RESUMEN
INTRODUCTION: A vaginal ring containing dapivirine is effective for HIV prevention as pre-exposure prophylaxis (PrEP). We evaluated the potential epidemiological impact and cost-effectiveness of dapivirine vaginal ring PrEP among 22- to 45-year-old women in KwaZulu-Natal, South Africa. METHODS: Using mathematical modelling, we studied dapivirine vaginal ring PrEP implementation, either unprioritized, or prioritized based on HIV incidence (≥3% per year), age (22 to 29 years) or female sex worker status, alongside the implementation of voluntary medical male circumcision and antiretroviral therapy scaled-up to UNAIDS Fast-Track targets. Outcomes over the intervention (2019 to 2030) and lifetime horizons included cumulative HIV infections, life-years lived, costs and cost-effectiveness. We assessed the incremental cost-effectiveness ratios against the revealed willingness to pay ($500) and the standard (2017 per capita gross domestic product; $6161) cost-effectiveness thresholds for South Africa. RESULTS: Compared to a reference scenario without PrEP, implementation of dapivirine vaginal ring PrEP, assuming 56% effectiveness and covering 50% of 22 to 29-year-old or high-incidence women, prevented 10% or 11% of infections by 2030 respectively. Equivalent, unprioritized coverage (30%) prevented fewer infections (7%), whereas 50% coverage of female sex workers had the least impact (4%). Drug resistance attributable to PrEP was modest (2% to 4% of people living with drug-resistant HIV). Over the lifetime horizon, dapivirine PrEP implementation among female sex workers was cost-saving, whereas incidence-based PrEP cost $1898 per life-year gained, relative to PrEP among female sex workers and $989 versus the reference scenario. In a scenario of 37% PrEP effectiveness, PrEP had less impact, but prioritization to female sex workers remained cost-saving. In uncertainty analysis, female sex worker PrEP was consistently cost-saving; and over the lifetime horizon, PrEP cost less than $6161 per life-year gained in over 99% of simulations, whereas incidence- and age-based PrEP cost below $500 per life-year gained in 61% and 49% of simulations respectively. PrEP adherence and efficacy, and the effectiveness of antiretroviral therapy for HIV prevention, were the principal drivers of uncertainty in the cost-effectiveness of PrEP. CONCLUSIONS: Dapivirine vaginal ring PrEP would be cost-saving in KwaZulu-Natal if prioritized to female sex workers. PrEP's impact on HIV prevention would be increased, with potential affordability, if prioritized to women by age or incidence.
Asunto(s)
Fármacos Anti-VIH/uso terapéutico , Dispositivos Anticonceptivos Femeninos/economía , Infecciones por VIH/prevención & control , Pirimidinas/uso terapéutico , Adolescente , Adulto , Dispositivos Anticonceptivos Femeninos/estadística & datos numéricos , Análisis Costo-Beneficio , Farmacorresistencia Viral , Femenino , Infecciones por VIH/economía , Infecciones por VIH/epidemiología , Humanos , Masculino , Persona de Mediana Edad , Modelos Teóricos , Profilaxis Pre-Exposición/economía , Trabajadores Sexuales , Sudáfrica/epidemiología , Adulto JovenRESUMEN
Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response.