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Right arrow Valve disease

Ann Thorac Surg 2003;76:75-83
© 2003 The Society of Thoracic Surgeons

Original article: cardiovascular

Current determinants of operative mortality in 1400 patients requiring aortic valve replacement

Ines Florath, PhDa, Ulrich P. Rosendahl, MDa, Amir Mortasawi, MDa, Stefan F. Bauer, MDa, Fatmir Dalladaku, MD, Ina C. Ennker, MDa, Juergen C. Ennker, MD, PhDa*

a Department of Cardiothoracic Surgery, Heart Institute Lahr/Baden, Lahr, Germany

Accepted for publication February 7, 2003.

* Address reprint requests to Dr Ennker, Heart Institute Lahr/Baden, Hohbergweg 2, D-77933 Lahr, Germany
e-mail: ennker{at}heart-lahr.com


   Abstract
Top
 Abstract
Introduction
 Patients and methods
 Results
 Comment
 Acknowledgments
 Appendix. Risk factors of...
 References
 
BACKGROUND: Determinants of operative mortality after aortic valve replacement vary with a changing patient population due to advances in operative management and increasing life expectancy. In order to predict current groups of high risk patients, a statistically valid large study population base recruited over a short period of time is required.

METHODS: Between January 1996 and June 2001, 1408 aortic valves were replaced in 1400 patients (572 of them with simultaneous coronary artery bypass grafting). The data were analyzed by multivariate logistic regression to evaluate the operative risk. Mean age of the study population was 68 ± 11 years (range 19 to 90 years old, 44% female).

RESULTS: Overall operative mortality (within 30 days) was 3.8%. Independent predictive factors for operative mortality were previous bypass surgery, emergency operation, simultaneous mitral valve replacement, renal dysfunction, more than 80 years old, simultaneous bypass surgery in female patients with a body mass index greater than 29 kg/m2, and height smaller than 1.57 m for patients more than 71 years old. Simultaneous coronary artery bypass grafting in general (p = 0.6), previous aortic valve replacement (p = 0.59), and implantation of stented bioprostheses (p = 0.39) or stentless bioprostheses (p = 0.7) were not identified as independent risk factors.

CONCLUSIONS: Certain groups of patients with a high operative risk were identified: patients more than 80 years old, women with a body mass index greater 29 kg/m2 undergoing simultaneous coronary artery bypass surgery, and "small" patients more than 71 years old.


   Introduction
Top
 Abstract
 Introduction
Patients and methods
 Results
 Comment
 Acknowledgments
 Appendix. Risk factors of...
 References
 
Quantification of operative risk for aortic valve replacement from preoperative patient characteristics has been available for nearly 20 years. However, due to advanced operative management and progress in diagnosis of aortic valve disease by improved noninvasive techniques, the operative risk is decreasing [1]. The likelihood of dying within the first 3 months after aortic valve replacement decreased threefold from 1967 to 1989 [1]. Patient characteristics that increased the operative risk in the past can be now safely managed. Therefore, as a result of the progress in perioperative management and the increased life expectancy, the patient characteristics have changed [2]. An increasing number of older patients with more concomitant diseases are considered for operation. In this changing patient population a number of "new" risk factors may become more relevant or the relative risk associated with well-known risk factors might change over time [3]. Nevertheless, real risk factors may be hidden behind the year of operation, which was identified as a risk factor in several reports recruiting patients over a 20-year period [3, 4].

Therefore, a continual monitoring of predictors for operative risk is necessary. Since patient characteristics and technical progress change over time, the study population should be recruited in a short period of time.

During the recent years numerous studies concerning prediction of operative risk have been published. However, many possible risk factors are still controversial, such as simultaneous coronary artery bypass grafting (CABG) [4–7], redo-cardiac surgery [4, 8–10], duration of cardiopulmonary bypass time [5, 11, 12], or implantation of small prosthetic valve size [12–14]. The appropriate method to identify predictive factors for mortality is the logistic regression analysis. Because this method is sensitive to extreme values, large study populations are required.

The goal of this study was to develop a general risk predicting model for aortic valve replacement (AVR), with an emphasis on the impact of the type and size of valve on operative risk, and the impact of simultaneous procedures on perioperative outcome.


   Patients and methods
Top
 Abstract
 Introduction
 Patients and methods
Results
 Comment
 Acknowledgments
 Appendix. Risk factors of...
 References
 
Patient population
Between January 1996 and June 2001, 9640 open heart operations were carried out at our institution, including 1666 aortic valve replacements. Different types of valves were used over the years. Only patients receiving stentless Medtronic Freestyle (n = 332; Medtronic, Inc, Minneapolis, MN, USA), stented Medtronic Mosaic bioprostheses (n = 246), mechanical St. Jude Medical (n = 394; St. Jude Medical, St. Paul, MN, USA), mechanical Medtronic Hall (n = 192), and mechanical Carbomedics (n = 244; Carbomedics, Austin, TX, USA) prostheses were included in the study. As 8 patients underwent reoperation at our institute, the study comprises 1400 patients and 1408 valve replacement procedures. Patients receiving other prosthetic valves (such as Carpentier-Edwards [Edwards Lifesciences, Irvine, CA, USA], Baxter Perimount [Baxter Healthcare, Deerfield, IL, USA], Tissuemed aortic valve cardiomedical [Tissuemed, Leeds, United Kingdom], Hancock [Medtronic, Inc], and different mechanical bileaflet valves or homografts) were excluded from the study because of the small number of each type of valve. Annulus enlargement procedures were carried out only for specific indications and these patients were excluded. Rarely performed types of procedures were excluded to avoid any bias due to the learning curve of surgeons and individual patient selection.

Prosthesis type and size selection were according to surgeon preference. Usually, older patients received biologic prostheses. Older patients with extensive calcification of the aortic wall or other anatomic findings making the implantation of a stentless valve difficult or dangerous usually received stented biologic prostheses. Detailed characteristics of the patient population, which were incorporated in the logistic regression analysis, are presented in the Appendix.

Statistical analysis
All continuous data were expressed as mean ± 1 standard deviation, whereas dichotomous variables were given in real numbers and percentages. All variables analyzed are presented in the Appendix and were univariately evaluated by {chi}2 test, Fischer’s exact test, or Wilcoxon rank-sum test with respect to operative mortality. Operative mortality was defined as any death within 30 days after the initial operation.

All variables that were univariately found to be associated with an operative mortality with a p value smaller than 0.25 [15] were tested by forward and backward logistic regression analysis using the likelihood ratio test.

To generate hypotheses with variables that would be further possible covariates of operative mortality, all patients were divided into four (quartiles) or eight subgroups with the same number in each group according to the interesting variables, such as body mass index or height. To look for linear dependence of the continuous variable age, we divided all patients into subgroups in steps of 10 years of age. The operative mortality for each group was then calculated and presented in tables or bar graphs to visualize the operative mortality for the analyzed variable. Variables exhibiting a high amount of operative mortality in one subgroup in comparison to the other groups were afterwards tested for significant improvement of the model by the likelihood ratio test (p < 0.05).

To compare our model with the risk predicting model of the EuroScore we calculated the c-index of the receiver operating curve (ROC) for our final model and a model containing only the EuroScore as a risk factor for the same group of patients. The c-index is equal to the area underneath the receiver operating curve and assesses the model performance. Models with a c-index higher than 0.7 are considered to have good discriminative power. This was necessary because there were 11% missing values in our EuroScore data.


   Results
Top
 Abstract
 Introduction
 Patients and methods
 Results
Comment
 Acknowledgments
 Appendix. Risk factors of...
 References
 
Because we were specifically interested in the impact of the more demanding and time consuming implantation technique of biologic stentless prostheses and the influence of prosthesis types in small aortic roots, we analyzed the patient characteristics for different valve types and size in more detail. Characteristics of patients receiving different valve types are presented in Table 1. Patients receiving biologic prostheses were older, of smaller height, and of smaller body surface area. These results may be confounded because there was a higher proportion of female patients receiving biologic valves. However, a separated analysis for male and female patients (Table 1, part II) revealed that generally, in comparison to patients receiving biologic valves, male patients receiving mechanical valves were of greater height but a valve of smaller size was implanted. In female patients height was not different in the five valve groups. However, mechanical valves of smaller sizes were implanted more often (Table 1, part II).


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  		Table 1. Patient Characteristics

 
Patients receiving a stented Mosaic bioprosthesis were older and had a higher preoperative risk profile (eg, more often previous myocardial infarction, diabetes mellitus, and renal dysfunction) than patients receiving any other valve type.

Overall operative mortality (within 30 days) was 3.8% (53/1408). The univariate analysis identified the following factors related to early mortality with a p value smaller than 0.25: age, body surface area, female gender, myocardial decompensation, arterial hypertension, diabetes mellitus, any infection before operation, chronic obstructive pulmonary disease, renal dysfunction, dialysis, no sinus rhythm, aortic stenosis, New York Heart Association (NYHA) class IV, previous bypass surgery, simultaneous mitral valve replacement (MVR), and emergency operation. Independent predictors of operative mortality were previous bypass surgery, emergency operation, simultaneous MVR, and renal dysfunction (Table 2, model 1). The model improvement was significant (p < 0.001) and forward and backward stepwise selection by the likelihood ratio test demonstrated the same results and included these four variables and age.


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  		Table 2. Multivariate Analysis of Operative Mortality (Model Building Strategies)

 
Impact of age on operative mortality
Including age into the model 1 (Table 2, model 2) the model further improved. However, the question remains whether age is linearly related to mortality rate. Therefore, the relation of age to operative mortality was analyzed by dividing all patients into seven age groups in steps of 10 years. Figure 1 illustrates the mortality rate for each group. Operative mortality was higher in patients more than 80 years old and less than 30 years old than for the other age groups (Fig 1). Adding these two variables, less than 30 years of age (model 3) and more than 80 years of age (model 4) to the model 1 instead of age as a continuous variable (model 2), the improvement was significant if age more than 80 years old was included. The improvement was also higher (deviance smaller) and the c-index increased in comparison in the model containing age as a continuous variable. Including age less than 30 years old did not improve the model, which means that the operative mortality increased by chance.



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  		Fig 1. Effect of age on operative mortality for patients undergoing aortic valve replacement. The number of patients in each group is presented above each column.

 
Impact of body mass index on operative mortality
Including the body mass index (BMI) as a continuous variable in the model (Table 2, model 5), no improvement was found. However, high BMI is often suspected to increase operative risk. To analyze whether a low or high body mass index was a risk factor for operative mortality, the patient population was divided into quartiles for BMI (Table 3). In analysis A (Table 3) operative mortality was calculated for all patients in the different groups of BMI. Operative mortality for patients with a BMI less than 23.8 kg/m2 was higher than in the other groups. Including this term the model (Table 2, model 6) did not improve. In analysis B (Table 3) the dependence of mortality from BMI groups was separately calculated for patients with isolated AVR and for patients with simultaneous coronary artery bypass grafting (CABG). In contrast to the previous analysis, the mortality was now higher for patients receiving simultaneous CABG with a high BMI. There seems to be some confounding process present. Dividing the previous groups again according to their gender, we found an increased mortality rate for female patients receiving isolated AVR and a low BMI as well as for female patients receiving simultaneous CABG with a high BMI. Testing these two hypotheses by logistic regression models (Table 2, models 7 and 8), only simultaneous CABG in female patients with a BMI higher than 29 kg/m2 could be identified as an independent risk factor for operative death (Table 2, model 8).


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  		Table 3. Operative Mortality (%) in Dependence of BMI (All Patients Were Divided Into Four Groups According to the Quartiles of the BMI)

 
Impact of implantation of small sized valves on operative mortality
The implantation of valve sizes 21 mm, or even smaller, did not significantly increase the operative risk (Table 2, model 9), whereas including the implantation of valves size 19 mm improved the model with a p value of 0.07 (Table 2, model 10). Body surface area (model 11) and body height (model 12) also improved the model with a p value of 0.08 (Table 2). The implantation of small aortic valves (valve sizes 19 and 21 mm) depended, of course, on the height of the patients (Fig 2 [top panel]) as well as on the age of patients (Fig 2 [bottom panel]). The question was whether the operative risk increases as a result of implantation of small sized valves or as a result of patient characteristics associated with the implantation of small valves, like small height and older age. Therefore we calculated the operative mortality with dependence on height and age. Figure 3 illustrates the operative mortality in eight different groups of height. We discovered that patients with a height below 1.57 m had an increased operative mortality (Fig 3). Patients with a height smaller than 1.57 m had an increased operative mortality if they were older than 71 years (Fig 4). Including this interacting term improved the significance of the model (Table 2, model 13). The c-index of the ROC curve increased from 0.736 for model 8 (Table 2) to 0.738 for model 13 (Table 2).



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  		Fig 2. Proportion of patients receiving small aortic valves in dependence of height (top) and age (bottom). The number of patients in each group is presented above each column. (m = meters.)

 


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  		Fig 3. Effect of height on operative mortality. The number of patients in each group is presented above each column. (m = meters.)

 


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  		Fig 4. Different effects of age on operative mortality for patients with height smaller than 1.57 meters and larger. The number of patients in each group is presented above each column.

 
Impact of type of valve on operative mortality
Neither the implantation of biologic prostheses (Table 2, model 14) nor stentless biologic prostheses (Table 2, model 15) improved the model significantly. Therefore, the more complex implantation technique for stentless valves was not an independent risk factor for operative mortality.

However, the type of valve implanted may reach significance if different sizes of valves are taken into consideration. This might particularly influence the outcome in small, old age patients. Including the interacting terms height less than 1.57 m, age more than 71 years, and mechanical prostheses, the model (model 16, Table 2) improves in significance, similar to the result of model 13 (interacting term height < 1.57 m by age > 71 years, Table 2). In contrast, including the interacting term height less than 1.57 m, age more than 71 years, and biologic prostheses (model 17, Table 2), whether for all biologic prostheses or for stentless biologic prostheses (model 18, Table 2) and stented bioprostheses separate (model 19, Table 2) the model did not improve. Hence, in our patient population of small, old age patients only the implantation of mechanical prostheses increased the risk for perioperative mortality.

Including cardiopulmonary bypass time as a continuous variable, the model improved with a significant level below 0.001 (model 20, Table 2). Operative mortality increased exponentially with cardiopulmonary bypass time (Fig 5).



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  		Fig 5. Exponential (continuous line) effect of cardiopulmonary bypass time on operative mortality. The number of patients in each group is presented above each column. (mts = minutes.)

 
The model with the highest c-index (0.738) was chosen for the final model (with exception of cardiopulmonary bypass time, see the Comments section). The following variables were included in the final model (Table 4): previous bypass surgery, emergency operation, simultaneous MVR, renal dysfunction, more than 80 years old, simultaneous bypass surgery for female patients with a BMI greater 29 kg/m2, and height less than 1.57 m for patients more than 71 years old.


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  		Table 4. Final Model

 
In order to validate this model we compared the results with the results of the EuroScore scoring system, which is an established scoring system for prediction of operative mortality. Although the EuroScore results were unknown in 155 patients, both models were calculated with the same number of patients. The model, consisting only of the EuroScore as a risk factor, reached a c-index of 0.666, whereas our model under these conditions resulted in a c-index of 0.729.


   Comment
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Comment
Acknowledgments
 Appendix. Risk factors of...
 References
 
Overall operative mortality was 3.1% for isolated AVR and 4.7% for AVR with simultaneous CABG. Our goal was to develop a general statistical model for AVR regardless of simultaneous procedures to determine the influence of these simultaneous procedures on perioperative risk. Simultaneous MVR was included in the final model and increased the operative risk nearly fivefold. Whether this result mirrors reality or results from an inadequate description of these patients in our final model is not apparent. A new model for MVR is needed for further analysis of this issue.

High BMI was reported to increase the risk of postoperative morbidity, but not operative mortality for CABG [16–18]. Aranki and colleagues [6] found an increased risk for simultaneous CABG and AVR only in females. As a result of the progress in perioperative management, previously discovered individual risk factors might only influence perioperative mortality, if several of these risk factors are simultaneously presenting in one patient.

Even though previous studies found simultaneous CABG to be an independent risk factor [1, 13, 19], our final model did not include simultaneous CABG, meaning that it did not increase the operative risk in general. Concomitant bypass surgery was only a risk factor for women with a high body mass index (> 29 kg/m2).

Controversial data exists concerning previous CABG as a single risk factor [4, 8, 9]. The only study negating previous CABG as an operative risk factor compared two patient groups (the first group having primary AVR with simultaneous bypass surgery and the second group having AVR with simultaneous bypass surgery or isolated AVR after previous bypass surgery) by {chi}2 test not considering any confounding factors [10]. The identification of previous CABG as a risk factor in AVR influences the recommendations concerning operative strategy in the presence of moderate aortic valve disease at the time of coronary artery bypass surgery.

We identified renal dysfunction, emergency operation, previous CABG, simultaneous MVR, age greater than 80 years old, simultaneous CABG for women with a body mass index more than 29 kg/m2, and height less than 1.57 m in patients older than 71 years old as independent risk factors for operative mortality after AVR. Risk factors of operative mortality like old age, renal dysfunction, emergency operation, and previous cardiac operations were also established by several authors analyzing large series [1, 2, 4, 8].

Most favorable surgical management in elderly patients, often presenting with a small aortic root, is still controversial. To avoid patient-prosthesis mismatch, aortic root enlargement procedures have been recommended [20, 21]. With the development of new generations of prosthetic valves, patient-prosthesis mismatch has been reported to be negligible [14, 22]. Therefore, smaller sized prosthetic valves are increasingly being implanted in many centers. In 1999 Adams and colleagues [13] reported a study of 366 patients older than 70 years old; implantation of prosthetic valves with the 19-mm size increased the operative risk in male patients. Moreover, our data indicated that the implantation of prosthetic valves with the 19-mm size increased the operative mortality risk, but mainly in patients receiving smaller sized valves who were older and generally of smaller build. Each of these individual factors independently predict a higher mortality. Because height and body surface area are correlated, our results are supported by Bloomstein and colleagues [12] describing small body surface area as a risk factor in patients older than 70 years.

In our study we included patients undergoing AVR using five different, frequently implanted prosthetic valve types (SJM, Carbomedics, Medtronic Freestyle, Medtronic Mosaic, and Medtronic Hall). Rarely performed procedures were excluded to avoid any bias due to the learning curve of surgeons and patient selection. The implanted type of valve (mechanical, stented or stentless biologic prosthesis) was not an independent risk factor for operative mortality. Nevertheless, operative risk was statistically increased in small patients older than 71 years old undergoing AVR with mechanical prosthetic valves. On the contrary, patients with the same characteristics undergoing AVR by means of stented or stentless biologic prostheses had no increased perioperative risk. Astor and colleagues [23] found a higher operative risk in patients undergoing AVR with biologic prostheses, but this association became weaker after adjusting it for patient characteristics and did not reach significance in a model for isolated AVR. According to the authors these findings are explained by differences in patient characteristics, namely comorbid conditions, which were not included in their model.

Including the interacting terms small height by older age and small height by older age by mechanical prostheses did contribute only a little to the discriminating power of our model. In accord with previous findings [12, 13, 23], variables such as height and valve type may be of relevance regarding operative risk and should be further analyzed in risk modeling.

In 1985 Scott and colleagues [4] pointed out that aortic cross-clamp time cannot be predicted preoperatively and, therefore, has limited utility in prediction of operative risk. In contrast, cardiopulmonary bypass time has been identified as an operative risk factor in many studies [5, 11, 12, 19]. Certainly there is an increase in operative mortality with rising cardiopulmonary bypass time (Fig 5). Furthermore, including this variable into the risk predicting model will improve the predicting power of any model. As expected, the area under the ROC curve increased as well, from 0.738 to 0.767, by including cardiopulmonary bypass time in the model in our study. But this factor, like the cross-clamp time, cannot be predicted preoperatively, and it is not valuable for creating models for prediction of operative mortality.

We compared our model with a model purely relying on the EuroScore [24] as a predictive factor. The performance of our model for aortic valve replacement was better than the EuroScore model (c-index of 0.729 and 0.666, respectively). Differences in the discriminative power between the EuroScore model and our model may be explained by several fundamental differences between the models: first, different procedures were included in the models (AVR vs all open-heart procedures); second, the results may be influenced by procedure-specific hospital volume (single center vs multi center investigations with different hospital volume [23]); and third, diversity in patient population unquestionably influences the results.

Risk predicting models can help surgeons in decision making, particularly if simultaneous or redo operations are planned. In order to take into account the huge variety of risk associated diseases, more sophisticated, wide-ranging models are necessary. However, those models may have less predictive power for the included subpopulations. Further investigations are required in order to optimize general models with a good discriminative power for all included patients.


   Acknowledgments
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Comment
 Acknowledgments
Appendix. Risk factors of...
 References
 
The authors thank Dr Wade Knight for language editing this manuscript.


   Appendix. Risk factors of aortic valve replacement (n = 1408)
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Comment
 Acknowledgments
 Appendix. Risk factors of...
References
 

Dichotomous Variables
Yes
Unknown
Female gender 614 (44) 0
Myocardial infarction 152 (11) 19 (1.3)
Syncope 200 (14) 9 (0.6)
Embolism 38 (2.7) 2 (0.1)
Cardial decompensation 336 (24) 1 (0.1)
2
Hypertension 747 (53) 12 (0.9)
Hyperlipidemia 562 (40) 15 (1.1)
Atrial fibrillation 190 (14) 0
No sinus rhythm 213 (15) 0
Advanced NYHA class (IV) 206 (15) 0
Physiologic subgroup
Aortic stenosis 604 (43) 26 (1.8)
Aortic insufficiency 180 (13) 26 (1.8)
Aortic stenosis and insuffiency 598 (42) 26 (1.8)
Endocarditits 10 (0.7) 0
Any other infection before operation 50 (3.6) 0
Diabetes mellitus 290 (21) 10 (0.7)
Renal dysfunction 163 (12) 3 (0.2)
Dialysis 14 (1) 0
Chronic obstructive pulmonary disease 169 (12) 2 (0.1)
Peripheral arterial occlusive disease 90 (6) 25 (1.8)
Previous coronary artery bypass grafting 21 (1.5) 0
Emergency procedure 43 (3.1) 0
Simultaneous mitral valve replacement 66 (4.7) 1 (0.1)
Simultaneous coronary artery bypass grafting 572 (41) 0

Continuous Variables
Mean ± SD
Unknown
Age 68 ± 11 years 0
Height 1.68 ± 0.09 m 0
Body surface area 1.86 ± 0.20 m2 0
Body mass index 26.6 ± 4.2 kg/m2 0
Euro Score 6.64 ± 3.25 155 (11)

Percentages are given in parentheses.

NYHA = New York Heart Association;

SD = standard deviation.


   References
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Comment
 Acknowledgments
 Appendix. Risk factors of...
 References
 

  1. McGiffin D.C., O’Brien M.F., Galbraith A.J., et al. An analysis of risk factors for death and mode-specific death after aortic valve replacement with allograft, xenograft and mechanical valves. J Thorac Cardiovasc Surg 1993;106:895-911.[Abstract]
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  6. Aranki S.F., Rizzo R.J., Couper G.S., et al. Aortic valve replacement in the elderly. Effect of gender and coronary artery disease on operative mortality. Circulation 1993;88(5 Suppl):II17-II23.
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  10. Sundt T.M., Murphy S.F., Barzilai B., et al. Previous coronary artery bypass grafting is not a risk factor for aortic valve replacement. Ann Thorac Surg 1997;64:651-658.[Abstract/Free Full Text]
  11. Kolh P., Kerzmann A., Lahaye L., et al. Cardiac surgery in octogenarians. Peri-operative outcome and long-term results. Eur Heart J 2001;22:1235-1243.[Abstract/Free Full Text]
  12. Bloomstein L.Z., Gielchinsky I., Bernstein A.D., et al. Aortic valve replacement in geriatric patients: determinants of in-hospital mortality. Ann Thorac Surg 2001;71:597-600.[Abstract/Free Full Text]
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Aortic valve replacement in octogenarians: utility of risk stratification with EuroSCORE.
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T. M. Dewey, D. L. Brown, T. S. Das, W. H. Ryan, J. E. Fowler, S. D. Hoffman, S. L. Prince, M. A. Herbert, D. Culica, and M. J. Mack
High-Risk Patients Referred for Transcatheter Aortic Valve Implantation: Management and Outcomes
Ann. Thorac. Surg., November 1, 2008; 86(5): 1450 - 1457.
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Y. S. Tjang, Y. van Hees, R. Korfer, D. E. Grobbee, and G. J.M.G. van der Heijden
Predictors of mortality after aortic valve replacement
Eur. J. Cardiothorac. Surg., September 1, 2007; 32(3): 469 - 474.
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F. B. Vanky, E. Hakanson, and R. Svedjeholm
Long-Term Consequences of Postoperative Heart Failure After Surgery for Aortic Stenosis Compared With Coronary Surgery
Ann. Thorac. Surg., June 1, 2007; 83(6): 2036 - 2043.
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S. J. Melby, A. Zierer, S. P. Kaiser, T. J. Guthrie, J. D. Keune, R. B. Schuessler, M. K. Pasque, J. S. Lawton, N. Moazami, M. R. Moon, et al.
Aortic Valve Replacement in Octogenarians: Risk Factors for Early and Late Mortality
Ann. Thorac. Surg., May 1, 2007; 83(5): 1651 - 1657.
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E. L. Hannan, C. Wu, E. V. Bennett, R. E. Carlson, A. T. Culliford, J. P. Gold, R. S.D. Higgins, C. R. Smith, and R. H. Jones
Risk Index for Predicting In-Hospital Mortality for Cardiac Valve Surgery
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E. H. Kincaid, A. R. Cordell, J. W. Hammon, S. M. Adair, and N. D. Kon
Coronary Insufficiency After Stentless Aortic Root Replacement: Risk Factors and Solutions
Ann. Thorac. Surg., March 1, 2007; 83(3): 964 - 968.
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I. Florath, A. Albert, W. Hassanein, B. Arnrich, U. Rosendahl, I. C. Ennker, and J. Ennker
Current determinants of 30-day and 3-month mortality in over 2000 aortic valve replacements: impact of routine laboratory parameters
Eur. J. Cardiothorac. Surg., November 1, 2006; 30(5): 716 - 721.
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A. I. Duncan, J. Lin, C. G. Koch, A. M. Gillinov, M. Xu, and N. J. Starr
The Impact of Gender on In-Hospital Mortality and Morbidity After Isolated Aortic Valve Replacement
Anesth. Analg., October 1, 2006; 103(4): 800 - 808.
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M. Cladellas, J. Bruguera, J. Comin, J. Vila, E. de Jaime, J. Marti, and M. Gomez
Is pre-operative anaemia a risk marker for in-hospital mortality and morbidity after valve replacement?
Eur. Heart J., May 1, 2006; 27(9): 1093 - 1099.
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J. M. Bernal, J. Lorca, D. Prieto-Salceda, I. Pulitani, A. Ponton, I. Garcia, and J. M. Revuelta
Performance at 10 years of the CarboMedics 'Top-Hat' valve. Postclamping time is a predictor of mortality
Eur. J. Cardiothorac. Surg., February 1, 2006; 29(2): 144 - 149.
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B. Iung, A. Cachier, G. Baron, D. Messika-Zeitoun, F. Delahaye, P. Tornos, C. Gohlke-Barwolf, E. Boersma, P. Ravaud, and A. Vahanian
Decision-making in elderly patients with severe aortic stenosis: why are so many denied surgery?
Eur. Heart J., December 2, 2005; 26(24): 2714 - 2720.
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R. Jin, G. L. Grunkemeier, A. Starr, and Providence Health System Cardiovascular Study Grou
Validation and Refinement of Mortality Risk Models for Heart Valve Surgery
Ann. Thorac. Surg., August 1, 2005; 80(2): 471 - 479.
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 HeartHome page
I Florath, A Albert, U Rosendahl, T Alexander, I C Ennker, and J Ennker
Mid term outcome and quality of life after aortic valve replacement in elderly people: mechanical versus stentless biological valves
Heart, August 1, 2005; 91(8): 1023 - 1029.
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 CirculationHome page
G. Ambler, R. Z. Omar, P. Royston, R. Kinsman, B. E. Keogh, and K. M. Taylor
Generic, Simple Risk Stratification Model for Heart Valve Surgery
Circulation, July 12, 2005; 112(2): 224 - 231.
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R. Bekeredjian and P. A. Grayburn
Valvular Heart Disease: Aortic Regurgitation
Circulation, July 5, 2005; 112(1): 125 - 134.
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A. Weerasinghe, M. Yusuf, T. Athanasiou, A. Wood, P. Magee, and R. Uppal
Role of transvalvular gradient in outcome from valve replacement for aortic stenosis
Ann. Thorac. Surg., April 1, 2004; 77(4): 1266 - 1271.
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