Chronic renal failure among HIV-1-infected patients
Mocroft, Amandaa; Kirk, Oleb; Gatell, Josec; Reiss, Peterd; Gargalianos, Panagiotise; Zilmer, Kaif; Beniowski, Marekg; Viard, Jean-Paulh; Staszewski, Schlomoi; Lundgren, Jens Db; for the EuroSIDA Study Group
From the aRoyal Free and University College Medical School, London, UK
bCopenhagen HIV Program, Hvidovre, Denmark
cHospital Clinic i Provincial, Barcelona, Spain
dAcademisch Medisch Centrum bij de Universiteit van Amsterdam, Amsterdam, The Netherlands
eAthens General Hospital, Athens, Greece
fWest-Tallinn Central Hospital, Tallinn, Estonia
gMedical University of Silesia in Katowice, Chorzow, Poland
hHôpital Necker-Enfants Malades, Paris, France
iJW Goethe University Hospital, Frankfurt, Germany.
Received 18 December, 2006
Revised 24 January, 2007
Accepted 8 February, 2007
Correspondence to Dr A Mocroft, Royal Free Centre for HIV Medicine and Dept Primary Care and Population Sciences, Royal Free and University College London Medical Schools, Royal Free Campus, Rowland Hill St, London, NW3 2PF, UK. E-mail: firstname.lastname@example.org
Background: The role of exposure to antiretrovirals in chronic renal failure (CRF) is not well understood. Glomerular filtration rates (GFR) are estimated using the Cockcroft–Gault (CG) or Modification of Diet in Renal Disease (MDRD) equations.
Methods: Baseline was arbitrarily defined as the first recorded GFR; patients with two consecutive GFR ≤ 60 ml/min per 1.73 m2 were defined as having CRF. Logistic regression was used to determine odds ratio (OR) of CRF at baseline. ART exposure (yes/no or cumulative exposure) prior to baseline was included in multivariate models (adjusted for region of Europe, age, prior AIDS, CD4 cell count nadir, viral load, hypertension and use of nephrotoxic anti-infective therapy).
Results: Using CG, the median GFR at baseline (n = 4474) was 94.4 (interquartile range, 80.5–109.3); 158 patients (3.5%) had CRF. Patients with CRF were older (median, 61.9 versus 43.1 years), had lower CD4 cell count nadirs (median, 80 versus 137 cells/μl), and were more likely to be diagnosed with AIDS (44.3 versus 30.4%), diabetes (16.5 versus 4.3%) or hypertension (53.8 versus 26.4%), all P < 0.001. In a multivariate model any use of indinavir [odds ratio (OR) 2.49; 95% confidence interval (CI), 1.62–3.83] or tenofovir (OR, 2.18; 95% CI, 1.25–3.81) was associated with increased odds of CRF, as was cumulative exposure to indinavir (OR, 1.15 per year of exposure; 95% CI, 1.06–1.25) or tenofovir (OR, 1.60; 95% CI, 1.20–2.15). Highly consistent results were seen using the MDRD formula.
Conclusions: Among antiretrovirals, only exposure to indinavir or tenofovir was associated with increased odds of CRF. We used a confirmed low GFR to define CRF to increase the robustness of our analysis, although there are several potential biases associated with this cross-sectional analysis.
HIV infection is associated with several types of renal dysfunction, including HIV-associated nephropathy (HIVAN), immune complex kidney disease and acute renal failure [1,2]. HIVAN was more commonly diagnosed in patients of Black African ethnic origin [3,4], and has declined since the introduction of combination antiretroviral therapy (cART) . In addition, a number of antiretrovirals and other drugs commonly used to treat HIV infection may be associated with nephrotoxicity. Acute renal failure and decline in renal function have been reported in association with indinavir, which can result in asymptomatic crystalluria in two-thirds of patients , and with ritonavir, although a causal mechanism has not been identified [7,8]. Cidofovir and adefovir have been associated with the development of nephrotoxicity at higher doses [9,10], and proximal tubular dysfunction and acute tubular necrosis have been reported in a small number of patients starting tenofovir [11–14]. Non-nucleoside reverse transcriptase inhibitors and fusion inhibitors have not been reported to be associated with nephrotoxicity . In addition, a number of antimicrobials used to treat opportunistic infections, such as acyclovir, amphotericin B, foscarnet and pentamidine, may each be associated with nephrotoxicity [15–17].
Early detection and management of chronic renal failure (CRF) in HIV infection can prompt discontinuation of antiretrovirals or antimicrobials prior to serious complications and kidney failure. One of the challenges, however, is that the early stages of renal dysfunction are silent and are detectable only through laboratory analyses. The glomerular filtration rate (GFR) is considered to be a direct measure of kidney function in populations with and without kidney disease and reduces before the onset of symptoms of kidney failure [18,19]. A decrease in GFR correlates with the severity of kidney disease . The Cockcroft–Gault (CG) and Modification of Diet in Renal Disease (MDRD) equations estimate GFR using serum creatinine measurements and antropometric variables [21,22]. In non-HIV infected persons, the CG equation may provide a more reliable estimate of GFR in younger patients aged less than 65 years .
Most previous studies of the relationship between nephrotoxicity and antiretrovirals have been case reports, retrospective, based on small patient groups and have tended to concentrate on tenofovir [1–4,24–28]. An understanding of both the prevalence of CRF, and the factors related to CRF, in a heterogeneous, large patient group, is important to establish the relationship between antiretrovirals, CRF and HIV infection. The aims of this study were therefore to characterize patients with CRF, as measured by two consecutive abnormally reduced GFR measurements (≤ 60 ml/min per 1.73 m2), and to describe antiretroviral treatment and experience in relation to CRF.
EuroSIDA is a prospective, European study of patients with HIV-1 infection in 93 centres across Europe (including Israel and Argentina as non-European representatives – see below). Details of the study have been published elsewhere . In brief, at recruitment, in addition to demographic and clinical information, a complete antiretroviral history was collected, together with the eight most recent CD4 cell counts and viral load measurements. Data is extracted from patient notes onto follow-up forms at 6-monthly intervals. This analysis includes data to autumn 2005, and includes details on dates of starting and stopping all antiretrovirals. Serum creatinine has been collected since the beginning of 2004 and up to four measurements are available for each patient.
Members of the coordinating office visited all centres to ensure correct patient selection and that accurate data was provided by checking the information provided against case-notes for all reported clinical events and a random sample of 10% of all other patients.
Patients from the EuroSIDA study with a minimum of two serum creatinine measurements measured after 1 January 2004 (when information on serum creatinine was first routinely collected as part of the EuroSIDA study) were included providing they had weight measured within 6 months of the serum creatinine measurement and had height recorded. Baseline was defined as the date of the first GFR measurement. GFR was calculated using the CG formula  and MDRD formula . There is no general consensus of whether the CG equation should be standardized for body surface area (BSA), although this adjustment has been incorporated into more recent analyses , and therefore both were standardized for body surface area using the Mostellar formula :
Equation (Uncited)Image Tools
Characteristics of patients with CRF at baseline, as defined by a confirmed GFR of ≤ 60 ml/min per 1.73 m2, were compared with patients without CRF. For each antiretroviral drug, and specific combinations of antiretrovirals, the number of days exposure to each drug was calculated using all start and stop dates, as previously described . Logistic regression was used to determine the factors related to CRF at baseline. All demographic variables were included in a forward selection model with P < 0.1 significance for entry into the model. Variables included in the model were gender, race, exposure group, region of Europe, prior AIDS diagnosis, CD4 cell count and viral load at baseline, CD4 cell count nadir, peak viral load, haemoglobin at baseline and age. Prior atherosclerotic cardio and/or cerebrovascular disease (as evidenced by myocardial infarction, stroke, coronary artery bypass graft, or carotic endarterectomy diagnosed before baseline), diabetes (diagnosis of diabetes mellitus prior to baseline, or taking oral antidiabetic agents or insulin at baseline) and hypertension (baseline systolic blood pressure ≥ 140 mmHg or baseline diastolic blood pressure ≥ 90 mmHg or taking angiotensin-converting enzyme inhibitors/antihypertensive agents at baseline) were included as yes, no or unknown. Smoking status was included as current, past, never or unknown. A backward selection model confirmed the demographic variables selected for inclusion into the model.
Use of each antiretroviral was then added to a model containing the relevant demographic factors. EuroSIDA does not collect information on dose of antiretrovirals used. Use of ritonavir was therefore considered as use of ritonavir in a single protease inhibitor (PI)-containing regimen, or as use of ritonavir in a regimen containing two or more PIs. This categorization most likely represents ritonavir used at high and low doses, respectively. Use of tenofovir and ritonavir in the same regimen occurred almost exclusively (96.8% of exposure) in patients taking ritonavir as part of a regimen containing two or more PIs, and therefore was not subdivided further. Antiretroviral use was modelled as ever exposed (yes/no), and cumulative exposure prior to baseline (continuous and categorical).
A total of 4474 patients satisfied the inclusion criterion and are described in Table 1, stratified by CRF at baseline. The median date of baseline was June 2004 [interquartile range (IQR), May 2004–September 2004]. There was a high degree of correlation between the CG and MDRD methods (correlation coefficient 0.773; P < 0.0001); this correlation dropped to 0.518 if the CG method was not adjusted for BSA. The CG formula generally gave higher GFR values in comparison with the MDRD formula, with a mean difference of 4.57 [95% confidence interval (CI) 4.04–5.10 ml/min per 1.73 m2]. This difference was greatest among the youngest patients. For example, in patients aged 40 years or less, the mean difference was 8.29 (95% CI, 7.33–9.25 ml/min per 1.73 m2) and in patients aged over 50 years of age, the MDRD formula gave a higher value, with mean difference 1.17 (95% CI, 0.27–2.07 ml/min per 1.73 m2).
A total of 158 patients (3.5%) had CRF at baseline using the CG formula and 209 patients (4.7%) had CRF at baseline using the MDRD formula. A total of 101 patients (2.3%) had CRF using both the CG and MDRD formulae. In general, using either formula, patients with CRF were more likely to have atherosclerotic cardio- and/or cerebrovascular disease, diabetes, and hypertension, were significantly older than patients without CRF and had significantly lower CD4 cell count nadirs (all P < 0.05 for comparison between those with and without CRF and for both CG and MDRD formulae). The median time between serum creatinine measurements was 6 months (IQR, 5–7 months) and there were no differences between patients with or without CRF, using either formula, in terms of median time between the two serum creatinine measurements used to determine the presence of CRF (CG, P = 0.97; MDRD, P = 0.91). Due to the similarity of results when using the CG or MDRD formulae, analyses concentrate on using the CG formula to define CRF.
Figure 1 illustrates the proportion of patients exposed to each antiretroviral, or combination of antiretrovirals, stratified according to CRF at baseline, using the CG formula. There were a number of differences, as indicated in the figure. For example, of 2118 patients never exposed to indinavir prior to baseline, 38 had CRF (1.8%), in comparison with 120 of 2356 patients who had been exposed to indinavir (5.1%). Similarly, of 3213 patients never exposed to tenofovir prior to baseline, 98 had CRF (3.1%), in comparison with 60 of 1261 patients who had been exposed to tenofovir (4.8%). The relationship between any exposure to renal-toxic drugs (acyclovir, pentamidine, cidofovir, amphotericin B and foscarnet) and CRF was also investigated. Of note, a significantly higher proportion of patients with CRF at baseline had previously taken pentamidine in comparison with patients without CRF (15.8 versus 9.6%; P = 0.010), similarly for cidofovir (3.8 versus 0.5%; P < 0.0001) and foscarnet (26.0 versus 15.1%, P = 0.063). Overall, 43 patients (27.2%) with CRF at baseline had been exposed to a renal-toxic drug, compared to 684 patients (15.9%) without CRF at baseline (P < 0.0001).
The results of the logistic regression models are shown in Table 2. Antiretroviral use and exposure to other renal-toxic drugs was included as ever exposed (yes versus no). The figures shown are the odds ratios (OR) of having CRF at baseline, using the CG formula. Note that although the diagnosis of diabetes at baseline was associated with an increased risk of CRF in univariate analyses, this variable was not selected using the forward selection model building process and was therefore excluded from the final multivariate model. Patients from Eastern Europe were significantly younger than patients from other regions of Europe (median ages of 37.5 and 44.0 years, respectively). Hence in the multivariate model which adjusted for age, patients from Eastern Europe had increased odds of CRF in comparison with patients from southern Europe (OR, 2.45; 95% CI, 1.35–4.45; P = 0.0033). Older patients also had increased odds of CRF at baseline (OR per 10 years older, 5.47; 95% CI, 4.4–6.72; P < 0.0001), whereas there was an increased, but not statistically significant, odds of CRF at baseline in patients with hypertension (OR, 1.34; 95% CI, 0.92–1.95; P = 0.12), in patients with a prior AIDS diagnosis (OR, 1.34; 95% CI, 0.88–2.02; P = 0.17), and in patients with prior exposure to renal-toxic prophylactic drugs (OR, 1.47; 95% CI, 0.94–2.30; P = 0.089). Patients with a higher CD4 nadir had decreased odds of CRF (OR per doubling of CD4 nadir 0.90; 95% CI, 0.82–0.99; P = 0.028), whereas patients with a higher viral load at baseline had marginally significantly increased odds of CRF at baseline (OR, 1.54; 95% CI, 0.98–2.41). There was also a temporal relationship, such that those patients under follow-up in more recent years had increased odds of CRF.
In the same model, use of ritonavir as a single or as part of a boosted antiretroviral regimen (OR, 0.89; 95% CI, 0.56–1.43; P = 0.64 and OR, 1.27; 95% CI, 0.82–1.98; P = 0.29, respectively) was not associated with increased odds of CRF at baseline. Use of indinavir (OR, 2.49; 95% CI, 1.62–3.83; P < 0.0001) or tenofovir (OR, 2.18; 95% CI, 1.25–3.81; P = 0.0061) was associated with increased odds of CRF at baseline. Use of enfurvitide was associated with decreased odds of CRF at baseline (OR, 0.13; 95% CI, 0.03–0.65; P = 0.013). Use of tenofovir and ritonavir (i.e., patients were taking both ritonavir and tenofovir as part of the same regimen) was not associated with increased odds of CRF at baseline (OR, 0.77; 95% CI, 0.39–1.50; P = 0.44), No other antiretrovirals were associated with CRF at baseline. Including diabetes in this model did not alter these findings (data not shown).
The analyses were repeated using cumulative exposure to antiretroviral drugs, with highly consistent results. For example, after adjustment for region of Europe, prior AIDS diagnosis, age, CD4 cell count nadir, baseline date, viral load, and presence of hypertension, each additional year of exposure to indinavir increased the odds of CRF at baseline by 15% (OR, 1.15; 95% CI, 1.05–1.24; P = 0.0013) and each additional year of exposure to tenofovir increased the odds of CRF at baseline by 60% (OR, 1.60; 95% CI, 1.20–2.15; P = 0.0015). Similar results were seen when cumulative exposure was included as a categorical variable; there was increasing odds of CRF as exposure increased, with no clear threshold value of exposure associated with CRF (data not shown). The findings were also similar when MDRD was used to define CRF.
This is one of the largest studies to date that assessed kidney function in HIV-infected patients. In addition to the risk factors reported in persons without HIV-1 infection, we found some evidence that patients treated with either tenofovir or indinavir had increased odds of CRF at the time of first serum creatinine measurement within the EuroSIDA study.
There are a potential number of benefits associated with antiretroviral therapy in patients with kidney disease; there has been a decrease in the incidence of HIVAN since the widespread introduction of cART , which is currently recommended for the management of chronic kidney disease in patients with HIVAN . It was reassuring therefore that despite extensive exposure to antiretrovirals, including tenofovir or indinavir, the two antiretroviral drugs which were related to CRF in this patient group, only a small proportion of patients had CRF.
Previous studies, which have mainly concentrated on the relationship between tenofovir and nephrotoxicity, have also reported a low frequency of serious renal impairment, using a range of definitions [27,28]. Current treatment guidelines suggest that, in patients with evidence of pre-existing renal insufficiency, tenofovir dose adjustment may be necessary , or that adjustment of the dosing interval may be required . It is not yet clear if this strategy will reduce tenofovir-associated nephrotoxicity in patients with renal impairment . There was also an association between the use of indinavir and CRF. Previous studies have demonstrated that nephrolithiasis is one of the most frequent adverse events of indinavir, with reported incidence of up to 27% [34,35], with infrequent reports of acute renal failure [36,37]. The risk of toxicities in patients taking a boosted indinavir regimen has also been shown to be increased [38,39]. Prolonged use of indinavir may also be associated with elevations in serum creatinine and therefore a decrease in GFR [36,40]. Dose modification of indinavir has led to improvements in measures of renal function, but not a return to pre-therapy levels . Whereas previous studies have concentrated on the relationships between specific antiretrovirals and nephrotoxicity, the results presented here also consider the impact of other factors, such as age, hypertension, and the use of commonly prescribed drugs to treat opportunistic infections.
We found older age and hypertension were associated with CRF at baseline, and these are well-known risk factors associated with renal impairment in uninfected persons [42,43]. Diabetes was not associated with CRF at baseline after adjustment, and this may be due to the majority of patients with diabetes also having hypertension, or due to a lack of power. In addition, it was interesting to note that HIV-1-infected patients with a lower CD4 cell count nadir and a prior diagnosis of AIDS had increased the odds of CRF at baseline in this patient population. Previous studies have also demonstrated a relationship between low CD4 cell counts and nephrotoxicity [44–48]. Patients with low CD4 cell counts were more likely to have a prior diagnosis of AIDS and therefore more likely to have been exposed to other drugs used in HIV infection, such as foscarnet, acyclovir and pentamidine, which may be nephrotoxic [1,49]. Foscarnet for example, may induce direct injury to tubular cells and the formation of crystals in the glomerular capillaries ; thus prior exposure may have a long-term effect on kidney function even after exposure ceases.
We have used both the CG and MDRD formula to calculate GFR, and found highly consistent results using either formula in this patient population, and a high degree of correlation between the two measurements. However, this may be an imprecise estimate and urinary clearance of intravenously infused inulin or labeled tracers with timed urine collection is regarded as one of the more accurate methodologies for determination of GFR . There is no consensus in HIV-infected patients as to the most appropriate formula to use for estimation of GFR, although in uninfected patients, the CG formula may be more appropriate in younger people who are not obese . The mean difference between the CG and MDRD formulae was least in older patients. The MDRD formula was mainly derived in patients with altered kidney function and therefore may be less reliable in populations without a high prevalence of CRF . Further work is required to identify which estimation of creatinine clearance most accurately reflects kidney function in HIV-infected patients.
There are several limitations to this study which should be noted. We do not have information on proteinuria, or other markers of kidney disease. The EuroSIDA cohort is predominantly of white ethnic origin, and therefore our results may not be generalizable to minority populations at a higher risk of kidney disease. Our baseline was arbitrarily defined according to when information on serum creatinine was first routinely collected in EuroSIDA. This means that only a small proportion of patients currently have serum creatinine measurements predating antiretrovirals or other potentially nephrotoxic drugs. This analysis is cross-sectional in nature, and it is impossible to say whether the observed CRF occurred before or after exposure to specific drugs and whether it was a consequence of exposure to the drugs. A causal relationship cannot be identified using a cross sectional analysis, and ideally, serum creatinine should be measured prior to starting drugs so that the effect on serum creatinine can be described in detail, both while on treatment and if treatment stops. We used a confirmed low GFR to define CRF rather than a single measurement to try and reduce the reliance of our findings on a single time point and to take account of some of the inherent variability in serum creatinine levels. In addition, EuroSIDA is an observational study and patients have not been randomized to treatment. Even after careful adjustment, there will be a number of factors, both unknown or unmeasured, such as use of non-steroidal anti-inflammatory drugs which may be related to CRF which we cannot adjust for [51,52]. The relationships between specific antiretrovirals and CRF should therefore be interpreted with caution and prompt further careful analyses. Further information is now routinely recorded as part of the EuroSIDA study, which will, with longer follow-up, allow analysis of serial estimated measurements of creatinine clearance, and consideration of how this changes after patients initiate antiretrovirals, and in patients who stop antiretrovirals because of concerns about nephrotoxicity, whether serum creatinine clearance return to pre treatment levels.
In conclusion, we have described the prevalence and risk factors for CRF in HIV-infected patients, including the most important risk factors known from the general population. Among antiretrovirals, only exposure to indinavir or tenofovir was associated with increased odds of CRF. We used a confirmed low GFR to define CRF to increase the robustness of our analysis, although there are several potential biases associated with this cross-sectional analysis. Future analyses should focus on confirmed changes in GFR, requiring large numbers of patients and serial estimates of creatinine clearance.
The multicentre study group on EuroSIDA (national coordinators in parenthesis)
Argentina (M. Losso), A. Duran, Hospital J.M. Ramos Mejia, Buenos Aires. Austria (N. Vetter) Pulmologisches Zentrum der Stadt Wien, Vienna. Belarus (I. Karpov), A. Vassilenko, Belarus State Medical University, Minsk. Belgium (N. Clumeck) S. De Wit, B. Poll, Saint-Pierre Hospital, Brussels; R. Colebunders, Institute of Tropical Medicine, Antwerp. Czech Republic (L. Machala) H. Rozsypal, Faculty Hospital Bulovka, Prague; D. Sedlacek, Charles University Hospital, Plzen. Denmark (J. Nielsen) J. Lundgren, T. Benfield, O. Kirk, Hvidovre Hospital, Copenhagen; J. Gerstoft, T. Katzenstein, A.-B. E. Hansen, P. Skinhøj, Rigshospitalet, Copenhagen; C. Pedersen, Odense University Hospital, Odense. Estonia (K. Zilmer) West-Tallinn Central Hospital, Tallinn. France (C. Katlama) Hôpital de la Pitié-Salpétière, Paris; J.-P. Viard, Hôpital Necker-Enfants Malades, Paris; P.-M. Girard, Hospital Saint-Antoine, Paris; T. Saint-Marc, Hôpital Edouard Herriot, Lyon; P. Vanhems, University Claude Bernard, Lyon; C. Pradier, Hôpital de l’Archet, Nice; F. Dabis, Unité INSERM, Bordeaux. Germany M. Dietrich, C. Manegold, Bernhard-Nocht-Institut for Tropical Medicine, Hamburg; J. van Lunzen, H.-J. Stellbrink, Eppendorf Medizinische Kernklinik, Hamburg; S. Staszewski, M. Bickel, J.W. Goethe University Hospital, Frankfurt; F.-D. Goebel, Medizinische Poliklinik, Munich; G. Fätkenheuer, Universität Köln, Cologne; J. Rockstroh, Universitäts Klinik Bonn; R. Schmidt, Medizinische Hochschule Hannover. Greece (J. Kosmidis) P. Gargalianos, H. Sambatakou, J. Perdios, Athens General Hospital, Athens; G. Panos, A. Filandras, E. Karabatsaki, 1st IKA Hospital, Athens. Hungary (D. Banhegyi) Szent Lásló Hospital, Budapest. Ireland (F. Mulcahy) St. James's Hospital, Dublin. Israel (I. Yust) D. Turner, M. Burke, Ichilov Hospital, Tel Aviv; S. Pollack, G. Hassoun, Rambam Medical Center, Haifa: Z. Sthoeger, Kaplan Hospital, Rehovot; S. Maayan, Hadassah University Hospital, Jerusalem. Italy (S. Vella, A. Chiesi) Istituto Superiore di Sanita, Rome; C. Arici, Ospedale Riuniti, Bergamo; R. Pristerá, Ospedale Generale Regionale, Bolzano; F. Mazzotta, A. Gabbuti, Ospedale S. Maria Annunziata, Florence; R. Esposito, A. Bedini, Università di Modena, Modena; A. Chirianni, E. Montesarchio, Presidio Ospedaliero A.D. Cotugno, Naples; V. Vullo, P. Santopadre, Università di Roma La Sapienza, Rome; P. Narciso, A. Antinori, P. Franci, M. Zaccarelli, Ospedale Spallanzani, Rome; A. Lazzarin, R. Finazzi, Ospedale San Raffaele, Milan; A. D’Arminio Monforte, Osp. S. Paolo, Milan; A.L. Ridolfo, Osp. L. Sacco, Milan. Latvia (L. Viksna) Infectology Centre of Latvia, Riga. Lithuania (S. Chaplinskas) Lithuanian AIDS Centre, Vilnius. Luxembourg (R. Hemmer), T. Staub, Centre Hospitalier, Luxembourg. Netherlands (P. Reiss) Academisch Medisch Centrum bij de Universiteit van Amsterdam, Amsterdam. Norway (J. Bruun) A. Maeland, V. Ormaasen, Ullevål Hospital, Oslo. Poland (B. Knysz) J. Gasiorowski, Medical University, Wroclaw; A. Horban, Centrum Diagnostyki i Terapii AIDS, Warsaw; D. Prokopowicz, A. Wiercinska-Drapalo, Medical University, Bialystok; A. Boron-Kaczmarska, M. Pynka, Medical Univesity, Szczecin; M. Beniowski, E. Mularska, Osrodek Diagnostyki i Terapii AIDS, Chorzow; H. Trocha, Medical University, Gdansk. Portugal (F. Antunes) E. Valadas, Hospital Santa Maria, Lisbon; K. Mansinho, Hospital de Egas Moniz, Lisbon; F. Matez, Hospital Curry Cabral, Lisbon. Romania (D. Duiculescu) Spitalul de Boli Infectioase si Tropicale: V. Babes, Bucarest; A. Streinu-Cercel, Institute of Infectious Diseases, Bucarest. Russia E. Vinogradova, St Petersburg AIDS Centre; A. Rakhmanova, Medical Academy Botkin Hospital, St Petersburg. Serbia and Montenegro (D. Jevtovic), The Institute for Infectious and Tropical Diseases, Belgrade. Slovakia (M. Mokráš) D. Staneková, Dérer Hospital, Bratislava. Spain (J. González-Lahoz) P. Barreiro, T. García-Benayas, L. Martin-Carbonero, V. Soriano, Hospital Carlos III, Madrid; B. Clotet, A. Jou, J. Conejero, C. Tural, Hospital Germans Trias i Pujol, Badalona; J.M. Gatell, J.M. Miró, Hospital Clinic i Provincial, Barcelona. Sweden (A. Blaxhult) Karolinska University Hospital, Solna; A. Karlsson, Karolinska University Hospital, Stockholm; P. Pehrson, Karolinska University Hospital, Huddinge. Switzerland (B. Ledergerber) R. Weber, University Hospital, Zürich; P. Francioli, A. Telenti, Centre Hospitalier Universitaire Vaudois, Lausanne; B. Hirschel, V. Soravia-Dunand, Hospital Cantonal Universitaire de Geneve, Geneve; H. Furrer, Inselspital Bern, Bern. Ukraine (N. Chentsova) Kyiv Centre for AIDS, Kyiv. United Kingdom (S. Barton) St. Stephen's Clinic, Chelsea and Westminster Hospital, London; A.M. Johnson, D. Mercey, Royal Free and University College London Medical School, London (University College Campus); A. Phillips, M.A. Johnson, A. Mocroft, Royal Free and University College Medical School, London (Royal Free Campus); M. Murphy, Medical College of Saint Bartholomew's Hospital, London; J. Weber, G. Scullard, Imperial College School of Medicine at St. Mary's, London; M. Fisher, Royal Sussex County Hospital, Brighton; R. Brettle, Western General Hospital, Edinburgh.
Virology group: B. Clotet (Central coordinators) plus ad hoc virologists from participating sites in the EuroSIDA Study.
Steering committee: F. Antunes, B. Clotet, D. Duiculescu, J. Gatell, B. Gazzard, A. Horban, A. Karlsson, C. Katlama, B. Ledergerber (Chair), A. D’Arminio Montforte, A. Phillips, A. Rakhmanova, P. Reiss (Vice-chair), J. Rockstroh.
Coordinating Centre Staff: J. Lundgren (project leader), I. Gjørup, O. Kirk, A. Mocroft, N. Friis-Møller, A. Cozzi-Lepri, W. Bannister, M. Ellefson, A. Borch, D. Podlekareva, C. Holkmann Olsen, J. Kjær.
Sponsorship: The European Commission BIOMED 1 (CT94-1637), BIOMED 2 (CT97-2713) and the 5th framework (QLK2-2000-00773) programmes were the primary sponsors of the study. Unrestricted grants were also provided by Bristol-Myers Squibb, Gilead, GlaxoSmithKline, Roche and Boehringer-Ingelheim. The participation of centres from Switzerland in EuroSIDA was supported by a grant from the Swiss Federal Office for Education and Science.
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