Computational Models as Predictors of HIV Treatment Outcomes for the Phidisa Cohort in South Africa

Background: Selecting the optimal combination of HIV drugs for an individual in resourcelimited settings is challenging because of the limited availability of drugs and genotyping.Objective: The evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype; using cases from the Phidisa cohort in South Africa.Methods: Cases from Phidisa of treatment change following failure were identified that had the following data available: baseline CD4 count and viral load; details of failing and previous antiretroviral drugs; drugs in new regimen and time to follow-up. The HIV Resistance Response Database Initiative's (RDI's) models used these data to predict the probability of a viral load 50 copies/mL at follow-up. The models were also used to identify effective alternative combinations of three locally available drugs.Results: The models achieved accuracy (area under the receiver-operator characteristic curve) of 0.72 when predicting response to therapy; which is less accurate than for an independent global test set (0.80) but at least comparable to that of genotyping with rules-based interpretation. The models were able to identify alternative locally available three-drug regimens that were predicted to be effective in 69% of all cases and 62% of those whose new treatment failed in the clinic.Conclusion: The predictive accuracy of the models for these South African patients together with the results of previous studies suggest that the RDI's models have the potential to optimise treatment selection and reduce virological failure in different patient populations; without the use of a genotype

The impact of the roll-out of rapid molecular diagnostic testing for tuberculosis on empirical treatment in Cape Town, South Africa

Objective To investigate the impact of introducing a rapid test as the first-line diagnostic test for drug-sensitive tuberculosis in Cape Town, South Africa. Methods Xpert® MTB/RIF (Xpert®), an automated polymerase-chain-reaction-based assay, was rolled out between 2011 and 2013. Data were available on 102 007 adults treated for pulmonary tuberculosis between 2010 and 2014. Tuberculosis notification rates per 100 000 population were calculated for each calendar year and for each year relative to the test roll-out locally, overall and by bacteriological confirmation. Empirical treatment was defined as treatment given without bacteriological confirmation by Xpert®, sputum smear microscopy or sputum culture. Findings. Between 2010 and 2014, the proportion of human immunodeficiency virus (HIV)-negative patients treated empirically for tuberculosis declined from 23% (2445/10 643) to 11% (1149/10 089); in HIV-positive patients, it declined from 42% (4229/9985) to 27% (2364/8823). The overall tuberculosis notification rate decreased by 12% and 19% among HIV-negative and HIV-positive patients, respectively; the rate of bacteriologically confirmed cases increased by 1% and 3%, respectively; and the rate of empirical treatment decreased by 56% and 49%, respectively. These changes occurred gradually following the test's introduction and stabilized after 3 years. Conclusion Roll-out of the rapid test in a setting with a high prevalence of pulmonary tuberculosis and HIV infection was associated with a halving of empirical treatment that occurred gradually after the test's introduction, possibly reflecting the time needed for full implementation. More than a quarter of HIV-positive patients with tuberculosis were still treated empirically, highlighting the diagnostic challenge in these patients