CROI 2025 Abstract eBook

Abstract eBook

Poster Abstracts

Methods: This retrospective observational cohort study was conducted using the Optum database (study period: May 2020-December 2023), with a combination of self-reported and imputed race data. The study sample included 88,416 hospitalizations by patients in the US aged ≥18 years who were hospitalized with a COVID-19 primary diagnosis and enrolled in the database ≥6 months prior to hospitalization. Patients could have >1 hospitalization, with a washout period of 4 months between hospitalizations. 3456 patient attributes associated with higher risk for COVID-19 diagnosis as defined by the CDC were identified in the dataset and grouped into 46 risk factor categories. Recursive Feature Elimination was applied over different variant periods (pre-Delta: May 2, 2020-May 31, 2021; Delta: June 1- November 30, 2021; Omicron: December 1, 2021-December 31, 2023) to select the most informative risk factor categories associated with delayed (≥3 days after admission) or no RDV treatment in each variant period. Multivariate logistic regression was then utilized to estimate the risk of delayed or no RDV treatment for each risk factor category. Results: During the Omicron variant period, patients with chronic kidney disease, type 1 or 2 diabetes, or limited activities of daily living, and those who were Black showed significantly greater risk of receiving delayed or no RDV treatment. Patients who were diagnosed as bipolar or as smokers were more likely to receive timely RDV treatment. Conclusions: Using machine learning, we showed that select patient attributes influenced the likelihood of receiving delayed or no RDV treatment upon hospitalization for COVID-19; this methodology has the benefit of allowing near real-time analysis of treatment patterns in the context of a pandemic. These results identified factors that have influenced treatment practices, which can help guide future efforts to reduce healthcare disparities and enhance health equity.

and quartiles of the incidence rates were calculated to characterize mutation frequency patterns over time. Results: The mutation with the highest peak incidence was E802D, with a maximum of 0.0315% of total sequenced samples in one month. V792I also demonstrated a significant peak incidence of 0.0367%. Mutations such as C799F, D484Y, and C799R exhibited lower but consistent incidences, with means ranging from 0.0020% to 0.0031%. Notably, A376V and S759A exhibited very low incidence rates, with means of 0.00007% and 0.00003%, respectively, indicating rare occurrences. The data showed periodic surges in certain mutations, particularly E802D and V792I, which could indicate selective pressures or shifts in viral fitness during the study period (Fig). Conclusions: The study highlights that while RDV resistance mutations remain relatively rare, specific mutations such as E802D and V792I have demonstrated transient increases in prevalence. This suggests potential adaptive advantages in certain populations or during specific time frames. Continued genomic surveillance is critical to monitor these mutations, assess their clinical significance, and guide therapeutic strategies for SARS-CoV-2 treatment, especially in the context of antiviral resistance. Further research should explore the underlying mechanisms driving these periodic surges and evaluate the clinical implications of RDV resistance in treatment outcomes.

Poster Abstracts

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Nirmatrelvir/Ritonavir for Chronic COVID-19: Outcome in 16 Severely Immunocompromised Patients Hanne Lamberink, Sammy Huygens, Hannelore Bax, Corine Geurtsvankessel, Bart Rijnders Erasmus University Medical Center, Rotterdam, Netherlands Background: Nirmatrelvir/ritonavir (N/r) is approved for the outpatient treatment of COVID-19 within 5 days of symptom onset and is typically used in patients at increased risk for severe disease. In severely immunocompromised patients, COVID-19 occasionally evolves into a chronic infection. The potential value of N/r in this context including optimal treatment duration is unknown. We describe the off-label use of N/r in severely immunocompromised patients with persistent COVID-19 related symptoms and viremia. Methods: Data from patients in care at Erasmus University Medical Center in Rotterdam, the Netherlands, were collected when 1. immunocompromised, 2. SARS-CoV-2 PCR positive (>10.500 IU/ml) with persisting symptoms ≥7 days and 3. treated with N/r. Viral clearance (VC) was defined as a viral load <10,500 IU/ml without occurrence of viral rebound. Underlying immunocompromising condition, time from symptom onset, time to confirmed VC, percentage of patients with VC 28 days after treatment initiation, additional antiviral therapies and mortality were registered. Results: Between November 2022 and March 2024, 16 immunocompromised patients with persistent COVID-19 were treated with off-label N/r with a median symptom duration of 53 days (IQR 31-75). 12/16 received N/r for 5 and 4/16 for 10 days. In 10/16, N/r was combined with COVID-19 convalescent plasma. 7/16 were hospitalized at N/r initiation (1 at ICU). Despite the long-lasting infection, the median time to confirmed VC was very short at 7 days (IQR 6–23). VC was observed in 12/16 within 28 days after treatment initiation and in 13/16 within 60 days. 2/4 patients with a viral rebound successfully received a second course of N/r at day 20 (10-day course) and 77 (5-day course), followed by VC at day 23 and 98, respectively. One of the 16 patients died from COVID-19-related ARDS on day 4 post-treatment; all others were alive at day 60. Figure 1 illustrates virological treatment response of all 16 patients. Conclusions: In severely immunocompromised patients with non-resolving COVID-19, off-label treatment with N/r resulted in prompt viral clearance in 75%. Whether the simultaneous use of CCP in 10 of 16 patients contributed to

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Tracking Key Remdesivir Resistance Mutations in SARS-CoV-2 Using GISAID Data (2020-2024) Toshibumi Taniguchi, Misuzu Yahaba, Hiroshi Yoshikawa, Hidetoshi Igari Chiba University, Chiba, Japan Background: The emergence of remdesivir (RDV) resistance mutations poses a potential challenge in treating SARS-CoV-2 infections, especially as the virus continues to evolve globally. GISAID data provides an extensive resource for tracking such mutations. This study aims to investigate the temporal trends and incidence rates of RDV resistance-associated mutations, specifically those conferring a >2.5-fold reduction in susceptibility, to understand their prevalence and patterns over time. Methods: Genomic surveillance data from GISAID, covering the period from January 2020 to August 2024, were analyzed. The focus was on nine mutations associated with RDV resistance which confers > 2.5-fold reduction in susceptibility per The Stanford Coronavirus Resistance Database (CoV-RDB): A376V, E802D, C799F, D484Y, E802A, C799R, E796G, V792I, and S759A. Monthly incidence rates were calculated by dividing the number of samples carrying each mutation by the total number of samples sequenced each month. Descriptive statistics were used to summarize trends, and the mean, standard deviation,

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