Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings.

OBJECTIVES: Genotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs. METHODS: Random forest models were trained to predict the probability of response to ART (≤400 copies HIV RNA/mL) using the following data from 14 891 treatment change episodes (TCEs) after virological failure, from well-resourced countries: viral load and CD4 count prior to treatment change, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. Models were assessed by cross-validation during development, with an independent set of 800 cases from well-resourced countries, plus 231 cases from Southern Africa, 206 from India and 375 from Romania. The area under the receiver operating characteristic curve (AUC) was the main outcome measure. RESULTS: The models achieved an AUC of 0.74-0.81 during cross-validation and 0.76-0.77 with the 800 test TCEs. They achieved AUCs of 0.58-0.65 (Southern Africa), 0.63 (India) and 0.70 (Romania). Models were more accurate for data from the well-resourced countries than for cases from Southern Africa and India (P < 0.001), but not Romania. The models identified alternative, available drug regimens predicted to result in virological response for 94% of virological failures in Southern Africa, 99% of those in India and 93% of those in Romania. CONCLUSIONS: We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation. These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available.

Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings.

In the absence of widespread access to individualized laboratory monitoring, which forms an integral part of HIV patient management in resource-rich settings, the roll-out of highly active antiretroviral therapy (HAART) in resource-limited settings has adopted a public health approach based on standard HAART protocols and clinical/immunological definitions of therapy failure. The cost-effectiveness of HIV-1 viral load monitoring at the individual level in such settings has been debated, and questions remain over the long-term and population-level impact of managing HAART without it. Computational models that accurately predict virological response to HAART using baseline data including CD4 count, viral load and genotypic resistance profile, as developed by the Resistance Database Initiative, have significant potential as an aid to treatment selection and optimization. Recently developed models have shown good predictive performance without the need for genotypic data, with viral load emerging as by far the most important variable. This finding provides further, indirect support for the use of viral load monitoring for the long-term optimization of HAART in resource-limited settings.

Acute necrotizing pancreatitis following inadvertent extensive splenic artery embolisation for trauma.

We present a case of splenic artery embolisation (SAE) after traumatic splenic injury that was complicated by acute necrotizing pancreatitis, caused by inadvertently extensive embolisation of the splenic artery. Although SAE is increasingly used for splenic preservation in trauma, there is insufficient knowledge on its efficacy and pitfalls. This report aims to draw attention to a rare but potentially serious complication of SAE.

[AIDS treatment in Africa: the risk of antiretroviral resistance]. / Aidsbehandeling in Afrika: het risico van antiretrovirale resistentie.

--In recent years, implementation of antiretroviral therapy in developing countries with a high prevalence of HIV-1 has been recognised as a public health priority. Consequently, the availability ofantiretroviral combination therapy for people with HIV is increasing rapidly in sub-Saharan Africa. --HIV treatment programmes are implemented according to the standardised, simplified public health guidelines developed by the World Health Organization (WHO). --However, the implementation of treatment programmes in Africa is hindered by several factors, including the lack of adequate immunological and virological laboratory monitoring, insufficient support for adherence to therapy, vulnerable health care systems and the use of suboptimal drug combinations. --These suboptimal treatment conditions increase the risk that resistant virus strains will emerge that are less susceptible to standard first-line combination therapy, thus threatening the long-term success of the treatment programmes. --The WHO has initiated HIVResNet, an international expert advisory board that has developed a global strategy for surveillance and prevention of antiretroviral drug resistance. --The Dutch initiative known as 'PharmAccess African studies to evaluate resistance' (PASER) is contributing to this strategy by creating a surveillance network in sub-Saharan Africa.