CROI 2018 Abstract eBook

Abstract eBook

Poster Abstracts

Methods: Main Parisian sequence databases were screened for the new recombinant profile. For each identified patient, available sequences and clinical data were extracted. HIV subtyping was confirmed by phylogenetic analysis for protease (PR-299bps), reverse transcriptase (RT-774bps), integrase (INT-696bps) and envelope (328bps) with the LANL reference sequences dataset and using FastTree 2.1. A first analysis of recombination points was performed on available sequences with RDP4. The time of the most recent common ancestor was estimated from PR-RT using BEAST 1.8. Results: 30 infected patients were identified so far. PR and INT clustered with CRF02_AG while RT and env clustered with subtype B. This profile and the first recombination point identified, at position 2709 (HXB2), do not correspond to previously described recombinants. No drug resistance mutation was identified. All patients formed a recent transmission cluster in PR-RT (branch support value >99% and maximum genetic distance <2.8%). Patients were diagnosed in 2013 (n=2), 2015 (7), 2016 (11), and 2017 (10). 29/30 are male, 17/18 declared a MSM route of transmission, 8/19 were diagnosed as primo-infections. Median viral loads were at 144,295 [IQR: 58,700-326,829] and 539,808 [IQR: 156,254- 3,305,809] copies/mL for non-primo- and primo-infections, respectively. Only 3 patients had CD4 <100 cells/mm 3 . Most of these patients are living in the Eastern Paris suburb area. tMRCA analysis estimated the emergence of this cluster in July 2012. Conclusion: A new CRF02_AG/B recombinant, proposed as CRF94_B02, has been identified. All detected patients so far are included in a single recent transmission cluster and were recently diagnosed, underlying the rapid spread of this strain among MSM in Paris suburb area. A full genome analysis and a research for other patients at a national scale are undergoing. 959 PREDICTIVE MODEL FOR HIV TRANSMISSION CLUSTER GROWTH IN NORTH CAROLINA Rachael Billock 1 , Kimberly A. Powers 1 , Erika Samoff 2 , Victoria L. Mobley 2 , William C. Miller 3 , Joseph J. Eron 1 , Ann M. Dennis 1 1 University of North Carolina Chapel Hill, Chapel Hill, NC, USA, 2 North Carolina Division of Public Health, Raleigh, NC, USA, 3 The Ohio State University, Columbus, OH, USA Background: HIV transmission cluster identification shows promise as a tool to help prioritize public health intervention. We assessed cluster-level characteristics associated with temporal cluster growth and incorporated these findings into a predictive model for cluster growth in North Carolina (NC). Methods: HIV-1 pol sequences generated from routine genotypic resistance testing from 11/2010 through 09/2016 in NC (n=8923 persons) were matched to HIV surveillance data and used to identify putative transmission clusters size ≥2 members via pairwise genetic distance differences <1.5%. Of 782 clusters, 275 (35%) were established by 03/2015 and recently active (included any sequences from prior 2 years). Cluster members were categorized as baseline members (sequences prior to 03/2015), hidden members (diagnosed prior to 03/2015 with sequences after 03/2015), and newmembers (diagnosed after 03/2015) [Figure]. Clusters were retrospectively assessed for growth (any newmembers) over 18 months (03/2015 – 09/2016). We developed a predictive model for cluster growth incorporating demographic, clinical, and contact tracing characteristics of baseline members and evaluated the model using the area under the receiver operating characteristic curve (ROC AUC). Results: Of 275 established, recently active clusters (n=1625 persons; size=2- 44 persons), 64 (23%) grew over 18 months of follow-up. Growing clusters had a larger median size at baseline than non-growing clusters (6 vs. 2 persons). Persons in growing clusters showed younger median ages (33 vs. 38 years) and were more likely to report male sex (89% vs. 77%), black race (77% vs. 41%), and MSM status (71% vs. 51%) than those in non-growing clusters. Persons in growing clusters had shorter median times to care entry after diagnosis than those in non-growing clusters (45 vs. 71 days) but were less likely to have been in care at the start of follow-up for cluster growth (81% vs. 84%). The final predictive model included terms for cluster size, median time to care entry after diagnosis, median age, and percent with no identified contacts among baseline members, and showed an ROC AUC of 0.85 in the validation sample. Conclusion: This model has strong predictive ability to forecast new HIV diagnoses in recently active, established genetic clusters in NC and could be adapted to diverse HIV control settings. Identification of HIV transmission clusters that are likely to grow over time could guide prioritization of public health interventions.

and clinical predictors of clustering and cluster growth in the network using multivariate logistic regression (growth statistic ≥1.4 vs. <1.4). We then calculated assortativity for each epidemiological variable. Results: 2743/8351 (32.85%) sequences clustered, distributed across 643 clusters sized from 2 to 162. The cluster growth statistic varied between 0 and 2.56, and 569 (6.8%) individuals were in high growth clusters. MSM (p<0.05), Hispanic (p<0.001) and white (p<0.05) race/ethnicity, higher CD4 (p<0.01), high viral loads (p<0.01), and incident cases (p<0.01) were associated with clustering. In comparison, when evaluating high cluster growth, race/ethnicity and higher CD4 were no longer significant, but young age at diagnosis (0-20) and current young age (20-30) became predictive (p<0.05). Incident cases (p<0.05), high viral loads (p<0.05) and being an MSM remained significantly associated. The transmission network was highly assortative by race/ethnicity (Assortativity = 0.31, p<0.001), but not strongly assortative for transmission risk group (A=0.13, p<0.01) or age (A=0.12, p<0.01); suggesting individuals linked to others of the same race/ethnicity, but not necessarily the same transmission risk group or age category. Conclusion: We found that variables associated with high cluster growth in Illinois were different from those associated with clustering. Most notably, younger age stood out as being highly predictive of cluster growth but race/ ethnicity did not. Given that the number of individuals in growing clusters is much smaller than the total number of individuals clustered, cluster growth measures should be optimized to best use limited resources to inform and prioritize local public health interventions.

Poster Abstracts

958 A NEW B/CRF02 CIRCULATING RECOMBINANT SPREADING QUICKLY IN PARIS AREA, FRANCE Marc Wirden 1 , Alexandre Storto 2 , Magali Bouvier-Alias 3 , Karine Grenet 4 , Marie- Laure Chaix Baudier 5 , Philippe Simon 4 , Eric Froguel 4 , Thuy T. Nguyen 1 , Cathia Soulie 1 , Vincent Calvez 1 , Diane Descamps 1 , Anne-Geneviève Marcelin 1 , Benoit Visseaux 2 1 Pitié-Salpêtrière Hospital, Paris, France, 2 Bichat–Claude Bernard Hospital, Paris, France, 3 Hôpital Henri Mondor, Créteil, France, 4 Groupe Hospitalier de l’Est Francilien, Jossigny, France, 5 Hôpital Saint-Louis, Paris, France Background: The two majors circulating HIV-1 clades in France, subtype B and CRF02_AG, were originally present in distinct populations, Caucasian and West African population, respectively. However, CRF02_AG is increasing among all HIV populations and several recombinant forms, 7 URF and CRF56_cpx, were already identified. In this study, we describe a new B/CRF02_AG recombinant spreading quickly and forming a recent transmission cluster among an MSM population.

CROI 2018 366

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