Predictors of antiretroviral treatment failure to the first line therapy: a cross-sectional study among Iranian HIV-positive adults

BACKGROUND

HIV virological failure is one of the main problems in HIV-infected patients, and identifying the main predictors of such treatment failure may help in combating HIV/AIDS.

METHODOLOGY

This cross-sectional study included 1800 HIV-infected patients with either virological failure or treatment response. HIV viral load, CD4 count, and other tests were performed. Statistical analysis was used to determine the predictors of virological failure.

RESULTS

Clinical stage, treatment with reverse transcriptase inhibitors (RTIs), under therapy for three years or more, suboptimal adherence to antiretroviral treatment (ART), age > 40 years, CD4 count < 200 cells/mm3, unemployment, being infected through sex, and the presence of symptoms were the predominant risk factors for virological failure. In addition, 55% of patients who experienced virological failure failed to experience immunological and/or clinical failure.

CONCLUSION

As the first study in southern Iran and the second in Iran, Iranian policymakers should focus on intensive counseling and adherence support and emphasize more effective treatment regimens such as protease and integrase inhibitors (PIs and INTIs), to increase the chance of a treatment response to ART. The accuracy of identifying clinical and immunological criteria in resource-limited settings is not promising. The present findings can be used to determine effective measures to control HIV treatment failure and design efficient strategies for the ambitious 95-95-95 plan.

Determining hypertensive patients' beliefs towards medication and associations with medication adherence using machine learning methods

Background

This study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients' adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients' adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature.

Methods

Data from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.

Result

Machine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.

Conclusion

This study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients' adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.