Predicting Antituberculosis Drug–Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study (Preprint)

Abstract

BACKGROUND Tuberculosis (TB) is a pandemic, being one of the top 10 causes of death and the main cause of death from a single source of infection. Drug-induced liver injury (DILI) is the most common and serious side effect during the treatment of TB. OBJECTIVE We aim to predict the status of liver injury in patients with TB at the clinical treatment stage. METHODS We designed an interpretable prediction model based on the XGBoost algorithm and identified the most robust and meaningful predictors of the risk of TB-DILI on the basis of clinical data extracted from the Hospital Information System of Shenzhen Nanshan Center for Chronic Disease Control from 2014 to 2019. RESULTS In total, 757 patients were included, and 287 (38%) had developed TB-DILI. Based on values of relative importance and area under the receiver operating characteristic curve, machine learning tools selected patients’ most recent alanine transaminase levels, average rate of change of patients’ last 2 measures of alanine transaminase levels, cumulative dose of pyrazinamide, and cumulative dose of ethambutol as the best predictors for assessing the risk of TB-DILI. In the validation data set, the model had a precision of 90%, recall of 74%, classification accuracy of 76%, and balanced error rate of 77% in predicting cases of TB-DILI. The area under the receiver operating characteristic curve score upon 10-fold cross-validation was 0.912 (95% CI 0.890-0.935). In addition, the model provided warnings of high risk for patients in advance of DILI onset for a median of 15 (IQR 7.3-27.5) days. CONCLUSIONS Our model shows high accuracy and interpretability in predicting cases of TB-DILI, which can provide useful information to clinicians to adjust the medication regimen and avoid more serious liver injury in patients.

A nomogram model to predict the risk of drug-induced liver injury in patients receiving anti-tuberculosis treatment

“…By using the XGBoost algorithm, Zhong et al have established a model that can predict DILI with high accuracy and interpretability. However, their model involves the accumulative dose of anti-TB agents used during a treatment course, and this dose cannot be foreseen before starting the treatment (Zhong et al, 2021). In the validation step, two groups were enrolled in this study, representing for populations with comparable baselines and with different baselines, respectively.…”

“…Prediction model can facilitate the screening for the significant risk factors in the real world, which has been found essential in diagnosis, treatment, and prognosis. Although several risk factors have been found associated with DILI, there are limited reports on the application of nomogram to predict DILI-associated risks in a large sample size (Ashby et al, 2021;Raj Mani et al, 2021;Zhong et al, 2021;Zhao et al, 2022). Here, a cohort of TB patients spanning 13 years were retrospectively reviewed, and a nomogram prediction model was established to evaluate the DILI risk following anti-TB treatment among patients who were negative for baseline liver diseases and liver protective drugs.…”