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Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis
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REVIEW
Heart, Lung and Circulation (2019) -, -–1443-9506/19/$36.00 https://doi.org/10.1016/j.hlc.2019.09.013
Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis Ayame Ochi, BMedSci a, Kevin Cheng, BMedSci, MBBS b, Bing Zhao, MD, MSc a, Ashutosh A. Hardikar, MBBS a,c, Kazuaki Negishi, MD, PhD a,b,d,* a
Menzies Institute for Medical Research, University of Tasmania, Hobart, Tas, Australia Department of Cardiology, Royal Hobart Hospital, Hobart, Tas, Australia c Department of Cardiothoracic Surgery, Royal Hobart Hospital, Hobart, Tas, Australia d Faculty of Medicine and Health, Sydney Medical School Nepean, University of Sydney, Sydney, NSW, Australia b
Received 5 August 2019; accepted 22 September 2019; online published-ahead-of-print xxx
The choice of valve type for aortic valve replacement surgery is sometimes challenging. The main risk for bioprostheses is structural valve degeneration (SVD); however, little is known about what the most important risk factors are. We conducted a systematic review and meta-analysis to identify the risk factors and estimate their pooled effect sizes to aid the prosthesis choice for replacement. We followed PRISMA guidelines and systematically searched three electronic databases (PubMed, Scopus, and Web of Science) using appropriate key terms: ‘aortic valve’, ‘bioprosthesis’, ‘degeneration’, ‘durability’, ‘prosthesis failure’, etc. Hazard ratio (HR) and odds ratio (OR) and associated 95% confidence intervals (CI) were extracted. Pooled risk estimates were calculated using a random-effects model. Twenty-nine (29) observational studies were included with a total of 25,490 patients, 981 of whom developed SVD over a mean follow-up time of 18.5 years. Four (4) factors influencing bioprosthetic SVD were identified: increasing age was a protective factor (per 1-yr increase, HR: 0.91 [95% CI 0.89, 0.94], p,0.0001), whereas increased body surface area (HR 1.77 [1.04, 3.01], p=0.034), patient2prosthesis mismatch (HR 1.95 [1.56, 2.43], p,0.001), and smoking (HR 2.28 [1.37, 3.79], p=0.0015) were risk factors for SVD. We found younger age, patient2prosthesis mismatch, body surface area, and smoking, as risk factors for aortic SVD, which should be considered for valve selection. This study generates a further hypothesis that accelerated flow across the valve is a shared key component in the pathophysiology of SVD, thus future research should consider other high cardiac output states. Keywords
Structural valve degeneration Risk factors Aortic valve Meta-analysis
Introduction Aortic valve disease is the most common valve disease in the developed world, predominantly attributable to aortic stenosis, with the prevalence rising due to an ageing population [1]. Over time, bioprosthesis use for aortic valve disease management has increased to over 80% of replacements [2].
Structural valve degeneration (SVD) is the major drawback of bioprostheses, resulting in patients re-experiencing valvular heart disease symptoms, and potentially requiring reintervention. Given the rising number of patients requiring replacement and the increasing use of bioprostheses, it is important to identify patients who are at highest risk for bioprosthetic SVD to aid prosthesis choice.
*Corresponding author at: Faculty of Medicine and Health, Sydney Medical School Nepean, Charles Perkins Centre Nepean, The University of Sydney, Level 5 South Block, Nepean Clinical School, PO Box 63, Penrith, NSW 2751, Australia. Tel.: 161 2 4734 4278; fax: 161 2 4734 2614., Email: [email protected] Ó 2019 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.
Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
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Numerous patient risk factors have been reported in the literature; however, there is no consensus on the effect the majority of these factors have on degeneration. Consequently, current guidelines for choosing an aortic valve prosthesis have not incorporated patient factors influencing bioprosthetic SVD, with the exception of age [1,3]. To aid in identifying patients at risk, we sought to: (1) conduct a systematic review of literature for studies reporting patient risk factors for bioprosthetic aortic valve degeneration; and (2) estimate the pooled means of their effect sizes by meta-analysis.
Methods The search strategy followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [4]. A systematic literature search was performed using PubMed, Web of Science, and Scopus electronic databases for studies reporting risk factors for bioprosthetic aortic valve degeneration. The search terms used were: ‘aortic valve’, ‘bioprosthesis’, ‘heart valve prosthesis’, ‘valve replacement’, ‘heart valve prosthesis implantation’, ‘degeneration’, ‘durability’, and ‘prosthesis failure’. Full details of search strategies used are presented in Online Appendix A. The search was limited to humans and studies published in English language. Studies were limited to those reporting aortic valve replacement from the last 20 years to encompass valves used in current clinical practice. Review articles, editorials, and letters were also excluded. No previous systematic reviews of this nature were identified. The last search was performed on 4 May 2017. The review was registered with the Prospective Registration of Systematic Reviews (http://www.crd.york. ac.uk/PROSPERO/display_record.php?ID=CRD4201809452), as PROSPERO 2018 CRD42018094522. Studies were included if the following criteria were met: (1) studies reporting risk factors for bioprosthetic aortic SVD following surgical aortic valve replacement, with or without concomitant procedures; (2) studies carried out on human adults (.18 years); and (3) studies from full-length publications in peer-reviewed English language journals. Studies were excluded for the following criteria: (1) studies with a population size n,50; (2) studies reporting results from homograft use, mechanical valve use, Ross Procedures (pulmonary autograft) or transcatheter aortic valve replacements; (3) studies reporting on endocarditis; and (4) review articles, editorial comments, letters to the editor, or articles without full text. Two independent investigators (AO and KC) reviewed the eligibility of the studies, first through screening by title and abstract, then review of the full text. Discrepancies were reviewed by another reviewer (KN) and resolved by consensus. The primary outcome of interest was the incidence of SVD as identified on echocardiography or reintervention (surgical explantation or transcatheter intervention). A full list of definitions used is presented in Online Appendix B. The following data were extracted: author’s name, date of publication, year range of aortic valve implantation, mean age, sex distribution, follow-up time, the number of
A. Ochi et al.
participants, the incidence of SVD, methods of assessment for SVD, valve type used, and risk factors discussed by each paper. A summary list of reported risk factors is presented in Online Appendix C. Adjusted hazard ratios (HR) from Cox proportional hazard models, or odds ratios (OR) from logistic regression models, and associated 95% confidence intervals (CI) were extracted as outcome measures. When only count data of SVD incidence were presented, the risk estimate and associated 95% CI was calculated and reported as an odds ratio (OR) in lieu of a HR. Where multiple articles represented data from the same dataset, only the largest dataset was included in the analysis. Statistical analysis, including testing for heterogeneity and publication bias, was performed using R version 3.4.1 (R Foundation for Statistical Computing, 2016) using the metafor package. Hazard ratios and OR, and associated 95% Cis, were pooled using a random effects model to determine a cumulative risk estimate and 95% CI. Sensitivity analysis was conducted, dividing studies based on the method of SVD assessment using three subgroups: echocardiography, reintervention, and both echocardiography and reintervention. A p-value of ,0.05 was considered statistically significant. Heterogeneity was assessed using I2 and Cochran Q tests. 2 I values of 25%, 50%, and 75% were considered low, moderate, and high levels of heterogeneity respectively. A Cochran Q p-value of ,0.05 was considered significant, indicating heterogeneity. Publication bias was assessed visually using funnel plots, with and without Duval and Tweedie trim and fill. Egger’s test was able to be used if the risk factor was analysed in three or more studies. An Egger’s test p-value of ,0.10 was considered significant, indicating possible publication bias. Study quality was assessed using the Newcastle Ottawa Scale (NOS) for non-randomised studies in meta-analyses [5].
Results The process of article selection based on PRISMA guidelines is presented in Figure 1. A total of 2,964 articles were identified. Following removal of 1,011 duplicates, the titles and abstracts of 1,953 articles were screened for eligibility. Among them, 165 were included and assessed by full text. A total of 58 articles were eligible for the qualitative synthesis and 29 were eligible for the meta-analysis. Articles included were published from 2006 to 2017, with aortic valve replacement occurring from 1970 to 2014. Baseline demographics of the 29 included studies are summarised in Table 1. Cumulatively, there were 25,490 subjects. The mean (weighted) age of subjects was 67.3 years (range 39.1–76 years), and 57.6% (range 17–79%) were male. The mean total follow-up was 18.5 years (range 6–32.4 years) during which 981 patients developed SVD as identified by echocardiography or reintervention (3.8% of 25,490). From our systematic review, the following 18 factors were identified as risk factors for bioprosthetic SVD: younger age, sex (both male and female), prosthesis brand, prosthesis size
Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
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Risk Factors for Structural Valve Degeneration
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Figure 1 PRISMA flow chart.
(,19 mm, ,21 mm, ,23 mm), patient2prosthesis mismatch (PPM), absence of anticalcification preparation, concomitant coronary artery bypass graft surgery, subcoronary implantation technique, postoperative pressure gradient, dyslipidaemia, smoking, metabolic syndrome, presence and absence of lipid lowering medication including statins, raised body mass index and body surface area (BSA), and renal disease. One study [6] identified the absence of anticoagulant therapy as a risk factor; however, no other studies reported the influence of anticoagulant or antiplatelet therapy on SVD. Factors involved in calcium homeostases such as calcium, vitamin D, phosphate and parathyroid hormone levels; osteoporosis and bone mineral density; bisphosphonates;
and calcium and vitamin D supplementation were not reported in any studies identified on systematic review. Variables reported two or more times were selected for quantitative analysis. This included age (both continuous and categorical), BSA, prosthesis size, subcoronary root implantation, anticalcification preparation, PPM, smoking, hypertension, diabetes mellitus, dyslipidaemia, chronic renal failure, and chronic obstructive pulmonary disease. Of these, four factors were identified as significant determinants for bioprosthetic SVD. Increasing age was associated with a 9% decrease in risk for SVD per 1-year of age increase (HR 0.91; 95% CI 0.8920.94; p,0.0001) (Figure 2A). The protective nature of increasing age was confirmed when analysed
Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
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Author
Publish (year)
Date implantation (years)
Total (n)
Alvarez et al. [27]
2009
1993 2 2006
491
Aupart et al. [28]
2006
1984 2 2003
1,133
Follow-Up (years)
Incident SVD (n)
SVD defined by
Age (years)
Male (%)
Valve type (if specific type used)
Risk estimate used
Risk factor(s) discussed
11.7
27
Echo
76.5 6 4
46.8
Mitroflow 12A
HR
Age
18
19
Echo
72.6
63.2
Perimount
OR
Age Age, subcoronary vs root
Bach and Kon [29]
2014
1992 2 2004
725
15
31
Explant
71.7 6 7.9
55.4
Freestyle
OR
Bach et al. [30]
2007
1997 2 2006
725
13.3
16
Explant
71.7 6 7
55.4
Freestyle
OR
Age
Bergoend et al. [31]
2010
1984 2 2003
1,857
20
48
Echo
69.8
69
Perimount
OR
Age
24.6
157
Echo
70.7 6 10.4
68.4
Perimount
HR
Age
6
Echo
74.0
46.4
CryoLife-O’Brien
OR
Valve size
87 23
Both Echo
67 6 11 75.9 6 5.3
76.2 17
Hancock II Mitroflow
OR HR
Age Age, BMI, BSA, chronic renal
Bourguignon et al. [32]
2015
1984 2 2008
2,659
Chambers et al. [33]
2011
1999 2 2008
166
David et al. [34] De Paulis et al. [35]
2010 2016
1982 2 2004 2005 2 2011
1,134 205
12 25 9.4
failure, concomitant procedures, COPD, diabetes, dyslipidaemia, ejection fraction, HTN, PPM, sex, valve size Flameng et al. [18]
2014
n/a
648
15.6
73
Both
73.8 6 4.9
52
-
HR
Anticalcification treatment, PPM
Goldman et al. [36]
2017
2007 2 2009
710
6
11
Both
72.4 6 9.3
66.3
Trifecta
OR
Valve size
Grunkemeier et al. [37]
2012
1976 2 2010
2,955
24
34
Explant
73
62.1
-
HR
Age, CABG pericardial valve, sex, valve size
Joshi et al. [38]
2014
1999 2 2013
281
10
15
Explant
67
n/a
Mitroflow
HR
Age, angina, atrial fibrillation, COPD, diabetes, elevated creatinine, extracardiac arteriopathy, HTN, poor left ventricular function, previous cardiac surgery, previous heart
2010
1990 2 2006
1,193
17.3
n/a
Both
72.4
59.8
-
HR
attack, smoking, Age, lipid-lowering therapy
Minakata et al. [40]
2014
1986 2 2001
574
10
n/a
Explant
71.9 6 8.6
52.4
Perimount
HR
Age, previous AVR
Mohammadi et al. [41]
2006
1993 2 2005
605
12.2
12
Explant
68 6 8.3
54.9
Freestyle
OR
Chronic renal failure, HTN, sex,
Mohammadi et al. [42]
2014
1993 2 2013
531
20.3
39
Explant
66.7
59.7
Freestyle
HR
Age, subcoronary vs root
subcoronary vs root replacement
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Kulik et al. [39]
A. Ochi et al.
Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
Table 1 Baseline characteristics of included studies.
Author
Publish (year)
Date Total (n) Follow-Up Incident SVD Age implantation (years) SVD (n) defined by (years) (years)
Male (%) Valve type (if specific type used)
Risk Risk factor(s) discussed estimate used HR
Mosquera et al. [43]
2016 2001 2 2014 1,023
10
31
Echo
75.6
49.2
Mitroflow
Nielsen et al. [44]
2016 1999 2 2014 2,392
15
14
Explant
74.7 6 6.8
60.4
Perimount & Mitroflow HR
Age, Mitroflow
Piccardo et al. [45]
2015 1994 2 2011 728
16.2
30
Echo
76 6 6
57
Mitroflow 12A & LX
Age, BSA, concomitant procedure,
Age, anticalcification treatment, valve size
OR
emergency procedure, endocarditis, PPM, sex, valve size, Reiss et al. [46]
2011 1994 2 1999 255
14
Explant
65
62.7
Mosaic
OR
Age
Repossini et al. [47]
2016 2004 2 2009 565
11.8
23
Echo
74.6 6 8.3
57.2
Freedom Solo
HR
Age, chronic renal failure, sex
Rizzoli et al. [48]
2006 1983 2 2002 809
12
21
Echo
68.4 6 8
75
Hancock II
HR
Age, incompetence of natural
Ruel et al. [49]
2005 1976 2 2002 309
26.7
9
Explant
39.1 6 8.1
75.8
-
HR
Smoking
Ruel et al. [50]
2004 1970 2 2002 1,360
32.4
n/a
Explant
n/a
n/a
-
HR
Age, BSA, current prosthesis,
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Risk Factors for Structural Valve Degeneration
valve, CAD
persistent left ventricular hypertrophy, sex, smoking, valve size Senage et al. [51]
2014 2002 2 2007 617
Stanger et al. [52]
2015 2005 2 2009 149
Une et al. [25]
2014 1982 2 2008 304
39
Echo
76.1 6 6.3
45.2
Mitroflow 12A & LX
HR
Dyslipidaemia, PPM, sex
9.6
26
Echo
73.6 6 8.7
54.4
Freedom Solo
HR
Age, diabetes, HTN, PPM, renal
27.5
85
Both
49.2 6 9
79
Hancock II
OR
Age, PPM
22.5
96
Both
57.9 6 19.1 69.5
-
HR
Age, PPM
478.1 18.5
981 42.4
67.3
57.6
157
76
79
39.1
17
16
dysfunction Urso et al. [53] Sum Mean (weighted)
2014 1974 2 2009 387 2006 - 2017 1970 2 2014 25,490 2,955
Minimum
149
32.4 6
6
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Maximum
Abbreviations: AVR, aortic valve replacement; BMI, body mass index; BSA, body surface area; CABG, coronary artery bypass graft; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; HTN, hypertension; OR, odds ratio; PPM, patient2prosthesis mismatch.
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Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
Table 1 (continued).
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Figure 2 Effect of age on increase on structural valve degeneration. (A) Forest plot of studies reporting the effect of age per 1 year increase. (B) Forest plot studies reporting the risk for patients older than 60 years. (C) Forest plot studies reporting the risk for patients older than 65 years. (D) Forest plot studies reporting the risk for patients older than 70 years.
dichotomously using age cut-offs of 60 years (OR 0.12; 95% CI 0.06–0.23; p,0.0001) (Figure 2B), 65 years (OR 0.06; 95% CI 0.02–0.21; p,0.0001) (Figure 2C) and 70 years (OR 0.06; 95% CI 0.01–0.28; p=0.0004) (Figure 2D). Patient2prosthesis mismatch (PPM) (HR 1.95; 95% CI 1.56–2.43; p,0.001) (Figure 3A), a raised BSA (HR 1.77; 95% CI 1.04–3.01; p=0.0339) (Figure 3B), and smoking (HR 2.28; 95% CI 1.37–3.79; p=0.0015) (Figure 3C) were associated with an increased risk for degeneration. Anticalcification preparation of the bioprosthetic valve was confirmed as a protective factor for degeneration (HR 0.41; 95% CI 0.19–0.89;
p=0.0245). All other factors were not statistically significant on analysis. Results from pooled HRs and ORs with associated 95% CIs are presented in Table 2. Sensitivity analysis based on the method of SVD assessment showed similar results, with the exception of sex. Subgroup analysis based on echocardiographic assessment found female sex as a risk factor for SVD (HR 2.34; 95% CI 1.03–5.35; p=0.0428); however, when based on reintervention, female sex was a protective factor for SVD (HR 0.68; 95% CI 0.48–0.96; p=0.027). Results from the sensitivity analysis are presented in Online Appendix D.
Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
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Risk Factors for Structural Valve Degeneration
Figure 3 Effect of patient2prosthesis mismatch, body surface area, and smoking on structural valve degeneration. (A) Forest plot of studies reporting the effect of patient2prosthesis mismatch. (B) Forest plot of studies reporting the effect of increasing body surface area. (C) Forest plot of studies reporting the effect of smoking.
Table 2 Results of meta-analysis — hazard ratio and odds ratio of risk variables for structural valve degeneration in aortic valve bioprostheses. Risk factor
Pooled OR
95% CI
P-value
Study (n)
#21 mm Prosthesis Size
0.76
0.24 – 2.38
0.64
2
Age .60 years
0.08
0.04 – 0.17
,0.0001
6
I2 (%) 0 70.52
Q-c2 0.53 16.71
Q-p value
Egger’s test (p-value)
0.47
*
0.0051
0.10
Age .65 years
0.06
0.02 – 0.21
,0.0001
2
40.89
1.69
0.19
*
Age .70 years
0.06
0.01 – 0.28
0.0004
3
65.75
6.12
0.0469
0.81
Subcoronary implantation
1.18
0.54 – 2.56
0.67
2
1.01
0.32
Risk factor
Pooled HR
95% CI
P-value
0.9 Study (n)
I2 (%)
Q-c2
Q-p value
Age .60 years
1.99
0.11 – 34.66
0.64
2
71.07
3.46
Age (per 1 year) (continuous)
0.91
0.89 – 0.94
,0.0001
10
93.23
132.92
,0.0001
Anticalcification preparation Female
0.41 1.33
0.19 – 0.89 0.65 – 2.73
0.025 0.44
2 5
33.04 76.06
1.49 16.71
0.22 0.0022
0.06
* Egger’s test (p-value) * 0.002 * 0.15
BSA (m2) (continuous)
1.77
1.04 – 3.01
0.034
2
0
0.65
0.42
*
Valve size (mm) (continuous)
0.89
0.74 – 1.08
0.24
3
0
0.69
0.71
0.95
,0.001
0.47
Patient2prosthesis mismatch
1.95
1.56 – 2.43
0.75
0.86
Smoking
2.28
1.37 – 3.79
0.0015
2
23.94
1.31
0.25
*
Dyslipidaemia
1.25
0.45 – 3.46
0.67
2
71.17
3.47
0.06
*
4
0
Hypertension
0.92
0.47 – 2.13
0.99
3
13.90
2.32
0.31
0.0632
Chronic renal failure Diabetes mellitus
1.42 1.46
0.42 – 4.82 0.79 – 2.71
0.57 0.23
3 3
51.88 0
4.16 1.53
0.13 0.47
0.0177 0.20
COPD
1.07
0.39 – 2.96
0.89
2
0.01
0.92
*
0
Abbreviations: BSA, body surface area; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; OR, odds ratio. *Egger’s test was only able to be used if the risk factor was reported in three or more studies.
Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
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Egger’s test indicated significant publication bias for age when assessed continuously (p=0.002), hypertension (p=0.063), and chronic renal failure (p=0.018). A high degree of inter-study heterogeneity was present for age analysed continuously (I2=93.2%). As a sub-group analysis, when age was dichotomised using cut-off values of .60, .65, or .70 years, the pooled effect sizes were similar (pool OR 0.06w0.12). The majority of studies were of good to fair quality, as assessed by the Newcastle Ottawa Scale (NOS) for cohort studies (Online Appendix E). The definitions used for bioprosthetic SVD varied between studies. The 13 studies referred to the 2008 ‘Guidelines for reporting mortality and morbidity after cardiac valve interventions’ defining SVD as “changes intrinsic to the valve, such as wear, fracture, poppet escape, calcification, leaflet tear, stent creep, and suture line disruption of components of a prosthetic valve” [7]; however, eight referred to the 1996 definition [8], two referred to the 1988 definition of these guidelines [9], and 12 used an unknown or independent definition. Furthermore, such definitions allow for incidence based on reintervention in addition to echocardiography: 11 studies based on reintervention, 13 based on echocardiography, and six based on both methods. Of the 19 studies using echocardiography, only 10 provided echocardiographic parameters on which SVD was based. Six (6) studies considered a mean gradient of .40 mmHg as indicative of stenotic SVD, with other parameters being inconsistent.
Discussion The American Heart Association/American College of Cardiology (AHA/ACC) and the European Society of Cardiology/European Association of Cardio-Thoracic Surgery (ESC/EACTS) Guidelines provide limited guidance on factors to consider when choosing a prosthesis, with the exception of age [1,3]. The AHA/ACC Guidelines, which state that patients aged between 50–70 years could reasonably choose either prosthesis, exemplify the existing uncertainty with regard to age as most patients are within this range. Identification of risk factors for bioprosthetic SVD could aid clinicians in identifying patients at highest risk, particularly for whom the choice is unclear such as patients aged 50–70 years. This systematic review and meta-analysis identified four patient factors influencing bioprosthetic SVD: increasing age as a protective factor, and smoking, raised BSA, and PPM as risk factors. Younger patient’s susceptibility to SVD is well established, hence recommendations for prosthesis choice are based around age [1,3]. Both continuous and categorical analysis in this meta-analysis confirmed that increasing age is a protective factor. The 2017 AHA/ACC Guidelines widened the age range whereby a patient could reasonably choose either a mechanical or a bioprosthesis from 60–70 to 50–70 years [3]. This may pose an issue as life expectancy increases, particularly in western countries where life expectancy is over 80 years [10,11]. Despite improved bioprosthesis durability [12],
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a patient aged 50 with expected bioprosthetic durability of 14.5 years[12] could expect to undergo at least two reoperations in their lifetime. Based on this meta-analysis patients younger than 60, 65, and 70 years are still at high risk for degeneration, thus caution is needed for patients aged 50–70 years, particularly those with additional risk factors for degeneration. The four factors identified in this meta-analysis share the commonality of increasing the flow rate across the valve prosthesis, inducing mechanical stress. Patients of a younger age, a raised BSA, and smokers all have an increased metabolic rate, which is associated with a higher cardiac output [13–15]. Increased flows generated by raised cardiac output exert shearing forces on valve leaflets[16], which may contribute significantly to the pathogenesis of bioprosthetic SVD. Other high cardiac output states warrant further investigation, such as thyrotoxicosis, anaemia, and haemodialysis with arteriovenous shunting. The outflow-obstruction-like effect created by PPM also generates increased flow velocity across the valve prosthesis, resulting in shearing forces exerted on valve leaflets [17,18]. This meta-analysis supports continued vigilance to avoid PPM, particularly in the presence of high cardiac output states where flows are further increased. Similarly, an increased risk of degeneration is reported in transcatheter valve-in-valve procedures, which have a similar PPM effect by further reducing an already narrowed effective orifice area of the previously inserted valve [6]. This requires continued study as the number of transcatheter procedures increase and long-term durability data is established. Analysis of PPM may have been affected by the variation in definition of PPM between studies. Whole group analysis found no effect of sex on SVD development. However, subgroup analysis found female sex as a risk factor for degeneration when assessed using echocardiography but protective when based on reintervention, accounting for the insignificant result of whole group analysis. Results may be confounded by lack of consideration for factors such as raised BSA and PPM to which females are more susceptible [19]; however, two of three studies found female sex as a risk factor independent of PPM. Therefore, a conclusion cannot be made about the effect of sex on SVD, and requires further investigation such as independent patient data analysis. Given the histological evidence of atherosclerotic-like processes in bioprostheses with SVD, and the similarity of SVD to native aortic stenosis, atherosclerotic risk factors are theoretically also risk factors for bioprosthetic SVD [20]. However, diabetes mellitus, hypertension, and dyslipidaemia were not found to be significant risk factors for degeneration. Factors were analysed based on clinical diagnosis, which may not accurately assess their effect on SVD, particularly in the presence of medications altering patients’ biochemistry and physiology. Such pathologies exist on a continuum, thus analysis of biochemical (blood sugar levels and lipid levels) or physiological measures (blood pressure) is a more appropriate method to better understand their influence. Large-scale registry data is required to investigate these factors in this manner.
Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
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AHA/ACC 2014[21] and ESC/EACTS 2012 Guidelines [22] acknowledge the risk of accelerated SVD in those with renal disease. This is expected given the altered calcium metabolism, and anaemia and arteriovenous fistula raising cardiac output [23]; however, results from this meta-analysis did not identify chronic renal disease as a risk factor for SVD. This may be due to avoidance of bioprostheses in patients with severe renal dysfunction and haemodialysis, in keeping with recommendations. Reduced survival of patients with renal disease may have induced a survivor bias [22]. Analysis based on continuous measures may demonstrate the significance of renal dysfunction. Gring et al. [14] exemplified this, finding elevated creatinine clearance as a risk factor for SVD. The low overall rate of SVD at 3.8% found in this metaanalysis is likely an underestimation of the true incidence of degeneration, attributable to a variety of factors. Firstly, only institutions with excellent outcomes might have reported their data and/or been accepted for publication, inducing publication bias. Secondly, older populations may induce a survival bias due to death prior to SVD development. This may also overestimate the protective nature of age. Additionally, a large degree of interstudy variability was present in the definition, including echocardiographic parameters and method of assessment for bioprosthetic SVD. Dvir et al. [24] highlighted the problem of discrepancies between definitions of SVD, and thus the difficulty adequately comparing studies. Based on studies that use both methods of assessment, reintervention underestimates the true incidence of degeneration, as not all those with degeneration will undergo reintervention [18,25,26]. Further interstudy variability also existed in the echocardiographic parameters used. Studies should consider use of standardised SVD grading such as proposed by Dvir et al. [24] to facilitate accurate identification of SVD and future interstudy comparisons of risk factors. Meta-analyses are limited by the studies on which they are based. The studies included are observational studies, with flaws inherent in their design due to the inability to randomise and control for many patient factors. Fifty-one (51) studies were excluded due to lack of risk factor reporting, and 13 were excluded as the incidence of degeneration was not reported. This may account for the low overall incidence of degeneration reported in this study, and may have influenced the risk factors analysed (Appendix G). A variety of bioprostheses were used with varying characteristics, including some that have been superseded. Valve characteristics such as tissue type and presence or absence of stenting were unable to be controlled. Studies investigating homografts and autografts were also excluded, thus, data on SVD may have been lost during this exclusion. Follow-up times of included studies ranged from 6 to 32 years; however, due to the small number of studies analysed, subgroup analysis stratified by time was not possible. Future studies may consider the effect of combinations of risk factors, which could not be assessed in this study. Finally, factors that were not mentioned by studies include life expectancy, patient and
clinician preference, and ethnicity, which should be considered in future studies. This systematic review and meta-analysis has identified four main risk factors for bioprosthetic aortic valve degeneration: younger age, PPM, raised BSA, and smoking, all of which share the commonality of creating high flows across the valve. Identification of factors influencing bioprosthetic SVD will remain an important issue with the rise of transcatheter aortic valve implantation and increasing bioprosthesis use [2]. Incorporation of these factors may be useful in the prediction of patients at high risk, thus to help inform prosthesis choice, particularly in patients for whom age guidelines are unclear. Some patient factors such as BSA and smoking are amenable to modification through lifestyle change. Modification may help reduce the development of SVD in patients unable to receive a mechanical prosthesis. Future studies should consider other high cardiac output states that also increase flows, both physiological and pathological, in addition to continued research into atherosclerotic and calcium homeostatic factors.
Disclosures Ayame Ochi is supported by a scholarship from the National Heart Foundation of Australia, which had no role in the preparation of this manuscript. Kazuaki Negishi (grant number: N0025231) is supported by a fellowship award from the National Heart Foundation of Australia, which had no role in the preparation of this manuscript.
Funding The authors declare no funding was received for the preparation of this manuscript.
Supplementary Data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j. hlc.2019.09.013.
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[25] Une D, Ruel M, David TE. Twenty-year durability of the aortic Hancock II bioprosthesis in young patients: Is it durable enough? Eur J Cardiothorac Surg 2014;46(5):825–30. [26] Johnston DR, Soltesz EG, Vakil N, Rajeswaran J, Roselli EE, Sabik III JF, et al. Long-term durability of bioprosthetic aortic valves: Implications from 12,569 implants. Ann Thorac Surg 2015;99(4):1239–47. [27] Alvarez JR, Sierra J, Vega M, Adrio B, Martinez-Comendador J, Gude F, et al. Early calcification of the aortic Mitroflow pericardial bioprosthesis in the elderly. Interact Cardiovasc Thorac Surg 2009;9(5):842–6. [28] Aupart MR, Mirza A, Meurisse YA, Sirinelli AL, Neville PH, Marchand MA. Perimount pericardial bioprosthesis for aortic calcified stenosis: 18-year experience with 1,133 patients. J Heart Vavle Dis 2006;15(6):768–76. [29] Bach DS, Kon ND. Long-term clinical outcomes 15 years after aortic valve replacement with the freestyle stentless aortic bioprosthesis. Ann Thorac Surg 2014;97(2):544–51. [30] Bach DS, Metras J, Doty JR, Yun KL, Dumesnil JG, Kon ND. Freedom from structural valve deterioration among patients aged #60 years undergoing Freestyle® stentless aortic valve replacement. J Heart Valve Dis 2007;16(6):649–56. [31] Bergoënd E, Aupart MR, Mirza A, Meurisse YA, Sirinelli AL, Neville PH, et al. 20 years’ durability of Carpentier-Edwards Perimount stented pericardial aortic valve. Aortic Root Surgery: The Biological Solution 2010. p. 441251. [32] Bourguignon T, Bouquiaux-Stablo AL, Candolfi P, Mirza A, Loardi C, May MA, et al. Very long-term outcomes of the Carpentier-Edwards Perimount valve in aortic position. Ann Thorac Surg 2015;99(3):831–7. [33] Chambers JB, Parkin D, Rajani R, Shabbo F. The CryoLife-O’Brien stentless replacement aortic valve: Patterns of valve failure. J Heart Valve Dis 2011;20(4):433–8. [34] David TE, Armstrong S, Maganti M. Hancock II bioprosthesis for aortic valve replacement: The gold standard of bioprosthetic valves durability? Ann Thorac Surg 2010;90(3):775–81. [35] De Paulis R, D’Aleo S, Bellisario A, Salica A, Weltert LP, Scaffa R, et al. The fate of small-size pericardial heart valve prostheses in an older patient population. J Thorac Cardiovasc Surg 2017;153(1):31–9e2. [36] Goldman S, Cheung A, Bavaria JE, Petracek MR, Groh MA, Schaff HV. Midterm, multicenter clinical and hemodynamic results for the Trifecta aortic pericardial valve. J Thorac Cardiovasc Surg 2017;153(3):561–9.e2. [37] Grunkemeier GL, Furnary AP, Wu Y, Wang L, Starr A. Durability of pericardial versus porcine bioprosthetic heart valves. J Thorac Cardiovasc Surg 2012;144(6):1381–6. [38] Joshi V, Prosser K, Richens D. Early prosthetic valve degeneration with Mitroflow aortic valves: Determination of incidence and risk factors. Interact Cardiovasc Thorac Surg 2014;19(1):36–40. [39] Kulik A, Masters RG, Bedard P, Hendry PJ, Lam BK, Rubens FD, et al. Postoperative lipid-lowering therapy and bioprosthesis structural valve deterioration: Justification for a randomised trial? Eur J Cardiothorac Surg 2010;37(1):139–44. [40] Minakata K, Tanaka S, Takahara Y, Kaneko T, Usui A, Shimamoto M, et al. Long-term durability of pericardial valves in the aortic position in younger patients: When does reoperation become necessary? J Card Surg 2015;30(5):405–13. [41] Mohammadi S, Baillot R, Voisine P, Mathieu P, Dagenais F. Structural deterioration of the Freestyle aortic valve: Mode of presentation and mechanisms. J Thorac Cardiovasc Surg 2006;132(2):401–6. [42] Mohammadi S, Kalavrouziotis D, Voisine P, Dumont E, Doyle D, Perron J, et al. Bioprosthetic valve durability after stentless aortic valve replacement: The effect of implantation technique. Ann Thorac Surg 2014;97(6):2011–8. [43] Mosquera VX, Bouzas-Mosquera A, Velasco-Garcia C, Muniz J, EstevezCid F, Portela-Torron F, et al. Long-term outcomes and durability of the Mitroflow aortic bioprosthesis. J Card Surg 2016;31(5):264–73. [44] Nielsen PH, Hjortdal V, Modrau IS, Jensen H, Kimose HH, Terp K, et al. Durability after aortic valve replacement with the Mitroflow versus the Perimount pericardial bioprosthesis: A single-centre experience in 2393 patients. Eur J Cardiothorac Surg 2016;49(6):1705–10. [45] Piccardo A, Blossier JD, Le Guyader A, Orsel I, Sekkal S, Cornu E, et al. Fate of aortic bioprostheses: An 18-year experience. J Thorac Cardiovasc Surg 2016;151(3):754–61e1. [46] Riess FC, Bader R, Cramer E, Hansen L, Schiffelers S, Wallrath J, et al. The Mosaic porcine bioprosthesis: Role of age on clinical performance in aortic position. J Thorac Cardiovasc Surg 2011;141(6):1440–8.e1. [47] Repossini A, Fischlein T, Santarpino G, Schafer C, Claus B, Passaretti B, et al. Pericardial stentless valve for aortic valve replacement: Long-term results. Ann Thorac Surg 2016;102(6):1956–65.
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[48] Rizzoli G, Mirone S, Ius P, Polesel E, Bottio T, Salvador L, et al. Fifteenyear results with the Hancock II valve: A multicenter experience. J Thorac Cardiovasc Surg 2006;132(3):602–U26. [49] Ruel M, Kulik A, Lam BK, Rubens FD, Hendry PJ, Masters RG, et al. Long-term outcomes of valve replacement with modern prostheses in young adults. Eur J Cardiothorac Surg 2005;27(3):425–33. [50] Ruel M, Kulik A, Rubens FD, Bedard P, Masters RG, Pipe AL, et al. Late incidence and determinants of reoperation in patients with prosthetic heart valves. Eur J Cardiothorac Surg 2004;25(3):364–70. [51] Senage T, Le Tourneau T, Foucher Y, Pattier S, Cueff C, Michel M, et al. Early structural valve deterioration of Mitroflow aortic bioprosthesis
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REVIEW
Heart, Lung and Circulation (2019) -, -–1443-9506/19/$36.00 https://doi.org/10.1016/j.hlc.2019.09.013
Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis Ayame Ochi, BMedSci a, Kevin Cheng, BMedSci, MBBS b, Bing Zhao, MD, MSc a, Ashutosh A. Hardikar, MBBS a,c, Kazuaki Negishi, MD, PhD a,b,d,* a
Menzies Institute for Medical Research, University of Tasmania, Hobart, Tas, Australia Department of Cardiology, Royal Hobart Hospital, Hobart, Tas, Australia c Department of Cardiothoracic Surgery, Royal Hobart Hospital, Hobart, Tas, Australia d Faculty of Medicine and Health, Sydney Medical School Nepean, University of Sydney, Sydney, NSW, Australia b
Received 5 August 2019; accepted 22 September 2019; online published-ahead-of-print xxx
The choice of valve type for aortic valve replacement surgery is sometimes challenging. The main risk for bioprostheses is structural valve degeneration (SVD); however, little is known about what the most important risk factors are. We conducted a systematic review and meta-analysis to identify the risk factors and estimate their pooled effect sizes to aid the prosthesis choice for replacement. We followed PRISMA guidelines and systematically searched three electronic databases (PubMed, Scopus, and Web of Science) using appropriate key terms: ‘aortic valve’, ‘bioprosthesis’, ‘degeneration’, ‘durability’, ‘prosthesis failure’, etc. Hazard ratio (HR) and odds ratio (OR) and associated 95% confidence intervals (CI) were extracted. Pooled risk estimates were calculated using a random-effects model. Twenty-nine (29) observational studies were included with a total of 25,490 patients, 981 of whom developed SVD over a mean follow-up time of 18.5 years. Four (4) factors influencing bioprosthetic SVD were identified: increasing age was a protective factor (per 1-yr increase, HR: 0.91 [95% CI 0.89, 0.94], p,0.0001), whereas increased body surface area (HR 1.77 [1.04, 3.01], p=0.034), patient2prosthesis mismatch (HR 1.95 [1.56, 2.43], p,0.001), and smoking (HR 2.28 [1.37, 3.79], p=0.0015) were risk factors for SVD. We found younger age, patient2prosthesis mismatch, body surface area, and smoking, as risk factors for aortic SVD, which should be considered for valve selection. This study generates a further hypothesis that accelerated flow across the valve is a shared key component in the pathophysiology of SVD, thus future research should consider other high cardiac output states. Keywords
Structural valve degeneration Risk factors Aortic valve Meta-analysis
Introduction Aortic valve disease is the most common valve disease in the developed world, predominantly attributable to aortic stenosis, with the prevalence rising due to an ageing population [1]. Over time, bioprosthesis use for aortic valve disease management has increased to over 80% of replacements [2].
Structural valve degeneration (SVD) is the major drawback of bioprostheses, resulting in patients re-experiencing valvular heart disease symptoms, and potentially requiring reintervention. Given the rising number of patients requiring replacement and the increasing use of bioprostheses, it is important to identify patients who are at highest risk for bioprosthetic SVD to aid prosthesis choice.
*Corresponding author at: Faculty of Medicine and Health, Sydney Medical School Nepean, Charles Perkins Centre Nepean, The University of Sydney, Level 5 South Block, Nepean Clinical School, PO Box 63, Penrith, NSW 2751, Australia. Tel.: 161 2 4734 4278; fax: 161 2 4734 2614., Email: [email protected] Ó 2019 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.
Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
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Numerous patient risk factors have been reported in the literature; however, there is no consensus on the effect the majority of these factors have on degeneration. Consequently, current guidelines for choosing an aortic valve prosthesis have not incorporated patient factors influencing bioprosthetic SVD, with the exception of age [1,3]. To aid in identifying patients at risk, we sought to: (1) conduct a systematic review of literature for studies reporting patient risk factors for bioprosthetic aortic valve degeneration; and (2) estimate the pooled means of their effect sizes by meta-analysis.
Methods The search strategy followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [4]. A systematic literature search was performed using PubMed, Web of Science, and Scopus electronic databases for studies reporting risk factors for bioprosthetic aortic valve degeneration. The search terms used were: ‘aortic valve’, ‘bioprosthesis’, ‘heart valve prosthesis’, ‘valve replacement’, ‘heart valve prosthesis implantation’, ‘degeneration’, ‘durability’, and ‘prosthesis failure’. Full details of search strategies used are presented in Online Appendix A. The search was limited to humans and studies published in English language. Studies were limited to those reporting aortic valve replacement from the last 20 years to encompass valves used in current clinical practice. Review articles, editorials, and letters were also excluded. No previous systematic reviews of this nature were identified. The last search was performed on 4 May 2017. The review was registered with the Prospective Registration of Systematic Reviews (http://www.crd.york. ac.uk/PROSPERO/display_record.php?ID=CRD4201809452), as PROSPERO 2018 CRD42018094522. Studies were included if the following criteria were met: (1) studies reporting risk factors for bioprosthetic aortic SVD following surgical aortic valve replacement, with or without concomitant procedures; (2) studies carried out on human adults (.18 years); and (3) studies from full-length publications in peer-reviewed English language journals. Studies were excluded for the following criteria: (1) studies with a population size n,50; (2) studies reporting results from homograft use, mechanical valve use, Ross Procedures (pulmonary autograft) or transcatheter aortic valve replacements; (3) studies reporting on endocarditis; and (4) review articles, editorial comments, letters to the editor, or articles without full text. Two independent investigators (AO and KC) reviewed the eligibility of the studies, first through screening by title and abstract, then review of the full text. Discrepancies were reviewed by another reviewer (KN) and resolved by consensus. The primary outcome of interest was the incidence of SVD as identified on echocardiography or reintervention (surgical explantation or transcatheter intervention). A full list of definitions used is presented in Online Appendix B. The following data were extracted: author’s name, date of publication, year range of aortic valve implantation, mean age, sex distribution, follow-up time, the number of
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participants, the incidence of SVD, methods of assessment for SVD, valve type used, and risk factors discussed by each paper. A summary list of reported risk factors is presented in Online Appendix C. Adjusted hazard ratios (HR) from Cox proportional hazard models, or odds ratios (OR) from logistic regression models, and associated 95% confidence intervals (CI) were extracted as outcome measures. When only count data of SVD incidence were presented, the risk estimate and associated 95% CI was calculated and reported as an odds ratio (OR) in lieu of a HR. Where multiple articles represented data from the same dataset, only the largest dataset was included in the analysis. Statistical analysis, including testing for heterogeneity and publication bias, was performed using R version 3.4.1 (R Foundation for Statistical Computing, 2016) using the metafor package. Hazard ratios and OR, and associated 95% Cis, were pooled using a random effects model to determine a cumulative risk estimate and 95% CI. Sensitivity analysis was conducted, dividing studies based on the method of SVD assessment using three subgroups: echocardiography, reintervention, and both echocardiography and reintervention. A p-value of ,0.05 was considered statistically significant. Heterogeneity was assessed using I2 and Cochran Q tests. 2 I values of 25%, 50%, and 75% were considered low, moderate, and high levels of heterogeneity respectively. A Cochran Q p-value of ,0.05 was considered significant, indicating heterogeneity. Publication bias was assessed visually using funnel plots, with and without Duval and Tweedie trim and fill. Egger’s test was able to be used if the risk factor was analysed in three or more studies. An Egger’s test p-value of ,0.10 was considered significant, indicating possible publication bias. Study quality was assessed using the Newcastle Ottawa Scale (NOS) for non-randomised studies in meta-analyses [5].
Results The process of article selection based on PRISMA guidelines is presented in Figure 1. A total of 2,964 articles were identified. Following removal of 1,011 duplicates, the titles and abstracts of 1,953 articles were screened for eligibility. Among them, 165 were included and assessed by full text. A total of 58 articles were eligible for the qualitative synthesis and 29 were eligible for the meta-analysis. Articles included were published from 2006 to 2017, with aortic valve replacement occurring from 1970 to 2014. Baseline demographics of the 29 included studies are summarised in Table 1. Cumulatively, there were 25,490 subjects. The mean (weighted) age of subjects was 67.3 years (range 39.1–76 years), and 57.6% (range 17–79%) were male. The mean total follow-up was 18.5 years (range 6–32.4 years) during which 981 patients developed SVD as identified by echocardiography or reintervention (3.8% of 25,490). From our systematic review, the following 18 factors were identified as risk factors for bioprosthetic SVD: younger age, sex (both male and female), prosthesis brand, prosthesis size
Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
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Figure 1 PRISMA flow chart.
(,19 mm, ,21 mm, ,23 mm), patient2prosthesis mismatch (PPM), absence of anticalcification preparation, concomitant coronary artery bypass graft surgery, subcoronary implantation technique, postoperative pressure gradient, dyslipidaemia, smoking, metabolic syndrome, presence and absence of lipid lowering medication including statins, raised body mass index and body surface area (BSA), and renal disease. One study [6] identified the absence of anticoagulant therapy as a risk factor; however, no other studies reported the influence of anticoagulant or antiplatelet therapy on SVD. Factors involved in calcium homeostases such as calcium, vitamin D, phosphate and parathyroid hormone levels; osteoporosis and bone mineral density; bisphosphonates;
and calcium and vitamin D supplementation were not reported in any studies identified on systematic review. Variables reported two or more times were selected for quantitative analysis. This included age (both continuous and categorical), BSA, prosthesis size, subcoronary root implantation, anticalcification preparation, PPM, smoking, hypertension, diabetes mellitus, dyslipidaemia, chronic renal failure, and chronic obstructive pulmonary disease. Of these, four factors were identified as significant determinants for bioprosthetic SVD. Increasing age was associated with a 9% decrease in risk for SVD per 1-year of age increase (HR 0.91; 95% CI 0.8920.94; p,0.0001) (Figure 2A). The protective nature of increasing age was confirmed when analysed
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4
Author
Publish (year)
Date implantation (years)
Total (n)
Alvarez et al. [27]
2009
1993 2 2006
491
Aupart et al. [28]
2006
1984 2 2003
1,133
Follow-Up (years)
Incident SVD (n)
SVD defined by
Age (years)
Male (%)
Valve type (if specific type used)
Risk estimate used
Risk factor(s) discussed
11.7
27
Echo
76.5 6 4
46.8
Mitroflow 12A
HR
Age
18
19
Echo
72.6
63.2
Perimount
OR
Age Age, subcoronary vs root
Bach and Kon [29]
2014
1992 2 2004
725
15
31
Explant
71.7 6 7.9
55.4
Freestyle
OR
Bach et al. [30]
2007
1997 2 2006
725
13.3
16
Explant
71.7 6 7
55.4
Freestyle
OR
Age
Bergoend et al. [31]
2010
1984 2 2003
1,857
20
48
Echo
69.8
69
Perimount
OR
Age
24.6
157
Echo
70.7 6 10.4
68.4
Perimount
HR
Age
6
Echo
74.0
46.4
CryoLife-O’Brien
OR
Valve size
87 23
Both Echo
67 6 11 75.9 6 5.3
76.2 17
Hancock II Mitroflow
OR HR
Age Age, BMI, BSA, chronic renal
Bourguignon et al. [32]
2015
1984 2 2008
2,659
Chambers et al. [33]
2011
1999 2 2008
166
David et al. [34] De Paulis et al. [35]
2010 2016
1982 2 2004 2005 2 2011
1,134 205
12 25 9.4
failure, concomitant procedures, COPD, diabetes, dyslipidaemia, ejection fraction, HTN, PPM, sex, valve size Flameng et al. [18]
2014
n/a
648
15.6
73
Both
73.8 6 4.9
52
-
HR
Anticalcification treatment, PPM
Goldman et al. [36]
2017
2007 2 2009
710
6
11
Both
72.4 6 9.3
66.3
Trifecta
OR
Valve size
Grunkemeier et al. [37]
2012
1976 2 2010
2,955
24
34
Explant
73
62.1
-
HR
Age, CABG pericardial valve, sex, valve size
Joshi et al. [38]
2014
1999 2 2013
281
10
15
Explant
67
n/a
Mitroflow
HR
Age, angina, atrial fibrillation, COPD, diabetes, elevated creatinine, extracardiac arteriopathy, HTN, poor left ventricular function, previous cardiac surgery, previous heart
2010
1990 2 2006
1,193
17.3
n/a
Both
72.4
59.8
-
HR
attack, smoking, Age, lipid-lowering therapy
Minakata et al. [40]
2014
1986 2 2001
574
10
n/a
Explant
71.9 6 8.6
52.4
Perimount
HR
Age, previous AVR
Mohammadi et al. [41]
2006
1993 2 2005
605
12.2
12
Explant
68 6 8.3
54.9
Freestyle
OR
Chronic renal failure, HTN, sex,
Mohammadi et al. [42]
2014
1993 2 2013
531
20.3
39
Explant
66.7
59.7
Freestyle
HR
Age, subcoronary vs root
subcoronary vs root replacement
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Kulik et al. [39]
A. Ochi et al.
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Table 1 Baseline characteristics of included studies.
Author
Publish (year)
Date Total (n) Follow-Up Incident SVD Age implantation (years) SVD (n) defined by (years) (years)
Male (%) Valve type (if specific type used)
Risk Risk factor(s) discussed estimate used HR
Mosquera et al. [43]
2016 2001 2 2014 1,023
10
31
Echo
75.6
49.2
Mitroflow
Nielsen et al. [44]
2016 1999 2 2014 2,392
15
14
Explant
74.7 6 6.8
60.4
Perimount & Mitroflow HR
Age, Mitroflow
Piccardo et al. [45]
2015 1994 2 2011 728
16.2
30
Echo
76 6 6
57
Mitroflow 12A & LX
Age, BSA, concomitant procedure,
Age, anticalcification treatment, valve size
OR
emergency procedure, endocarditis, PPM, sex, valve size, Reiss et al. [46]
2011 1994 2 1999 255
14
Explant
65
62.7
Mosaic
OR
Age
Repossini et al. [47]
2016 2004 2 2009 565
11.8
23
Echo
74.6 6 8.3
57.2
Freedom Solo
HR
Age, chronic renal failure, sex
Rizzoli et al. [48]
2006 1983 2 2002 809
12
21
Echo
68.4 6 8
75
Hancock II
HR
Age, incompetence of natural
Ruel et al. [49]
2005 1976 2 2002 309
26.7
9
Explant
39.1 6 8.1
75.8
-
HR
Smoking
Ruel et al. [50]
2004 1970 2 2002 1,360
32.4
n/a
Explant
n/a
n/a
-
HR
Age, BSA, current prosthesis,
9
Risk Factors for Structural Valve Degeneration
valve, CAD
persistent left ventricular hypertrophy, sex, smoking, valve size Senage et al. [51]
2014 2002 2 2007 617
Stanger et al. [52]
2015 2005 2 2009 149
Une et al. [25]
2014 1982 2 2008 304
39
Echo
76.1 6 6.3
45.2
Mitroflow 12A & LX
HR
Dyslipidaemia, PPM, sex
9.6
26
Echo
73.6 6 8.7
54.4
Freedom Solo
HR
Age, diabetes, HTN, PPM, renal
27.5
85
Both
49.2 6 9
79
Hancock II
OR
Age, PPM
22.5
96
Both
57.9 6 19.1 69.5
-
HR
Age, PPM
478.1 18.5
981 42.4
67.3
57.6
157
76
79
39.1
17
16
dysfunction Urso et al. [53] Sum Mean (weighted)
2014 1974 2 2009 387 2006 - 2017 1970 2 2014 25,490 2,955
Minimum
149
32.4 6
6
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Maximum
Abbreviations: AVR, aortic valve replacement; BMI, body mass index; BSA, body surface area; CABG, coronary artery bypass graft; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; HTN, hypertension; OR, odds ratio; PPM, patient2prosthesis mismatch.
5
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Table 1 (continued).
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A. Ochi et al.
Figure 2 Effect of age on increase on structural valve degeneration. (A) Forest plot of studies reporting the effect of age per 1 year increase. (B) Forest plot studies reporting the risk for patients older than 60 years. (C) Forest plot studies reporting the risk for patients older than 65 years. (D) Forest plot studies reporting the risk for patients older than 70 years.
dichotomously using age cut-offs of 60 years (OR 0.12; 95% CI 0.06–0.23; p,0.0001) (Figure 2B), 65 years (OR 0.06; 95% CI 0.02–0.21; p,0.0001) (Figure 2C) and 70 years (OR 0.06; 95% CI 0.01–0.28; p=0.0004) (Figure 2D). Patient2prosthesis mismatch (PPM) (HR 1.95; 95% CI 1.56–2.43; p,0.001) (Figure 3A), a raised BSA (HR 1.77; 95% CI 1.04–3.01; p=0.0339) (Figure 3B), and smoking (HR 2.28; 95% CI 1.37–3.79; p=0.0015) (Figure 3C) were associated with an increased risk for degeneration. Anticalcification preparation of the bioprosthetic valve was confirmed as a protective factor for degeneration (HR 0.41; 95% CI 0.19–0.89;
p=0.0245). All other factors were not statistically significant on analysis. Results from pooled HRs and ORs with associated 95% CIs are presented in Table 2. Sensitivity analysis based on the method of SVD assessment showed similar results, with the exception of sex. Subgroup analysis based on echocardiographic assessment found female sex as a risk factor for SVD (HR 2.34; 95% CI 1.03–5.35; p=0.0428); however, when based on reintervention, female sex was a protective factor for SVD (HR 0.68; 95% CI 0.48–0.96; p=0.027). Results from the sensitivity analysis are presented in Online Appendix D.
Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
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Risk Factors for Structural Valve Degeneration
Figure 3 Effect of patient2prosthesis mismatch, body surface area, and smoking on structural valve degeneration. (A) Forest plot of studies reporting the effect of patient2prosthesis mismatch. (B) Forest plot of studies reporting the effect of increasing body surface area. (C) Forest plot of studies reporting the effect of smoking.
Table 2 Results of meta-analysis — hazard ratio and odds ratio of risk variables for structural valve degeneration in aortic valve bioprostheses. Risk factor
Pooled OR
95% CI
P-value
Study (n)
#21 mm Prosthesis Size
0.76
0.24 – 2.38
0.64
2
Age .60 years
0.08
0.04 – 0.17
,0.0001
6
I2 (%) 0 70.52
Q-c2 0.53 16.71
Q-p value
Egger’s test (p-value)
0.47
*
0.0051
0.10
Age .65 years
0.06
0.02 – 0.21
,0.0001
2
40.89
1.69
0.19
*
Age .70 years
0.06
0.01 – 0.28
0.0004
3
65.75
6.12
0.0469
0.81
Subcoronary implantation
1.18
0.54 – 2.56
0.67
2
1.01
0.32
Risk factor
Pooled HR
95% CI
P-value
0.9 Study (n)
I2 (%)
Q-c2
Q-p value
Age .60 years
1.99
0.11 – 34.66
0.64
2
71.07
3.46
Age (per 1 year) (continuous)
0.91
0.89 – 0.94
,0.0001
10
93.23
132.92
,0.0001
Anticalcification preparation Female
0.41 1.33
0.19 – 0.89 0.65 – 2.73
0.025 0.44
2 5
33.04 76.06
1.49 16.71
0.22 0.0022
0.06
* Egger’s test (p-value) * 0.002 * 0.15
BSA (m2) (continuous)
1.77
1.04 – 3.01
0.034
2
0
0.65
0.42
*
Valve size (mm) (continuous)
0.89
0.74 – 1.08
0.24
3
0
0.69
0.71
0.95
,0.001
0.47
Patient2prosthesis mismatch
1.95
1.56 – 2.43
0.75
0.86
Smoking
2.28
1.37 – 3.79
0.0015
2
23.94
1.31
0.25
*
Dyslipidaemia
1.25
0.45 – 3.46
0.67
2
71.17
3.47
0.06
*
4
0
Hypertension
0.92
0.47 – 2.13
0.99
3
13.90
2.32
0.31
0.0632
Chronic renal failure Diabetes mellitus
1.42 1.46
0.42 – 4.82 0.79 – 2.71
0.57 0.23
3 3
51.88 0
4.16 1.53
0.13 0.47
0.0177 0.20
COPD
1.07
0.39 – 2.96
0.89
2
0.01
0.92
*
0
Abbreviations: BSA, body surface area; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; OR, odds ratio. *Egger’s test was only able to be used if the risk factor was reported in three or more studies.
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Egger’s test indicated significant publication bias for age when assessed continuously (p=0.002), hypertension (p=0.063), and chronic renal failure (p=0.018). A high degree of inter-study heterogeneity was present for age analysed continuously (I2=93.2%). As a sub-group analysis, when age was dichotomised using cut-off values of .60, .65, or .70 years, the pooled effect sizes were similar (pool OR 0.06w0.12). The majority of studies were of good to fair quality, as assessed by the Newcastle Ottawa Scale (NOS) for cohort studies (Online Appendix E). The definitions used for bioprosthetic SVD varied between studies. The 13 studies referred to the 2008 ‘Guidelines for reporting mortality and morbidity after cardiac valve interventions’ defining SVD as “changes intrinsic to the valve, such as wear, fracture, poppet escape, calcification, leaflet tear, stent creep, and suture line disruption of components of a prosthetic valve” [7]; however, eight referred to the 1996 definition [8], two referred to the 1988 definition of these guidelines [9], and 12 used an unknown or independent definition. Furthermore, such definitions allow for incidence based on reintervention in addition to echocardiography: 11 studies based on reintervention, 13 based on echocardiography, and six based on both methods. Of the 19 studies using echocardiography, only 10 provided echocardiographic parameters on which SVD was based. Six (6) studies considered a mean gradient of .40 mmHg as indicative of stenotic SVD, with other parameters being inconsistent.
Discussion The American Heart Association/American College of Cardiology (AHA/ACC) and the European Society of Cardiology/European Association of Cardio-Thoracic Surgery (ESC/EACTS) Guidelines provide limited guidance on factors to consider when choosing a prosthesis, with the exception of age [1,3]. The AHA/ACC Guidelines, which state that patients aged between 50–70 years could reasonably choose either prosthesis, exemplify the existing uncertainty with regard to age as most patients are within this range. Identification of risk factors for bioprosthetic SVD could aid clinicians in identifying patients at highest risk, particularly for whom the choice is unclear such as patients aged 50–70 years. This systematic review and meta-analysis identified four patient factors influencing bioprosthetic SVD: increasing age as a protective factor, and smoking, raised BSA, and PPM as risk factors. Younger patient’s susceptibility to SVD is well established, hence recommendations for prosthesis choice are based around age [1,3]. Both continuous and categorical analysis in this meta-analysis confirmed that increasing age is a protective factor. The 2017 AHA/ACC Guidelines widened the age range whereby a patient could reasonably choose either a mechanical or a bioprosthesis from 60–70 to 50–70 years [3]. This may pose an issue as life expectancy increases, particularly in western countries where life expectancy is over 80 years [10,11]. Despite improved bioprosthesis durability [12],
A. Ochi et al.
a patient aged 50 with expected bioprosthetic durability of 14.5 years[12] could expect to undergo at least two reoperations in their lifetime. Based on this meta-analysis patients younger than 60, 65, and 70 years are still at high risk for degeneration, thus caution is needed for patients aged 50–70 years, particularly those with additional risk factors for degeneration. The four factors identified in this meta-analysis share the commonality of increasing the flow rate across the valve prosthesis, inducing mechanical stress. Patients of a younger age, a raised BSA, and smokers all have an increased metabolic rate, which is associated with a higher cardiac output [13–15]. Increased flows generated by raised cardiac output exert shearing forces on valve leaflets[16], which may contribute significantly to the pathogenesis of bioprosthetic SVD. Other high cardiac output states warrant further investigation, such as thyrotoxicosis, anaemia, and haemodialysis with arteriovenous shunting. The outflow-obstruction-like effect created by PPM also generates increased flow velocity across the valve prosthesis, resulting in shearing forces exerted on valve leaflets [17,18]. This meta-analysis supports continued vigilance to avoid PPM, particularly in the presence of high cardiac output states where flows are further increased. Similarly, an increased risk of degeneration is reported in transcatheter valve-in-valve procedures, which have a similar PPM effect by further reducing an already narrowed effective orifice area of the previously inserted valve [6]. This requires continued study as the number of transcatheter procedures increase and long-term durability data is established. Analysis of PPM may have been affected by the variation in definition of PPM between studies. Whole group analysis found no effect of sex on SVD development. However, subgroup analysis found female sex as a risk factor for degeneration when assessed using echocardiography but protective when based on reintervention, accounting for the insignificant result of whole group analysis. Results may be confounded by lack of consideration for factors such as raised BSA and PPM to which females are more susceptible [19]; however, two of three studies found female sex as a risk factor independent of PPM. Therefore, a conclusion cannot be made about the effect of sex on SVD, and requires further investigation such as independent patient data analysis. Given the histological evidence of atherosclerotic-like processes in bioprostheses with SVD, and the similarity of SVD to native aortic stenosis, atherosclerotic risk factors are theoretically also risk factors for bioprosthetic SVD [20]. However, diabetes mellitus, hypertension, and dyslipidaemia were not found to be significant risk factors for degeneration. Factors were analysed based on clinical diagnosis, which may not accurately assess their effect on SVD, particularly in the presence of medications altering patients’ biochemistry and physiology. Such pathologies exist on a continuum, thus analysis of biochemical (blood sugar levels and lipid levels) or physiological measures (blood pressure) is a more appropriate method to better understand their influence. Large-scale registry data is required to investigate these factors in this manner.
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Risk Factors for Structural Valve Degeneration
AHA/ACC 2014[21] and ESC/EACTS 2012 Guidelines [22] acknowledge the risk of accelerated SVD in those with renal disease. This is expected given the altered calcium metabolism, and anaemia and arteriovenous fistula raising cardiac output [23]; however, results from this meta-analysis did not identify chronic renal disease as a risk factor for SVD. This may be due to avoidance of bioprostheses in patients with severe renal dysfunction and haemodialysis, in keeping with recommendations. Reduced survival of patients with renal disease may have induced a survivor bias [22]. Analysis based on continuous measures may demonstrate the significance of renal dysfunction. Gring et al. [14] exemplified this, finding elevated creatinine clearance as a risk factor for SVD. The low overall rate of SVD at 3.8% found in this metaanalysis is likely an underestimation of the true incidence of degeneration, attributable to a variety of factors. Firstly, only institutions with excellent outcomes might have reported their data and/or been accepted for publication, inducing publication bias. Secondly, older populations may induce a survival bias due to death prior to SVD development. This may also overestimate the protective nature of age. Additionally, a large degree of interstudy variability was present in the definition, including echocardiographic parameters and method of assessment for bioprosthetic SVD. Dvir et al. [24] highlighted the problem of discrepancies between definitions of SVD, and thus the difficulty adequately comparing studies. Based on studies that use both methods of assessment, reintervention underestimates the true incidence of degeneration, as not all those with degeneration will undergo reintervention [18,25,26]. Further interstudy variability also existed in the echocardiographic parameters used. Studies should consider use of standardised SVD grading such as proposed by Dvir et al. [24] to facilitate accurate identification of SVD and future interstudy comparisons of risk factors. Meta-analyses are limited by the studies on which they are based. The studies included are observational studies, with flaws inherent in their design due to the inability to randomise and control for many patient factors. Fifty-one (51) studies were excluded due to lack of risk factor reporting, and 13 were excluded as the incidence of degeneration was not reported. This may account for the low overall incidence of degeneration reported in this study, and may have influenced the risk factors analysed (Appendix G). A variety of bioprostheses were used with varying characteristics, including some that have been superseded. Valve characteristics such as tissue type and presence or absence of stenting were unable to be controlled. Studies investigating homografts and autografts were also excluded, thus, data on SVD may have been lost during this exclusion. Follow-up times of included studies ranged from 6 to 32 years; however, due to the small number of studies analysed, subgroup analysis stratified by time was not possible. Future studies may consider the effect of combinations of risk factors, which could not be assessed in this study. Finally, factors that were not mentioned by studies include life expectancy, patient and
clinician preference, and ethnicity, which should be considered in future studies. This systematic review and meta-analysis has identified four main risk factors for bioprosthetic aortic valve degeneration: younger age, PPM, raised BSA, and smoking, all of which share the commonality of creating high flows across the valve. Identification of factors influencing bioprosthetic SVD will remain an important issue with the rise of transcatheter aortic valve implantation and increasing bioprosthesis use [2]. Incorporation of these factors may be useful in the prediction of patients at high risk, thus to help inform prosthesis choice, particularly in patients for whom age guidelines are unclear. Some patient factors such as BSA and smoking are amenable to modification through lifestyle change. Modification may help reduce the development of SVD in patients unable to receive a mechanical prosthesis. Future studies should consider other high cardiac output states that also increase flows, both physiological and pathological, in addition to continued research into atherosclerotic and calcium homeostatic factors.
Disclosures Ayame Ochi is supported by a scholarship from the National Heart Foundation of Australia, which had no role in the preparation of this manuscript. Kazuaki Negishi (grant number: N0025231) is supported by a fellowship award from the National Heart Foundation of Australia, which had no role in the preparation of this manuscript.
Funding The authors declare no funding was received for the preparation of this manuscript.
Supplementary Data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j. hlc.2019.09.013.
References [1] Baumgartner H, Falk V, Bax JJ, De Bonis M, Hamm C, Holm PJ, et al. 2017 ESC/EACTS guidelines for the management of valvular heart disease. Eur Heart J 2017;38(36):2739–91. [2] Brown JM, O’Brien SM, Wu C, Sikora JAH, Griffith BP, Gammie JS. Isolated aortic valve replacement in North America comprising 108,687 patients in 10 years: Changes in risks, valve types, and outcomes in the Society of Thoracic Surgeons National Database. J Thorac Cardiovasc Surg 2009;137(1):82–90. [3] Nishimura RA, Otto CM, Bonow RO, Carabello BA, Erwin JP, Fleisher LA, et al. 2017 AHA/ACC focused update of the 2014 AHA/ ACC guideline for the management of patients with valvular heart disease: A report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines. Circulation 2017;135(13):1–59. [4] Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic
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Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013
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Risk Factors for Structural Valve Degeneration
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Please cite this article in press as: Ochi A, et al. Patient Risk Factors for Bioprosthetic Aortic Valve Degeneration: A Systematic Review and Meta-Analysis. Heart, Lung and Circulation (2019), https://doi.org/10.1016/j.hlc.2019.09.013