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Original Articles: Adult Cardiac

Effect of Prosthesis-Patient Mismatch on Long-Term Survival With Aortic Valve Replacement: Assessment to 15 Years

University of British Columbia, Vancouver, British Columbia, Canada

Accepted for publication August 25, 2009.

^_* Address correspondence to Dr Jamieson, 486 Burrard Bldg, St. Paul's Hospital, 1081 Burrard St, Vancouver, British Columbia, V6Z 1Y6, Canada (Email: eric.jamieson{at}vch.ca).

Presented at the Forty-fifth Annual Meeting of The Society of Thoracic Surgeons, San Francisco, CA, Jan 26–28, 2009.

	Abstract

Top Abstract Introduction Patients and Methods Results Comment Discussion Acknowledgments References

 
Background: The effect of prosthesis-patient mismatch on long-term survival after aortic valve replacement has received considerable attention but there remains controversy. This study was performed to determine the predictors of mortality after aortic valve replacement and influence of prosthesis-patient mismatch on survival.

Methods: Contemporary mechanical prostheses and bioprostheses were implanted in 3,343 patients with aortic valve replacement between 1982 and 2003. The mean age was 68.06 ± 11.20 years (median 70.06; range, 19 to 94), and the mean follow-up was 6.18 ± 4.96 years, for a total of 20,666 years of follow-up. Prosthesis-patient mismatch was classified by effective orifice area index categories: normal (>0.85 cm²/m²), 1,547 (46.3%); mild-to-moderate (>0.65 cm²/m² to 0.85 cm²/m²), 1,584 (47.4%); and severe (<0.65 cm²/m²), 212 (6.3%).

Results: The predictors of overall mortality were age, age categorization, New York Heart Association functional class III/IV, concomitant coronary artery bypass graft surgery, prosthesis type, preoperative congestive heart failure, diabetes mellitus, renal failure, and chronic obstructive pulmonary disease. All categories of effective orifice area indexes were not predictive of overall mortality, late mortality, or early mortality. The 15-year overall survival was differentiated by effective orifice area index categories: 38.1% ± 2.1%, 37.0% ± 2.2%, and 22.1% ± 6.5%, respectively, for the three categories. Survival adjusted for the covariates (effective orifice area index, age, basal mass index, and ejection fraction) determined no effect except severe effective orifice area index when adjusted for ejection fraction more than 50% (p = 0.049).

Conclusions: Prosthesis-patient mismatch is not a predictor of overall standard unadjusted mortality to 15 years after aortic valve replacement, regardless of the category of effective orifice area index.

	Introduction

Top Abstract Introduction Patients and Methods Results Comment Discussion Acknowledgments References

 
Prosthesis-patient mismatch (PPM) was first described in 1978 for aortic valve replacement when the in vivo effective orifice area (EOA) of the prosthetic valve is less than that of the native, nondiseased, human valve [1]. There has been extensive documentation on the role of PPM after aortic valve replacement, with left ventricular remodeling and enhanced survival [2–8]. Controversy still exists as to the influence of PPM on survival, both early and midterm [9–14].

There is opinion that control of mismatch has contributed to reduced congestive heart failure and regression of left ventricular mass, both influencing improved survival. The indication for surgical management of aortic stenosis is symptomatic severe aortic stenosis (less than 1.0 cm² valve area). In the majority of patients, this is equivalent to an EOA index (EOAI) at or below the level of severe mismatch.

The objective of the study was to determine the predictors for all levels of mortality and to detail the roles of PPM defined by effective orifice area index.

	Patients and Methods

Top Abstract Introduction Patients and Methods Results Comment Discussion Acknowledgments References

 
This is a retrospective study of prospectively collected data from the University of British Columbia Cardiac Valve Database. The database receives annual renewal from the University of British Columbia Clinical Research Ethics Board, which has a formal informed consenting process of patients.

From 1982 to 2003, 3,343 patients had aortic valve replacements at the affiliated teaching hospitals (St. Paul's Hospital, Vancouver General Hospital, and Royal Columbian Hospital) of the University of British Columbia. There were 2,493 bioprostheses (BP) and 850 mechanical prostheses (MP). The prostheses in this study are all currently marketed worldwide. The prostheses utilized were as follows: Carpentier-Edwards Perimount pericardial (Edwards Lifesciences, Irvine, CA) in 667 patients, Carpentier-Edwards supra-annular porcine in 1,250 patients, Medtronic Mosaic porcine (Medtronic, Minneapolis, MN) in 576 patients, St. Jude Medical mechanical (St. Jude Medical, St. Paul, MN) in 462 patients, and CarboMedics mechanical (Sorin-CarboMedics, Austin, TX) in 388 patients.

The patients were classified into three levels of EOAI based upon the documentation and verification from Laval University [2]. The reference EOAs were determined from the published literature for all prostheses (refer to footnote of Appendix).

The patients considered for the study had their first aortic valve procedure with the contemporary prostheses. Patients who had a subsequent valvular reoperative procedure with prosthesis replacement were censored alive on the date of the reoperative procedures. There were 178 of the 3,343 patients censored: BP 158 (6.3%) and MP 20 (2.4%). Structural valve deterioration was the cause of 105 BP reoperations (66%). This concept was to avoid accepting a hemodynamically different prosthesis at the time of reoperative explantation.

The follow-up was conducted periodically through the years with structured telephone interviews, review of hospital health records, review of consultative and echocardiographic documentation, and questionnaires to primary care physicians. The follow-up was 97.6% complete. The closing interval was a 4-month interval in early 2007.

The characteristics of the total population are detailed in Table 1, as well as characterizing the EOAI categories. The characteristics of the severe EOAI group (n = 212; 6.3%) are of a much more serious level than that of the overall population.

Statistical Methods
Descriptive statistics were used to summarize the data. The patient cohort was divided into three groups according to PPM categories. Continuous variables were reported as mean ± SD and compared for statistical significance by one-way analysis of variance for the three PPM groups, and the Bonferroni post-hoc test was used for multiple pair-wise comparisons. Follow-up years were tested by the nonparametric median tests. Categorical variables were reported as number of patients (%) and the ² or Fisher exact test for statistical significance.

The Kaplan-Meier method was used to assess the time-related outcome of survival. The survival analyses were conducted for aortic valve replacement overall mortality and late mortality. The survival curves were compared by log rank statistic. The effect of the preoperative variables on survival was assessed with the multivariable Cox proportional hazards model. Survival risk analyses compared EOAI as a continuous variable and the three categories of EOAI. Variables with a value of p of 0.25 or less on univariate analysis were incorporated into the models in a stepwise fashion. The EOAI (when not significant by univariate analysis) as a continuous variable and by categories was forced into the multivariable risk analyses because of the primary interest of the study.

Survival with covariates using multivariable Cox regression models built with the backward stepwise elimination method (significance determined by likelihood ratio) was used to determine the interaction of EOAI categories, age groups (60 years or less, more than 60 years), left ventricular ejection fraction (EF [50% or less, more than 50%]) and body mass index (less than 25 kg/m², 25 kg/m² or more).

	Results

Top Abstract Introduction Patients and Methods Results Comment Discussion Acknowledgments References

 
Of the total population of 3,343 patients, 1,547 (46.3%) had nonsignificant PPM, 1,584 (47.4%) had mild-to-moderate PPM, and 212 (6.3%) had severe PPM. The mean follow-up was 6.2 ± 5.0 years. During follow-up, 1,050 (31.4%) died: early mortality, 114 (3.4% [BP 4.0% (99), MP 1.8% (15)]; p = 0.002); late mortality, 1,050 (31.8%); cardiac-related mortality, 419 (12.5%); and valve-related mortality, 190 (5.7%). The early mortality for nonsignificant/normal PPM patients (PPM group A) was 3.4% (52); for mild-to-moderate PPM patients (PPM group B), it was 3.5% (56); and for severe PPM patients (PPM group C), it was 2.8% (6).

The univariate determinants for independent variables (p values) are detailed in Table 2. The multivariable analyses of risk factors for early, late, and overall mortality was conducted with the model using categorical variables (Table 3). The EOAI as a categorical variable was not an independent predictor of overall, early, or late mortality.

The risk analysis was also conducted with EOAI as a continuous variable. Continuous EOAI variable in the univariate regression was for early mortality (p = 0.549), late mortality (p = 0.0001), and overall mortality (p = 0.0001). The multivariable regression analysis (using all variables as continuous except EF) did not reveal EOAI as an independent risk factor for late mortality (p = 0.623, HR 0.8, CI: 0.4 to 1.8) or overall mortality (p = 0.257, HR 0.7, CI: 0.3 to 1.3). The independent risk factors, otherwise, were not different from categorical modeling (Table 3) except for the elimination of valve size (p = 0.064) and the addition of BMI (p = 0.025) for early mortality. Valve type was eliminated for late mortality (p = 0.177) and overall mortality (p = 0.314).

Figure 1 denotes survival by EOAI categories for overall mortality. The freedom from overall mortality for severe PPM was 22.1% ± 6.5% at 15 years, significantly different (p = 0.040) from 38.1% ± 2.1% for no PPM. The same relationship was observed for freedom from late mortality: 22.9% ± 6.7% for severe PPM and 39.5% ± 2.1% for no PPM at 15 years (p = 0.020).

View larger version (16K): [in this window] [in a new window]   Fig 1. Freedom from overall mortality by three effective orifice area index (EOAI) groups (n = 3,343): E1 (solid line), not significant; E2 (long-dash line), mild to moderate; and E3 (short-dash line), severe. (AVR = aortic valve replacement; BP = bioprosthesis; MP = mechanical prosthesis.)

The freedom from overall mortality by EOAI categories for age groups more than 60 years and 60 years or less, EF groupings greater than 50% and 50% or less, BMI levels 25 kg/m² or greater and less than 25 kg/m² is summarized in Table 4. Suvival was adjusted to determine the effect of covariates (EOAI, age, BMI, and EF; Table 5). Severe EOAI had no other interaction effect on adjusted survival for the evaluated covariates except EF greater than 50%.

	Comment

Top Abstract Introduction Patients and Methods Results Comment Discussion Acknowledgments References

The predominant investigators in the area of prosthesis-patient mismatch have been Pibarot and Dumesnil, with several important publications and editorials [2, 15–19]. Pibarot and Dumesnil [2], in 2000, proposed the concept that valve PPM can be predicted at the time of surgery by projected in vivo indexed effective orifice area. The literature-derived EOA can be substituted by transthoracic echocardiogram EOAs taken in the first month after surgery.

This study identified the risk factors for mortality after aortic valve replacement, considering specifically EOAI categories. The significant findings of the study are related to significantly reduced long-term survival at 15 years for the severe PPM group in the modeling of late and overall mortality, but not related to PPM. There was no influence of mild-to-moderate and severe PPM on early mortality.

There have been discrepancies resulting in controversy in published reports on the impact of PPM on postoperative clinical outcomes. Numerous studies, including those of Pibarot and Dumesnil, have reported that PPM is an independent predictor of mortality and cardiac events after aortic valve replacement [2–8]. Several studies have failed to demonstrate a significant effect of PPM on clinical outcomes [9–14].

The Laval University group evaluated the influence of PPM on early mortality in the report by Blais and associates [3] in 2003. The independent predictors in that study included EF less than 40%, infectious endocarditis, emergent status, cardiopulmonary bypass time, and PPM. The hazard ratio for severe PPM was 11.4, and for moderate PPM, it was 2.1. The magnitude of influence was even greater for severe and moderate PPM with left ventricular dysfunction. Prosthesis-patient mismatch of any category was not influential in the authors' study.

The same investigators from Laval University reported on the influence of PPM on late mortality recently, in 2009, by Mohty and colleagues [4]. Severe PPM influenced late mortality and cardiovascular mortality with adjustment for age, BMI, and left ventricular dysfunction. Moderate PPM was a predictor of mortality with EF less than 50% but not in the presence of preserved left ventricular function. The authors' study did not confirm these findings. Mohty and coworkers [4] developed propensity scoring to avoid selection bias, but the use of a propensity score in the risk modeling did not change the results.

Mohty and coworkers [4] evaluated the relationship of PPM, age, basal mass index, and left ventricular dysfunction on survival. The evaluation revealed that severe PPM increased late mortality on patients less than 70 years old and BMI less than 30 kg/m². These investigators identified no impact in the elderly or obese. The authors' study revealed that severe PPM did not influence survival adjusted for age, EOAI, BMI, and EF for age (60 or less, and more than 60 years), BMI (less than 25 kg/m², and 25 kg/m² or more), and EF 50% or less. These contradictory findings will need to be further evaluated, considering that both studies have provided conflicting results.

Considerable literature has evaluated PPM on survival with aortic valve replacement. Several studies utilized in vitro EOA and geometric EOA rather than in vivo EOA and, consequently, the reported results have less reliability [9, 11, 14, 20].

There have been several other very important studies. Mohty and investigators [5] of the Mayo Clinic revealed that severe PPM (17% of the population), in a cohort using 19-mm or 21-mm St. Jude Medical prostheses, is an independent predictor of long-term mortality and congestive heart failure. Florath and colleagues [6] also identified that severe PPM estimated by echocardiographic-determined EOA was an independent risk factor of long-term survival. Tasca and associates [7], working in collaboration with Pibarot, reported the EOAI of 0.80 cm²/m² or less was an independent predictor of late mortality and cardiac events.

The impact of PPM (EOAI less than 0.75 cm²/m²), reported by Moon and coworkers [8], was detrimental to survival in patients younger than 60 years. This confirms the finding by Mohty and colleagues [4] of reduced survival at less than 70 years of age.

There have been a number of studies reporting the lack of impact of PPM on survival [10–13]. Hanayama and colleagues [10] found that severe PPM was rare and that there was no influence on left ventricular mass index or intermediate-term survival. Vicchio and coworkers [13] also found, in an elderly population, that incidence of PPM was low and without influence on survival. Kato and colleagues [12] reported no negative influence on survival at 10 years for PPM 0.85 cm²/m² or less; however, left ventricular mass regression was lower.

Monin and coworkers [21], reporting from a multicenter study, found that PPM of 0.85 cm²/m² or less had no influence on early mortality, confirming the authors' findings, but contrary to those of Blais and colleagues [3]. Ruel and colleagues have extended the study of prosthesis-patient mismatch beyond survival [22, 23]. In 2004, they reported that an EOAI of 0.80 cm²/m² or less contributed to persistent or recurrent congestive heart failure after aortic valve replacement without influence on survival. In 2006, they documented that an EOAI of 0.80 cm²/m² or less in the presence of impaired left ventricular dysfunction reduced survival. Mohty and coauthors [4] have identified that moderate and severe PPM reduced survival in EF less than 50%, whereas the authors have identified nonsignificant reductions. Severe PPM reduced survival (p = 0.049) in the presence of normal ventricular function (EF greater than 50%) [4].

Bioprostheses were a predictor of mortality in all the modeling that was performed. The reason why BP is a predictor over MP is not fully understood. Bioprostheses were used more predominantly in patients over the age of 60 and, to a greater extent, over the age of 70. There is the possibility that degenerative changes in the bioprostheses could be an identified factor that is contributing to BP being an independent predictor of mortality.

The limitations of this study are that it is a retrospective study that can be subject to selection biases and unidentified confounders that may be influencing the results. The authors' series reports survival to 15 years whereas Mohty and colleagues [4] reported survival to 12 years. The majority of all other publications report survival only to 5 to 8 years.

Severe PPM as identified by an EOAI of less than 0.65 cm²/m² is not an independent predictor of early mortality, late mortality, or overall mortality after aortic valve replacement. That does not mean that surgeons should leave patients with severe mismatch; rather, they should maintain a prospective strategy of implanting an adequate size aortic prosthesis that will avoid patients in the category of severe mismatch (near equivalent to indications for intervention in severe aortic stenosis). It will obviously remain that a significant portion of patients having aortic valve replacement will have a degree of mild-to-moderate PPM owing to the obstructive nature of most prostheses. This study should provide confidence to surgeons and cardiologists that mild-to-moderate PPM is unlikely detrimental to survival, except that the influence of left ventricular dysfunction and obesity require further, extensive investigation to evaluate the relationship with mild-to-moderate and severe PPM.

	Appendix

 
Normal Reference Values of Effective Orifice Areas for the Aortic Prosthetic Valves

Prosthetic Valve Size (mm) 19 21 23 25 27 29 Stented bioprosthetic valves  Medtronic Mosaic 1.1 1.3 1.5 1.8 1.9 2.2  Carpentier-Edwards Perimount 1.1 1.3 1.5 1.8 1.8 2.2  Carpentier-Edwards SAV 1.1 1.4 1.5 1.7 1.9 2.2 Mechanical valves  St. Jude Medical Standard 1.0 1.3 1.5 1.8 2.4 2.7  St. Jude Medical HP 1.2 1.3 1.8 2.4 2.7 —  St. Jude Medical Regent 1.6 2.0 2.2 2.5 3.6 4.4  CarboMedics Standard 1.1 1.5 1.6 2.0 2.4 2.6  CarboMedics R Series 1.1 1.5 1.6 2.0 2.4 2.6  CarboMedics Top Hat 1.1 1.5 1.6 2.0 2.4 —

Reference values derived from the following contributions to the literature: Ann Thorac Surg 2002;74:2003–9; J Thorac Cardiovasc Surg 2002;124:333–9; J Thorac Cardiovasc Surg 2005;129:1056–63; Circulation 1991;83:213–23; Thorac Cardiovasc Surg 2003;51:126–9; J Am Coll Cardiol 1993;21:398–405; Ann Thorac Surg 1999;67:1299–303; J Thorac Cardiovasc Surg 2005;130:994–1000; and J Thorac Cardiovasc Surg 2005;130:759–64.

	Discussion

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DR PAUL KURLANSKY (Miami, FL): As always from your group, this was a very interesting, well-conducted study. It raises some very provocative questions, because your findings are exactly in contradiction to some other findings regarding specifically the group of patients with reduced ejection fraction (EF). The question that I have, though, is regarding the multivariable analysis where the severe patient mismatch was not a predictor, but in that model you have also valve size and body surface area (BSA), which are both covariants with patient-prosthesis mismatch, and both of those seem to affect mortality whereas PPM did not. So I was just wondering if you could comment on the potential for a statistical confounding in the multivariable analysis that might have obscured or confused the potential effect of patient-prosthesis mismatch? Thank you.

DR JAMIESON: Yes, that is a good question. We did not use BSA in the risk analysis but we did use valve size, and valve size was only predictive in early mortality and it was not in late or overall. We did keep valve size in, because the relationship of EOAI is related to EOA and to BSA, and we felt that this was appropriate. But, I agree with you, it may change the results if we analyzed it without valve size. But we did not use BSA in the model.

DR ROBERT A. DION (Genk, Belgium): My first question is, do we still have to take into account mild or moderate mismatch, because I can see that the great majority of the bioprostheses have some kind of mismatch. Also I have noted that we have a very important point with the EF. So my question is, when addressing mismatch, why does not a stress test belong to the testing, because obviously the patient with a good EF will then produce a higher cardiac output than the patient with a mediocre EF. Maybe is it going to be much more indicative of survival than the passive measurement?

DR JAMIESON: I think it is very emphatically demonstrated here that mild to moderate mismatch does not change survival, and it does not change survival in the work of Ross and Braumald in the natural history studies. The work from Laval has indicated that mild to moderate mismatch may be a predictor in certain subsets of patients. But we don't exercise our patients. We don't operate on patients with mild to moderate mismatch of the native valve, I think this really tries to attempt to clarify those issues that we do have to avoid severe mismatch because we are operating in a category very close to what we do for native disease. But what we tried to differentiate here is those patients with LV dysfunction and obesity, certain subsets of patients.

There was a very interesting paper published last week in JACC by the Laval group, which showed findings somewhat at variance. They showed that severe mismatch influenced patients less than 70 years and we found it greater than 60 years, and they also found that mismatch influenced those patients who were not obese where we found that it had influence in obesity. That paper was only published a few days ago, and we have tried to incorporate some of this into this presentation.

DR TOMISLAV MIHALJEVIC (Cleveland, OH): Based on this nice presentation and your results, how has this actually influenced your clinical practice? If you were to summarize, after you found what you found, what do you do differently in your preoperative planning? And the second question is, how have you dealt with the patients who had a severe prosthesis mismatch after you found it postoperatively? How many people were reoperated for it?

DR JAMIESON: Well, we have not reoperated on any patients. I think it is really important that you size the annulus preoperatively or intraoperatively. You know your reference EOAs, we always have these on our operating theater walls, and we calculate what we are going to leave the patient with. We should not leave the patient with severe mismatch regardless of whether or not it influences or does not influence survival in certain subgroups. But in patients with left ventricular dysfunction, young active patients, and obese patients, I think we have to, even though these data don't necessarily clarify totally those indications. I think that is where we have to be very cautious in those patients. We should never leave a patient with severe mismatch.

DR MICHAEL A. ACKER (Philadelphia, PA): So to be clear, this is an important point, Dr Jamieson, you are suggesting that 0.65 cm²/m² should become your trigger, let's say, an annular enlargement procedure, versus 0.85 cm²/m², as all the charts turn red at 0.85 cm²/m²? Is 0.65 cm²/m² your trigger? If you can't get a valve that will fit that patient with better than 0.65 cm²/m², you will enlarge the annulus?

DR JAMIESON: Well, we can either enlarge the annulus or we can select another prosthesis.

DR ACKER: But that is your trigger, not 0.85 cm²/m²?

DR JAMIESON: No, no, not 0.85 cm²/m² at all, and especially in older, sedentary people, you can go down to 0.7 or closer to 0.65 cm²/m². I like sort of 0.7 cm²/m² as clinically relevant. But, no, the 0.85 cm²/m², that is normal. There is no way that we are going to reoperate on patients and leave everybody with 0.85 cm²/m² or above.

DR VINOD H. THOURANI (Atlanta, GA): On that same note, what percentage of your patients are getting a root enlargement?

DR JAMIESON: Very, very small numbers, because we have had a lot of prostheses through the years that have excellent hemodynamics, and we very rarely do root enlargements.

DR GIOVANNI SPEZIALI (Pittsburgh, PA): Have you tried to run a correlation between preoperative mismatch and postoperative transvalvular gradient, meaning, have you confirmed the existence of mismatch with a postoperative echo study that effectively showed that you still had a 30 to 40 mm peak gradient across the prosthesis?

DR JAMIESON: Well, I think occasionally we may have. We don't do routine echoes, and we are looking at echoes afterwards to see whether or not the reference EOA has fallen over time, and I think that is another study and another discussion for another day. We do have data in that regard, but I don't think it is pertinent to this presentation.

	Acknowledgments

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The authors acknowledge the contribution of Dr Phillippe Pibarot and Dr Jean Dumesnil from Laval University in the stage of original design of this study. The authors also acknowledge the work of Kevin Shillitto for his diligent work in the word processing of this manuscript.

	References

Top Abstract Introduction Patients and Methods Results Comment Discussion Acknowledgments References

 

1. Rahimtoola SH. The problem of valve prosthesis-patient mismatch Circulation 1978;58:20-24.[Abstract/Free Full Text]
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