Browsing by Subject "Drug Resistance"

Now showing 1 - 3 of 3
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    Open Access
    A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data
    (2017) Nasejje, Justine B; Mwambi, Henry; Sabur, Natasha F; Lesosky, Maia
    Abstract Background Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF) are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate. Methods In this study, we compare the random survival forest model to the conditional inference model (CIF) using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points). The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB) which consists of mainly categorical covariates with two levels (few split-points). Results The study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points. Conclusion Although survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question.
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    Open Access
    Case management of malaria: Treatment and chemoprophylaxis
    (2013) Ukpe, I S; Moonasar, D; Raman, J; Barnes, K I; Baker, L; Blumberg, L
    Malaria case management is a vital component of programmatic strategies for malaria control and elimination. Malaria case management encompasses prompt and effective treatment to minimise morbidity and mortality, reduce transmission and prevent the emergence and spread of antimalarial drug resistance. Malaria is an acute illness that may progress rapidly to severe disease and death, especially in non-immune populations, if not diagnosed early and promptly treated with effective drugs. In this article, the focus is on malaria case management, addressing treatment, monitoring for parasite drug resistance, and the impact of drug resistance on treatment policies; it concludes with chemoprophylaxis and treatment strategies for malaria elimination in South Africa.
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    Open Access
    Management of HIV-associated tuberculosis in resource-limited settings: a state-of-the-art review
    (BioMed Central Ltd, 2013) Lawn, Stephen; Meintjes, Graeme; McIlleron, Helen; Harries, Anthony; Wood, Robin
    The HIV-associated tuberculosis (TB) epidemic remains a huge challenge to public health in resource-limited settings. Reducing the nearly 0.5 million deaths that result each year has been identified as a key priority. Major progress has been made over the past 10 years in defining appropriate strategies and policy guidelines for early diagnosis and effective case management. Ascertainment of cases has been improved through a twofold strategy of provider-initiated HIV testing and counseling in TB patients and intensified TB case finding among those living with HIV. Outcomes of rifampicin-based TB treatment are greatly enhanced by concurrent co-trimoxazole prophylaxis and antiretroviral therapy (ART). ART reduces mortality across a spectrum of CD4 counts and randomized controlled trials have defined the optimum time to start ART. Good outcomes can be achieved when combining TB treatment with first-line ART, but use with second-line ART remains challenging due to pharmacokinetic drug interactions and cotoxicity. We review the frequency and spectrum of adverse drug reactions and immune reconstitution inflammatory syndrome (IRIS) resulting from combined treatment, and highlight the challenges of managing HIV-associated drug-resistant TB.