresponding clinical model having a poor predictive performance (AUC of 0.685). Fifty-seven differential radiomics features were identified, and the 10 most important features were utilized for modelling. Seven ML radiomics models were developed with a mean AUC of 0.87 ± 0.02, higher than the clinical model. Furthermore, the clinical-radiomics models showed similar predictive performance to the radiomics models (AUC of 0.87 ± 0.03).

CONCLUSION

ML radiomics models based on CECT are valuable in predicting ER in ICC.

Impact of Sex and Gender on Clinical Management of Patients with Advanced Chronic Liver Disease and Type 2 Diabetes

Gender differences in the epidemiology, pathophysiological mechanisms and clinical features in chronic liver diseases that may be associated with type 2 diabetes (T2D) have been increasingly reported in recent years. This sexual dimorphism is due to a complex interaction between sex- and gender-related factors, including biological, hormonal, psychological and socio-cultural variables. However, the impact of sex and gender on the management of T2D subjects with liver disease is still unclear. In this regard, sex-related differences deserve careful consideration in pharmacology, aimed at improving drug safety and optimising medical therapy, both in men and women with T2D; moreover, low adherence to and persistence of long-term drug treatment is more common among women. A better understanding of sex- and gender-related differences in this field would provide an opportunity for a tailored diagnostic and therapeutic approach to the management of T2D subjects with chronic liver disease. In this narrative review, we summarized available data on sex- and gender-related differences in chronic liver disease, including metabolic, autoimmune, alcoholic and virus-related forms and their potential evolution towards cirrhosis and/or hepatocarcinoma in T2D subjects, to support their appropriate and personalized clinical management.

Global treatment rate and barriers to direct-acting antiviral therapy: A systematic review and meta-analysis of 146 studies and 1 760 352 hepatitis C virus patients

BACKGROUND

Global data on the treatment rate with direct-acting antivirals (DAAs) for chronic hepatitis C (CHC) are sparse. We aimed to evaluate the CHC treatment rate and barriers to treatment in the DAA era.

METHODS

We searched PubMed, EMBASE and Cochrane from inception to 5 August 2021, for relevant articles. Patients treated with DAAs without interferon (IFN) therapy were categorized as IFN-free DAAs. Patients receiving DAA with IFN or unclear IFN status were categorized as DAA/IFN.

RESULTS

We identified and analysed data from 146 studies (1 760 352 CHC patients). DAA/IFN treatment rate was 16.0% (95% CI: 9.9-23.3, 49 studies, 886 535 patients). IFN-free DAA treatment rate was 52.3% (95% CI: 46.2-58.4, 123 studies, 1 276 754 patients): 45.4% in North America, 64.2% in South America (1 study), 90.4% in Africa (most data from Egypt), 54.4% in Europe, 60.7% in Australia and 60.5% in Asia, (p < .0001); 49% with hepatitis B co-infection and 32.3% with hepatocellular carcinoma (HCC). Treatment was not a priority in 22.8% of patients in Europe and 16.7% in Australia, compared to only 4.8% in North America and 2.1% in Asia (p < .0001). Poor adherence to clinical follow-up was the cause of no treatment in 74.7% of patients in Australia, 37.0% in North America, 7.9% in Europe and 14.3% in Asia (p < .0001).

CONCLUSION

Though a marked improvement from IFN/DAA, the treatment rate with IFN-free DAA remains suboptimal (52.3% overall, 32.3% in HCC patients). Non-adherence to clinical follow-up and lack of disease awareness were treatment barriers.

Artificial intelligence models for the diagnosis and management of liver diseases

With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.

Evaluation of machine learning algorithms for predicting direct-acting antiviral treatment failure among patients with chronic hepatitis C infection

Despite the availability of efficacious direct-acting antiviral (DAA) therapy, the number of people infected with hepatitis C virus (HCV) continues to rise, and HCV remains a leading cause of liver-related morbidity, liver transplantation, and mortality. We developed and validated machine learning (ML) algorithms to predict DAA treatment failure. Using the HCV-TARGET registry of adults who initiated all-oral DAA treatment, we developed elastic net (EN), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) ML algorithms. Model performances were compared with multivariable logistic regression (MLR) by assessing C statistics and other prediction evaluation metrics. Among 6525 HCV-infected adults, 308 patients (4.7%) experienced DAA treatment failure. ML models performed similarly in predicting DAA treatment failure (C statistic [95% CI]: EN, 0.74 [0.69-0.79]; RF, 0.74 [0.69-0.80]; GBM, 0.72 [0.67-0.78]; FNN, 0.75 [0.70-0.80]), and all 4 outperformed MLR (C statistic [95% CI]: 0.51 [0.46-0.57]), and EN used the fewest predictors (n = 27). With Youden index, the EN had 58.4% sensitivity and 77.8% specificity, and nine patients were needed to evaluate to identify 1 DAA treatment failure. Over 60% treatment failure were classified in top three risk decile subgroups. EN-identified predictors included male sex, treatment < 8 weeks, treatment discontinuation due to adverse events, albumin level < 3.5 g/dL, total bilirubin level > 1.2 g/dL, advanced liver disease, and use of tobacco, alcohol, or vitamins. Addressing modifiable factors of DAA treatment failure may reduce the burden of retreatment. Machine learning algorithms have the potential to inform public health policies regarding curative treatment of HCV.