Epidemiology and Social: Concise Communications
Could better tolerated HIV drug regimens improve patient outcome?
Smit, Mikaelaa; Smit, Coletteb; Cremin, Idea; Garnett, Geoffrey P.a; Hallett, Timothya; de Wolf, Frankb
aDepartment of Infectious Disease Epidemiology, Imperial College, Faculty of Medicine, London, UK
bHIV Monitoring Foundation, Amsterdam, The Netherlands.
Correspondence to Mikaela Smit, Department of Infectious Disease Epidemiology, Faculty of Medicine at St Mary's Campus, Imperial College London, W2 1PG. UK. E-mail: firstname.lastname@example.org
Received 18 April, 2012
Revised 14 June, 2012
Accepted 19 June, 2012
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Objectives: To quantify the performance of existing first-line and second-line combination antiretroviral therapy (cART) regimens on patient's clinical outcomes in the Netherlands using ATHENA data and to evaluate the potential for new drug regimens to improve patient's clinical outcomes using a data-based mathematical model.
Design and methods: We analysed data from 3995 patients from the Dutch ATHENA national observational cohort between 2000 and 2010. We quantified the main drug-related reasons for switching from first-line and second-line cART, classified as toxicity, simplification/new medication becoming available, virological failure, or other reasons. We developed a deterministic model describing HIV infection and treatment in the Netherlands parameterized on the basis of these data. The model simulated how a new drug regimen, with either improved toxicity or virological failure profile, could impact on patient's clinical outcomes.
Results: The main reason for switching current first-line and second-line regimens was toxicity, accounting for around 50% of switching from first-line and from second-line cART. The model found that a new drug regimen with increased tolerability profile could have the highest potential impact on patient's outcomes, especially as a first-line treatment. A new first-line drug regimen with improved tolerability could increase the time patients spend on first-line cART, decrease their risk of switching from first-line cART and thus simplify patient management.
Conclusion: New drug regimens with improved toxicity profiles could have the greatest impact on patient outcomes and simplify patient management in the Netherlands.
Since the introduction of combination antiretroviral therapy (cART) in 1996, HIV treatment has improved substantially [1–3]. The improved efficacy of cART in supressing HIV replication has reduced mortality of HIV-patients on treatment [4–6], and transformed HIV into a long-term chronic infection for many patients [2,7], with mortality rates among HIV-infected patients on cART approaching those of the general population .
There is a growing range of antiretroviral drugs that are used to provide life-long therapy. Although the most limiting factors of HIV treatment have shifted over time , there are still a number of problems associated with cART that challenge the regimens durability, such as considerable cross-resistance within drug-classes , and potential, sometime serious, adverse effects, which can make adherence to regimens difficult [9–11].
Prolonging the durability of first-line cART is particularly important for the management of HIV infection. Patients on subsequent lines often receive more complex and expensive regimens [4–6], and switching is associated with ‘transactional’ costs, including clinical visits, and laboratory tests. Moreover, patients, who achieve undetectable levels of viral load on first-line have a greater chance of long-term success on HIV-treatment .
The aim of this study is to quantify the performance of existing first-line and second-line cART regimens on patient's clinical outcomes in the Netherlands using ATHENA data and to investigate the potential of new drug regimens using a data-based mathematical model.
ATHENA is a national observational cohort that includes all HIV-patients followed in the 25 designated HIV treatment centres in the Netherlands since 1998. The design of this cohort has been described in detail previously . Clinical, biological and immunological data on HIV-infected patients are collected upon entry and at each follow-up visit. Reasons for stopping a current regimen are determined by physicians at the date of regimen change from a list of reasons provided by the ATHENA cohort. This analysis included all Dutch-born male patients, who were treatment-naive when beginning cART and who began cART between 1 January 2000 and June 2010 (n = 3995). cART was defined as regimens containing three or more antiretroviral drugs. Women were not included in this analysis because of the treatment changes that occur during pregnancy.
We defined a switch as a change in regimen that included at least one new antiretroviral drug. Reasons for switching were classified as first, toxicity; second, simplification/new medication becoming available; third, virological failure, and fourth, other reasons. ‘Toxicity’ is defined as the need to change regimen due to side effects. ‘Simplification/new medication becomes available’ refers to the situation in which patients switch regimen for a simpler cART regimen, for example a once-daily regimen, or because a new, better cART regimen has become available. ‘Virological failure’ refers to a switch due to poor virological response, including resistance. ‘Other reasons’ included ‘pharmacological’, ‘caution’, ‘other’ and ‘unknown’ reasons as classified by the ATHENA cohort. ‘Pharmacologic reason’ refers to an interaction with co-medication and ‘caution’ to the situation in which a patient is starting an additional treatment, for example chemotherapy, and the regimen is changed as the combination of side effects may be too heavy. Apart from the four categories of reasons, (a) to (d) defined above, there were other drug-related reasons for switching treatment (compliance, co-infection, hospital admittance, and contra-indication), which were excluded as they accounted for less than 1% of switches.
A deterministic model describing HIV infection and treatment in the Netherlands was developed. Technical details are available in the supplement. The model represents a cohort of hypothetical HIV-infected individuals, who were assumed to have been infected with HIV at the same time and age, and who were 42 years old in 2005. The model represents patients from 2005 onwards to reflect the improvement of treatment regimens since 2005. Individuals transition through different stages representing the natural course of infection and treatment, including first-line and second-line cART (Fig. 1 Supplement, http://links.lww.com/QAD/A231).
The same baseline epidemiological, healthcare and demographic inputs, based on ATHENA data, are used throughout the analysis, except where otherwise specified (Table 1). HIV-related mortality was modelled as independent of time on cART, as the estimated hazard of dying was not significantly dependent on time on cART in the ATHENA data.
Table 1-a Baseline p...Image Tools
The model allows tracking of patient's outcomes, including calculating survival time and average time on first-line and second-line cART. The model simulates changes in patient's outcomes when prescribed a new drug regimen, with either improved toxicity or virological failure profiles, two potential targets for future drug development.
Table 1-b Baseline p...Image Tools
The incidence of switching regimens has declined between the periods of 2000–2005 and 2005–2010 (Fig. 1), suggesting that cART regimens have improved considerably over the course of the last decade. The decline is particularly evident in first-line cART (Fig. 1a). Incidence of virological failure and switching due to simplification/new medication becoming available more than halved, from 31 events [95% confidence interval (CI), 25–38] and 94 events (95% CI, 83–106) in 2000–2005 to 13 (95% CI, 10–17) and 38 (95% CI, 32–44) events per 1000 person-years in 2005–2010, respectively. Incidence of switching due to toxicity and for other reasons decreased from 161 events (95% CI, 147–177) and 46 events (95% CI, 38–54) in 2000–2005 to 90 (95% CI, 82–99) and 31 (95% CI, 26–36) events per 1000 person-years in 2005–2010, respectively.
Analysis of the Dutch ATHENA data found that among Dutch-born men, diagnosed with HIV from 1996 onward, the main reason for switching current first-line and second-line regimens was toxicity, accounting for around 50% of switching first-line and second-line cART (Fig. 1). Despite the decline in the incidence of switching from first-line cART between 2000–2005 and 2005–2010, toxicity is still the dominant reason for switching cART regimens.
We simulated the impact of a new drug regimen, with improved profile on patient's outcomes using a mathematical model. Model simulations found that a new drug regimen would have the highest impact if it had an improved toxicity profile (Fig. 1c). A new drug regimen with a 50% reduction (from 90 to 45 events per 1000 person-years) in incidence of toxicity compared with current cART would result in three additional life-months per patient (Fig. 1c) to an average survival time of 32 years after start of treatment. In comparison, a 50% reduction in virological failure would add less than 1 month to patient's survival time after treatment initiation (Fig. 1c).
The results further showed that a new drug regimen would have the highest impact if used as first-line cART. Used as first-line, a new drug regimen with a 50% reduction (from 90 to 45 events per 1000 person-years) in incidence of toxicity can potentially add 5 months to patient's survival time compared with no change in survival time if used as second-line.
A new first-line drug regimen would also simplify patient management, as fewer patients experience problems with their regimen and therefore remain on first-line cART for longer. A new drug regimen with a 50% reduction in incidence of toxicity compared with current treatment regimens could reduce the number of patients, who have to switch first-line cART by 16% and increase the average duration on first-line cART by nine months per patient.
Among HIV-infected men in the Netherlands, the main reason for switching regimens is toxicity, and model simulations found that use of a new drug regimen with a reduction in these toxicity rates could translate into a potential increase in survival time and time on first-line cART, to the benefit of patient management. The model highlighted the importance of using new improved drug regimens as first-line cART in order to maximise clinical outcomes and simplify patient management. The relatively low impact a new drug regimen can have on survival is due to the high performance of existing regimens as evident by the improvement in performance shown by the analysis of ATHENA data and marked increase in life-expectancy of HIV-infected individuals in European countries, including the Netherlands since 1996 [1,15,16]. Moreover, patients, who experience toxicity could switch regimen without experiencing virological failure, thus having little impact on survival. However, although newer, bettertolerated regimens may have a limited impact on patients’ survival, they will simplify patient management and improve patient's quality of life.
Our findings are consistent with other studies. Research conducted into the reasons for switching treatment suggests that toxicity is the main reason patients switch their regimens [17–20]. Model results are consistent with studies documenting that effective treatment has increased patients’ life expectancy [1,16,21,22]. There may be a number of mechanisms driving the increased mortality rate in patients switching first-line due to toxicity. Patients could have difficulty adhering to the regimen because of the side effects and consequently do less well virologically and/or clinically. These could be patients who are more likely to experience side effects, for example due to their reduced body mass, so that higher concentrations of the medication are in the body [19,23].
To our knowledge, this analysis of ATHENA data is the first attempt to look at the factors that drive switching cART and the potential impact of new drug regimens in the Netherlands. Coupled with the model, it provides a valuable insight into the potential of new drug regimens and the impact these can have on patients’ outcomes and patient management in the Netherlands in the future. The results suggest that the data requires further analysis to identify the side effects that result in switching, as well as their effect on patients’ long-term outcome. Interpretation of the model output would be wrong if switching and reduced survival on second-line is entirely due to switching being a marker for low adherence. The model analysis is limited by its reliance on assumptions and on data from the literature, in which ATHENA data was not available or sufficient, especially for biological assumptions regarding disease progression. The model uses long-term extrapolations from the patient data and assumes that switching rates are independent of CD4 cell count. The mathematical model did not include certain complexities, for example the inclusion of failure rates for different treatment regimens, as it has been well documented that earlier regimens, such as stavudine and some zidovudine-based regimens are associated with high toxicity rates [24–26] or including time-dependent variation of switching rates, as the incidence of these have been shown to vary during the first and subsequent years on cART [27,28]. Incorporation of individual-based adherence into the model, which has been shown to be associated with the development of resistance [27,29–31], could assist in investigating the reason for poor response to second-line treatment . However, we believe that this model provides a suitable framework with which to describe potentials of new drug regimens in the future.
In conclusion, new drug regimens with improved toxicity profiles will have the greatest impact on patient prognosis and patient management in the Netherlands.
M.S. would like to thank the Medical Research Council for funding this research.
M.S. formulated the research question, constructed the model, interpreted results and wrote the first draft of the article. C.S. carried out the data analysis of the ATHENA cohort for the data and model part of the work. I.C. assisted in the model construction. G.P.G., T.H. and F.W. contributed to the formulation of the research question, and the interpretation of the data and model output. All authors contributed to the re-drafting of the article and in the process of approving the final draft.
Conflicts of interest
Conflict of interest and source of funding: The funder had no role in the analysis or the decision to publish. There are no conflicts of interest.
1. The Antiretroviral Therapy Cohort Collaboration. Life expectancy of individuals on combination antiretroviral therapy in high-income countries: a collaborative analysis of 14 cohort studies. Lancet 2008; 372:293–299.
2. Willig JH, Abroms S, Westfall AO, Routman J, Adusumilli S, Varshney M, et al. Increased regimen durability in the era of once-daily fixed-dose combination antiretroviral therapy. AIDS 2008; 22:1951–1960.
3. Palmisano L, Vella S. A brief history of antiretroviral therapy of HIV infection: success and challenges. Ann Ist Super Sanita 2011; 47:44–48.
4. Mocroft A, Vella S, Benfield TL, Chiesi A, Miller V, Gargalianos P, et al. Changing patterns of mortality across Europe in patients infected with HIV-1. EuroSIDA Study Group. Lancet 1998; 352:1725–1730.
5. Lohse N, Hansen A-BE, Pedersen G, Kronborg G, Gerstoft J, Sørensen HT, et al. Survival of persons with and without HIV infection in Denmark, 1995–2005. Ann Intern Med 2007; 146:87–95.
6. Bhaskaran K, Hamouda O, Sannes M, Boufassa F, Johnson A, Lambert P, et al. Changes in the risk of death after HIV seroconversion compared with mortality in the general population. JAMA 2008; 300:51–59.
7. Randell P, Moyle G. Antiretroviral therapy with heart. Am J Ther 2009; 16:579–584.
8. Tozzi V, Zaccarelli M, Bonfigli S, Lorenzini P, Liuzzi G, Trotta MP, et al. Drug-class-wide resistance to antiretrovirals in HIV-infected patients failing therapy: prevalence, risk factors and virological outcome. Antivir Ther 2006; 11:553–560.
9. Montessori V, Press N, Harris M, Akagi L, Montaner JSG. Adverse effects of antiretroviral therapy for HIV infection. CMAJ 2004; 170:229–238.
10. Miron RE, Smith RJ. Modelling imperfect adherence to HIV induction therapy. BMC Infect Dis 2010; 10:6.
11. Ghosh RK, Ghosh SM, Chawla S. Recent advances in antiretroviral drugs. Expert Opin Pharmacother 2011; 12:31–46.
12. Ananworanich J. Reaching undetectable viral loads after initial HIV treatment. Future HIV Therapy 2007; 1:81–89.
13. van Sighem AI, van de Wiel MA, Ghani AC, Jambroes M, Reiss P, Gyssens IC, et al. Mortality and progression to AIDS after starting highly active antiretroviral therapy. AIDS 2003; 17:2227–2236.
14. CBS. Statistics Netherlands. http://www.cbs.nl/en-GB/menu/home/default.htm
[Accessed 29 July 2012].
15. van Sighem A, Danner S, Ghani AC, Gras L, Anderson RM, de Wolf F. Mortality in patients with successful initial response to highly active antiretroviral therapy is still higher than in non-HIV-infected individuals. J Acquir Immune Defic Syndr 2005; 40:212–218.
16. The Antiretroviral Therapy Cohort CollaborationMortality of HIV-infected patients starting potent antiretroviral therapy: comparison with the general population in nine industrialized countries. Int J Epidemiol 2009; 38:1624–1633.
17. d’ Arminio Monforte A, Lepri AC, Rezza G, Pezzotti P, Antinori A, Phillips AN, et al. Insights into the reasons for discontinuation of the first highly active antiretroviral therapy (HAART) regimen in a cohort of antiretroviral naïve patients. I.CO.N.A. Study Group. Italian Cohort of Antiretroviral-Naïve Patients. AIDS 2000; 14:499–507.
18. Yuan Y, L’italien G, Mukherjee J, Iloeje UH. Determinants of discontinuation of initial highly active antiretroviral therapy regimens in a US HIV-infected patient cohort. HIV Med 2006; 7:156–162.
19. Mocroft A, Phillips AN, Soriano V, Rockstroh J, Blaxhult A, Katlama C, et al. Reasons for stopping antiretrovirals used in an initial highly active antiretroviral regimen: increased incidence of stopping due to toxicity or patient/physician choice in patients with hepatitis C coinfection. AIDS Res Hum Retrovir 2005; 21:743–752.
20. Sabundayo BP, McArthur JH, Langan SJ, Gallant JE, Margolick JB. High frequency of highly active antiretroviral therapy modifications in patients with acute or early human immunodeficiency virus infection. Pharmacotherapy 2006; 26:674–681.
21. The HIV-CAUSAL Collaboration. The effect of combined antiretroviral therapy on the overall mortality of HIV-infected individuals. AIDS 2010; 24:123–137.
22. May M, Gompels M, Delpech V, Porter K, Post F, Johnson M, et al.Impact of late diagnosis and treatment on life expectancy in people with HIV-1: UK Collaborative HIV Cohort (UK CHIC) Study. BMJ 2011; 343. doi:10.1136/bmj.d6016
23. Ledergerber B, Egger M, Opravil M, Telenti A, Hirschel B, Battegay M, et al. Clinical progression and virological failure on highly active antiretroviral therapy in HIV-1 patients: a prospective cohort study. Lancet 1999; 353:863–868.
24. Ribera E, Paradiñeiro JC, Curran A, Sauleda S, García-Arumí E, Castella E, et al. Improvements in subcutaneous fat, lipid profile, and parameters of mitochondrial toxicity in patients with peripheral lipoatrophy when stavudine is switched to tenofovir (LIPOTEST study). HIV Clin Trials 2008; 9:407–417.
25. Bygrave H, Ford N, van Cutsem G, Hilderbrand K, Jouquet G, Goemaere E, et al. Implementing a tenofovir-based first-line regimen in rural Lesotho: clinical outcomes and toxicities after two years. J Acquir Immune Defic Syndr 2011; 56:e75–e78.
26. Jouquet G, Bygrave H, Kranzer K, Ford N, Gadot L, Lee J, et al. Cost and cost-effectiveness of switching from d4T or AZT to a TDF-based first-line regimen in a resource-limited setting in rural Lesotho. J Acquir Immune Defic Syndr 2011; 58:e68–e74.
27. Rosenblum M, Deeks SG, van der Laan M, Bangsberg DR. The risk of virologic failure decreases with duration of HIV suppression, at greater than 50% adherence to antiretroviral therapy. PLoS ONE 2009; 4:e7196.
28. Pujades-Rodríguez M, Balkan S, Arnould L, Brinkhof MAW, Calmy A. Treatment failure and mortality factors in patients receiving second-line HIV therapy in resource-limited countries. JAMA 2010; 304:303–312.
29. Bangsberg DR, Kroetz DL, Deeks SG. Adherence-resistance relationships to combination HIV antiretroviral therapy. Curr HIV/AIDS Rep 2007; 4:65–72.
30. Gardner EM, Sharma S, Peng G, Hullsiek KH, Burman WJ, MacArthur RD, et al. Differential adherence to combination antiretroviral therapy is associated with virological failure with resistance. AIDS 2008; 22:75–82.
31. Ford N, Darder M, Spelman T, Maclean E, Mills E, Boulle A. early adherence to antiretroviral medication as a predictor of long-term hiv virological suppression: five-year follow up of an observational cohort. PLoS One 2010; 5. doi:10.1371/journal.pone.0010460
32. Phillips, Pillay D, Miners AH, Bennett DE, Gilks CF, Lundgren JD. Outcomes from monitoring of patients on antiretroviral therapy in resource-limited settings with viral load, CD4 cell count, or clinical observation alone: a computer simulation model. Lancet 2008; 371:1443–1451.
antiretroviral therapy; ATHENA; mathematical models; new drug regimens; toxicity
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