(Circulation. 2001;103:1535.)
© 2001 American Heart Association, Inc.
Clinical Investigation and Reports |
Prognosis After Aortic Valve Replacement With a Bioprosthesis
Predictions Based on Meta-Analysis and Microsimulation
From the Center for Clinical Decision Sciences, Department of Public Health (J.P.A.P., E.W.S., M.J.C.E., J.D.F.H.), and the Department of Cardio-thoracic Surgery (J.J.M.T., L.A.v.H., A.J.J.C.B.), Erasmus University Medical Center, Rotterdam, The Netherlands.
Correspondence to Dr E.W. Steyerberg, Department of Public Health, Ee2091, Erasmus University Medical Center Rotterdam, PO Box 1738, 3000 DR Rotterdam, Netherlands. E-mail steyerberg{at}mgz.fgg.eur.nl
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Abstract |
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Background—Bioprostheses are widely used as an aortic valve substitute, but knowledge about prognosis is still incomplete. The purpose of this study was to provide insight into the age-related life expectancy and actual risks of reoperation and valve-related events of patients after aortic valve replacement with a porcine bioprosthesis.
Methods and Results—We conducted a meta-analysis of 9 selected reports on stented porcine bioprostheses, including 5837 patients with a total follow-up of 31 874 patient-years. The annual rates of valve thrombosis, thromboembolism, hemorrhage, and nonstructural dysfunction were 0.03%, 0.87%, 0.38%, and 0.38%, respectively. The annual rate of endocarditis was estimated at 0.68% for >6 months of implantation and was 5 times as high during the first 6 months. Structural valve deterioration was described with a Weibull model that incorporated lower risks for older patients. These estimates were used to parameterize, calibrate, and validate a mathematical microsimulation model. The model was used to predict life expectancy and actual risks of reoperation and valve-related events after implantation for patients of different ages. For a 65-year-old male, these figures were 11.3 years, 28%, and 47%, respectively.
Conclusions—The combination of meta-analysis with microsimulation enabled a detailed insight into the prognosis after aortic valve replacement with a bioprosthesis for patients of different ages. This information will be useful for patient counseling and clinical decision making. It also could serve as a baseline for the evaluation of newer valve types.
Key Words: heart diseases • surgery • valves • meta-analysis • prognosis • survival
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Introduction |
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Nearly 40 years after the pioneering efforts of Starr and Edwards in heart valve replacement, a wide variety of mechanical, bioprosthetic, and human tissue prostheses are now available for clinical use. Mechanical valves have a greater durability and consequently lower reoperation rates than other valve types. However, they are associated with a greater risk of thromboembolism, which necessitates regular anticoagulation with the concomitant risk of hemorrhage. In contrast, bioprostheses have a low thrombogeneity, which in most patients obviates the need for regular anticoagulation and consequently reduces hemorrhagic accidents. However, the main factor limiting their use is the propensity to undergo tissue degeneration, often necessitating reoperation. Human tissue valves have a relatively low rate of thromboembolism and endocarditis. However, the long-term incidence of structural valve deterioration (SVD) of these valves is uncertain, and human valves are scarce.1 2 3
With the aging of the general population, the number of elderly patients requiring aortic valve replacement has increased rapidly during recent years. Hence, the choice and long-term performance of a valve prosthesis becomes of paramount importance. Currently, bioprostheses are recommended for elderly patients who do not have risk factors for thromboembolism. These valves may also be used in younger patients presenting with a contraindication to long-term anticoagulation.3
Because of the limited life expectancy (LE) of elderly patients, the benefits of avoiding anticoagulation may outweigh the disadvantages of a possible reoperation, ie, the valve will probably outlive the patient. However, in younger patients, reoperations will be frequent, and reoperation-free LE and event-free LE are important considerations in making decisions about implantation.
The purpose of this study was to provide insight into the prognosis of patients of different ages after implantation with a stented porcine bioprosthesis. We incorporated data from various smaller clinical studies in a mathematical microsimulation model.
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Methods |
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Meta-Analysis
Literature Search
We conducted a literature search of the PubMed and Medline databases for the period January 1990 to December 1999. The terms used for the search were both MeSH terms and the text words "heart valve prostheses," "aortic valve," or "bioprostheses" in combination with "porcine," "stented," "Hancock," "Carpentier-Edwards," or "modified orifice." The search was limited to "human" and to the English language. We then screened the titles and abstracts of the remaining studies to select those that examined the valve-related events, outcomes, or survival of patients after aortic valve replacement with a bioprosthetic valve. Reports that considered stentless and pericardial valves were excluded during this process. The references in the reports were cross-checked for other potentially relevant studies. This resulted in 53 published reports.
We stipulated 5 criteria to obtain a group of similar
studies: (1) studies that described 1 or more of the following stented
porcine bioprostheses: Carpentier-Edwards standard and
Carpentier-Edwards supra-annular valves (Baxter Healthcare Corp) or
Hancock standard, Hancock modified orifice, and Hancock II valves
(Medtronic Inc); (2) isolated valve implantation in the aortic
position; (3) valves 
19 mm in size implanted in patients >15
years of age; (4) valve-related events defined according to the
standard definitions published in
19884 and
19965 (valve-related events
included valve thrombosis, thromboembolism, hemorrhage,
endocarditis, nonstructural dysfunction, and SVD; for this
analysis, we only included studies that contained data on at
least 1 of these valve-related events); and (5) no duplicate
publication or overlapping patient population. When these criteria were
used, 44 studies were excluded, leaving 9 studies for the present
analysis.6 7 8 9 10 11 12 13 14
Data Extraction and Analysis
We reviewed the 9 reports to obtain the input data
required for the microsimulation model. The annual hazards of valve
thrombosis, thromboembolism, hemorrhage, and nonstructural
dysfunction were assumed to be constant over time. Hence, combined
estimates of the linearized occurrence rates for these events were
calculated as the ratio of the sums of the number of events and
patient-years of follow-up in the individual reports. The combined
mortality and reoperation rates after an event were similarly
calculated.
Pooling of time-to-event curves was performed for survival, freedom from endocarditis, and SVD. Published curves were scanned and enlarged in a graphical computer package. The heights of these curves were measured at each year, and corresponding survival probabilities were calculated with their complementary log-log transformations. These transformed probabilities were pooled with weighting according to the estimated number of patients at risk at each year and transformed back to obtain a summary curve.15 Homogeneity of the curves was assessed graphically and judged satisfactory.
The risk of endocarditis was assumed to take 2 phases of constant hazard, with a hazard during the first 6 months greater than the subsequent period. Therefore, we fitted a 2-period exponential model on the pooled freedom-from-endocarditis curve, which was based on 3 reports.7 10 13
The risk of SVD depended on the time elapsed since valve replacement and the age of the patient at implantation. This relationship was described by a Weibull model.16 17 This model is a generalization of the exponential distribution to accommodate a changing risk over time. The shape parameter of the Weibull model was estimated from the average freedom-from-SVD curve, which was pooled from 4 reports.7 8 12 13 The age effect was incorporated in the scale parameter of the Weibull model, based on 1 study.12
Microsimulation Model
Parameters in the Model
We used the estimates from the meta-analysis
to parameterize a previously developed microsimulation
model
(Figure 1
).18 The
model incorporates SVD (age dependent), other valve-related events
(valve thrombosis, thromboembolism, hemorrhage, endocarditis,
and nonstructural dysfunction), and the background mortality of aortic
valve recipients (non–valve-related deaths). The simulation model
calculates patient survival rates by superimposing the mortality
associated with valve-related events on a background mortality. The
background mortality may well exceed that of the general population
owing to the aortic valve disease as such,
cardiomyopathy, and the valve replacement
procedure.19 20 21
Therefore, hazard ratios were applied to the age-specific survival
rates of the Dutch population to calibrate the model outputs with the
age-specific survival curves obtained from the
literature.12 Operative
mortality was estimated as 1.5% for a 40-year-old man, increasing with
odds ratios of 1.022 for age (per year) and 1.7 for every
reoperation.18 21
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Evaluation and Validation
Microsimulation is a type of Monte Carlo
simulation.17 For our
evaluations, 10 000 virtual life histories were randomly drawn. Age at
death and occurrence of events and reoperation were registered for each
simulated patient. This enabled us to calculate the LE, event-free LE,
and reoperation-free LE, as well as actual risks of valve-related
events and reoperation, for a patient of a given age and sex. The model
output was validated against the pooled survival curve, as obtained
from 3
reports.8 10 12
Sensitivity Analyses
We performed 1-way sensitivity analyses to
investigate the effect of uncertainty in the parameter
estimates. When we varied the estimates of valve-related events
according to their 95% CIs, we found only very small variations in
event-free LE. We therefore defined larger ranges for the valve-related
events, ie, from half to double the baseline parameter
values. The mortality hazard ratio was assumed to exceed 1, ie,
mortality was at least at the level of the general
population.
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Results |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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Literature Search
The 9 selected reports contained data on 5837 bioprosthetic valve recipients with a total follow-up of 31 874 patient-years (Table 1
).6 7 8 9 10 11 12 13 14
The majority of patients were male, and the mean age of the population
was 64.6 years, although differences between the component studies were
noted. Most patients were in New York Heart Association class III or IV
(on average 71%), and a coronary artery bypass graft was
present in approximately one third (on average
36%).
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Data Extraction and Analysis
A summary of the meta-analysis is given in
Table 2. Adequate data on valve thrombosis were available
in only 4
reports,7 9 11 13
which yielded 3 events from 9925 patient-years of follow-up. Two of
these patients died, giving a death rate of 67% for this rare event.
Assuming a constant hazard, a linearized occurrence rate was calculated
for each of 4 types of valve complications, of which thromboembolism
was the highest with 0.87% per patient-year. The incidence of
endocarditis was estimated as 0.68% per patient-year beyond the first
6 months after valve replacement and 3.4% per patient-year before that
(ie, 5 times as high).
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The average incidence of SVD was estimated by a Weibull
model, as shown in
Figure 2. The formula for freedom from SVD was S(t)
=e-(t/
)^ß,
where S(t) indicates the probability of being free from SVD at time
t, and and ß indicate the
scale and shape parameters in the Weibull model,
respectively. The value of depended on age: =
e2.11+0.0112xage, and the value of ß was
3.49. With these parameters, the median time until SVD was
17.1 years for a 65-year-old patient.
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Model Calibration
The simulation model was calibrated by comparing
survival curves produced by the model (for both males and females of
varying ages) with empirical survival curves of the corresponding age
ranges.12 Hazard ratios of
8.0, 3.6, 1.5, 1.1, and 1.0 were found adequate for the background
mortality in men aged 35, 45, 55, 65, and 75 years,
respectively.
Model Validation
An overall impression of the validity of the model was
obtained by comparison of expected and observed overall survival. The
observed survival was obtained by pooling the curves from 3 reports in
which the mean age was 61.5
years.8 10 12
The expected survival was calculated with the model for male and female
patients aged 62 years. These curves closely approximated the pooled
survival curve
(Figure 3, top).
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Age-Specific Results
Survival curves were estimated for men of different
ages at implantation of the valve
(Figure 3
, bottom). The area under each survival curve equals
the LE. The LE decreases with advancing age, ie, from 17.1 to 7.2 years
for men aged 35 to 75 years. The reoperation-free LE and event-free LE
show a remarkable pattern: an increase to age 55 years, followed by a
decrease
(Figure 4). The increase is caused by the age dependency of
the SVD risk (decreasing with age), whereas the eventual decrease is
caused by the dominating effect of background mortality at older age.
For a 65-year-old man, the LE, reoperation-free LE, and event-free LE
were 11.3, 9.5, and 8.4 years, respectively.
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We further calculated the actual lifetime risk of a
reoperation or a valve-related event after aortic valve replacement.
The lower these risks, the better the prognosis. As shown in
Figure 5, the probability of ever undergoing a reoperation
or experiencing a valve-related event rapidly decreased with age at
implantation, from 63% and 83% at 35 years to 11% and 24%,
respectively, at 75 years.
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Furthermore, we compared the LE of male
bioprosthesis recipients with the LE of men in the general
Dutch population. The relative LE increased with the age of valve
replacement
(Figure 6). We also estimated the relative LE of a
hypothetical valve recipient, were he immune to valve-related events.
The relevant parameters in the model were set to zero. This
enabled us to quantify the impact of the increased background
mortality. This impact was large for young patients (eg, 46% for
35-year-old men) and decreased to 0% for 75-year-old men,
corresponding to the decrease in hazard ratio to 1. The difference
between the curves in
Figure 6 represents the loss in LE due to the
occurrence of valve-related events. This relative difference was
12% for all ages. On an absolute scale, the difference decreases
with age.
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Sensitivity Analyses
The event-free LE of a 65-year-old male patient is
shown in
Table 3 for extreme values of the valve-related events
while other parameters are kept at baseline values. Changes
in SVD risk had the largest influence. A doubling of the median failure
time would increase the event-free LE by 1.5 years (from 8.4 to 9.9
years) and a halving would reduce the event-free LE by 2.8 years (from
8.4 to 5.6 years). Furthermore, increasing the hazard ratio associated
with the background mortality from 1.1 to 1.5 resulted in an event-free
LE of 7.6 instead of 8.4 years.
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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We used a meta-analysis of empirical data and a mathematical microsimulation model to predict the LE and actual lifetime risk of reoperation and valve-related events for patients after implantation with a stented porcine bioprosthesis. The microsimulation model generates the life histories of a large number of virtual patients. Compared with standard statistical methods, the added value of modeling is that it provides detailed insights into the occurrence of valve-related and non–valve-related events. For a 65-year-old man, for example, the model predicted an LE of 11.3 years and a lifetime risk of reoperation and a valve-related event of 28% and 47%, respectively, after implantation with a bioprosthesis. Such information will be useful for patient counseling and for the surgeon and patient in making decisions. We envision being able to present a user-friendly version of the model on the Internet in the near future that could serve as a bedside tool to the surgeon. The model results may also serve as a baseline for the evaluation of newer valve types.
We chose 5 types of stented porcine bioprostheses, both first and second generation, which were not markedly different from one another. The Hancock standard prosthesis and the Carpentier-Edwards standard prosthesis, 2 of the initial stented porcine valves, were introduced in the early 1970s.2 22 The composite Hancock modified orifice prosthesis was designed to improve hemodynamic performance by substituting the septal leaflet with a nonseptal leaflet from a second porcine valve.6 23 In contrast to the above, the second-generation Carpentier-Edwards supra-annular bioprosthesis and the Hancock II bioprosthesis were introduced in the 1980s and incorporated several considered improvements, including a supra-annular configuration, to maximize the effective orifice of the prosthesis.7 8
Similarities in the performance of these valve types have been documented in the literature. A randomized prospective comparison of the Hancock standard and the Carpentier-Edwards standard valves showed no clear difference in durability or other valve-related complications after 10 years.22 Also, no important differences were found in durability or other valve-related complications between the Hancock modified orifice valve and the 2 standard valve types.23 The second-generation porcine bioprostheses (Carpentier-Edwards supra-annular, Hancock II) were designed to improve clinical performance by reducing the incidence of SVD. However, Jamieson and others24 failed to demonstrate clinically relevant differences with regard to freedom from SVD between the Carpentier-Edwards standard and Carpentier-Edwards supra-annular valves, except for the 21- to 40-year-old age group. The risk of valve-related complications with the Hancock II prosthesis has been reported to be similar to the previously mentioned valve types.25 26 However, the limited improvement in durability of the second-generation valves could be related to enhanced surveillance and early intervention.
Ideally, for the application of simulation methodology, a
sufficiently comprehensive "super data set" should be
analyzed.19 Such a
data set should contain detailed information on patients who underwent
aortic valve replacement, have complete and long-term follow-up for all
patients, and consider all relevant valve-related events. However, no
such databases are available as yet, although reports on larger series
with long-term follow-up have become more
frequent.27 28 We
pooled the results of selected reports that satisfied strict criteria
and calculated quantitative estimates for the parameters of
interest
(Table 2). An advantage of pooling was that the estimates
represented the experience of many institutions with
possibly slightly varying patient populations. Single-center results
may be less generalizable because of typical patient populations and
unique surgical practices.
Standard actuarial statistical techniques (eg, Kaplan-Meier)
have been used in many studies to assess the survival of patients and
the performance of valve prostheses, while "actual"
analysis has recently gained
interest.29 For survival,
the actuarial and actual methods provide identical estimates. However,
when the actuarial method is applied to nonfatal complications, such as
SVD, the risk described is that which patients would experience
provided they were immortal. Patients with valve disease have
relatively high annual risks of death. Hence, a more relevant estimate
of valve failure is the actual percentage of patients who will
experience an event before they
die.27 29 30
The simulation model provides estimates of the actual risk of
reoperation and of valve-related events according to age
(Figure 5). This information is more meaningful than
actuarial risks or actual risks for "average" patients. The
estimates by Grunkemeier and
colleagues29 for the actual
risk of ever experiencing an SVD (20% for the age group 70 to 73 years
and 40% for the 59- to 63-year-old age group) were rather similar to
our model estimates (18% for 71-year-old and 42% for 61-year-old
males, respectively).
The microsimulation model calculates patient survival rates by superimposing the mortality associated with valve-related events on a background mortality rate. The background mortality is the non–valve-related mortality of the valve recipients. It was previously assumed that in the absence of morbid valve events, patients would follow the trajectory of the general population.18 This assumption may not be tenable, because valve disease, cardiomyopathy, and the valve replacement procedure per se may cause higher non–valve-related mortality than noted in the general population.19 20 21 By applying age-specific hazard ratios to the age-specific survival curves of the general population, we aimed to obtain a more accurate prediction of patient prognosis. For young patients, the increase in background mortality was substantial compared with the general population (eg, a 36% lower LE for a 45-year-old man).
Limitations of our microsimulation model included that certain structural assumptions had to be made. For example, a constant hazard was assumed for valve thrombosis, thromboembolism, hemorrhage, and nonstructural dysfunction, where in fact, these hazards may be time and age dependent. Furthermore, endocarditis risk was assumed to be piecewise constant before and after 6 months of follow-up, and SVD risk was described with a Weibull model. Additional studies need to address these assumptions. Furthermore, survival after aortic valve replacement will not only depend on age and sex but also on many risk factors, including preoperative New York Heart Association class and the presence of coronary heart disease.
In addition to structural assumptions, uncertainty existed in parameter values owing to small or moderate numbers of events. The time to SVD was the most important factor for event-free LE. This is of interest in assessing the value of newer bioprostheses, eg, stentless types.31 32 When more data become available on such valves, these can easily be included in our model to quantify the impact on patient prognosis. Furthermore, a change in background mortality resulted in a marked variation in the LE. This illustrates the need for incorporation of more detailed information on the clinical characteristics of the patients into the model. Also, updating of the model with the growing experience with bioprostheses is essential to provide valid estimates of prognosis in the future.
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Acknowledgments |
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This study was supported in part by grant 99141 from the Dutch Board of Health Care Insurance.
Received August 11, 2000; revision received November 21, 2000; accepted November 29, 2000.
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References |
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 J. P.A. Puvimanasinghe, J. J.M. Takkenberg, M. J.C. Eijkemans, L. A. van Herwerden, W.R. E. Jamieson, G. L. Grunkemeier, J. D. F. Habbema, and A. J.J.C. Bogers Comparison of Carpentier-Edwards pericardial and supraannular bioprostheses in aortic valve replacement Eur. J. Cardiothorac. Surg., March 1, 2006; 29(3): 374 - 379. [Abstract] [Full Text] [PDF]
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 J. P.A. Puvimanasinghe, J. J.M. Takkenberg, M. J.C. Eijkemans, E. W. Steyerberg, L. A. van Herwerden, G. L. Grunkemeier, J. D. F. Habbema, and A. J.J.C. Bogers Prognosis After Aortic Valve Replacement With the Carpentier-Edwards Pericardial Valve: Use of Microsimulation Ann. Thorac. Surg., September 1, 2005; 80(3): 825 - 831. [Abstract] [Full Text] [PDF]
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E. B. Schelbert, M. S. Vaughan-Sarrazin, K. F. Welke, and G. E. Rosenthal Hospital Volume and Selection of Valve Type in Older Patients Undergoing Aortic Valve Replacement Surgery in the United States Circulation, May 3, 2005; 111(17): 2178 - 2182. [Abstract] [Full Text] [PDF]
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 J P A Puvimanasinghe, J J M Takkenberg, M B Edwards, M J C Eijkemans, E W Steyerberg, L A van Herwerden, K M Taylor, G L Grunkemeier, J D F Habbema, and A J J C Bogers Comparison of outcomes after aortic valve replacement with a mechanical valve or a bioprosthesis using microsimulation Heart, October 1, 2004; 90(10): 1172 - 1178. [Abstract] [Full Text] [PDF]
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J. P.A. Puvimanasinghe, J. J.M. Takkenberg, M. J.C. Eijkemans, E. W. Steyerberg, L. A. van Herwerden, G. L. Grunkemeier, J. D. F. Habbema, and A. J.J.C. Bogers Choice of a mechanical valve or a bioprosthesis for AVR: does CABG matter? Eur. J. Cardiothorac. Surg., May 1, 2003; 23(5): 688 - 695. [Abstract] [Full Text] [PDF]
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J. J.M. Takkenberg, J. P.A. Puvimanasinghe, and G. L. Grunkemeier Simulation models to predict outcome after aortic valve replacement Ann. Thorac. Surg., May 1, 2003; 75(5): 1372 - 1376. [Full Text] [PDF]
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J. J.M. Takkenberg, M. J.C. Eijkemans, L. A. van Herwerden, E. W. Steyerberg, M. M. Lane, R. C. Elkins, J.D. F. Habbema, and A. J.J.C. Bogers Prognosis after aortic root replacement with cryopreserved allografts in adults Ann. Thorac. Surg., May 1, 2003; 75(5): 1482 - 1489. [Abstract] [Full Text] [PDF]
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 J.J.M. Takkenberg, J.P.A. Puvimanasinghe, L.A. van Herwerden, E.W. Steyerberg, M.J.C. Eijkemans, J.D.F Habbema, and A.J.J.C. Bogers Prognosis after aortic valve replacement with St. Jude Medical bileaflet prostheses: impact on outcome of varying thromboembolic and bleeding hazards Eur. Heart J. Suppl., December 1, 2001; 3(suppl_Q): Q27 - Q32. [Abstract] [PDF]
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J. J.M. Takkenberg, M. J.C. Eijkemans, and E. W. Steyerberg Simulation techniques to support prosthetic valve choice in aortic valve replacement Ann. Thorac. Surg., November 1, 2001; 72(5): 1795 - 1795. [Full Text] [PDF]
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