After 15 years of widespread treatment with combination antiretroviral therapy (cART), mortality rates in HIV-infected patients in the Western world have decreased and life expectancies have significantly improved.1 As a result of treatment, HIV is gradually becoming a serious chronic condition rather than an invariably fatal one. Mortality is increasingly more likely related to non-AIDS causes such as cardiovascular, liver, and renal diseases, and certain non-AIDS cancers.2–6
The increasing proportion of deaths related to non-AIDS causes can be attributed to multiple factors. HIV infection has been suggested to possibly accelerate ageing. In combination with the prolonged survival and already increased age of HIV-infected patients, this could also contribute to an increase in non-AIDS diseases, many of which are associated with ageing.7 A large fraction of HIV-infected patients have lifestyle-related risk factors like smoking or excessive alcohol consumption, or are co-infected with hepatitis B or C, conditions that are all related to an increased risk of non-AIDS diseases.8,9 Moreover, the risk of certain non-AIDS diseases may also be increased as a result of antiretroviral treatment.3,10
Although the proportion of deaths due to non-AIDS causes is increasing, the absolute rate of non-AIDS deaths after the start of the cART era has decreased, suggesting that HIV infection itself influences the risk of non-AIDS diseases.11 Previously, several studies found an association between the level of immunodeficiency and the occurrence of non-AIDS diseases.4,12–15 Consistent evidence for an association with CD4 counts has been reported for liver-related diseases, non-AIDS malignancies, and renal disease, but not for cardiovascular diseases. In addition to CD4 counts, latest plasma HIV RNA levels seem to be independently related with an increased risk of some non-AIDS diseases.4,13–15
The Strategies for Management of Antiretroviral Therapy (SMART) study showed that amongst HIV-infected patients enrolled with CD4 cell counts above 350 cells per cubic millimeter, the risk of non-AIDS diseases was elevated in patients on episodic use of cART guided by CD4 cell counts compared with those on continuous cART.12 In the present study, we further examined the relation between HIV viremia and the risk of selected fatal and nonfatal non-AIDS diseases in a large group of patients on antiretroviral treatment from the longstanding national HIV cohort in the Netherlands, where structured treatment interruptions were never standard of care. Our aim was to investigate the effect of transient episodes of viremia and nonstructured treatment interruptions on the occurrence of non-AIDS diseases, irrespective of the underlying cause of such episodes and without restrictions on CD4 counts at enrolment.
HIV-1–infected patients included in this study were selected from the ATHENA national observational HIV clinical cohort in the Netherlands, which by July 2010 consisted of 17,327 patients.16 Patients were eligible for inclusion if they were 13 years of age or older and antiretroviral therapy naive when starting cART, defined as a combination of at least 3 drugs from at least 2 different drug classes. Moreover, within 48 weeks after starting cART, patients had to achieve initial treatment success, which was defined as the first date that HIV RNA levels were (not necessarily confirmed) below 50 copies per milliliter. Furthermore, at initial treatment success, a CD4+ T-cell count measurement was required, which was determined by taking the CD4 value closest to the date of initial success measured between start of cART and 60 weeks after start. Disease stage was defined as the most serious Centers for Disease Control and Prevention event ever recorded up to the time of initial success.17 CD4 count and viral load at start of cART were determined by taking the value closest to start of cART measured within 12 weeks before start. Chronic hepatitis B virus (HBV) co-infection was defined by a positive HBV surface antigen (HBsAg) or a positive HBV DNA test, whereas hepatitis C virus (HCV) co-infection was defined as a positive HCV RNA test.
Follow-up started at the first date at which RNA was <50 copies per milliliter. During follow-up, progression to 3 different non-AIDS–defining endpoints was considered in 3 separate analyses, including (1) major cardiovascular disease, (2) liver fibrosis or cirrhosis, and (3) chronic renal failure. These non-AIDS diseases were used as secondary endpoints in the SMART study and have been collected in ATHENA in a structured and well-defined manner since 1998.12 Major cardiovascular diseases included myocardial infarction, stroke, and invasive coronary procedures and were collected as described previously.3 Liver fibrosis and cirrhosis were collected as written down in the medical file. For a subset of patients with fibrosis or cirrhosis, additional information was available in terms of the METAVIR score of a liver biopsy (grade F0 to F4 with F4 being cirrhosis), or results of a fibroscan or ultrasound of the abdomen or upper gastrointestinal endoscopy consistent with signs of cirrhosis and portal hypertension. Chronic renal failure was defined as a confirmed (≥2 consecutive measurements at least 3 months apart) estimated glomerular filtration rate (eGFR) ≤60 mL/min/1.73 m2 for patients with eGFR >60 mL/min/1.73 m2 at initial treatment success, or a confirmed 25% decline in eGFR for patients with eGFR ≤60 mL/min/1.73 m2 at initial success.18,19 Glomerular filtration rates were estimated using the Cockcroft–Gault equation after standardization for body surface area using the Mosteller formula.20,21 Estimated GFR at initial success was defined as the measurement closest to the date of initial success within a period of 3 months before or after that date. Also, a composite endpoint was considered, defined as the first occurrence of any of the abovementioned non-AIDS diseases. Patients with documented prior cardiovascular disease or liver fibrosis/cirrhosis before initial therapy success were excluded from the analysis, whereas analysis of chronic renal failure and the composite endpoint was limited to patients who had an estimated GFR at treatment success and at least 2 GFR estimates thereafter. Patients who did not reach the endpoint before the end of follow-up were censored at their last follow-up visit.
From initial therapy success onwards, treatment interruptions and episodes of viral suppression or viremia were considered. Treatment interruptions (or treatment discontinuations in case therapy was not restarted) were defined as episodes of any duration without antiretroviral drugs. Patients with an interruption or discontinuation lasting more than 1 year were censored at 1 year after the start of the interruption or discontinuation. Episodes of viremia and viral suppression were defined during treatment but not during interruptions, as there were no viral load measurements during 37.6% of the interruptions. Episodes of viremia started when the viral load rose above 50 copies per milliliter and ended when it returned to below 50 copies per milliliter. Analogously, an episode of viral suppression started with a viral load below 50 copies per milliliter and ended when it rose above 50 copies per milliliter. Episodes of viremia were subdivided into low-level viremia if the median viral load during the episode was between 50 and 400 copies per milliliter or high-level viremia if the median viral load was above 400 copies per milliliter. The cut-off of 400 copies/ per milliliter was chosen such that one-third of RNA measurements >50 copies per milliliter were above 400 copies per milliliter.22 In sensitivity analyses, the threshold of high-level viremia was changed from 400 to 1000 copies per milliliter, whereas in another sensitivity analysis, patients were censored at the start of their first treatment interruption.
The association between episodes of viral suppression, viremia, and treatment interruptions on the one hand, and each of the 3 non-AIDS endpoints and the composite endpoint on the other hand was studied using proportional hazards models, in which each patient's follow-up time was divided into 1-week intervals. All models were adjusted for age, sex, transmission category, country of birth, disease stage at initial success, time-updated CD4 cell counts, diabetes, smoking status (never, current/former, unknown), history of alcohol abuse, chronic hepatitis B or C co-infection, viral load at start of cART, and, in case of the chronic renal failure endpoint, eGFR at initial success. For the cardiovascular disease endpoint, serum lipid levels (levels of total cholesterol, high-density lipoprotein cholesterol, and triglycerides) were also taken into account. Also, the effect of exposure to different antiretroviral drugs was investigated by including duration of exposure to each drug as the cumulative time each drug was used since initial therapy success.
In the first set of models, we studied the relation between outcomes and the most recent episode of viremia or treatment interruption by including the type of episode at the start of each time interval as time-updated binary status indicators. In a second set of models, the effect of viremia and interruptions on outcome was studied by including the cumulative follow-up time spent in each type of episode at the start of each time interval as a time-updated variable, which may better reflect the patients' overall exposure to viremia or interruptions. We also considered models in which the 4 types of episodes were combined in a single dichotomous variable with categories ≤400 and >400 copies per milliliter. In this case, treatment interruptions with a duration of 2 weeks or shorter were considered as a continuation of the previous episode, whereas interruptions longer than 2 weeks were considered as episodes with RNA >400 copies/ per milliliter, unless the median viral load during the interruption indicated otherwise.
All analyses were performed with SAS software (version 9.2; SAS Institute, Cary, NC).
The total population included in our study consisted of 6440 patients with total follow-up of 24,603 person-years since initial therapy success (Table 1). Of these patients, 102 had a major cardiovascular event, of which 15 were fatal, including 52 myocardial infarctions, 31 strokes, 14 invasive coronary procedures, and 5 fatal other events. In total, 70 patients had liver fibrosis (46 patients) or cirrhosis (24 patients, 5 fatal), and 56 (81%) of them had chronic hepatitis B or C coinfection. For 36 patients with fibrosis, information on the METAVIR grade was available, including 31 cases of F1/F2, and 5 F3, whereas 18 cases of cirrhosis were confirmed by either a biopsy (7 cases) or an ultrasound or a CT scan (9 and 2 cases, respectively, of which 6 with circumstantial clinical evidence, including portal hypertension, ascites, and hepatic encephalopathy). Chronic renal failure was found in 54 patients, and their median decrease in eGFR was 31% [interquartile range (IQR), 17-41]. The overall incidence was 0.42 [95% confidence interval (CI) 0.35 to 0.51] per 100 person-years for cardiovascular events, 0.49 (0.37 to 0.64) for chronic renal failure, and 0.29 (0.23 to 0.37) for liver fibrosis/cirrhosis.
Of the 6440 patients, 3406 (52.9%) always had viral suppression, 1082 (16.8%) interrupted cART at least once, 2647 (41.1%) patients experienced at least 1 episode of viremia, and 1083 (16.8%) patients had at least 1 episode of high-level viremia. Of the patients with treatment interruptions, 458 (42.3%) experienced at least 1 episode of high-level viremia although on treatment, which was more often (P < 0.001) than patients without interruptions of whom 625 (11.7%) experienced high-level viremia. During follow-up, there were 10,277 episodes of viral suppression, accounting for 88.5% of the total follow-up time, with median duration of 15.6 (IQR 5.5–38.2) months. The median duration of interruptions was 2.4 (IQR: 0.6–10.7) months. In total, 75.8% of 3104 episodes of low-level viremia consisted of a single viral load measurement compared with 35.4% of 1490 episodes of high-level viremia (Table 2). Overall, the median time between viral load measurements was 3.2 (IQR: 1.9–4.2) months.
The overall incidence of our composite non-AIDS endpoint was 1.41 (95% CI: 0.73 to 2.46) per 100 person-years when CD4 counts were <200 cells per cubic millimeter, 1.73 (1.23 to 2.38) for CD4 counts 200 to 349 cells/mm3, 1.37 (0.97 to 1.88) for CD4 counts 350 to 499 cells per cubic millimeter, and 0.71 (0.49 to 1.00) for CD4 counts ≥500 cells per cubic millimeter (Fig. 1). Incidence rates were not different between episodes of interruption, viral suppression, and viremia, but confidence intervals were wide as the majority (200 events, 88%) of the non-AIDS events occurred during episodes of viral suppression.
In the models including the latest type of episode (viral suppression, viremia, or treatment interruption), episodes of high-level viremia were associated with a higher hazard of cardiovascular disease (RH: 2.69, 95% CI: 1.29 to 5.63; P = 0.009), but not of chronic renal failure, liver fibrosis/cirrhosis, or the composite endpoint (Table 3). Episodes of low-level viremia or treatment interruptions were not associated with any of the 3 non-AIDS endpoints or the composite endpoint. In the models including the cumulative exposure to episodes of viremia or treatment interruption, a longer duration of interruption or low-level viremia compared with viral suppression was generally not associated with any of the non-AIDS endpoints. Only longer exposure to high-level viremia was associated with cardiovascular disease (RH: 1.37, 1.04 to 1.81, per year increase in duration; P = 0.03). When the 4 types of episodes were combined in a single dichotomous variable RNA ≤400 or >400 copies per milliliter, cumulative exposure to RNA >400 copies per milliliter was associated with an increased hazard of cardiovascular disease (RH: 1.32, 95% CI: 1.01 to 1.73; P = 0.04), but not with chronic renal failure (1.13, 0.66 to 1.92; P = 0.7), liver fibrosis/cirrhosis (0.86, 0.51 to 1.44; P = 0.6), and the composite endpoint (1.15, 0.84 to 1.57; P = 0.6).
Compared with CD4 counts ≥500 cells per cubic millimeter, relative hazards of the composite non-AIDS endpoint were 1.85 (95% CI: 1.14 to 2.99) for CD4 counts 350 to 499, 1.68 (1.01 to 2.80) for CD4 200–349, and 1.27 (0.61 to 2.64) for CD4 <200 cells per cubic millimeter. Similar associations were found for chronic renal failure and liver fibrosis/cirrhosis, but not for cardiovascular disease (Table 3). There was no strong evidence for an association with CD4 counts when including CD4 counts as a continuous variable: 1.01 (95% CI: 0.93 to 1.10) per 100 cells per cubicmillimeter increase for cardiovascular disease, 0.90 (0.78 to 1.04) for chronic renal failure, 0.90 (0.80 to 1.01) for liver fibrosis/cirrhosis, and 0.93 (0.85-1.01) for the composite endpoint. Associations were similar when log-transformed instead of untransformed CD4 cell counts were included (Table 3).
Older age was associated with a higher hazard of cardiovascular disease (RH: 2.28, 95% CI: 1.89 to 2.75 per 10 years older) and chronic renal failure (1.86, 1.42 to 2.43), but not liver fibrosis/cirrhosis (1.00, 0.75 to 1.33). Patients with chronic hepatitis B (RH: 4.04, 95% CI: 2.30 to 7.09) or C co-infection (11.3, 6.38 to 20.0) had a significantly higher hazard of liver fibrosis/cirrhosis. Diabetes was a risk factor for cardiovascular disease (RH: 3.11, 95% CI: 1.57 to 6.15), as was being a current or former smoker (2.02, 1.14 to 3.56), but we found no additional independent effect of serum lipid levels. A higher eGFR at initial success was associated with a lower hazard of chronic renal failure (RH: 0.68, 0.61 to 0.76, per 10 units increase). No other significant associations between covariates and our 3 non-AIDS endpoints and the composite endpoint were observed. In particular, exposure to antiretroviral drugs was not associated with any of the non-AIDS endpoints, except for tenofovir, which was related with a higher hazard of chronic renal failure (RH: 1.39, 1.11 to 1.74, per additional year of exposure).
Model results were similar when the threshold between low-level and high-level viremia was 1000 instead of 400 copies per milliliter. In this case, low-level viremia became marginally associated with outcome in the models including cumulative time, that is, 1.56 (95% CI: 1.05 to 2.32, P = 0.03) for cardiovascular disease, 0.73 (0.27 to 1.96, P = 0.5) for chronic renal failure, 1.55 (1.01 to 2.40, P = 0.05) for liver fibrosis/cirrhosis, and 1.63 (1.11 to 2.39, P < 0.001) for the composite endpoint. In a sensitivity analysis in which patients with at least 1 interruption were censored at the start of the first interruption, the association between high-level viremia and the risk of cardiovascular events disappeared, RH: 0.52 (95% CI: 0.07 to 3.80).
Our study found that the risk of chronic renal failure and liver fibrosis/cirrhosis, but not cardiovascular events, was associated with the level of immunodeficiency, whereas plasma HIV RNA levels above 400 copies per milliliter seemed to be associated only with cardiovascular events. These associations were robust under different sensitivity analyses. However, low-level viremia between 50 and 400 copies per milliliter although on cART was not associated with any of the endpoints.
A major strength of our study is the large patient population and systematic collection of non-AIDS diseases for more than 10 years. Nevertheless, several limitations need to be mentioned. Despite the sizable population, only a limited number of non-AIDS diseases were observed. In addition, the amount of follow-up time spent in episodes of viremia or treatment interruptions was relatively short. As a result, the power to detect an association between episodes of viremia and the risk of serious non-AIDS events is likely to have been reduced. This is particularly true for renal failure, which could be studied in only 51% of the patients, because creatinine measurements were not available for the entire population. Another limitation was the relatively short follow-up time, even though more than 25% of the patients were followed for 6 years or longer. Therefore, our study could only detect short-term effects of viremia, whereas longer-term effects remained undisclosed. Also, limited data were available on smoking, alcohol consumption, and other risk factors that are generally associated with non-AIDS diseases. Finally, 16 liver fibrosis/cirrhosis events were only based on clinical chart entries.
To our knowledge, so far only a limited number of studies have investigated the relation between immunodeficiency, viral load levels, and the risk of serious but nonfatal non-AIDS diseases. The SMART trial found that patients on CD4 cell count–guided intermittent antiretroviral treatment had a 1.7 (95% CI: 1.1 to 2.5) times higher risk of a composite endpoint of serious cardiovascular, renal, and hepatic disease compared with patients who were treated continuously.12 Overall, there was a 0.94 (95% CI: 0.90 to 0.98) times lower risk of non-AIDS events per 100 CD4 cells higher which agrees well with the value of 0.93 (0.85 to 1.01) found in our study.15 The FIRST trial, which compared outcomes for patients randomized to 1 of 3 different initial treatment strategies, found a similar relative hazard of 0.86 (0.77 to 0.96) per 100 CD4 cells per cubic millimeter higher.13
Our data showed that high levels of plasma HIV RNA but not lower CD4 counts were associated with an increased risk of cardiovascular disease.3,13,15,23 The association with RNA disappeared when patients were censored at the first treatment interruption, likely because treatment interruptions and high viral load although on cART would be expected to be tightly correlated in such circumstances. The association between HIV RNA and an increased risk of cardiovascular disease has been observed before in studies that looked at cardiovascular disease as cause of death, but there was no consistent evidence for such an association in the SMART study.14,23 In contrast to what was seen for cardiovascular disease, lower CD4 counts seemed to be associated with a higher risk of chronic renal failure and liver fibrosis/cirrhosis.13,15,24 This association with CD4 counts was more pronounced in studies that investigated the relation between non-AIDS causes of death and CD4 counts.4,14
Apart from immunological, virological, and other clinical parameters, there may also be a contribution of antiretroviral drugs in the development of non-AIDS diseases. Tenofovir, indinavir, and atazanavir have been associated with an increased risk of nephrotoxicity, whereas didanosine, nevirapine, and efavirenz are related with liver disease.19,25,26 Our study could only establish an association between chronic renal failure and cumulative exposure to tenofovir. However, our ability to find associations with treatment may have been limited not only due to lack of power but also because patients may have their medication changed if renal or liver markers deteriorate. Also, we were unable to look at associations with other non-cART medication with potential hepatotoxic and nephrotoxic effects because their use is generally less well registered in the medical files.
There are several plausible mechanisms by which infection with HIV could raise the risk of non-AIDS morbidity. Plasma RNA levels have been correlated with certain markers of inflammation and coagulation, which are factors that have been linked to an increased risk of cardiovascular events, chronic renal failure, and all-cause mortality.24,27 The relation between HIV and renal function is consistent with studies that have shown that HIV can infect kidney epithelial cells and thus form a renal viral reservoir with local replication, but the exact mechanism how HIV leads to renal failure is not yet clear.28 Infection with HIV also accelerates the progression of fibrosis and hepatocellular cancer in patients coinfected with hepatitis B or C.4,15,29 It has been found, however, that cirrhosis is also associated with low CD4 counts in HIV-negative individuals.30 Hence, one cannot rule out that the increased risk of liver events might be the cause of low CD4 counts rather than a consequence.
As there is a growing body of evidence that lower CD4 counts and higher HIV RNA levels are associated with a greater likelihood of non-AIDS events, treatment with cART—by increasing CD4 counts and reducing RNA levels—may reduce the risk not only of AIDS but also of non-AIDS events. This argues in favor of starting cART earlier when CD4 counts are still well above 350 cells per cubic millimeter. Currently, most guidelines recommend starting cART only when CD4 counts fall below that threshold because of concerns over toxicities and long-term risk of resistance. However, in recent years, antiretroviral drug regimens have become more potent and less toxic thus reducing the risk of resistance. In addition, there are indications that starting cART earlier in the HIV infection may improve prognosis.31–33 A large randomized trial (Strategic Timing of Antiretroviral Treatment [START]) is currently ongoing to definitely address the effects earlier initiation of ART will have on a range of non-AIDS events.
Given that the number of events was limited and that they mostly occurred during viral suppression, larger collaborative studies would be needed to further investigate the possible association between HIV RNA and risk of non-AIDS diseases. The extent to which viral load levels affect the risk of non-AIDS events could also be investigated in cohorts of untreated patients in whom HIV RNA levels will be more diverse than in treated patients.13 A limitation in untreated cohorts, however, would be the amount of follow-up time before start of antiretroviral treatment.
In conclusion, we found that amongst patients on antiretroviral treatment lower CD4 counts were associated with a higher risk of chronic renal failure and liver fibrosis/cirrhosis but not cardiovascular events. We also found evidence that exposure to HIV RNA levels above 400 copies per milliliter was independently associated with a higher risk of cardiovascular diseases. To further strengthen evidence for a relation between non-AIDS diseases and HIV RNA, larger collaborative studies would be needed.
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PHYSICIANS AND DATA ANALYSTS
The physicians and data analysts include (*site coordinating physicians): Prof Dr. F. de Wolf (director), Dr. D. O. Bezemer, Drs. L. A. J. Gras, Dr. R. Holman, Drs. A. M. Kesselring, Dr. A. I. van Sighem, Dr. C. Smit, Drs. S. Zhang (data analysis group), Drs. S. Zaheri (data collection), Stichting HIV Monitoring, Amsterdam; Academisch Medisch Centrum bij de Universiteit van Amsterdam, Amsterdam: Prof. Dr. J. M. Prins*, Prof Dr T. W. Kuijpers, Dr. H. J. Scherpbier, Dr. K. Boer, Dr. J. T. M. van der Meer, Dr. F. W. M. N. Wit, Dr. M. H. Godfried, Prof. Dr. P. Reiss, Prof Dr. T. van der Poll, Dr. F. J. B. Nellen, Prof Dr. J. M. A. Lange, Dr. S. E. Geerlings, Dr. M. van Vugt, Drs. D. Pajkrt, Drs. J. C. Bos, Drs. M. van der Valk, Drs. M. L. Grijsen, Dr. W. J. Wiersinga; Academisch Ziekenhuis Maastricht, Maastricht: Dr. G. Schreij*, Dr. S. Lowe, Dr. A. Oude Lashof; Catharina-ziekenhuis, Eindhoven: Drs. M. J. H. Pronk*, Dr. B. Bravenboer; Erasmus Medisch Centrum, Rotterdam: Dr. M. E. van der Ende*, Drs. T. E. M. S. de Vries-Sluijs, Dr. C. A. M. Schurink, Drs. M. van der Feltz, Dr. J. L. Nouwen, Dr. L. B. S. Gelinck, Dr. A. Verbon, Drs. B. J. A. Rijnders, Dr. L. Slobbe; Erasmus Medisch Centrum–Sophia, Rotterdam: Dr. N. G. Hartwig, Dr. G. J. A. Driessen; Flevoziekenhuis. Almere: Dr. J. Branger*; HagaZiekenhuis, Den Haag: Dr. E. F. Schippers*, Dr. C. van Nieuwkoop. Isala Klinieken, Zwolle: Dr. P. H. P. Groeneveld*, Dr. M. A. Alleman, Drs. J. W. Bouwhuis; Kennemer Gasthuis: Prof Dr. R. W. ten Kate*, Dr. R. Soetekouw; Leids Universitair Medisch Centrum, Leiden: Dr. F.P. Kroon*, Prof. dr. P.J. van den Broek, Prof. dr. J.T. van Dissel, Dr. S.M. Arend, Drs. C. van Nieuwkoop, Drs. M.G.J. de Boer, Drs. H. Jolink; Maasstadziekenhuis, Rotterdam: Dr. J.G. den Hollander*, Dr. K. Pogany; Medisch Centrum Alkmaar, Alkmaar: Drs. G. van Twillert*, Drs. W. Kortmann*; Medisch Centrum Haaglanden, Den Haag: Dr. R. Vriesendorp*, Dr. E.M.S. Leyten. Medisch Spectrum Twente, Enschede: Dr. C.H.H. ten Napel*, Drs. G.J. Kootstra; Onze Lieve Vrouwe Gasthuis, Amsterdam: Prof. dr. K. Brinkman*, Dr. W.L. Blok, Dr. P.H.J. Frissen, Drs. W.E.M. Schouten, Drs. G.E.L. van den Berk; Sint Elisabeth Ziekenhuis, Tilburg: Dr. J.R. Juttmann*, Dr. M.E.E. van Kasteren, Drs. A.E. Brouwer; Sint Lucas Andreas Ziekenhuis, Amsterdam: Dr. J. Veenstra*, Dr. K.D. Lettinga; Slotervaartziekenhuis, Amsterdam: Dr. J.W. Mulder*, Dr. E.C.M. van Gorp, Drs. P.M. Smit, Drs. S.M.E. Vrouenraets, Dr. F.N. Lauw; Stichting Medisch Centrum Jan van Goyen, Amsterdam: Drs. A. van Eeden*, Dr. D.W.M. Verhagen*; Universitair Medisch Centrum Groningen, Groningen: Dr. H.G. Sprenger*, Dr. R. Doedens, Dr. E.H. Scholvinck, Drs. S. van Assen, Drs. W.F.W. Bierman; Universitair Medisch Centrum Sint Radboud, Nijmegen: Dr. P.P. Koopmans*, Prof. dr. R. de Groot, Dr. M. Keuter, Dr. A.J.A.M. van der Ven, Dr. H.J.M. ter Hofstede, Dr. M. van der Flier, Drs. A.M. Brouwer, Dr. A.S.M. Dofferhoff; Universitair Medisch Centrum Utrecht, Utrecht: Prof. dr. A.I.M. Hoepelman*, Dr. T. Mudrikova, Dr. M.M.E. Schneider, Drs. C.A.J.J. Jaspers, Dr. P.M. Ellerbroek, Dr. J.J. Oosterheert, Dr. J.E. Arends, Dr. M.W.M. Wassenberg, Dr. R.E. Barth; Vrije Universiteit Amsterdam, Amsterdam: Dr. M.A. van Agtmael*, Drs. J. de Vocht, Dr. R.M. Perenboom, Drs. F.A.P. Claessen, Drs. E.A. bij de Vaate; Wilhelmina Kinderziekenhuis, Utrecht: Dr. S.P.M. Geelen, Dr. T.F.W. Wolfs, Dr. L.J. Bont; Ziekenhuis Rijnstate, Arnhem: Dr. C. Richter*, Dr. J.P. van der Berg, Dr. E.H. Gisolf; Admiraal De Ruyter Ziekenhuis, Vlissingen: Drs. M. van den Berge*, Drs. A. Stegeman; Medisch Centrum Leeuwarden, Leeuwarden: Dr. M.G.A. van Vonderen*, Drs. D.P.F. van Houte; Sint Elisabeth Hospitaal, Willemstad, Curaçao: Dr. C. Winkel, Drs. F. Muskiet, Drs. Durand, Drs. R. Voigt. Cited Here...