EClinicalMedicine

BACKGROUND: As mortality remains high for patients with Ebola virus disease (EVD) despite new treatment options, the ability to level up the provided supportive care and to predict the risk of death is of major importance. This analysis of the EVISTA cohort aims to describe advanced supportive care provided to EVD patients in the Democratic Republic of the Congo (DRC) and to develop a simple risk score for predicting in-hospital death, called PREDS. METHODS: In this prospective cohort (NCT04815175), patients were recruited during the 10(th) EVD outbreak in the DRC across three Ebola Treatment Centers (ETCs). Demographic, clinical, biological, virological and treatment data were collected. We evaluated factors known to affect the risk of in-hospital death and applied univariate and multivariate Cox proportional-hazards analyses to derive the risk score in a training dataset. We validated the score in an internal-validation dataset, applying C-statistics as a measure of discrimination. FINDINGS: Between August 1(st) 2018 and December 31(th) 2019, 711 patients were enrolled in the study. Regarding supportive care, patients received vasopressive drug (n = 111), blood transfusion (n = 101), oxygen therapy (n = 250) and cardio-pulmonary ultrasound (n = 15). Overall, 323 (45%) patients died before day 28. Six independent prognostic factors were identified (ALT, creatinine, modified NEWS2 score, viral load, age and symptom duration). The final score range from 0 to 13 points, with a good concordance (C = 86.24%) and calibration with the Hosmer-Lemeshow test (p = 0.12). INTERPRETATION: The implementation of advanced supportive care is possible for EVD patients in emergency settings. PREDS is a simple, accurate tool that could help in orienting early advanced care for at-risk patients after external validation. FUNDING: This study was funded by ALIMA

Assessing Biases in the Evaluation of Classification Assays for HIV Infection Recency

Identifying recent HIV infection cases has important public health and clinical implications. It is essential for estimating incidence rates to monitor epidemic trends and evaluate the effectiveness of interventions. Detecting recent cases is also important for HIV prevention given the crucial role that recently infected individuals play in disease transmission, and because early treatment onset can improve the clinical outlook of patients while reducing transmission risk. Critical to this enterprise is the development and proper assessment of accurate classification assays that, based on cross-sectional samples of viral sequences, help determine infection recency status. In this work we assess some of the biases present in the evaluation of HIV recency classification algorithms that rely on measures of within-host viral diversity. Particularly, we examine how the time since infection (TSI) distribution of the infected subjects from which viral samples are drawn affect performance metrics (e.g., area under the ROC curve, sensitivity, specificity, accuracy and precision), potentially leading to misguided conclusions about the efficacy of classification assays. By comparing the performance of a given HIV recency assay using six different TSI distributions (four simulated TSI distributions representing different epidemic scenarios, and two empirical TSI distributions), we show that conclusions about the overall efficacy of the assay depend critically on properties of the TSI distribution. Moreover, we demonstrate that an assay with high overall classification accuracy, mainly due to properly sorting members of the well-represented groups in the validation dataset, can still perform notoriously poorly when sorting members of the less represented groups. This is an inherent issue of classification and diagnostics procedures that is often underappreciated. Thus, this work underscores the importance of acknowledging and properly addressing evaluation biases when proposing new HIV recency assays

Estimating influenza attack rates in the United States using a participatory cohort

We considered how participatory syndromic surveillance data can be used to estimate influenza attack rates during the 2012–2013 and 2013–2014 seasons in the United States. Our inference is based on assessing the difference in the rates of self-reported influenza-like illness (ILI, defined as presence of fever and cough/sore throat) among the survey participants during periods of active vs. low influenza circulation as well as estimating the probability of self-reported ILI for influenza cases. Here, we combined Flu Near You data with additional sources (Hong Kong household studies of symptoms of influenza cases and the U.S. Centers for Disease Control and Prevention estimates of vaccine coverage and effectiveness) to estimate influenza attack rates. The estimated influenza attack rate for the early vaccinated Flu Near You members (vaccination reported by week 45) aged 20–64 between calendar weeks 47–12 was 14.7%(95% CI(5.9%,24.1%)) for the 2012–2013 season and 3.6%(−3.3%,10.3%) for the 2013–2014 season. The corresponding rates for the US population aged 20–64 were 30.5% (4.4%, 49.3%) in 2012–2013 and 7.1%(−5.1%, 32.5%) in 2013–2014. The attack rates in women and men were similar each season. Our findings demonstrate that participatory syndromic surveillance data can be used to gauge influenza attack rates during future influenza seasons

Hypothetical Time Since Infection distributions.

<p>These distributions have a Beta distribution kernel with parameters <i>a</i> and <i>b</i>. They are meant to represent different epidemic scenarios (akin to those in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139735#pone.0139735.ref018" target="_blank">18</a>]). The blue line represents the case of an “emerging” epidemic; the orange line a “waning” epidemic; the green line a “stable” epidemic; and the black line an epidemic that has been partially controlled for a period of time, but has recently resurged. The latter scenario is arguably the least likely to be found in reality, and we have included it to mimic the properties of the TSI distribution of D561 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139735#pone.0139735.g001" target="_blank">Fig 1</a>.</p

Empirical <i>time since infection</i> distributions of two available datasets.

<p>On the left, D228 represents 228 samples (from 42 subjects) with recent infection of subtype C in Botswana from 2004 to 2008. Subjects were followed longitudinally for no more than 755 days [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139735#pone.0139735.ref017" target="_blank">17</a>]. On the right, D561 represents a meta database (freely available at Los Alamos HIV public database; accessed August 2014) of 561 samples (from 462 subjects) with subtype B and C. The maximum TSI is 8888 days.</p

Classification performance of the same assay using the two TSI distributions from the empirical datasets.

<p>With <i>u</i> = 0.2, <i>h</i> = 500, recency at 6 months. The 95% prediction bounds are obtained from 1000 simulations.</p