HIV-1 Virologic Failure in the RESINA cohort: Lessons from Two Decades of Real- World Data

HIV-1 Virologic Failure in the RESINA cohort: Lessons from Two Decades of Real- World Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article HIV-1 Virologic Failure in the RESINA cohort: Lessons from Two Decades of Real- World Data Smaranda Gliga, Micha Böhm, Nadine Lübke, Alexander Killer, Falk Hüttig, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7722983/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Dec, 2025 Read the published version in Infection → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose To quantify virologic failure (VF), identify predictors, characterize resistance patterns at failure, and evaluate time to resuppression in the RESINA cohort. Methods ART-naïve adults initiating ART in 2001–2024 were followed. VF was confirmed HIV-1 RNA > 200 copies/mL after suppression or ≥ 0.5-log₁₀ rebound. Participants were grouped by treatment era (2001–2007, 2008–2013, ≥ 2014), reflecting availability of drug classes. Genotypes at baseline and VF were interpreted using the HIV-GRADE algorithm. Predictors of VF were assessed with logistic regression; time to resuppression (< 50 copies/mL) after first VF with Cox models and Kaplan–Meier plots. Results Among 5,136 participants, 139 (2.7%) had VF; rates declined by era (4.7%, 2.6%, 1.7%). Independent predictors were injection-drug use (odds ratio [OR] 1.74,), CD4 < 200/µL (OR 2.32), and ART start in 2001–2007 (OR 1.95); MSM acquisition was protective (OR 0.32). At failure, 36% showed resistance, often multiclass (61%); INSTI resistance was rare (n = 5), including one R263K + G118R. After first VF, 122/139 cases resuppressed; 17 did not. Median time to resuppression was 147 days. Male sex predicted faster resuppression (hazard ratio [HR] 1.81); higher failure VL trended to slower resuppression (HR 0.84 per log₁₀); regimen switches showed a favorable, non-significant trend. Conclusion VF was uncommon and declined over time, reflecting improved regimen potency and tolerability. Failures were associated with late presentation and IDU, consistent with adherence barriers. Resistance often involved multiple classes, while INSTI resistance remained infrequent. Early, genotype-guided optimization, preferably to INSTI-based therapy, combined with targeted adherence support may improve outcomes. HIV virologic failure drug resistance re-suppression Cox regression RESINA Figures Figure 1 Figure 2 Figure 3 Introduction The widespread introduction of combination antiretroviral therapy (cART) has transformed HIV from a fatal illness to a manageable chronic disease, dramatically improving both survival and clinical outcomes for affected individuals [ 1 , 2 ]. In recent years, newer regimens featuring enhanced safety profiles, once-daily single-tablet formulations, and the approval of long-acting injectable agents have further simplified treatment and strengthened adherence [ 3 – 5 ]. Sustained adherence not only ensures durable viral suppression but is also associated with improved health-related quality of life (HR-QoL) [ 1 ]. Despite substantial therapeutic advances, virologic failure (VF) remains a significant challenge in HIV management. The principal drivers of VF include suboptimal adherence, preexisting resistance mutations, and viral subtype–specific characteristics that may accelerate rebound or increase the likelihood of resistance development [ 6 – 8 ]. Sociodemographic factors such as younger age, male sex, nonwhite ethnicity, migration from high-prevalence regions, injection drug use (IDU), and lower socioeconomic status have also been linked to VF, primarily through their impact on adherence, continuity of care, and access to treatment [ 9 – 13 ]. Clinical determinants including low baseline CD4 T-cell count, elevated HIV-1 RNA levels at initiation, and delayed diagnosis or late presentation consistently predict poorer outcomes [ 5 , 11 , 14 , 15 ]. In addition, comorbid conditions, such as obesity, which is particularly relevant among individuals receiving long-acting injectable regimens, and structural barriers, including limited healthcare access in rural settings, may further increase the risk of VF [ 16 – 18 ]. Given these diverse contributors, understanding how resistance mutations evolve and persist is vital for informing treatment decisions, optimizing regimen selection and preventing ongoing viral replication. Previous studies have catalogued the range of resistance mutations that compromise cART efficacy and necessitate regimen switches [ 8 , 19 – 21 ]. However, the extent to which specific resistance mutation patterns influence VF outcomes in real-world cohorts with limited prior treatment exposure remains unclear. The RESINA cohort provides a unique opportunity to address these gaps, as it systematically collects longitudinal virologic and clinical data on previously untreated individuals. This enables a detailed analysis of both the frequency and the clinical consequences of resistance-associated mutations. In this analysis we aimed to quantify the frequency and determinants of VF, and to characterize the associated resistance mutations in a cohort of previously untreated participants with HIV from the RESINA cohort. As a secondary objective, we aimed to determine the proportion of participants who regained virologic resuppression after VF, and to identify the clinical and virologic factors associated with subsequent resuppression. Such insights are essential for optimizing first-line regimens, guiding timely regimen switches, and improving long-term HIV outcomes. Materials and methods This analysis used data from the RESINA cohort, a multicenter observational study investigating transmitted HIV drug resistance in ART-naive individuals from North Rhine-Westphalia, Germany. We included all participants diagnosed with HIV and initiating ART between 2001 and 2024. Study entry was defined as the date of written informed consent and collection of the first study sample before ART, whose initiation varied according to treatment guidelines across the observation period. try. Late presentation/diagnosis of HIV was defined in this study as a CD4 cell count below 200 cells/µL at the time of HIV diagnosis. This differs from the European Centre for Disease Prevention and Control (ECDC) definition, which classifies late presentation as a CD4 count <350 cells/µL or an AIDS-defining event within three months of diagnosis. [22]. We applied the stricter threshold of 200 copies/mL following prior suppression (<50 copies/mL) or, in some patients, as virologic rebound (a confirmed ≥0.5 log₁₀ increase from the post-ART nadir after an initial decline), even if the nadir remained above the assay’s lower limit of quantification (LLQ). Subsequent VF episodes were defined using the same virologic criteria (HIV-1 RNA >200 copies/mL), provided they occurred after a confirmed resuppression (<50 copies/mL). For patients with VF, time to resuppression was the interval between the date of first VF and the first subsequent HIV-1 RNA <50 copies/mL. Data Collection Clinical data included demographics, regimen, adherence (documented treatment interruption; missed ART-related appointments; descriptive only) , and HBV/HCV/HDV coinfections. Virologic data comprised plasma HIV-1 RNA (viral load (VL)) and the timing of genotypic resistance testing. Participants were stratified into three ART-initiation eras reflecting availability of key drug classes in Germany: Group 1 (2001–2007; pre-darunavir/raltegravir), Group 2 (2008–2013; post-darunavir/raltegravir), and Group 3 (2014 onwards; post-dolutegravir (DTG)). Resistance Testing Genotypic resistance was assessed at study entry and again at the time of VF. Plasma HIV‑1 RNA was amplified and sequenced with conventional Sanger assay from the start of the study and Illumina next-generation sequencing from 2015 onward. 10% consensus FASTAs were used to ensure comparability with earlier Sanger sequences, population‑based assay; whenever amplification from RNA was unsuccessful, paired proviral DNA extracted from peripheral blood mononuclear cells was analyzed as an accepted backup template to avoid loss of resistance information [23]. The composite nucleotide sequences spanning the protease (PR), reverse‑transcriptase (RT) and integrase (IN) regions were interpreted with the HIV‑GRADE algorithm, which translates codon changes into drug‑specific susceptibility scores. All major and accessory mutations were cataloged across nucleos(t)ide and non‑nucleoside RT inhibitors, protease inhibitors and integrase‑strand‑transfer inhibitors (INSTI), with particular attention to the canonical mutation pathways that underlie the stepwise evolution of raltegravir resistance described by Sichtig et al [19]. Statistical Analysis All analyses were conducted in SPSS v27 (IBM Corp., Armonk, NY). Baseline was defined as study entry. Descriptive statistics (frequencies/percentages; median/range) summarized characteristics and mutation distributions; group comparisons used t-tests/ANOVA or Mann–Whitney/Kruskal–Wallis as appropriate. To identify predictors of VF, we fit a cohort-wide binary logistic regression model including all 5,136 participants, restricted to one record per individual (VF = 1 if ≥1 VF occurred during follow-up; VF = 0 otherwise). Covariates were prespecified based on prior evidence, biological plausibility, and data completeness. Age was modeled in 10-year increments. Sex was coded as male versus female/other. Route of acquisition was represented by two dummy variables for MSM and IDU (1 = yes, 0 = no), with heterosexual contact as the reference group. Baseline CD4 count was categorized as <200 cells/µL, ≥200 cells/µL, or missing (reference), and baseline HIV-1 RNA as <100,000 copies/mL, ≥100,000 copies/mL, or missing (reference). Year of ART initiation was grouped into 2001–2007, 2008–2013, and ≥2014 (reference). This specification yielded 10 predictors for 139 VF events, remaining within accepted limits to avoid model overfitting. To further assess the robustness of the model, we conducted additional multivariable logistic regression models extending the core analysis. Each supplementary model included one additional covariate of interest: HIV-1 subtype (B vs non-B), region of origin (Western Europe vs other), or transmitted drug resistance (TDR, any vs none). Because adherence ascertainment was inconsistent and frequently missing, this variable was used for descriptive summaries only and was not included in multivariable models. All models were restricted to the first VF episode per patient and adjusted for the same set of demographic, clinical, and treatment-era variables as in the core model. For time to virologic resuppression after the first VF event, we used a Cox proportional hazards model including age, sex, presence of resistance mutations, regimen switch at failure, log₁₀ HIV-1 RNA at failure, and HIV-1 subtype B. Patients lost to follow-up before resuppression were censored at the date of their last available HIV-1 RNA measurement; deaths prior to re-suppression were censored at the date of death. We did not model death as a competing risk because the number of deaths was small and the primary endpoint was time to resuppression. For logistic regression, we report odds ratios (ORs) with 95% confidence intervals (95% CI) and two-sided p-values (Wald tests). For Cox models, we report hazard ratios (HRs) with 95% CI and two-sided p-values (Wald). All tests were two-sided with α = 0.05 . p-values are shown up to three decimals; values <0.001 are reported as p < 0.001. Kaplan–Meier curves stratified by sex were used to visualize time to resuppression, with differences assessed by the log-rank test. Ethical Considerations The study protocol was reviewed and approved by the Ethics Committee of the Heinrich Heine University (Study No. 4862), and all participants provided written informed consent according to the Declaration of Helsinki. Results Baseline characteristics and rates of virologic failure Out of the 5,136 patients included in the RESINA cohort, 139 experienced VF, corresponding to an overall VF rate of 2.7%. The patient flow diagram is presented in Figure 1, and baseline characteristics by VF status are summarized in Table 1. Patients with VF were slightly younger (median age 37 years, range 18–75 vs. 39 years, range 18–82), and a lower proportion were male compared with patients without VF (65.5% vs. 82.1%, p <0.001). Acquisition routes differed markedly: in the VF group, heterosexual transmission (including high-prevalence regions) and IDU were more common (30.3% vs. 19.5% and 12.2% vs. 5.4%, respectively), whereas men who have sex with men (MSM) transmission predominated among patients without VF (54.4% vs. 23%, p <0.0001). Regarding region of origin, 57 % of VF patients originated from Germany, compared with 66 % without VF. Sub-Saharan Africa was more frequently represented in the VF group (17.3% vs. 5.2%, p < 0.0001). Patients with VF initiated ART at lower CD4 cell counts (median 85/µL vs. 273/µL; <200 cells/µL in 43.2% vs. 18.9%) and higher HIV-1 RNA levels (≥100,000 copies/mL in 38.8% vs. 26.5%, p = 0.005). Late presentation was significantly more frequent among VF patients (43.2% vs. 18.9%, p < 0.0001). HIV-1 subtype B remained the most common subtype in both groups but was less predominant among VF patients (54.7% vs. 58.6%), with higher proportions of CRF02_AG (15.1% vs. 5.4%) and subtype C (5.8% vs. 2.6%, p < 0.0001). VF occurred more often in earlier treatment eras, with 40.3% of VF patients starting ART before 2007, compared to 22.9% in the no-VF group ( p < 0.0001). TDR mutations were rare in both groups (2.9% vs. 7.1%, p = 0.061). Overall mortality was higher in the VF group (9.4% vs. 4%, p = 0.004). Although more than half of VF patients (79/139) were lost to follow-up at some point, the median follow-up time was 9 years (range 1–24), and 85% (118/139) were followed for at least 5 years. At ART initiation, the most common regimens in the VF group combined two NRTIs, usually TDF/FTC, with either an NNRTI (most often nevirapine or efavirenz) or a boosted PI (most frequently lopinavir or darunavir) (Figure 2). More than half of VF cases (77/139, 55.4%) occurred under the first ART regimen, and 34/139 (24.5%) occurred during the second. First VF/rebound occurred after a median of 608 days on ART (range 59–5,186 ). VF rates by treatment era were: group 1, 4.7% (56/1,200); group 2, 2.6% (48/1,882); and group 3, 1.7% (35/2,054). Almost half (64/139) experienced a single VF event during follow-up, 21% (29/139) had two, and the remainder had three or more. In a cohort-wide binary logistic regression model restricted to the first VF episode per patient, three covariates emerged as independent risk factors for VF: IDU (OR 1.74, 95% CI 1.00–3.00, p = 0.048), CD4 <200 cells/µL at ART initiation (OR 2.32, 95% CI 1.56–3.45, p < 0.001), and ART initiation during 2001–2007 (OR 1.95, 95% CI 1.24–3.06, p = 0.004) (Table 2a). By contrast, acquisition through MSM contact was associated with a lower risk of VF (OR 0.32, 95% CI 0.20–0.50, p < 0.001). Other factors, including age, sex, baseline viral load, and ART initiation during 2008–2013, were not significantly associated with VF. In supplementary models, neither HIV-1 subtype nor region of origin was independently associated with VF. Adjustment for subtype attenuated the effect of ART initiation during 2001–2007, indicating partial confounding by subtype distribution. TDR was significantly associated with a lower risk of VF (OR <1, p = 0.035) (Supplementary Tables 1a-c). Adherence data were inconsistently recorded; among participants with VF, 83/139 (59.7%) had documented non-adherence (treatment pause and/or missed appointment). We did not evaluate adherence in adjusted models. HIV Resistance mutations and cross-resistance patterns in the VF group TDR mutations in HIV were identified in eight patients, while acquired resistance mutations emerged in 28 during follow-up. Of those with acquired mutations, 11 had single-class resistance and 17 (61%) showed cross-resistance to two or more drug classes (Supplementary Table 1). The most frequently observed NRTI resistance mutation was M184V/I (16 M184V and 6 M184I) detected in 22 patients (2 transmitted, 20 acquired), followed by K70R/E in five patients (1 transmitted, 4 acquired). Among NNRTI mutations, Y181C/Y occurred in 10 patients (1 transmitted, 9 acquired), K103N in eight patients (2 transmitted, 6 acquired), and K101E/K in six patients (all acquired). PI resistance was uncommon ( n = 6 ), with V82A in four patients (1 transmitted, 3 acquired). INSTI resistance was identified in five patients: N155H (n = 2), Y143C/R/S (n = 1), E92Q (n = 1), and R263K + G118R (n = 1). The latter pattern reflects dolutegravir resistance and has been described previously [24]. Supplementary Table 2 highlights extensive cross‑resistance in several patients (e.g., M184V + T215Y + K103N + V82A conferring NRTI, NNRTI, and PI resistance). Fourteen patients with transmitted or acquired mutations that do not impair cabotegravir (CAB) or rilpivirine (RPV) susceptibility would still be eligible to receive injectables (CAB + RPV LA), which might be an option to improve future adherence. Treatment outcomes post- failure in the VF group Of 139 participants with any VF, 44 (31.7%) were unsuppressed at last follow-up (Fig. 1). After the first VF, 122 (87.8%) ever achieved resuppression , while 17 (12.2%) never resuppressed (HIV-1 RNA ≥50 copies/mL). Median time to resuppression was 147 days (range 13–2,015 ). The higher end-of-follow-up count reflects patients who later failed again or ended follow-up unsuppressed. Of the total of 139 patients experiencing VF, 74 patients remained on the same drug class after VF (predominantly PI-based regimens, but also some NNRTI-, NRTI-only, and INSTI-based therapies), while 65 were switched to a different drug class. Common switches were PI→PI (n=23) , PI→INSTI (n=14) and PI→other (n=20) ; fewer moved NNRTI→PI (n=6) or NNRTI→INSTI (n=6) , NRTI-only→PI (n=5) , and other→PI/INSTI (n=6/2). Switches to INSTI-based regimens (PI → INSTI 3/3, NNRTI → INSTI 4/4, other → INSTI 7/7) and NRTI-only → PI (6/6) were the most consistently successful, whereas remaining on PI regimens (16/22, 73%) or switching NNRTI → PI (4/6, 67%) showed lower rates of re-suppression, and NRTI-only → NNRTI was least effective (1/2, 50%) Patients who failed to resuppress had significantly higher VL at failure compared with those who did resuppress (mean log₁₀ VL 4.7 vs. 3.8, p = 0.004). In multivariable analysis, male sex was independently associated with faster re-suppression (HR 1.81, 95 % CI 1.15–2.86, p = 0.011), a finding also reflected in Kaplan–Meier curves (log-rank p = 0.009; Figure 3). Regimen switch at failure showed a non-significant trend toward faster resuppression (HR 0.72, 95 % CI 0.49–1.05, p = 0.087), while higher VL at failure trended toward slower resuppression (HR 0.84 per log₁₀ increase, 95 % CI 0.71–1.00, p = 0.056). Age, resistance-mutation status, and subtype B were not significantly associated with resuppression. Overall, these results indicate that, once switch strategy is taken into account, male sex and lower VL at failure are the strongest predictors of faster resuppression, whereas age, resistance-mutation status, and subtype B appear to have little influence (Table 2b). Discussion In this large, multicenter cohort of ART-naïve patients followed for more than two decades, VF was uncommon overall (2.7%) and declined markedly across calendar eras, from nearly 5% in the early 2000s to below 2% in the most recent decade. This decline reflects the impact of increasingly potent and tolerable regimens, particularly INSTI–based therapy, which has demonstrated superior effectiveness and durability compared with NNRTI- or PI-anchored regimens. These findings align with clinical trial and cohort data confirming faster suppression and improved durability under INSTI-based regimens [ 3 , 4 , 25 ]. Independent predictors of VF in our cohort, IDU and late presentation (CD4 < 200/µL), point to adherence and structural barriers rather than intrinsic regimen limitations. Evidence consistently links active substance use to suboptimal adherence and worse virologic outcomes, while opioid substitution therapy improves ART uptake, adherence, and suppression; this likely explains a sizable share of the excess VF we observed in people who inject drugs [ 26 ]. Similarly, late diagnosis is repeatedly associated with delayed virologic control and higher failure risk in European cohorts; our signal is aligned with those reports and with contemporary European cohort data [ 14 , 15 ]. The higher VF in the earliest era also reflects historical context (less potent backbones, lower resistance barriers) and a different subtype mix; adjusting for subtype attenuated that effect in our sensitivity analyses, consistent with era- and subtype-related differences described elsewhere [ 15 ]. At failure, the pattern in our cohort, M184V and M184I among NRTIs and K103N/Y181C among NNRTIs, matches long-standing pathways seen with TDF/FTC + NNRTI backbones and explains much of the NNRTI cross-resistance we recorded [ 8 ]. Resistance to PIs and INSTIs was rare; we observed only isolated major INSTI mutations and a single case with DTG-associated changes, consistent with reviews showing that emergent DTG resistance remains uncommon but does occur under ongoing viremia [ 27 ]. Notably, TDR was not associated with higher VF risk here. This is biologically plausible: several common TDR mutations carry fitness costs and tend to revert in the absence of drug pressure, blunting clinical impact when potent modern regimens are used [ 28 ]. Many patients exhibited multiclass resistance (e.g., concurrent NRTI, NNRTI, and PI mutations), underscoring the need to favor INSTI-based regimens and, when necessary, newer antiretroviral classes to secure durable suppression in complex resistance profiles [ 20 , 21 , 29 ]. In addition, in our cohort, baseline resistance testing was systematically performed and considered when selecting initial therapy. This strategy likely prevented the use of compromised regimens and underscores the importance and effectiveness of baseline resistance screening in routine care. Post-failure outcomes were generally favorable: 87.8% achieved at least one resuppression after the first VF (median 147 days). Two signals from our models mirror external data: higher VL at failure was linked with slower re-suppression, and switching therapy trended toward faster control, both consistent with reports that viral burden at switch predicts time-to-suppression and that timely regimen change improves outcomes. Current guidelines likewise recommend prompt reassessment (adherence + genotype) and early switch once VF is confirmed [ 5 ]. Prompt switch after confirmed VF is therefore prudent to limit reservoir seeding and additional resistance, in line with guideline recommendations [ 5 , 30 ] . Our patient-level switch analysis is clinically instructive. Switches to INSTI-based regimens were uniformly successful and NRTI-only→PI switches also performed exceptionally well. In contrast, remaining on PI after failure had lower therapy success (73%), and NNRTI→PI achieved suppression in two-thirds. This hierarchy aligns with randomized and real-world evidence showing INSTI-anchored therapy to be the most reliable strategy after failure and supports choosing DTG- or BIC-based regimens when resistance and tolerability permit [ 31 ]. Male sex also predicted faster resuppression in our cohort (HR 1.81), whereas prior studies show inconsistent sex effects on suppression/resuppression, likely reflecting differences in adherence, pregnancy-related ART exposure, and care engagement [ 9 , 32 , 33 ]. Although we did not collect systematic adverse effects (AE) data, improved tolerability of modern regimens likely contributed to the era-wise decline in VF and the strong performance of INSTI-based switches. INSTIs are associated with fewer discontinuations and better tolerability than NNRTI- or PI-based anchors, in contrast to legacy regimens such as efavirenz, which frequently caused neuropsychiatric adverse events [ 31 , 34 ]. The uniform success of INSTI-based switches in our cohort is consistent with this profile. While weight gain under INSTIs and injection-site reactions with long-acting CAB/RPV require monitoring, these rarely lead to treatment withdrawal, and patient-reported outcomes generally favor modern INSTI-based or long-acting therapies [ 5 , 16 , 18 ]. Given that nearly 60% of our patients reported non-adherence prior to VF, long-acting CAB/RPV may help selected patients once suppressed and appropriately screened. Phase 3 trials show non-inferiority to oral maintenance, while recent analyses clarify risk factors for long-acting failure (e.g., pre-existing RPV resistance, A6/A1 subtype, obesity). Our eligibility screen identified a subset who might benefit from this approach as part of an adherence-support package [ 5 , 16 ]. Key strengths of our study include its large, multicenter design, the prospective follow-up of over two decades spanning three major treatment eras, and the availability of standardized virologic monitoring with genotypes at the time of failure. Together, these features provide a robust, real-world view of long-term ART effectiveness, durability, and resistance evolution. Limitations are those inherent to observational cohorts: residual confounding by unmeasured social determinants, reliance on self-reported or pharmacy-based adherence data, incomplete genotyping in all failures, and small patient numbers within some switch categories. These factors may have limited power to detect certain associations and prevent definitive causal inference. In the current treatment era, VF primarily reflects adherence and healthcare access rather than regimen potency. Our findings support three priorities: (i) prevent late presentation and support people with substance use through integrated adherence services; (ii) reassess adherence and resistance promptly and switch early after confirmed VF; and (iii) favor INSTI-based salvage, or boosted PI when INSTIs are unsuitable, given their superior resuppression rates. In practice, this requires intensified outreach and education to promote earlier HIV testing, and integration of adherence and resistance checks into routine follow-up visits or structured check-up programs. Conclusion In this comprehensive analysis of the RESINA cohort, VF has become rare but persists among individuals facing social, structural, and clinical barriers. Multiclass resistance highlights the need for timely genotyping and access to newer drug classes. Male sex and lower viral burden at failure were associated with faster resuppression, supporting targeted adherence support and rapid regimen optimization to sustain long-term virologic control. Declarations Funding : The RESINA study was previously funded by the Federal Ministry for Health and Social Services, the EU project CHAIN, and the Heinz-Ansmann-Stiftung for AIDS Research. Competing interests : The authors declare no competing interests. Ethics approval and consent to participate : The study protocol was approved by the Ethics Committee of Heinrich Heine University Düsseldorf (Study No. 4862) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to inclusion. Data availability : De-identified participant data are not publicly available due to privacy and institutional restrictions (GDPR). Aggregate results and the analysis code, along with a limited de-identified dataset sufficient to reproduce the main findings, are available from the corresponding author upon reasonable request. Author contributions SG and MB drafted the original manuscript, while SG, MB, BEJ, NL, AK, and RK critically revised and edited the text. Formal analysis was performed by SG and MB, and data curation was carried out by MB, CM, EH, and JB. Patient recruitment and clinical investigation were undertaken by SG, AK, FH, LH, GF, CL, MO, MH, HK, NS, SE, SS, NQ, KR, JR, and BEJ. Conceptualization and study design were provided by MO and RK, with additional supervision by JR, TL, and BEJ. Funding acquisition was primarily carried out by MO, with further contributions from RK and BEJ. All authors had full access to the data, contributed to the interpretation of results, participated in manuscript revisions, and approved the final version for submission. References Narváez M, Lins-Kusterer L, Valdelamar-Jiménez J, Brites C. Quality of Life and Antiretroviral Therapy Adherence: A Cross-Sectional Study in Colombia. AIDS Res Hum Retroviruses. 2022;38:660–9. Freedberg KA, Losina E, Weinstein MC, Paltiel AD, Cohen CJ, Seage GR, et al. The Cost Effectiveness of Combination Antiretroviral Therapy for HIV Disease. N Engl J Med. 2001;344:824–31. Cahn P, Madero JS, Arribas JR, Antinori A, Ortiz R, Clarke AE, et al. Durable Efficacy of Dolutegravir Plus Lamivudine in Antiretroviral Treatment–Naive Adults With HIV-1 Infection: 96-Week Results From the GEMINI-1 and GEMINI-2 Randomized Clinical Trials. JAIDS J Acquir Immune Defic Syndr. 2020;83:310–8. Raffi F, Rachlis A, Stellbrink H-J, Hardy WD, Torti C, Orkin C, et al. Once-daily dolutegravir versus raltegravir in antiretroviral-naive adults with HIV-1 infection: 48 week results from the randomised, double-blind, non-inferiority SPRING-2 study. The Lancet. 2013;381:735–43. Guidelines for the Use of Antiretroviral Agents in Adults and Adolescents With HIV. September 12, 2024. Available from: https://clinicalinfo.hiv.gov/en/guidelines/hiv-clinical-guidelines-adult-and-adolescent-arv/virologic-failure Lucas GM, Chaisson RE, Moore RD. Highly active antiretroviral therapy in a large urban clinic: risk factors for virologic failure and adverse drug reactions. Ann Intern Med. 1999;131:81–7. Management of virologic failure. [cited 2025 Apr 24]; Available from: https://www.hiv.uw.edu/go/antiretroviral-therapy/evaluation-management-virologic-failure/core-concept/all Tang MW, Shafer RW. HIV-1 Antiretroviral Resistance: Scientific Principles and Clinical Applications. Drugs. 2012;72:e1–25. Moore AL, Mocroft A, Madge S, Devereux H, Wilson D, Phillips AN, et al. Gender Differences in Virologic Response to Treatment in an HIV-Positive Population: A Cohort Study: J Acquir Immune Defic Syndr. 2001;26(2):159–63. Wu G, Zhou C, Zhang X, Zhang W, Lu R, Ouyang L, et al. Higher Risks of Virologic Failure and All-Cause Deaths Among Older People Living with HIV in Chongqing, China. AIDS Res Hum Retroviruses. 2019;35:1095–102. Mocroft A, Lundgren JD, Sabin ML, Monforte A d’Arminio, Brockmeyer N, Casabona J, et al. Risk Factors and Outcomes for Late Presentation for HIV-Positive Persons in Europe: Results from the Collaboration of Observational HIV Epidemiological Research Europe Study (COHERE). Sansom SL, editor. PLoS Med. 2013;10:e1001510. Segala FV, Di Gennaro F, Frallonardo L, De Vita E, Petralia V, Sapienza V, et al. HIV-related outcomes among migrants living in Europe compared with the general population: a systematic review and meta-analysis. Lancet HIV. 2024;11:e833–42. Przybyla S, Ashare RL, Cioffi L, Plotnik I, Shuter J, Seng EK, et al. Substance Use and Adherence to Antiretroviral Therapy among People Living with HIV in the United States. Trop Med Infect Dis. 2022;7:349. Darling KE, Hachfeld A, Cavassini M, Kirk O, Furrer H, Wandeler G. Late presentation to HIV care despite good access to health services: current epidemiological trends and how to do better. Swiss Med Wkly. 2016;146:w14348. Mondi A, Cozzi-Lepri A, Tavelli A, Cingolani A, Giacomelli A, Orofino G, et al. Persistent poor clinical outcomes of people living with HIV presenting with AIDS and late HIV diagnosis – results from the ICONA cohort in Italy, 2009-2022. Int J Infect Dis. 2024;142:106995. Van Welzen BJ, Van Lelyveld SFL, Ter Beest G, Gisolf JH, Geerlings SE, Prins JM, et al. Virological Failure After Switch to Long-Acting Cabotegravir and Rilpivirine Injectable Therapy: An In-depth Analysis. Clin Infect Dis. 2024;79:189–95. Weissman S, Duffus WA, Iyer M, Chakraborty H, Samantapudi AV, Albrecht H. Rural–Urban Differences in HIV Viral Loads and Progression to AIDS among New HIV Cases. South Med J. 2015;108:180–8. Eckard AR, McComsey GA. Weight gain and integrase inhibitors. Curr Opin Infect Dis. 2020;33:10–9. Sichtig N, Sierra S, Kaiser R, Däumer M, Reuter S, Schülter E, et al. Evolution of raltegravir resistance during therapy. J Antimicrob Chemother. 2009;64:25–32. Eron JJ, Clotet B, Durant J, Katlama C, Kumar P, Lazzarin A, et al. Safety and Efficacy of Dolutegravir in Treatment-Experienced Subjects With Raltegravir-Resistant HIV Type 1 Infection: 24-Week Results of the VIKING Study. J Infect Dis. 2013;207:740–8. Boltz VF, Shao W, Bale MJ, Halvas EK, Luke B, McIntyre JA, et al. Linked dual-class HIV resistance mutations are associated with treatment failure. JCI Insight. 2019;4:e130118. European Centre for Disease Prevention and Control/WHO Regional Office for Europe. HIV/AIDS surveillance in Europe 2022–2021 data. Stockholm: ECDC; 2022 [Internet]. Available from: https://www.ecdc.europa.eu/en/publications-data/hivaids-surveillance-europe-2022-2021-data Lübke N, Di Cristanziano V, Sierra S, Knops E, Schülter E, Jensen B, et al. Proviral DNA as a Target for HIV-1 Resistance Analysis. Intervirology. 2015;58:184–9. Lübke N, Jensen B, Hüttig F, Feldt T, Walker A, Thielen A, et al. Failure of Dolutegravir First-Line ART with Selection of Virus Carrying R263K and G118R. N Engl J Med. 2019;381:887–9. Aboud M, Kaplan R, Lombaard J, Zhang F, Hidalgo JA, Mamedova E, et al. Dolutegravir versus ritonavir-boosted lopinavir both with dual nucleoside reverse transcriptase inhibitor therapy in adults with HIV-1 infection in whom first-line therapy has failed (DAWNING): an open-label, non-inferiority, phase 3b trial. Lancet Infect Dis. 2019;19:253–64. Low AJ, Mburu G, Welton NJ, May MT, Davies CF, French C, et al. Impact of Opioid Substitution Therapy on Antiretroviral Therapy Outcomes: A Systematic Review and Meta-Analysis. Clin Infect Dis. 2016;63:1094–104. Chu C, Tao K, Kouamou V, Avalos A, Scott J, Grant PM, et al. Prevalence of Emergent Dolutegravir Resistance Mutations in People Living with HIV: A Rapid Scoping Review. Viruses. 2024;16:399. Kühnert D, Kouyos R, Shirreff G, Pečerska J, Scherrer AU, Böni J, et al. Quantifying the fitness cost of HIV-1 drug resistance mutations through phylodynamics. Stern A, editor. PLOS Pathog. 2018;14:e1006895. Gallant J, Lazzarin A, Mills A, Orkin C, Podzamczer D, Tebas P, et al. Bictegravir, emtricitabine, and tenofovir alafenamide versus dolutegravir, abacavir, and lamivudine for initial treatment of HIV-1 infection (GS-US-380-1489): a double-blind, multicentre, phase 3, randomised controlled non-inferiority trial. The Lancet. 2017;390:2063–72. Consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection: recommendations for a public health approach. Geneva, Switzerland; 2016. Jacobson K, Ogbuagu O. Integrase inhibitor-based regimens result in more rapid virologic suppression rates among treatment-naïve human immunodeficiency virus–infected patients compared to non-nucleoside and protease inhibitor–based regimens in a real-world clinical setting: A retrospective cohort study. Medicine (Baltimore). 2018;97:e13016. Melak D, Wedajo S, Dewau R. Time to Viral Re-suppression and Its Predictors among Adults on Second-Line Antiretroviral Therapy in South Wollo Zone Public Hospitals: Stratified Cox Model. HIVAIDS Auckl NZ. 2023;15:411–21. Novelli S, Delobel P, Bouchaud O, Avettand‐Fenoel V, Fialaire P, Cabié A, et al. Enhanced immunovirological response in women compared to men after antiretroviral therapy initiation during acute and early HIV‐1 infection: results from a longitudinal study in the French ANRS Primo cohort. J Int AIDS Soc. 2020;23:e25485. Law JKC, Butler LT, Hamill MM. Predictors of Discontinuation of Efavirenz as Treatment for HIV, Due to Neuropsychiatric Side Effects, in a Multi-Ethnic Sample in the United Kingdom. AIDS Res Hum Retroviruses. 2020;36:459–66. Tables Table 1. Baseline characteristics of patients in the RESINA cohort according to virologic outcome (without vs. with virologic failure) Group no VF VF p-value Number of patients (n) 4997 139 Age (median, range) Age categories (n, %) < 30 years 30-39 years 40-49 years 50-59 ³60 years unspecified 39 (18-82) 946 (18.9) 1647 (33) 1426 (28.5) 724 (14.5) 248 (5) 6 (0.1) 37 (18-75) 27 (19.4) 51 (36.7) 42 (30.2) 12 (8.6) 7 (5) 0.53 Gender (n, %) Male Female at birth Other/unspecified 4104 (82.1) 869 (17.4) 24 (0.5) 91 (65.5) 48 (34.5) <0.0001 Acquisition route (n, %) Heterosexual Heterosexual from high prevalence region MSM IDU Other/ unknown 976 (19.5) 413 (8.3) 2720 (54.4) 270 (5.4) 618 (12.4) 42 (30.3) 32 (23) 32 (23) 18 (12.2) 15 (11.5) <0.0001 Region of origin (n, %) German European (other) Sub Saharan Africa Asia Other/unknown 3276 (65.5) 118 (2.4) 258 (5.2) 115 (2.3) 1230 (24.6) 79 (56.8) 9 (6.5) 24 (17.3) 11 (7.9) 16 (11.5) <0.0001 Median CD4 (range) at ART initiation cells/ m L < 200 200-349 ³350 missing 273 (0-2006) 945 (18.9) 557 (11.1) 963 (19.3) 2532 (50.7) 85 (1-484) 60 (43.2) 15 (10.8) 6 (4.3) 58 (41.7) <0.0001 Median HIV-1 RNA (range) at ART initiation log 10 < 100.000 ³100.000 missing 4.6 (1.3-7) 2693 (53.9) 1326 (26.5) 978 (19.6) 4.8 (2.1- 6.3) 63 (45.3) 54 (38.8) 22 (15.9) 0.005 HIV-1 Subtype (n, %) B CRF02_AG C A1 A6 Other 2927 (58.6) 273 (5.4) 129 (2.6) 194 (3.9) 130 (2.6) 1344 (26.9) 76 (54.7) 21 (15.1) 8 (5.8) 7 (5) 4 (2.9) 23 (16.5) <0.0001 Year of ART initiation 2001-2007 2008-2013 ³2014 1144 (22.9) 1834 (36.7) 2019 (40.4) 56 (40.3) 48 (34.5) 35 (25.2) <0.0001 Transmitted drug mutations (TDR) any mutation none 357 (7.1) 4640 (92.9) 4 (2.9) 135 (97.1) 0.061 Late diagnosis/late presenters (n, %) 945 (18.9) 60 (43.2) <0.0001 Deaths from any cause (n, %) 202 (4) 13 (9.4) 0.004 Values are given as number (%) unless otherwise stated. Age, CD4 and HIV-1 RNA are presented as median (range). VF: virologic failure; MSM: men who have sex with men; IDU: intravenous drug use; TDR: transmitted drug resistance Table 2a. Multivariable logistic regression of risk factors for virologic failure Variable OR 95% CI p-value Age (per 10 years) 0.88 0.75 – 1.04 0.14 Male sex (vs female/other) 0.80 0.53 – 1.20 0.29 MSM (vs heterosexual) 0.32 0.20 – 0.50 <0.001 IDU (vs heterosexual) 1.74 1.00 – 3.00 0.048 CD4 <200 cells/µL 2.32 1.56 – 3.45 <0.001 CD4 ≥200 cells/µL 0.64 0.38 – 1.07 0.087 HIV-1 RNA <100,000 copies/mL 0.98 0.58 – 1.68 0.95 HIV-1 RNA ≥100,000 copies/mL 1.38 0.81 – 2.36 0.24 ART start 2001–2007 1.95 1.24 – 3.06 0.004 ART start 2008–2013 1.20 0.75 – 1.92 0.44 ART start ≥2014 1.0 (ref) - - Reference categories: female/other sex, heterosexual acquisition, missing CD4, missing HIV RNA, ART start ≥2014. OR = odds ratio; CI = confidence interval; MSM = men who have sex with men; IDU = intravenous drug use. N=5130, VF events=139 Table 2b. Cox proportional‑hazards regression of predictors for time to virologic re‑suppression after first virologic failure Predictor HR 95% CI p-value Age (per 10 years) 1.10 0.90 – 1.34 0.33 Sex (male vs. female) 1.81 1.15– 2.86 0.011 Resistance mutations (yes/no) 0.89 0.59 – 1.37 0.62 Regimen switch (yes/no) 0.72 0.49 – 1.05 0.088 log ₁₀ VL at failure 0.84 0.71 – 1.00 0.056 Subtype B vs non-B 0.93 0.61 – 1.41 0.72 Cox proportional ‑ hazards model of time (days) from first virologic failure to confirmed re ‑ suppression, adjusted for age, sex, presence of resistance mutations, regimen switch at failure, VL at VF and HIV ‑ 1 subtype B. HR = hazard ratio; CI = confidence interval. Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Published Journal Publication published 17 Dec, 2025 Read the published version in Infection → Version 1 posted Editorial decision: Revision requested 29 Nov, 2025 Reviews received at journal 19 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers invited by journal 13 Oct, 2025 Editor assigned by journal 27 Sep, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 26 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7722983","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533905712,"identity":"c3887699-39cb-46e7-b81f-0114c3307c34","order_by":0,"name":"Smaranda Gliga","email":"data:image/png;base64,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","orcid":"","institution":"Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf","correspondingAuthor":true,"prefix":"","firstName":"Smaranda","middleName":"","lastName":"Gliga","suffix":""},{"id":533905713,"identity":"8ae28df7-69e9-49cb-aeb7-2c543b446854","order_by":1,"name":"Micha Böhm","email":"","orcid":"","institution":"Institute of Virology, Faculty of Medicine and University Hospital of Cologne, University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Micha","middleName":"","lastName":"Böhm","suffix":""},{"id":533905716,"identity":"1725c276-9729-4233-bfaf-647c5ff5d676","order_by":2,"name":"Nadine Lübke","email":"","orcid":"","institution":"Institute of Virology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf","correspondingAuthor":false,"prefix":"","firstName":"Nadine","middleName":"","lastName":"Lübke","suffix":""},{"id":533905717,"identity":"8f816d29-8511-41ed-b6c0-1e44ce26288c","order_by":3,"name":"Alexander Killer","email":"","orcid":"","institution":"Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Killer","suffix":""},{"id":533905720,"identity":"3bdda5d3-c5e4-4a98-9980-b25bb7b4af76","order_by":4,"name":"Falk Hüttig","email":"","orcid":"","institution":"Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf","correspondingAuthor":false,"prefix":"","firstName":"Falk","middleName":"","lastName":"Hüttig","suffix":""},{"id":533905721,"identity":"46d28230-b599-40a2-b5ea-663a151739c3","order_by":5,"name":"Lila Haberl","email":"","orcid":"","institution":"Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf","correspondingAuthor":false,"prefix":"","firstName":"Lila","middleName":"","lastName":"Haberl","suffix":""},{"id":533905722,"identity":"7d106eb9-86ec-48eb-bb16-3ae1584273bd","order_by":6,"name":"Jörg Timm","email":"","orcid":"","institution":"Institute of Virology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf","correspondingAuthor":false,"prefix":"","firstName":"Jörg","middleName":"","lastName":"Timm","suffix":""},{"id":533905723,"identity":"33ff6db3-ab01-4e7e-a8b7-f8926e88bca3","order_by":7,"name":"Claudia Müller","email":"","orcid":"","institution":"Institute of Virology, Faculty of Medicine and University Hospital of Cologne, University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Müller","suffix":""},{"id":533905724,"identity":"180353e7-2a57-484d-aa00-6b2a5f7d27be","order_by":8,"name":"Eva Heger","email":"","orcid":"","institution":"Institute of Virology, Faculty of Medicine and University Hospital of Cologne, University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Heger","suffix":""},{"id":533905725,"identity":"24a5cc05-a38b-4f19-ba36-bef4452850bf","order_by":9,"name":"Joachim Büch","email":"","orcid":"","institution":"Institute of Virology, Faculty of Medicine and University Hospital of Cologne, University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Joachim","middleName":"","lastName":"Büch","suffix":""},{"id":533905726,"identity":"6589f290-71a8-484e-9649-b98dc7148c8e","order_by":10,"name":"Gerd Fätkenheuer","email":"","orcid":"","institution":"German Center for Infection Research (DZIF), partner site Bonn- Cologne","correspondingAuthor":false,"prefix":"","firstName":"Gerd","middleName":"","lastName":"Fätkenheuer","suffix":""},{"id":533905727,"identity":"6cad6e59-f95f-46f1-8d65-8736e0439c22","order_by":11,"name":"Clara Lehmann","email":"","orcid":"","institution":"German Center for Infection Research (DZIF), partner site Bonn- Cologne","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"","lastName":"Lehmann","suffix":""},{"id":533905728,"identity":"bbda7e5a-52ec-45de-a4f2-97f000c44b99","order_by":12,"name":"Mark Oette","email":"","orcid":"","institution":"Krankenhaus der Augustinerinnen","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Oette","suffix":""},{"id":533905729,"identity":"f8a059eb-470f-4ac8-9038-db5f6971f0b3","order_by":13,"name":"Martin Hower","email":"","orcid":"","institution":"Klinikum Dortmund gGmbH, Hospital of University Witten / Herdecke,","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Hower","suffix":""},{"id":533905730,"identity":"d367b453-2c03-44c6-8c7a-b00b0735b938","order_by":14,"name":"Heribert Knechten","email":"","orcid":"","institution":"Private practice","correspondingAuthor":false,"prefix":"","firstName":"Heribert","middleName":"","lastName":"Knechten","suffix":""},{"id":533905731,"identity":"98db029e-8125-48c1-a9c0-ab882054fc01","order_by":15,"name":"Niels Schübel","email":"","orcid":"","institution":"Klinikum Osnabrück","correspondingAuthor":false,"prefix":"","firstName":"Niels","middleName":"","lastName":"Schübel","suffix":""},{"id":533905732,"identity":"644f4eaf-ee61-443a-b4ed-b6f914803236","order_by":16,"name":"Stefan Esser","email":"","orcid":"","institution":"Department of Dermatology and Venerology, Faculty of Medicine and University Hospital of Essen, University of Duisburg-Essen","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Esser","suffix":""},{"id":533905733,"identity":"cd8449fa-c10f-481f-a780-065c01acaa34","order_by":17,"name":"Stephan Schneeweiß","email":"","orcid":"","institution":"Private practice Hohenstaufenring","correspondingAuthor":false,"prefix":"","firstName":"Stephan","middleName":"","lastName":"Schneeweiß","suffix":""},{"id":533905734,"identity":"11e2e457-b551-43a2-88d7-a4734e627898","order_by":18,"name":"Nazifa Qurishi","email":"","orcid":"","institution":"Private Practice Gotenring","correspondingAuthor":false,"prefix":"","firstName":"Nazifa","middleName":"","lastName":"Qurishi","suffix":""},{"id":533905735,"identity":"93d44271-ad39-4bb9-a874-781156613f1d","order_by":19,"name":"Katja Römer","email":"","orcid":"","institution":"Private Practice Gotenring","correspondingAuthor":false,"prefix":"","firstName":"Katja","middleName":"","lastName":"Römer","suffix":""},{"id":533905737,"identity":"9920ddb8-62bd-4708-9996-a57ff38989e6","order_by":20,"name":"Jürgen K Rockstroh","email":"","orcid":"","institution":"German Center for Infection Research (DZIF), partner site Bonn- Cologne","correspondingAuthor":false,"prefix":"","firstName":"Jürgen","middleName":"K","lastName":"Rockstroh","suffix":""},{"id":533905739,"identity":"8021e1f5-2791-42e0-bc14-1ef6978b9ded","order_by":21,"name":"Rolf Kaiser","email":"","orcid":"","institution":"Institute of Virology, Faculty of Medicine and University Hospital of Cologne, University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Rolf","middleName":"","lastName":"Kaiser","suffix":""},{"id":533905740,"identity":"9376919f-4e0e-4e36-bfa0-db03359b9dae","order_by":22,"name":"Tom Luedde","email":"","orcid":"","institution":"Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf","correspondingAuthor":false,"prefix":"","firstName":"Tom","middleName":"","lastName":"Luedde","suffix":""},{"id":533905741,"identity":"181a5dbc-d338-4a0c-92e9-a21bb4e98e7a","order_by":23,"name":"Björn Erik-Ole Jensen","email":"","orcid":"","institution":"Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf","correspondingAuthor":false,"prefix":"","firstName":"Björn","middleName":"Erik-Ole","lastName":"Jensen","suffix":""}],"badges":[],"createdAt":"2025-09-26 14:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7722983/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7722983/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s15010-025-02713-7","type":"published","date":"2025-12-17T15:57:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94480404,"identity":"a5258475-0f7a-4a0f-ba9a-eb1abbdea4af","added_by":"auto","created_at":"2025-10-27 16:10:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2137145,"visible":true,"origin":"","legend":"","description":"","filename":"VFResina.docx","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/011c411d06fe034232a4286b.docx"},{"id":94480949,"identity":"4d09e06d-c32d-4323-b64f-fb6ec43b49df","added_by":"auto","created_at":"2025-10-27 16:12:17","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21765,"visible":true,"origin":"","legend":"","description":"","filename":"274a7040c3b54a0faeda493529e664c5.json","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/c7f23c090774057b597e084d.json"},{"id":94480653,"identity":"ced0005f-cd36-464d-ae56-3e7ec11f09c1","added_by":"auto","created_at":"2025-10-27 16:11:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42792,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/7f57aa60d880540f56fcf0f5.docx"},{"id":94480462,"identity":"d5b0310f-414f-4db6-bbdd-3fcfca946fdd","added_by":"auto","created_at":"2025-10-27 16:11:15","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123255,"visible":true,"origin":"","legend":"","description":"","filename":"274a7040c3b54a0faeda493529e664c51enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/baf18a3ae8ddfb5c304c31b7.xml"},{"id":94480659,"identity":"150d102c-f182-49c1-b2ad-d39965b19953","added_by":"auto","created_at":"2025-10-27 16:11:34","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137647,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/5e6e9787c4a85bdcc0fbc904.png"},{"id":94480260,"identity":"7b0a7613-3b8f-476f-aea7-3ee3e859535e","added_by":"auto","created_at":"2025-10-27 16:10:26","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":360260,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/fab048a024b6ceca7a923842.jpeg"},{"id":94480783,"identity":"2bbde672-f114-4522-b960-d797af83933c","added_by":"auto","created_at":"2025-10-27 16:11:58","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1705726,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/4ba08b2c58779a8d16ff99b8.jpeg"},{"id":94480364,"identity":"ba06f9d6-22c1-4926-9bcb-04e390ab271f","added_by":"auto","created_at":"2025-10-27 16:10:48","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62007,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/09ebdab835573a4209f98cb7.png"},{"id":94480124,"identity":"e15e2c09-471b-4b58-9b42-ae089256ba07","added_by":"auto","created_at":"2025-10-27 16:09:48","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":59363,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/f7240069368ebb41ff5b111e.png"},{"id":94480339,"identity":"04f920d3-53bd-437c-94a3-bb674b80273c","added_by":"auto","created_at":"2025-10-27 16:10:37","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9543,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/ab0f3cc6607758e94bed8026.png"},{"id":94480332,"identity":"1a1ba7d5-f06b-4133-9093-53acc8a8c370","added_by":"auto","created_at":"2025-10-27 16:10:35","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120398,"visible":true,"origin":"","legend":"","description":"","filename":"274a7040c3b54a0faeda493529e664c51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/eaf22efba15532544d91adf1.xml"},{"id":94480244,"identity":"4b7a9680-ef08-48d7-bacf-681917977d64","added_by":"auto","created_at":"2025-10-27 16:10:17","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132705,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/1fbc45f63399afa944e23fe1.html"},{"id":94480261,"identity":"193da5d3-4307-4170-b6bb-f8e8f25b056b","added_by":"auto","created_at":"2025-10-27 16:10:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":158391,"visible":true,"origin":"","legend":"\u003cp\u003ePatient flow through the RESINA cohort (2001–2024), from ART initiation to first virologic failure and final suppression status.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003csub\u003e\u003cem\u003eOf 5136 ART‐treated participants, those with first virologic failure are shown by calendar period (2001–2007, 2008–2013, 2014–2024), then by disposition (retained in care, lost to follow-up, died) and by whether they achieved virologic suppression by end of follow-up. n: number\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/2ddd4894fe5a8a88fbc62b5e.png"},{"id":94480760,"identity":"00a2f93b-8a1b-4fa7-aeca-d4e882a405d3","added_by":"auto","created_at":"2025-10-27 16:11:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":379169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eART Regimens at treatment Initiation and resistance mutation frequencies in patients with VF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Distribution of first‐line ART regimen classes: 2NRTI + PI (two nucleoside reverse‐transcriptase inhibitors + protease inhibitor), 2NRTI + NNRTI (non‐nucleoside RTI), 2NRTI + PI/r or c (boosted PI), 2NRTI + INSTI (integrase inhibitor), and other regimens. Bars are labelled with patient counts (n) (left). Absolute number of patients initiating each individual antiretroviral drug (right). \u003cstrong\u003e(B)\u003c/strong\u003eAbsolute numbers of patients initiating therapy with each antiretroviral drug\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C)\u003c/strong\u003e Heatmap of the most common resistance-associated mutations, grouped by drug class (INSTI = integrase inhibitors; NNRTI = non-nucleoside RT inhibitors; NRTI = nucleoside RT inhibitors; PI = protease inhibitors). Color intensity indicates the number of patients harboring each mutation (scale 0–20).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/ef4c1410a3601d77aeca859a.png"},{"id":94480494,"identity":"6a4f86e1-a80c-455e-9c4c-af10325e5795","added_by":"auto","created_at":"2025-10-27 16:11:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33176,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier estimates of time to virologic resuppression, stratified by gender\u003c/strong\u003e\u003cbr\u003e\nKaplan–Meier survival curves showing the probability of remaining unsuppressed (HIV-1 RNA ≥50 copies/mL) over time after first virologic failure, stratified by sex. The green line represents male participants (n = 92), and the purple line represents female participants (n = 47). Censoring (patients lost to follow-up or without resuppression by study end) is indicated by tick marks. The log-rank test comparing the two curves yielded p = 0.009.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/b7f262ea667bd69f19685ac3.png"},{"id":98814075,"identity":"c99363aa-38d8-4555-9f34-bc31485df9a8","added_by":"auto","created_at":"2025-12-22 16:10:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2087691,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/3e897a50-e81f-4c08-b15e-c43d4a296844.pdf"},{"id":94480398,"identity":"da24b095-fc73-4a48-a2e0-299476b7c374","added_by":"auto","created_at":"2025-10-27 16:10:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":42792,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7722983/v1/6bedc9d0a4e092160e81c991.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"HIV-1 Virologic Failure in the RESINA cohort: Lessons from Two Decades of Real- World Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe widespread introduction of combination antiretroviral therapy (cART) has transformed HIV from a fatal illness to a manageable chronic disease, dramatically improving both survival and clinical outcomes for affected individuals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In recent years, newer regimens featuring enhanced safety profiles, once-daily single-tablet formulations, and the approval of long-acting injectable agents have further simplified treatment and strengthened adherence [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Sustained adherence not only ensures durable viral suppression but is also associated with improved health-related quality of life (HR-QoL) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite substantial therapeutic advances, virologic failure (VF) remains a significant challenge in HIV management. The principal drivers of VF include suboptimal adherence, preexisting resistance mutations, and viral subtype\u0026ndash;specific characteristics that may accelerate rebound or increase the likelihood of resistance development [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Sociodemographic factors such as younger age, male sex, nonwhite ethnicity, migration from high-prevalence regions, injection drug use (IDU), and lower socioeconomic status have also been linked to VF, primarily through their impact on adherence, continuity of care, and access to treatment [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Clinical determinants including low baseline CD4 T-cell count, elevated HIV-1 RNA levels at initiation, and delayed diagnosis or late presentation consistently predict poorer outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, comorbid conditions, such as obesity, which is particularly relevant among individuals receiving long-acting injectable regimens, and structural barriers, including limited healthcare access in rural settings, may further increase the risk of VF [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven these diverse contributors, understanding how resistance mutations evolve and persist is vital for informing treatment decisions, optimizing regimen selection and preventing ongoing viral replication. Previous studies have catalogued the range of resistance mutations that compromise cART efficacy and necessitate regimen switches [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the extent to which specific resistance mutation patterns influence VF outcomes in real-world cohorts with limited prior treatment exposure remains unclear. The RESINA cohort provides a unique opportunity to address these gaps, as it systematically collects longitudinal virologic and clinical data on previously untreated individuals. This enables a detailed analysis of both the frequency and the clinical consequences of resistance-associated mutations.\u003c/p\u003e\u003cp\u003eIn this analysis we aimed to quantify the frequency and determinants of VF, and to characterize the associated resistance mutations in a cohort of previously untreated participants with HIV from the RESINA cohort. As a secondary objective, we aimed to determine the proportion of participants who regained virologic resuppression after VF, and to identify the clinical and virologic factors associated with subsequent resuppression. Such insights are essential for optimizing first-line regimens, guiding timely regimen switches, and improving long-term HIV outcomes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThis analysis used data from the RESINA cohort, a multicenter observational study investigating transmitted HIV drug resistance in ART-naive individuals from North Rhine-Westphalia, Germany. We included all participants diagnosed with HIV and initiating ART between 2001 and 2024. Study entry was defined as the date of written informed consent and collection of the first study sample before ART, whose initiation varied according to treatment guidelines across the observation period. try. Late presentation/diagnosis of HIV was defined in this study as a CD4 cell count below 200 cells/\u0026micro;L at the time of HIV diagnosis. This differs from the European Centre for Disease Prevention and Control (ECDC) definition, which classifies late presentation as a CD4 count \u0026lt;350 cells/\u0026micro;L or an AIDS-defining event within three months of diagnosis. [22]. We applied the stricter threshold of \u0026lt;200 cells/\u0026micro;L to ensure a uniform classification across the cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003cbr\u003eThe \u003cstrong\u003eprimary binary outcome\u003c/strong\u003e was VF (\u0026ge;1 event vs none). VF was defined as either a confirmed HIV-1 RNA \u0026gt;200 copies/mL following prior suppression (\u0026lt;50 copies/mL) or, in some patients, as virologic rebound (a confirmed \u0026ge;0.5 log₁₀ increase from the post-ART nadir after an initial decline), even if the nadir remained above the assay\u0026rsquo;s lower limit of quantification (LLQ). \u003cstrong\u003eSubsequent VF episodes\u003c/strong\u003e were defined using the same virologic criteria (HIV-1 RNA \u0026gt;200 copies/mL), provided they occurred \u003cstrong\u003eafter a confirmed resuppression\u003c/strong\u003e (\u0026lt;50 copies/mL).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor patients with VF, \u003cstrong\u003etime to resuppression\u003c/strong\u003e was the interval between the date of first VF and the first subsequent HIV-1 RNA \u0026lt;50 copies/mL.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical data included demographics, regimen, \u003cstrong\u003eadherence (documented treatment interruption; missed ART-related appointments; descriptive only)\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e and HBV/HCV/HDV coinfections.\u0026nbsp;Virologic data comprised plasma HIV-1 RNA (viral load (VL)) and the timing of genotypic resistance testing. Participants were stratified into three ART-initiation eras reflecting availability of key drug classes in Germany: Group 1 (2001\u0026ndash;2007; pre-darunavir/raltegravir), Group 2 (2008\u0026ndash;2013; post-darunavir/raltegravir), and Group 3 (2014 onwards; post-dolutegravir (DTG)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResistance Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenotypic resistance was assessed at study entry and again at the time of VF.\u0026nbsp;Plasma HIV‑1 RNA was amplified and sequenced with conventional Sanger assay from the start of the study and Illumina next-generation sequencing from 2015 onward. 10% consensus FASTAs were used to ensure comparability with earlier Sanger sequences, population‑based assay; whenever amplification from RNA was unsuccessful, paired proviral DNA extracted from peripheral blood mononuclear cells was analyzed as an accepted backup template to avoid loss of resistance information [23]. The composite nucleotide sequences spanning the protease (PR), reverse‑transcriptase (RT) and integrase (IN) regions were interpreted with the HIV‑GRADE algorithm, which translates codon changes into drug‑specific susceptibility scores. All major and accessory mutations were cataloged across nucleos(t)ide and non‑nucleoside RT inhibitors, protease inhibitors and integrase‑strand‑transfer inhibitors (INSTI), with particular attention to the canonical mutation pathways that underlie the stepwise evolution of raltegravir resistance described by Sichtig et al [19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted in SPSS v27 (IBM Corp., Armonk, NY). \u003cstrong\u003eBaseline\u003c/strong\u003e was defined as study entry. Descriptive statistics (frequencies/percentages; median/range) summarized characteristics and mutation distributions; group comparisons used t-tests/ANOVA or Mann\u0026ndash;Whitney/Kruskal\u0026ndash;Wallis as appropriate.\u003c/p\u003e\n\u003cp\u003eTo identify predictors of VF, we fit a cohort-wide binary logistic regression model including all 5,136 participants, restricted to one record per individual (VF = 1 if \u0026ge;1 VF occurred during follow-up; VF = 0 otherwise). Covariates were prespecified based on prior evidence, biological plausibility, and data completeness. Age was modeled in 10-year increments. Sex was coded as male versus female/other. Route of acquisition was represented by two dummy variables for MSM and IDU (1 = yes, 0 = no), with heterosexual contact as the reference group. Baseline CD4 count was categorized as \u0026lt;200 cells/\u0026micro;L, \u0026ge;200 cells/\u0026micro;L, or missing (reference), and baseline HIV-1 RNA as \u0026lt;100,000 copies/mL, \u0026ge;100,000 copies/mL, or missing (reference). Year of ART initiation was grouped into 2001\u0026ndash;2007, 2008\u0026ndash;2013, and \u0026ge;2014 (reference). This specification yielded 10 predictors for 139 VF events, remaining within accepted limits to avoid model overfitting. To further assess the robustness of the model, we conducted additional multivariable logistic regression models extending the core analysis. Each supplementary model included one additional covariate of interest: HIV-1 subtype (B vs non-B), region of origin (Western Europe vs other), or transmitted drug resistance (TDR, any vs none). Because adherence ascertainment was inconsistent and frequently missing, this variable was used \u003cstrong\u003efor descriptive summaries only\u003c/strong\u003e and was \u003cstrong\u003enot included\u003c/strong\u003e in multivariable models.\u0026nbsp;All models were restricted to the first VF episode per patient and adjusted for the same set of demographic, clinical, and treatment-era variables as in the core model.\u003c/p\u003e\n\u003cp\u003eFor \u003cstrong\u003etime to virologic resuppression\u003c/strong\u003e after the first VF event, we used a \u003cstrong\u003eCox proportional hazards model\u003c/strong\u003e including age, sex, presence of resistance mutations, regimen switch at failure, log₁₀\u0026nbsp;HIV-1 RNA at failure, and HIV-1 subtype B. Patients lost to follow-up before resuppression were censored at the date of their last available HIV-1 RNA measurement; deaths prior to re-suppression were censored at the date of death. We did not model death as a competing risk because the number of deaths was small and the primary endpoint was time to resuppression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor logistic regression, we report \u003cstrong\u003eodds ratios (ORs)\u003c/strong\u003e with \u003cstrong\u003e95% confidence intervals (95% CI)\u003c/strong\u003e and two-sided \u003cstrong\u003ep-values\u003c/strong\u003e (Wald tests). For Cox models, we report \u003cstrong\u003ehazard ratios (HRs)\u003c/strong\u003e with \u003cstrong\u003e95% CI\u003c/strong\u003e and two-sided \u003cstrong\u003ep-values\u003c/strong\u003e (Wald). All tests were two-sided with \u003cstrong\u003e\u0026alpha;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 0.05\u003c/strong\u003e. p-values are shown up to three decimals; values \u0026lt;0.001 are reported as \u003cstrong\u003ep \u0026lt; 0.001.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier curves stratified by sex were used to visualize time to resuppression, with differences assessed by the log-rank test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Ethics Committee of the Heinrich Heine University (Study No. 4862), and all participants provided written informed consent according to the Declaration of Helsinki.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline characteristics and rates of virologic failure\u003c/p\u003e\n\u003cp\u003eOut of the 5,136 patients included in the RESINA cohort, 139 experienced VF, corresponding to an overall VF rate of 2.7%. The patient flow diagram is presented in Figure 1, and baseline characteristics by VF status are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003ePatients with VF were slightly younger (median age 37 years, range 18\u0026ndash;75 vs. 39 years, range 18\u0026ndash;82), and a lower proportion were male compared with patients without VF (65.5% vs. 82.1%, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). Acquisition routes differed markedly: in the VF group, heterosexual transmission (including high-prevalence regions) and IDU were more common (30.3% vs. 19.5% and 12.2% vs. 5.4%, respectively), whereas men who have sex with men (MSM) transmission predominated among patients without VF (54.4% vs. 23%, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003eRegarding region of origin, 57 % of VF patients originated from Germany, compared with 66 % without VF. Sub-Saharan Africa was more frequently represented in the VF group (17.3% vs. 5.2%, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001). Patients with VF initiated ART at lower CD4 cell counts (median 85/\u0026micro;L vs. 273/\u0026micro;L; \u0026lt;200 cells/\u0026micro;L in 43.2% vs. 18.9%) and higher HIV-1 RNA levels (\u0026ge;100,000 copies/mL in 38.8% vs. 26.5%, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.005). Late presentation was significantly more frequent among VF patients (43.2% vs. 18.9%, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003eHIV-1 subtype B remained the most common subtype in both groups but was less predominant among VF patients (54.7% vs. 58.6%), with higher proportions of CRF02_AG (15.1% vs. 5.4%) and subtype C (5.8% vs. 2.6%, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001). VF occurred more often in earlier treatment eras, with 40.3% of VF patients starting ART before 2007, compared to 22.9% in the no-VF group (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001). TDR mutations were rare in both groups (2.9% vs. 7.1%, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.061).\u003c/p\u003e\n\u003cp\u003eOverall mortality was higher in the VF group (9.4% vs. 4%, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.004). Although more than half of VF patients (79/139) were lost to follow-up at some point, the median follow-up time was 9 years (range 1\u0026ndash;24), and 85% (118/139) were followed for at least 5 years.\u003c/p\u003e\n\u003cp\u003eAt ART initiation, the most common regimens in the VF group combined two NRTIs, usually TDF/FTC, with either an NNRTI (most often nevirapine or efavirenz) or a boosted PI (most frequently lopinavir or darunavir) (Figure 2). More than half of VF cases (77/139, 55.4%) occurred under the first ART regimen, and 34/139 (24.5%) occurred during the second. First VF/rebound occurred after a median of \u003cstrong\u003e608 days\u003c/strong\u003e on ART (range \u003cstrong\u003e59\u0026ndash;5,186\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eVF rates by treatment era were: group 1, 4.7% (56/1,200); group 2, 2.6% (48/1,882); and group 3, 1.7% (35/2,054). Almost half (64/139) experienced a single VF event during follow-up, 21% (29/139) had two, and the remainder had three or more.\u003c/p\u003e\n\u003cp\u003eIn a cohort-wide binary logistic regression model restricted to the first VF episode per patient, three covariates emerged as independent risk factors for VF: \u003cstrong\u003eIDU\u003c/strong\u003e (OR 1.74, 95% CI 1.00\u0026ndash;3.00, \u003cem\u003ep\u003c/em\u003e = 0.048), \u003cstrong\u003eCD4 \u0026lt;200 cells/\u0026micro;L at ART initiation\u003c/strong\u003e (OR 2.32, 95% CI 1.56\u0026ndash;3.45, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and \u003cstrong\u003eART initiation during 2001\u0026ndash;2007\u003c/strong\u003e (OR 1.95, 95% CI 1.24\u0026ndash;3.06, \u003cem\u003ep\u003c/em\u003e = 0.004) (Table 2a). By contrast, acquisition through \u003cstrong\u003eMSM contact\u003c/strong\u003e was associated with a lower risk of VF (OR 0.32, 95% CI 0.20\u0026ndash;0.50, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Other factors, including age, sex, baseline viral load, and ART initiation during 2008\u0026ndash;2013, were not significantly associated with VF. In supplementary models, neither HIV-1 subtype nor region of origin was independently associated with VF. Adjustment for subtype attenuated the effect of ART initiation during 2001\u0026ndash;2007, indicating partial confounding by subtype distribution. TDR was significantly associated with a lower risk of VF (OR \u0026lt;1,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.035) (Supplementary Tables 1a-c).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdherence data were inconsistently recorded; among participants with VF, \u003cstrong\u003e83/139 (59.7%)\u003c/strong\u003e had documented non-adherence (treatment pause and/or missed appointment). We did not evaluate adherence in adjusted models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHIV Resistance mutations and cross-resistance patterns in the VF group\u003c/p\u003e\n\u003cp\u003eTDR mutations in HIV were identified in eight patients, while acquired resistance mutations emerged in 28 during follow-up. Of those with acquired mutations, 11 had single-class resistance and 17 (61%) showed cross-resistance to two or more drug classes (Supplementary Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe most frequently observed NRTI resistance mutation was M184V/I (16 M184V and 6 M184I) detected in 22 patients (2 transmitted, 20 acquired), followed by K70R/E in five patients (1 transmitted, 4 acquired). Among NNRTI mutations, Y181C/Y occurred in 10 patients (1 transmitted, 9 acquired), K103N in eight patients (2 transmitted, 6 acquired), and K101E/K in six patients (all acquired). PI resistance was uncommon (\u003cstrong\u003en = 6\u003c/strong\u003e), with V82A in four patients (1 transmitted, 3 acquired). \u003cstrong\u003eINSTI resistance was identified in five patients:\u003c/strong\u003e N155H (n = 2), Y143C/R/S (n = 1), E92Q (n = 1), and \u003cstrong\u003eR263K + G118R\u003c/strong\u003e (n = 1). The latter pattern reflects \u003cstrong\u003edolutegravir resistance\u003c/strong\u003e and has been described previously [24].\u003c/p\u003e\n\u003cp\u003eSupplementary Table 2 highlights extensive cross‑resistance in several patients (e.g., M184V + T215Y + K103N + V82A conferring NRTI, NNRTI, and PI resistance).\u003c/p\u003e\n\u003cp\u003eFourteen patients with transmitted or acquired mutations that do not impair cabotegravir (CAB) or rilpivirine (RPV) susceptibility would still be eligible to receive injectables (CAB + RPV LA), which might be an option to improve future adherence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTreatment outcomes post- failure in the VF group\u003c/p\u003e\n\u003cp\u003eOf 139 participants with any VF, \u003cstrong\u003e44 (31.7%)\u003c/strong\u003e were \u003cstrong\u003eunsuppressed at last follow-up\u003c/strong\u003e (Fig. 1). After the \u003cstrong\u003efirst\u003c/strong\u003e VF, \u003cstrong\u003e122 (87.8%)\u003c/strong\u003e ever achieved \u003cstrong\u003eresuppression\u003c/strong\u003e, while \u003cstrong\u003e17 (12.2%)\u003c/strong\u003e \u003cstrong\u003enever resuppressed\u003c/strong\u003e (HIV-1 RNA \u0026ge;50 copies/mL). \u003cstrong\u003eMedian time to resuppression\u003c/strong\u003e was \u003cstrong\u003e147 days\u003c/strong\u003e (range \u003cstrong\u003e13\u0026ndash;2,015\u003c/strong\u003e). The higher end-of-follow-up count reflects patients who later failed again or ended follow-up unsuppressed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOf the total of 139 patients experiencing VF, 74 patients remained on the same drug class after VF (predominantly PI-based regimens, but also some NNRTI-, NRTI-only, and INSTI-based therapies), while 65 were switched to a different drug class. Common switches were \u003cstrong\u003ePI\u0026rarr;PI (n=23)\u003c/strong\u003e, \u003cstrong\u003ePI\u0026rarr;INSTI (n=14)\u003c/strong\u003e and \u003cstrong\u003ePI\u0026rarr;other (n=20)\u003c/strong\u003e; fewer moved \u003cstrong\u003eNNRTI\u0026rarr;PI (n=6)\u003c/strong\u003e or \u003cstrong\u003eNNRTI\u0026rarr;INSTI (n=6)\u003c/strong\u003e, \u003cstrong\u003eNRTI-only\u0026rarr;PI (n=5)\u003c/strong\u003e, and \u003cstrong\u003eother\u0026rarr;PI/INSTI (n=6/2).\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e Switches to INSTI-based regimens (PI \u0026rarr; INSTI 3/3, NNRTI \u0026rarr; INSTI 4/4, other \u0026rarr; INSTI 7/7) and NRTI-only \u0026rarr; PI (6/6) were the most consistently successful, whereas remaining on PI regimens (16/22, 73%) or switching NNRTI \u0026rarr; PI (4/6, 67%) showed lower rates of re-suppression, and NRTI-only \u0026rarr; NNRTI was least effective (1/2, 50%)\u003c/p\u003e\n\u003cp\u003ePatients who failed to resuppress had significantly higher VL at failure compared with those who did resuppress (mean log₁₀\u0026nbsp;VL 4.7 vs. 3.8, \u003cem\u003ep\u003c/em\u003e = 0.004). In multivariable analysis, male sex was independently associated with faster re-suppression (HR 1.81, 95 % CI 1.15\u0026ndash;2.86, \u003cem\u003ep\u003c/em\u003e = 0.011), a finding also reflected in Kaplan\u0026ndash;Meier curves (log-rank \u003cem\u003ep\u003c/em\u003e = 0.009; Figure 3). Regimen switch at failure showed a non-significant trend toward faster resuppression (HR 0.72, 95 % CI 0.49\u0026ndash;1.05, p = 0.087), while higher VL at failure trended toward slower resuppression (HR 0.84 per log₁₀\u0026nbsp;increase, 95 % CI 0.71\u0026ndash;1.00, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.056). Age, resistance-mutation status, and subtype B were not significantly associated with resuppression. Overall, these results indicate that, once switch strategy is taken into account, male sex and lower VL at failure are the strongest predictors of faster resuppression, whereas age, resistance-mutation status, and subtype B appear to have little influence (Table 2b). \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, multicenter cohort of ART-na\u0026iuml;ve patients followed for more than two decades, VF was uncommon overall (2.7%) and declined markedly across calendar eras, from nearly 5% in the early 2000s to below 2% in the most recent decade. This decline reflects the impact of increasingly potent and tolerable regimens, particularly INSTI\u0026ndash;based therapy, which has demonstrated superior effectiveness and durability compared with NNRTI- or PI-anchored regimens. These findings align with clinical trial and cohort data confirming faster suppression and improved durability under INSTI-based regimens [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIndependent predictors of VF in our cohort, \u003cb\u003eIDU\u003c/b\u003e and \u003cb\u003elate presentation\u003c/b\u003e (CD4\u0026thinsp;\u0026lt;\u0026thinsp;200/\u0026micro;L), point to adherence and structural barriers rather than intrinsic regimen limitations. Evidence consistently links active substance use to suboptimal adherence and worse virologic outcomes, while opioid substitution therapy improves ART uptake, adherence, and suppression; this likely explains a sizable share of the excess VF we observed in people who inject drugs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similarly, late diagnosis is repeatedly associated with delayed virologic control and higher failure risk in European cohorts; our signal is aligned with those reports and with contemporary European cohort data [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe higher VF in the earliest era also reflects historical context (less potent backbones, lower resistance barriers) and a different subtype mix; adjusting for subtype attenuated that effect in our sensitivity analyses, consistent with era- and subtype-related differences described elsewhere [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt failure, the pattern in our cohort, M184V and M184I among NRTIs and K103N/Y181C among NNRTIs, matches long-standing pathways seen with TDF/FTC\u0026thinsp;+\u0026thinsp;NNRTI backbones and explains much of the NNRTI cross-resistance we recorded [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eResistance to PIs and INSTIs was rare; we observed only isolated major INSTI mutations and a single case with DTG-associated changes, consistent with reviews showing that emergent DTG resistance remains uncommon but does occur under ongoing viremia [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Notably, TDR was not associated with higher VF risk here. This is biologically plausible: several common TDR mutations carry fitness costs and tend to revert in the absence of drug pressure, blunting clinical impact when potent modern regimens are used [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Many patients exhibited multiclass resistance (e.g., concurrent NRTI, NNRTI, and PI mutations), underscoring the need to favor INSTI-based regimens and, when necessary, newer antiretroviral classes to secure durable suppression in complex resistance profiles [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition, in our cohort, baseline resistance testing was systematically performed and considered when selecting initial therapy. This strategy likely prevented the use of compromised regimens and underscores the importance and effectiveness of baseline resistance screening in routine care.\u003c/p\u003e\u003cp\u003ePost-failure outcomes were generally favorable: 87.8% achieved at least one resuppression after the first VF (median 147 days). Two signals from our models mirror external data: higher VL at failure was linked with slower re-suppression, and switching therapy trended toward faster control, both consistent with reports that viral burden at switch predicts time-to-suppression and that timely regimen change improves outcomes. Current guidelines likewise recommend prompt reassessment (adherence\u0026thinsp;+\u0026thinsp;genotype) and early switch once VF is confirmed [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Prompt switch after confirmed VF is therefore prudent to limit reservoir seeding and additional resistance, in line with guideline recommendations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] .\u003c/p\u003e\u003cp\u003eOur patient-level switch analysis is clinically instructive. Switches to INSTI-based regimens were uniformly successful and NRTI-only\u0026rarr;PI switches also performed exceptionally well. In contrast, remaining on PI after failure had lower therapy success (73%), and NNRTI\u0026rarr;PI achieved suppression in two-thirds. This hierarchy aligns with randomized and real-world evidence showing INSTI-anchored therapy to be the most reliable strategy after failure and supports choosing DTG- or BIC-based regimens when resistance and tolerability permit [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Male sex also predicted faster resuppression in our cohort (HR 1.81), whereas prior studies show inconsistent sex effects on suppression/resuppression, likely reflecting differences in adherence, pregnancy-related ART exposure, and care engagement [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough we did not collect systematic adverse effects (AE) data, improved tolerability of modern regimens likely contributed to the era-wise decline in VF and the strong performance of INSTI-based switches. INSTIs are associated with fewer discontinuations and better tolerability than NNRTI- or PI-based anchors, in contrast to legacy regimens such as efavirenz, which frequently caused neuropsychiatric adverse events [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The uniform success of INSTI-based switches in our cohort is consistent with this profile. While weight gain under INSTIs and injection-site reactions with long-acting CAB/RPV require monitoring, these rarely lead to treatment withdrawal, and patient-reported outcomes generally favor modern INSTI-based or long-acting therapies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven that nearly 60% of our patients reported non-adherence prior to VF, long-acting CAB/RPV may help selected patients once suppressed and appropriately screened. Phase 3 trials show non-inferiority to oral maintenance, while recent analyses clarify risk factors for long-acting failure (e.g., pre-existing RPV resistance, A6/A1 subtype, obesity). Our eligibility screen identified a subset who might benefit from this approach as part of an adherence-support package [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eKey strengths of our study include its large, multicenter design, the prospective follow-up of over two decades spanning three major treatment eras, and the availability of standardized virologic monitoring with genotypes at the time of failure. Together, these features provide a robust, real-world view of long-term ART effectiveness, durability, and resistance evolution. Limitations are those inherent to observational cohorts: residual confounding by unmeasured social determinants, reliance on self-reported or pharmacy-based adherence data, incomplete genotyping in all failures, and small patient numbers within some switch categories. These factors may have limited power to detect certain associations and prevent definitive causal inference.\u003c/p\u003e\u003cp\u003eIn the current treatment era, VF primarily reflects adherence and healthcare access rather than regimen potency. Our findings support three priorities: (i) prevent late presentation and support people with substance use through integrated adherence services; (ii) reassess adherence and resistance promptly and switch early after confirmed VF; and (iii) favor INSTI-based salvage, or boosted PI when INSTIs are unsuitable, given their superior resuppression rates. In practice, this requires intensified outreach and education to promote earlier HIV testing, and integration of adherence and resistance checks into routine follow-up visits or structured check-up programs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this comprehensive analysis of the RESINA cohort, VF has become rare but persists among individuals facing social, structural, and clinical barriers. Multiclass resistance highlights the need for timely genotyping and access to newer drug classes. Male sex and lower viral burden at failure were associated with faster resuppression, supporting targeted adherence support and rapid regimen optimization to sustain long-term virologic control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The RESINA study was previously funded by the Federal Ministry for Health and Social Services, the EU project CHAIN, and the Heinz-Ansmann-Stiftung for AIDS Research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: The study protocol was approved by the Ethics Committee of Heinrich Heine University D\u0026uuml;sseldorf (Study No. 4862) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to inclusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: De-identified participant data are not publicly available due to privacy and institutional restrictions (GDPR). Aggregate results and the analysis code, along with a limited de-identified dataset sufficient to reproduce the main findings, are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSG and MB drafted the original manuscript, while SG, MB, BEJ, NL, AK, and RK critically revised and edited the text. Formal analysis was performed by SG and MB, and data curation was carried out by MB, CM, EH, and JB. Patient recruitment and clinical investigation were undertaken by SG, AK, FH, LH, GF, CL, MO, MH, HK, NS, SE, SS, NQ, KR, JR, and BEJ. Conceptualization and study design were provided by MO and RK, with additional supervision by JR, TL, and BEJ. Funding acquisition was primarily carried out by MO, with further contributions from RK and BEJ. All authors had full access to the data, contributed to the interpretation of results, participated in manuscript revisions, and approved the final version for submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNarv\u0026aacute;ez M, Lins-Kusterer L, Valdelamar-Jim\u0026eacute;nez J, Brites C. Quality of Life and Antiretroviral Therapy Adherence: A Cross-Sectional Study in Colombia. AIDS Res Hum Retroviruses. 2022;38:660\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eFreedberg KA, Losina E, Weinstein MC, Paltiel AD, Cohen CJ, Seage GR, et al. The Cost Effectiveness of Combination Antiretroviral Therapy for HIV Disease. N Engl J Med. 2001;344:824\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eCahn P, Madero JS, Arribas JR, Antinori A, Ortiz R, Clarke AE, et al. Durable Efficacy of Dolutegravir Plus Lamivudine in Antiretroviral Treatment\u0026ndash;Naive Adults With HIV-1 Infection: 96-Week Results From the GEMINI-1 and GEMINI-2 Randomized Clinical Trials. JAIDS J Acquir Immune Defic Syndr. 2020;83:310\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eRaffi F, Rachlis A, Stellbrink H-J, Hardy WD, Torti C, Orkin C, et al. Once-daily dolutegravir versus raltegravir in antiretroviral-naive adults with HIV-1 infection: 48 week results from the randomised, double-blind, non-inferiority SPRING-2 study. The Lancet. 2013;381:735\u0026ndash;43. \u003c/li\u003e\n\u003cli\u003eGuidelines for the Use of Antiretroviral Agents in Adults and Adolescents With HIV. September 12, 2024. Available from: https://clinicalinfo.hiv.gov/en/guidelines/hiv-clinical-guidelines-adult-and-adolescent-arv/virologic-failure\u003c/li\u003e\n\u003cli\u003eLucas GM, Chaisson RE, Moore RD. Highly active antiretroviral therapy in a large urban clinic: risk factors for virologic failure and adverse drug reactions. Ann Intern Med. 1999;131:81\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eManagement of virologic failure. [cited 2025 Apr 24]; Available from: https://www.hiv.uw.edu/go/antiretroviral-therapy/evaluation-management-virologic-failure/core-concept/all\u003c/li\u003e\n\u003cli\u003eTang MW, Shafer RW. HIV-1 Antiretroviral Resistance: Scientific Principles and Clinical Applications. Drugs. 2012;72:e1\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eMoore AL, Mocroft A, Madge S, Devereux H, Wilson D, Phillips AN, et al. Gender Differences in Virologic Response to Treatment in an HIV-Positive Population: A Cohort Study: J Acquir Immune Defic Syndr. 2001;26(2):159\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eWu G, Zhou C, Zhang X, Zhang W, Lu R, Ouyang L, et al. Higher Risks of Virologic Failure and All-Cause Deaths Among Older People Living with HIV in Chongqing, China. AIDS Res Hum Retroviruses. 2019;35:1095\u0026ndash;102. \u003c/li\u003e\n\u003cli\u003eMocroft A, Lundgren JD, Sabin ML, Monforte A d\u0026rsquo;Arminio, Brockmeyer N, Casabona J, et al. Risk Factors and Outcomes for Late Presentation for HIV-Positive Persons in Europe: Results from the Collaboration of Observational HIV Epidemiological Research Europe Study (COHERE). Sansom SL, editor. PLoS Med. 2013;10:e1001510. \u003c/li\u003e\n\u003cli\u003eSegala FV, Di Gennaro F, Frallonardo L, De Vita E, Petralia V, Sapienza V, et al. HIV-related outcomes among migrants living in Europe compared with the general population: a systematic review and meta-analysis. Lancet HIV. 2024;11:e833\u0026ndash;42. \u003c/li\u003e\n\u003cli\u003ePrzybyla S, Ashare RL, Cioffi L, Plotnik I, Shuter J, Seng EK, et al. Substance Use and Adherence to Antiretroviral Therapy among People Living with HIV in the United States. Trop Med Infect Dis. 2022;7:349. \u003c/li\u003e\n\u003cli\u003eDarling KE, Hachfeld A, Cavassini M, Kirk O, Furrer H, Wandeler G. Late presentation to HIV care despite good access to health services: current epidemiological trends and how to do better. Swiss Med Wkly. 2016;146:w14348. \u003c/li\u003e\n\u003cli\u003eMondi A, Cozzi-Lepri A, Tavelli A, Cingolani A, Giacomelli A, Orofino G, et al. Persistent poor clinical outcomes of people living with HIV presenting with AIDS and late HIV diagnosis \u0026ndash; results from the ICONA cohort in Italy, 2009-2022. Int J Infect Dis. 2024;142:106995. \u003c/li\u003e\n\u003cli\u003eVan Welzen BJ, Van Lelyveld SFL, Ter Beest G, Gisolf JH, Geerlings SE, Prins JM, et al. Virological Failure After Switch to Long-Acting Cabotegravir and Rilpivirine Injectable Therapy: An In-depth Analysis. Clin Infect Dis. 2024;79:189\u0026ndash;95. \u003c/li\u003e\n\u003cli\u003eWeissman S, Duffus WA, Iyer M, Chakraborty H, Samantapudi AV, Albrecht H. Rural\u0026ndash;Urban Differences in HIV Viral Loads and Progression to AIDS among New HIV Cases. South Med J. 2015;108:180\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eEckard AR, McComsey GA. Weight gain and integrase inhibitors. Curr Opin Infect Dis. 2020;33:10\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eSichtig N, Sierra S, Kaiser R, D\u0026auml;umer M, Reuter S, Sch\u0026uuml;lter E, et al. Evolution of raltegravir resistance during therapy. J Antimicrob Chemother. 2009;64:25\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eEron JJ, Clotet B, Durant J, Katlama C, Kumar P, Lazzarin A, et al. Safety and Efficacy of Dolutegravir in Treatment-Experienced Subjects With Raltegravir-Resistant HIV Type 1 Infection: 24-Week Results of the VIKING Study. J Infect Dis. 2013;207:740\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eBoltz VF, Shao W, Bale MJ, Halvas EK, Luke B, McIntyre JA, et al. Linked dual-class HIV resistance mutations are associated with treatment failure. JCI Insight. 2019;4:e130118. \u003c/li\u003e\n\u003cli\u003eEuropean Centre for Disease Prevention and Control/WHO Regional Office for Europe. HIV/AIDS surveillance in Europe 2022\u0026ndash;2021 data. Stockholm: ECDC; 2022 [Internet]. Available from: https://www.ecdc.europa.eu/en/publications-data/hivaids-surveillance-europe-2022-2021-data\u003c/li\u003e\n\u003cli\u003eL\u0026uuml;bke N, Di Cristanziano V, Sierra S, Knops E, Sch\u0026uuml;lter E, Jensen B, et al. Proviral DNA as a Target for HIV-1 Resistance Analysis. Intervirology. 2015;58:184\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eL\u0026uuml;bke N, Jensen B, H\u0026uuml;ttig F, Feldt T, Walker A, Thielen A, et al. Failure of Dolutegravir First-Line ART with Selection of Virus Carrying R263K and G118R. N Engl J Med. 2019;381:887\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eAboud M, Kaplan R, Lombaard J, Zhang F, Hidalgo JA, Mamedova E, et al. Dolutegravir versus ritonavir-boosted lopinavir both with dual nucleoside reverse transcriptase inhibitor therapy in adults with HIV-1 infection in whom first-line therapy has failed (DAWNING): an open-label, non-inferiority, phase 3b trial. Lancet Infect Dis. 2019;19:253\u0026ndash;64. \u003c/li\u003e\n\u003cli\u003eLow AJ, Mburu G, Welton NJ, May MT, Davies CF, French C, et al. Impact of Opioid Substitution Therapy on Antiretroviral Therapy Outcomes: A Systematic Review and Meta-Analysis. Clin Infect Dis. 2016;63:1094\u0026ndash;104. \u003c/li\u003e\n\u003cli\u003eChu C, Tao K, Kouamou V, Avalos A, Scott J, Grant PM, et al. Prevalence of Emergent Dolutegravir Resistance Mutations in People Living with HIV: A Rapid Scoping Review. Viruses. 2024;16:399. \u003c/li\u003e\n\u003cli\u003eK\u0026uuml;hnert D, Kouyos R, Shirreff G, Pečerska J, Scherrer AU, B\u0026ouml;ni J, et al. Quantifying the fitness cost of HIV-1 drug resistance mutations through phylodynamics. Stern A, editor. PLOS Pathog. 2018;14:e1006895. \u003c/li\u003e\n\u003cli\u003eGallant J, Lazzarin A, Mills A, Orkin C, Podzamczer D, Tebas P, et al. Bictegravir, emtricitabine, and tenofovir alafenamide versus dolutegravir, abacavir, and lamivudine for initial treatment of HIV-1 infection (GS-US-380-1489): a double-blind, multicentre, phase 3, randomised controlled non-inferiority trial. The Lancet. 2017;390:2063\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eConsolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection: recommendations for a public health approach. Geneva, Switzerland; 2016. \u003c/li\u003e\n\u003cli\u003eJacobson K, Ogbuagu O. Integrase inhibitor-based regimens result in more rapid virologic suppression rates among treatment-na\u0026iuml;ve human immunodeficiency virus\u0026ndash;infected patients compared to non-nucleoside and protease inhibitor\u0026ndash;based regimens in a real-world clinical setting: A retrospective cohort study. Medicine (Baltimore). 2018;97:e13016. \u003c/li\u003e\n\u003cli\u003eMelak D, Wedajo S, Dewau R. Time to Viral Re-suppression and Its Predictors among Adults on Second-Line Antiretroviral Therapy in South Wollo Zone Public Hospitals: Stratified Cox Model. HIVAIDS Auckl NZ. 2023;15:411\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eNovelli S, Delobel P, Bouchaud O, Avettand‐Fenoel V, Fialaire P, Cabi\u0026eacute; A, et al. Enhanced immunovirological response in women compared to men after antiretroviral therapy initiation during acute and early HIV‐1 infection: results from a longitudinal study in the French ANRS Primo cohort. J Int AIDS Soc. 2020;23:e25485. \u003c/li\u003e\n\u003cli\u003eLaw JKC, Butler LT, Hamill MM. Predictors of Discontinuation of Efavirenz as Treatment for HIV, Due to Neuropsychiatric Side Effects, in a Multi-Ethnic Sample in the United Kingdom. AIDS Res Hum Retroviruses. 2020;36:459\u0026ndash;66. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eBaseline characteristics of patients in the RESINA cohort according to virologic outcome (without vs. with virologic failure)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eno VF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of patients (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (median, range)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAge categories (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt; 30 years\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 30-39 years\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 40-49 years\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 50-59\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026sup3;60 years\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eunspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e39 (18-82)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e946 (18.9)\u003c/p\u003e\n \u003cp\u003e1647 (33)\u003c/p\u003e\n \u003cp\u003e1426 (28.5)\u003c/p\u003e\n \u003cp\u003e724 (14.5)\u003c/p\u003e\n \u003cp\u003e248 (5)\u003c/p\u003e\n \u003cp\u003e6 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e37 (18-75)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e27 (19.4)\u003c/p\u003e\n \u003cp\u003e51 (36.7)\u003c/p\u003e\n \u003cp\u003e42 (30.2)\u003c/p\u003e\n \u003cp\u003e12 (8.6)\u003c/p\u003e\n \u003cp\u003e7 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eMale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Female at birth\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other/unspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4104 (82.1)\u003c/p\u003e\n \u003cp\u003e869 (17.4)\u003c/p\u003e\n \u003cp\u003e24 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91 (65.5)\u003c/p\u003e\n \u003cp\u003e48 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcquisition route (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHeterosexual\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Heterosexual from high prevalence\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; region\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; MSM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; IDU\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other/ unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e976 (19.5)\u003c/p\u003e\n \u003cp\u003e413 (8.3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2720 (54.4)\u003c/p\u003e\n \u003cp\u003e270 (5.4)\u003c/p\u003e\n \u003cp\u003e618 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42 (30.3)\u003c/p\u003e\n \u003cp\u003e32 (23)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32 (23)\u003c/p\u003e\n \u003cp\u003e18 (12.2)\u003c/p\u003e\n \u003cp\u003e15 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion of origin (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eGerman\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; European (other)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Sub Saharan Africa\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Asia\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other/unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3276 (65.5)\u003c/p\u003e\n \u003cp\u003e118 (2.4)\u003c/p\u003e\n \u003cp\u003e258 (5.2)\u003c/p\u003e\n \u003cp\u003e115 (2.3)\u003c/p\u003e\n \u003cp\u003e1230 (24.6)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e79 (56.8)\u003c/p\u003e\n \u003cp\u003e9 (6.5)\u003c/p\u003e\n \u003cp\u003e24 (17.3)\u003c/p\u003e\n \u003cp\u003e11 (7.9)\u003c/p\u003e\n \u003cp\u003e16 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian CD4 (range) at ART initiation cells/\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt; 200\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 200-349\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026sup3;350\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emissing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e273 (0-2006)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e945 (18.9)\u003c/p\u003e\n \u003cp\u003e557 (11.1)\u003c/p\u003e\n \u003cp\u003e963 (19.3)\u003c/p\u003e\n \u003cp\u003e2532 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e85 (1-484)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e60 (43.2)\u003c/p\u003e\n \u003cp\u003e15 (10.8)\u003c/p\u003e\n \u003cp\u003e6 (4.3)\u003c/p\u003e\n \u003cp\u003e58 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian HIV-1 RNA (range) at ART initiation log\u003csub\u003e10\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026lt; 100.000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026sup3;100.000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4.6 (1.3-7)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2693 (53.9)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1326 (26.5)\u003c/p\u003e\n \u003cp\u003e978 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4.8 (2.1- 6.3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e63 (45.3)\u003c/p\u003e\n \u003cp\u003e54 (38.8)\u003c/p\u003e\n \u003cp\u003e22 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV-1 Subtype (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eB\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CRF02_AG\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; C\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; A1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; A6\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2927 (58.6)\u003c/p\u003e\n \u003cp\u003e273 (5.4)\u003c/p\u003e\n \u003cp\u003e129 (2.6)\u003c/p\u003e\n \u003cp\u003e194 (3.9)\u003c/p\u003e\n \u003cp\u003e130 (2.6)\u003c/p\u003e\n \u003cp\u003e1344 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e76 (54.7)\u003c/p\u003e\n \u003cp\u003e21 (15.1)\u003c/p\u003e\n \u003cp\u003e8 (5.8)\u003c/p\u003e\n \u003cp\u003e7 (5)\u003c/p\u003e\n \u003cp\u003e4 (2.9)\u003c/p\u003e\n \u003cp\u003e23 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear of ART initiation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e2001-2007\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2008-2013\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026sup3;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1144 (22.9)\u003c/p\u003e\n \u003cp\u003e1834 (36.7)\u003c/p\u003e\n \u003cp\u003e2019 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56 (40.3)\u003c/p\u003e\n \u003cp\u003e48 (34.5)\u003c/p\u003e\n \u003cp\u003e35 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransmitted drug mutations (TDR)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eany mutation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e357 (7.1)\u003c/p\u003e\n \u003cp\u003e4640 (92.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (2.9)\u003c/p\u003e\n \u003cp\u003e135 (97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLate diagnosis/late presenters (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e945 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e60 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeaths from any cause (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e202 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u003csub\u003eValues are given as number (%) unless otherwise stated. Age, CD4 and HIV-1 RNA are presented as median (range). VF: virologic failure; MSM: men who have sex with men; IDU: intravenous drug use; TDR: transmitted drug resistance\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 2a. Multivariable logistic regression of risk factors for virologic failure\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (per 10 years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.75 \u0026ndash; 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale sex (vs female/other)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.53 \u0026ndash; 1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSM (vs heterosexual)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.20 \u0026ndash; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIDU (vs heterosexual)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.00 \u0026ndash; 3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD4 \u0026lt;200 cells/\u0026micro;L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.56 \u0026ndash; 3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD4 \u0026ge;200 cells/\u0026micro;L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.38 \u0026ndash; 1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV-1 RNA \u0026lt;100,000 copies/mL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.58 \u0026ndash; 1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV-1 RNA \u0026ge;100,000 copies/mL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.81 \u0026ndash; 2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eART start 2001\u0026ndash;2007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.24 \u0026ndash; 3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eART start 2008\u0026ndash;2013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.75 \u0026ndash; 1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eART start \u0026ge;2014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.0 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u003csub\u003eReference categories: female/other sex, heterosexual acquisition, missing CD4, missing HIV RNA, ART start \u0026ge;2014. OR = odds ratio; CI = confidence interval; MSM = men who have sex with men; IDU = intravenous drug use. \u003cstrong\u003eN=5130, VF events=139\u003c/strong\u003e\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2b.\u0026nbsp;\u003c/strong\u003eCox proportional‑hazards regression of predictors for time to virologic re‑suppression after first virologic failure\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (per 10 years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e0.90 \u0026ndash; 1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (male vs. female)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.15\u0026ndash; 2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResistance mutations\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(yes/no)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e0.59 \u0026ndash; 1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegimen switch (yes/no)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e0.49 \u0026ndash; 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elog\u003c/strong\u003e\u003cstrong\u003e₁₀\u003c/strong\u003e\u003cstrong\u003eVL at failure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e0.71 \u0026ndash; 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.056\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubtype B vs non-B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e0.61 \u0026ndash; 1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csub\u003eCox proportional\u003c/sub\u003e\u003csub\u003e‑\u003c/sub\u003e\u003csub\u003ehazards model of time (days) from first virologic failure to confirmed re\u003c/sub\u003e\u003csub\u003e‑\u003c/sub\u003e\u003csub\u003esuppression, adjusted for age, sex, presence of resistance mutations, regimen switch at failure, VL at VF and HIV\u003c/sub\u003e\u003csub\u003e‑\u003c/sub\u003e\u003csub\u003e1 subtype B. HR = hazard ratio; CI = confidence interval.\u003c/sub\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"infection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infe","sideBox":"Learn more about [Infection](http://link.springer.com/journal/15010)","snPcode":"15010","submissionUrl":"https://submission.nature.com/new-submission/15010/3","title":"Infection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"HIV, virologic failure, drug resistance, re-suppression, Cox regression, RESINA","lastPublishedDoi":"10.21203/rs.3.rs-7722983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7722983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo quantify virologic failure (VF), identify predictors, characterize resistance patterns at failure, and evaluate time to resuppression in the RESINA cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eART-na\u0026iuml;ve adults initiating ART in 2001\u0026ndash;2024 were followed. VF was confirmed HIV-1 RNA\u0026thinsp;\u0026gt;\u0026thinsp;200 copies/mL after suppression or \u0026ge;\u0026thinsp;0.5-log₁₀ rebound. Participants were grouped by treatment era (2001\u0026ndash;2007, 2008\u0026ndash;2013, \u0026ge;\u0026thinsp;2014), reflecting availability of drug classes. Genotypes at baseline and VF were interpreted using the HIV-GRADE algorithm. Predictors of VF were assessed with logistic regression; time to resuppression (\u0026lt;\u0026thinsp;50 copies/mL) after first VF with Cox models and Kaplan\u0026ndash;Meier plots.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 5,136 participants, 139 (2.7%) had VF; rates declined by era (4.7%, 2.6%, 1.7%). Independent predictors were injection-drug use (odds ratio [OR] 1.74,), CD4\u0026thinsp;\u0026lt;\u0026thinsp;200/\u0026micro;L (OR 2.32), and ART start in 2001\u0026ndash;2007 (OR 1.95); MSM acquisition was protective (OR 0.32). At failure, 36% showed resistance, often multiclass (61%); INSTI resistance was rare (n\u0026thinsp;=\u0026thinsp;5), including one R263K\u0026thinsp;+\u0026thinsp;G118R. After first VF, 122/139 cases resuppressed; 17 did not. Median time to resuppression was 147 days. Male sex predicted faster resuppression (hazard ratio [HR] 1.81); higher failure VL trended to slower resuppression (HR 0.84 per log₁₀); regimen switches showed a favorable, non-significant trend.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eVF was uncommon and declined over time, reflecting improved regimen potency and tolerability. Failures were associated with late presentation and IDU, consistent with adherence barriers. Resistance often involved multiple classes, while INSTI resistance remained infrequent. Early, genotype-guided optimization, preferably to INSTI-based therapy, combined with targeted adherence support may improve outcomes.\u003c/p\u003e","manuscriptTitle":"HIV-1 Virologic Failure in the RESINA cohort: Lessons from Two Decades of Real- World Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 15:21:31","doi":"10.21203/rs.3.rs-7722983/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-29T12:50:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T21:11:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59554441713581243925388231516285342986","date":"2025-11-19T18:04:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29497657303938649256329607025205803255","date":"2025-11-18T17:09:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T11:11:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99897919542226123529089387800503738978","date":"2025-10-15T15:16:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T10:46:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T05:11:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-27T00:32:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Infection","date":"2025-09-26T14:41:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"infection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infe","sideBox":"Learn more about [Infection](http://link.springer.com/journal/15010)","snPcode":"15010","submissionUrl":"https://submission.nature.com/new-submission/15010/3","title":"Infection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"afb81907-01ae-4bcf-8a4d-75372a593e3f","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:04:08+00:00","versionOfRecord":{"articleIdentity":"rs-7722983","link":"https://doi.org/10.1007/s15010-025-02713-7","journal":{"identity":"infection","isVorOnly":false,"title":"Infection"},"publishedOn":"2025-12-17 15:57:35","publishedOnDateReadable":"December 17th, 2025"},"versionCreatedAt":"2025-10-27 15:21:31","video":"","vorDoi":"10.1007/s15010-025-02713-7","vorDoiUrl":"https://doi.org/10.1007/s15010-025-02713-7","workflowStages":[]},"version":"v1","identity":"rs-7722983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7722983","identity":"rs-7722983","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}