Characteristics of HBV infection in 705 HIV-infected patients under lamivudine-based antiretroviral treatment from three regions in China

Outcomes of ABO-incompatible liver transplantation in end-stage liver disease patients co-infected with hepatitis B and human immunodeficiency virus

BACKGROUND

Human immunodeficiency virus (HIV)-positive patients coinfected with hepatitis B virus (HBV) are eligible for liver transplantation (LT) in Africa and Southeast Asia, particularly China. However, the outcome of HIV-HBV coinfected patients referred for ABO-incompatible LT (ABOi-LT) is unknown.

AIM

To clarify the outcome of ABOi-LT for HIV-HBV coinfected patients with end-stage liver disease (ESLD).

METHODS

We report on two Chinese HIV-HBV coinfected patients with ESLD who underwent A to O brain-dead donor LT and reviewed the literature on HIV-HBV coinfected patients treated with ABO-compatible LT. The pretransplantation HIV viral load was undetectable, with no active opportunistic infections. Induction therapy consisted of two sessions of plasmapheresis and a single dose of rituximab in two split doses, followed by an intraoperative regimen of intravenous immunoglobulin, methylprednisolone, and basiliximab. Post-transplant maintenance immunosuppressive agents consisted of tacrolimus and mycophenolate mofetil, and prednisone.

RESULTS

At the intermediate-term follow-up, patients showed undetectable HIV viral load, CD4(+) T cell counts greater than 150 cells/μL, no HBV recurrence, and stable liver function. A liver allograft biopsy showed no evidence of acute cellular rejection. Both patients survived at 36-42 mo of follow-up.

CONCLUSION

This is the first report of ABOi-LT in HIV-HBV recipients with good intermediate-term outcomes, suggesting that ABOi-LT may be feasible and safe for HIV-HBV coinfected patients with ESLD.

A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China

Objective

To establish a machine learning model for identifying patients coinfected with hepatitis B virus (HBV) and human immunodeficiency virus (HIV) through two sexual transmission routes in Jiangsu, China.

Methods

A total of 14197 HIV cases transmitted by homosexual and heterosexual routes were recruited. After data processing, 12469 cases (HIV and HBV, 1033; HIV, 11436) were left for further analysis, including 7849 cases with homosexual transmission and 4620 cases with heterosexual transmission. Univariate logistic regression was used to select variables with significant P value and odds ratio for multivariable analysis. In homosexual transmission and heterosexual transmission groups, 10 and 6 variables were selected, respectively. For identifying HIV individuals coinfected with HBV, a machine learning model was constructed with four algorithms, including Decision Tree, Random Forest, AdaBoost with decision tree (AdaBoost), and extreme gradient boosting decision tree (XGBoost). The detective value of each variable was calculated using the optimal machine learning algorithm.

Results

AdaBoost algorithm showed the highest efficiency in both transmission groups (homosexual transmission group: accuracy = 0.928, precision = 0.915, recall = 0.944, F − 1 = 0.930, and AUC = 0.96; heterosexual transmission group: accuracy = 0.892, precision = 0.881, recall = 0.905, F − 1 = 0.893, and AUC = 0.98). Calculated by AdaBoost algorithm, the detective value of PLA was the highest in homosexual transmission group, followed by CR, AST, HB, ALT, TBIL, leucocyte, age, marital status, and treatment condition; in the heterosexual transmission group, the detective value of PLA was the highest (consistent with the condition in the homosexual group), followed by ALT, AST, TBIL, leucocyte, and symptom severity.

Conclusions

The univariate logistics regression combined with the AdaBoost algorithm could accurately screen the risk factors of HBV in HIV coinfection without invasive testing. Further studies are needed to evaluate the utility and feasibility of this model in various settings.