Development and External Validation of an Algorithm for Identifying HDV RNA-Positive Patients.

BACKGROUND AND AIMS: Accurate detection and treatment of hepatitis D virus (HDV)-infected patients can reduce disease-related morbidity and mortality. This study developed and validated real-world evidence-based algorithms to detect ribonucleic acid (RNA)-positive HDV patients from administrative claims data.

METHODS

This retrospective observational study identified hepatitis B virus and HDV patients from laboratory testing data linked to administrative claims (HealthVerity; 2015-2022), with external validation performed using electronic health records (TriNetX; 2005-2023). Both diagnosis-based and machine learning algorithms were evaluated. Performance metrics included area under the receiver-operating characteristic curve (AUROC), area under the precision-recall curve, sensitivity, specificity, positive predictive value, and accuracy. Internal validation results showed that diagnosis code-based algorithms identified HDV RNA-positivity with ≥88% accuracy.

RESULTS

The best-performing algorithm-based approach in terms of optimism-adjusted AUROC was a random forest model with the following covariates: age, gender, hepatitis complications, HDV, and hepatitis B virus diagnosis, inpatient/outpatient diagnosis, hepatitis medication, and physician specialty (AUROC 97%). Decision curve analysis showed that the random forest algorithm with covariates excluding physician specialty, both inpatient/outpatient diagnosis, and physician specialty performed best. External validation indicated the random forest algorithm using equivalent covariates but without physician specialty had the best performance (positive predictive value 34%; accuracy 92%).

CONCLUSION

Algorithms based on HDV diagnosis were more effective for identifying HDV RNA-positive patients than algorithms using HDV-based variables from claims data. Further research into improving such algorithms is needed. Although fair discrimination may occur without claims data, key metrics were not able to replace traditional testing methods. The proposed models could help identify high-risk patients where certain strategies could be prioritized.