Vergleich der Effektivität von multiplen dynamischen Behandlungsstrategien unter Nutzung der Target-Trial-Emulierung

Hintergrund

Therapieentscheidungen, die durch „Wenn-dann“-Algorithmen basierend auf bspw. Krankheitsverläufen oder vergangenen Therapien geregelt werden, sind dynamische Fragestellungen. Die Effektivität von dynamischen Therapiestrategien wird häufig mit Real World Data (RWD), d. h. Realdaten, untersucht. Einerseits bieten RWD ein großes Potenzial, da hiermit viele unterschiedliche in der Routineversorgung vorkommende Therapiestrategien analysiert werden können. Andererseits bergen Effektschätzer aus RWD-Analysen ein hohes Verzerrungspotenzial.

Ziel der Arbeit

Dieser Artikel beschreibt, wie dynamische Behandlungsstrategien mithilfe von RWD adäquat verglichen und damit die optimale Therapiestrategie identifiziert werden können.

Material und Methoden

Wir beschreiben, wie die Kombination aus drei Ansätzen eine kausale Interpretation der Ergebnisse erlaubt. Hierzu gehören (1) Kausaldiagramme, (2) Target-Trial-Emulierung sowie (3) statistische g‑Methoden. Der beschriebene kausale Ansatz und die genannten Begriffe und Konzepte werden erläutert und anhand eines Fallbeispiels verdeutlicht, in welchem untersucht wird, wann die antivirale Therapie bei behandlungsnaiven Patient:innen mit HIV-Infektion begonnen werden sollte.

Ergebnisse

Kausaldiagramme visualisieren kausale Prozesse, die der Datengenerierung zugrunde liegen. Sie helfen, Parameter zu identifizieren, die in der Analyse berücksichtigt werden müssen. Die Target-Trial-Emulierung simuliert eine randomisierte klinische Studie, indem alle möglichen dynamischen Strategien definiert, die Patientendaten kopiert („geklont“) und jede:r Patient:in jedem Behandlungsarm zugewiesen werden. In einer kausalen Per-Protokoll-Analyse werden alle Patient:innen, die das jeweilige Protokoll einer Behandlungsstrategie verletzen, zensiert. Durch g‑Methoden wird für informatives Zensieren adjustiert. Die erwarteten Outcomes jeder Behandlungsstrategie werden simuliert und miteinander verglichen.

Schlussfolgerung

Dynamische Behandlungsstrategien können mithilfe von RWD adäquat verglichen werden, wenn drei kausale Ansätze kombiniert werden und die erforderlichen Daten vorliegen. Diese Ansätze sind (1) Kausaldiagramme, (2) Target-Trial-Emulierung sowie (3) statistische g‑Methoden.

Seemingly Unrelated Regression Analysis of the Cost and Health-Related Quality of Life Outcomes of the REVAMP Randomized Clinical Trial

OBJECTIVE

This study aimed to evaluate the 9-month cost and health-related quality of life (HRQOL) outcomes of resistance versus viral load testing strategies to manage virological failure in low-middle income countries.

METHODS

We analyzed secondary outcomes from the REVAMP clinical trial: a pragmatic, open label, parallel-arm randomized trial investigating resistance versus viral load testing for individuals failing first-line treatment in South Africa and Uganda. We collected resource data, valued according to local cost data and used the 3-level version of EQ-5D to measure HRQOL at baseline and 9 months. We applied seemingly unrelated regression equations to account for the correlation between cost and HRQOL. We conducted intention-to-treat analyses with multiple imputation using chained equations for missing data and performed sensitivity analyses using complete cases.

RESULTS

For South Africa, resistance testing and opportunistic infections were associated with statistically significantly higher total costs, and virological suppression was associated with lower total cost. Higher baseline utility, higher cluster of differentiation 4 (CD4) count, and virological suppression were associated with better HRQOL. For Uganda, resistance testing and switching to second-line treatment were associated with higher total cost, and higher CD4 was associated with lower total cost. Higher baseline utility, higher CD4 count, and virological suppression were associated with better HRQOL. Sensitivity analyses of the complete-case analysis confirmed the overall results.

CONCLUSION

Resistance testing showed no cost or HRQOL advantage in South Africa or Uganda over the 9-month REVAMP clinical trial.

Management of Virologic Failure and HIV Drug Resistance

Approximately 20% of people with HIV in the United States prescribed antiretroviral therapy are not virally suppressed. Thus, optimal management of virologic failure has a critical role in the ability to improve viral suppression rates to improve long-term health outcomes for those infected and to achieve epidemic control. This article discusses the causes of virologic failure, the use of resistance testing to guide management after failure, interpretation and relevance of HIV drug resistance patterns, considerations for selection of second-line and salvage therapies, and management of virologic failure in special populations.

Antiretroviral resistance testing in HIV-positive people

BACKGROUND

Resistance to antiretroviral therapy (ART) among people living with human immunodeficiency virus (HIV) compromises treatment effectiveness, often leading to virological failure and mortality. Antiretroviral drug resistance tests may be used at the time of initiation of therapy, or when treatment failure occurs, to inform the choice of ART regimen. Resistance tests (genotypic or phenotypic) are widely used in high-income countries, but not in resource-limited settings. This systematic review summarizes the relative merits of resistance testing in treatment-naive and treatment-exposed people living with HIV.

OBJECTIVES

To evaluate the effectiveness of antiretroviral resistance testing (genotypic or phenotypic) in reducing mortality and morbidity in HIV-positive people.

SEARCH METHODS

We attempted to identify all relevant studies, regardless of language or publication status, through searches of electronic databases and conference proceedings up to 26 January 2018. We searched MEDLINE, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), in the Cochrane Library, the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP), and ClinicalTrials.gov to 26 January 2018. We searched Latin American and Caribbean Health Sciences Literature (LILACS) and the Web of Science for publications from 1996 to 26 January 2018.

SELECTION CRITERIA

We included all randomized controlled trials (RCTs) and observational studies that compared resistance testing to no resistance testing in people with HIV irrespective of their exposure to ART.Primary outcomes of interest were mortality and virological failure. Secondary outcomes were change in mean CD4-T-lymphocyte count, clinical progression to AIDS, development of a second or new opportunistic infection, change in viral load, and quality of life.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed each reference for prespecified inclusion criteria. Two review authors then independently extracted data from each included study using a standardized data extraction form. We analysed data on an intention-to-treat basis using a random-effects model. We performed subgroup analyses for the type of resistance test used (phenotypic or genotypic), use of expert advice to interpret resistance tests, and age (children and adolescents versus adults). We followed standard Cochrane methodological procedures.

MAIN RESULTS

Eleven RCTs (published between 1999 and 2006), which included 2531 participants, met our inclusion criteria. All of these trials exclusively enrolled patients who had previous exposure to ART. We found no observational studies. Length of follow-up time, study settings, and types of resistance testing varied greatly. Follow-up ranged from 12 to 150 weeks. All studies were conducted in Europe, USA, or South America. Seven studies used genotypic testing, two used phenotypic testing, and two used both phenotypic and genotypic testing. Only one study was funded by a manufacturer of resistance tests.Resistance testing made little or no difference in mortality (odds ratio (OR) 0.89, 95% confidence interval (CI) 0.36 to 2.22; 5 trials, 1140 participants; moderate-certainty evidence), and may have slightly reduced the number of people with virological failure (OR 0.70, 95% CI 0.56 to 0.87; 10 trials, 1728 participants; low-certainty evidence); and probably made little or no difference in change in CD4 cell count (mean difference (MD) -1.00 cells/mm³, 95% CI -12.49 to 10.50; 7 trials, 1349 participants; moderate-certainty evidence) or progression to AIDS (OR 0.64, 95% CI 0.31 to 1.29; 3 trials, 809 participants; moderate-certainty evidence). Resistance testing made little or no difference in adverse events (OR 0.89, 95% CI 0.51 to 1.55; 4 trials, 808 participants; low-certainty evidence) and probably reduced viral load (MD -0.23, 95% CI -0.35 to -0.11; 10 trials, 1837 participants; moderate-certainty evidence). No studies reported on development of new opportunistic infections or quality of life. We found no statistically significant heterogeneity for any outcomes, and the I² statistic value ranged from 0 to 25%. We found no subgroup effects for types of resistance testing (genotypic versus phenotypic), the addition of expert advice to interpretation of resistance tests, or age. Results for mortality were consistent when we compared studies at high or unclear risk of bias versus studies at low risk of bias.

AUTHORS' CONCLUSIONS

Resistance testing probably improved virological outcomes in people who have had virological failure in trials conducted 12 or more years ago. We found no evidence in treatment-naive people. Resistance testing did not demonstrate important patient benefits in terms of risk of death or progression to AIDS. The trials included very few participants from low- and middle-income countries.

A Longitudinal Analysis of Healthcare Costs after Treatment Optimization following Genotypic Antiretroviral Resistance Testing: Does Resistance Testing pay off?

Objective

To assess the impact of antiretroviral therapy optimized by genotypic antiretroviral resistance testing (GRT) on healthcare costs over a 2-year period in patients after antiretroviral treatment failure.

Study design

Non-randomized, prospective, tertiary care, clinic-based study.

Patients

One-hundred and forty-two HIV patients enrolled in the ‘ZIEL’ study and the Swiss HIV Cohort Study who experienced virological treatment failure.

Methods

For all patients GRT was used to optimize the antiretroviral treatment regimen. All healthcare costs during 2 years following GRT were assessed using micro-costing. Costs were separated into ART medication costs and healthcare costs other than ART medication (that is, non-ART medication costs, in-patient costs and ambulatory [out-patient] costs). These cost estimates were then split into four consecutive 6-month periods (period 1–4) and the accumulated cost for each period was calculated. Univariate and multivariate regression modelling techniques for repeated measurements were applied to assess the changes of healthcare costs over time and factors associated with healthcare costs following GRT.

Results

Overall healthcare costs after GRT decreased over time and were significantly higher in period 1 (32%; 95% confidence interval [CI]: 18–47) compared with period 4. ART medication costs significantly increased by 1,017 (95% CI: 22–2,014) Swiss francs (CHF) from period 1–4, whereas healthcare costs other than ART medication costs decreased substantially by a factor of 3.1 (95% CI: 2.6–3.7) from period 1 to period 4. Factors mostly influencing healthcare costs following GRT were AIDS status, costs being 15% (95% CI: 6–24) higher in patients with AIDS compared with patients without AIDS, and baseline viral load, costs being 12% (95% CI: 6–17) higher in patients with each log increase in plasma RNA.

Conclusions

Optimized antiretroviral treatment regimens following GRT lead to a reduction of healthcare costs in patients with treatment failure over 2 years. Patients in a worse health state (that is, a positive AIDS status and high baseline viral load) will experience higher overall costs.