Effectiveness of Prophylactic Use of Hepatoprotectants for Tuberculosis Drug-Induced Liver Injury: A Population-Based Cohort Analysis Involving 6,743 Chinese Patients

Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study

BACKGROUND

The objective of this research was to create and validate an interpretable prediction model for drug-induced liver injury (DILI) during tuberculosis (TB) treatment.

METHODS

A dataset of TB patients from Ningbo City was used to develop models employing the eXtreme Gradient Boosting (XGBoost), random forest (RF), and the least absolute shrinkage and selection operator (LASSO) logistic algorithms. The model's performance was evaluated through various metrics, including the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR) alongside the decision curve. The Shapley Additive exPlanations (SHAP) method was used to interpret the variable contributions of the superior model.

RESULTS

A total of 7,071 TB patients were identified from the regional healthcare dataset. The study cohort consisted of individuals with a median age of 47 years, 68.0% of whom were male, and 16.3% developed DILI. We utilized part of the high dimensional propensity score (HDPS) method to identify relevant variables and obtained a total of 424 variables. From these, 37 variables were selected for inclusion in a logistic model using LASSO. The dataset was then split into training and validation sets according to a 7:3 ratio. In the validation dataset, the XGBoost model displayed improved overall performance, with an AUROC of 0.89, an AUPR of 0.75, an F1 score of 0.57, and a Brier score of 0.07. Both SHAP analysis and XGBoost model highlighted the contribution of baseline liver-related ailments such as DILI, drug-induced hepatitis (DIH), and fatty liver disease (FLD). Age, alanine transaminase (ALT), and total bilirubin (Tbil) were also linked to DILI status.

CONCLUSION

XGBoost demonstrates improved predictive performance compared to RF and LASSO logistic in this study. Moreover, the introduction of the SHAP method enhances the clinical understanding and potential application of the model. For further research, external validation and more detailed feature integration are necessary.

Highlighting the Use of the Hepatoprotective Nutritional Supplements among Patients with Chronic Diseases

Cross-sectional studies, while not considered glamorous endeavors, are firmly anchored in data and statistics, providing essential insights about public health. The aim of the study is to see the frequency of hepatoprotective (HP) nutritional supplement consumption among patients with chronic diseases (other than chronic liver disorders) and analyzes the habits related to the consumption of nutritional supplements among these patients. A total of 954 patients, seeking medical prescriptions for chronic diseases under various payment arrangements (compensated, gratuity, or full payment) were carefully selected over a 12-month period from four private pharmaceutical facilities. We examined the frequency of HP consumption in relation with a number of prescribed medications for chronic conditions. All these patients were invited to complete a questionnaire about their supplement consumption habits and were provided the option to participate in a nutritional status assessment. One hundred ninety-five patients consented to participate in the survey, and 65 patients agreed to undergo a nutritional status evaluation. Of the 954 patients, 77.2% incorporate HP into their regimen. The most frequent consumption (83.33%) was recorded in a group with seven drugs, followed by a group with three drugs (82.84%). Women have a higher usage rate of HP (80.58%; 444 from 551) compared to men (62.60%; 293 from 383), and most of the patients (59.5%) used extracts of Silybum marianum L. In the survey, 64.61% of participants were using supplements, with most (59.52%) consuming HP. Only 32.54% of patients rely on recommendations from healthcare professionals. Of the patients who use supplements, 55.56% reported improvements in their health status. Furthermore, patients who integrate supplements into their daily routine tend to achieve better overall nutritional status.

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

Objectives: To establish an individualized nomogram to predict the probability of drug-induced liver injury (DILI) in tuberculosis patients receiving anti-tuberculosis treatment. Methods: The clinical information of patients admitted to a tertiary hospital between January 2010 and December 2022 was retrospectively reviewed from the clinical records. Patients with baseline liver diseases (hepatis B or C infection and fatty liver) or taking liver protective drugs were excluded. The maximum values in liver function test within 180 days after anti-tuberculosis treatment were collected to determine the occurrence of DILI. The candidate variables used for establishing prediction model in this study were the last results within the 30 days before the treatment onset. The final variables were included after univariate and multivariate logistic regression analyses and applied to establish the nomogram model. The discrimination power and prediction accuracy of the prediction model were assessed using the area under the receiver operating characteristic (AUC) curve and a calibration chart. The clinical effectiveness was assessed via decision curve analysis (DCA). The established model was validated in two validation groups. Results: A total of 1979 patients with 25 variables were enrolled in this study, and the incidence of DILI was 4.2% (n = 83). The patients with complete variables were divided into training group (n = 1,121), validation group I (n = 492) and validation group II (n = 264). Five variables were independent factors for DILI and included in the final prediction model presented as nomogram: age (odds ratio [OR] 1.022, p = 0.023), total bilirubin ≥17.1 μmol/L (OR 11.714, p < 0.001), uric acid (OR 0.977, p = 0.047), neutrophil count (OR 2.145, 0.013) and alcohol consumption (OR 3.209, p = 0.002). The AUCs of the prediction model in the training group, validation group I and validation group II were 0.833, 0.668, and 0.753, respectively. The p-values of calibration charts in the three groups were 0.800, 0.996, and 0.853. The DCA curves of the prediction model were above the two extreme curves. Conclusion: The nomogram model in this study could effectively predict the DILI risk among patients under anti-tuberculosis drug treatment.

Risk of peripheral facial palsy following parenteral inactivated influenza vaccination in the elderly Chinese population

Background

Concern about the risk of peripheral facial palsy (PFP) following vaccination is one reason for hesitancy in influenza vaccination. However, the association between the flu vaccine and PFP is still controversial, and further evidence is urgently needed.

Methods

This self-controlled case series study evaluated PFP risk following inactivated influenza vaccine in the elderly using a large linked database in Ningbo, China. Relative incidence ratios (RIRs) and 95% confidence intervals (CIs) estimated using conditional Poisson regression were utilized to determine whether the risk of PFP was increased after vaccination.

Results

This study included 467 episodes, which occurred in 244 females and 220 males. One hundred twenty-four episodes happened within 1-91 days after vaccination, accounting for 26.7%. The adjusted RIRs within 1-30 days, 31-60 days, 61-91 days, and 1-91 days after influenza vaccination were 0.95 (95% CI 0.69-1.30), 1.08 (95% CI 0.78-1.49), 1.01 (95% CI 0.70-1.45), and 1.00 (95% CI 0.81-1.24), respectively. Similar results were found in subgroup analyses and sensitivity analyses.

Conclusions

Influenza vaccination does not increase PFP risk in the elderly population. This finding provides evidence to overcome concerns about facial paralysis after influenza vaccination.