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ARTICLE IN PRESS
Transcatheter Aortic Valve Implantation Futility Risk
Model Development and Validation Among Treated
Patients With Aortic Stenosis
Oren Zusman, MDa,b, Ran Kornowski, MDa,b,*, Guy Witberg, MDa,b, Adi Lador, MDa,b,
Katia Orvin, MDa,b, Amos Levi, MDa,b, Abid Assali, MDa,b, Hana Vaknin-Assa, MDa,b,
Ram Sharony, MDb,c, Yaron Shapira, MDa,b, Alexander Sagie, MDa,b, and Uri Landes, MDa,b
Risk-benefit assessment for transcatheter aortic valve implantation (TAVI) is still evolving. A sizeable group of patients do not fully benefit from intervention despite a technically
successful procedure. All patients who underwent TAVI with device success and with no
Valve Academic Research Consortium (VARC)-2 defined complications were included. Various
demographic data, clinical details, and echocardiographic findings were examined. The
outcome was defined as 1-year composite of mortality, stroke, lack of functional-class improvement (by New York Heart Association class), and readmissions (?1 month after the
procedure). Logistic regression was used to fit the prediction model. We used a 10-fold crossvalidation to validate our results. Of 543 patients, 435 met the inclusion criteria. The mean
age was 82 (�5) years, 43% were men, and the mean Society of Thoracic Surgeons score
was 6.6 (�7). At 1 year, 66 of 435 patients (15%) experienced the study end point. The
final logistic regression model included diabetes, baseline New York Heart Association functional class, diastolic dysfunction, need for diuretics, mean gradient, hemoglobin level, and
creatinine level. The area under the curve was 0.73 and was reduced to 0.71 after validation, with a 97% specificity using a single cutoff. Dividing to low-, medium-, and high-risk
groups for futility produced a corresponding prevalence of 6%, 19%, and 59% futility. A
web application for the prediction model was developed and provided. In conclusion, this
prediction score may provide an important insight and may facilitate identification of patients who, despite a technically successful and uncomplicated procedure, have risk that
may outweigh the benefit of a contemplated TAVI. � 2017 Elsevier Inc. All rights reserved.
(Am J Cardiol 2017;??:?????)
Transcatheter aortic valve implantation (TAVI) has
been shown by to be a valid alternative to surgical aortic
valve replacement1?4 and its use has grown exponentially.5 Although TAVI provides tremendous survival rate and symptom
benefit for most, some patients die or gain little improvement in the quality of life or functional status after the
procedure.6 Attention has previously been given to predicting periprocedural7,8 and 1-year9 mortality after TAVI, and to
quality of life-related measures.10 In addition, there might be
a substantial group of patients who would have a technically ?perfect? procedure and endure the periprocedural period,
but would still not reap the expected benefits; that is, that procedure will be futile. The option to consider palliative care
instead of an invasive procedure for these patients is increasingly recognized,11,12 and so is the need for an integrated
benefit-risk assessment and shared decision making. We have
therefore sought to identify and describe those who would
a
Department of Cardiology, Rabin Medical Center, Petah Tikva,
Israel; b Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv,
Israel; and cDepartment of Cardiothoracic Surgery, Rabin Medical Center,
Petah Tikva, Israel. Manuscript received July 27, 2017; revised manuscript
received and accepted September 6, 2017.
See page ?? for disclosure information.
*Corresponding author: Tel: +972 39377107; fax: +972 39249850.
E-mail address: ran.kornowski@gmail.com (R. Kornowski).
0002-9149/� 2017 Elsevier Inc. All rights reserved.
https://doi.org/10.1016/j.amjcard.2017.09.007
have a ?futile? outcome, even though they had a perfect procedure, and develop a prediction model and calculator to try
and identify these patients.
Methods
The TAVI screening policy at our medical center was described in detail previously.13 Briefly, each patient was initially
evaluated in the clinic and then referred to a dedicated consultation ?heart team? forum that includes a multidisciplinary
team. Severe aortic stenosis (AS) is defined as a valvular orifice
area of <1.0 cm2 or <0.6 cm2/m2 and/or a mean pressure gradient of >40 mm Hg and/or a jet velocity of >4.0 m/s. Selected
patients with low-flow, low-gradient severe AS are also considered for TAVI. All patients were assessed either by
transesophageal echocardiography and electrocardiographygated cardiac computed tomography. Vascular access and
coronary artery status were assessed by multislice computed tomography and peripheral and coronary angiography
as needed. Both the logistic EuroScore and the Society of Thoracic Surgeons (STS) score were calculated. Risk factors not
captured by traditional risk scores including frailty, porcelain aorta, severe pulmonary hypertension, hostile chest, severe
liver disease or cirrhosis are also taken into account. The choice
of therapy (i.e., surgical aortic valve replacement, TAVI,
balloon aortic valvuloplasty, or palliative care) was tailored
for each patient individually. Patients with an estimated life
www.ajconline.org
ARTICLE IN PRESS
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The American Journal of Cardiology (www.ajconline.org)
expectancy shorter than 1 year or a significant mental impairment were referred to palliative treatment and/or balloon
aortic valvuloplasty (during our TAVI registry years, there were
38 such patients). The default access for TAVI is transfemoral
and the valve choice is at the discretion of the heart team.
After the index procedure, all patients are prospectively
followed up in a dedicated TAVI clinic at 30 days, 6 and 12
months, and yearly thereafter. Clinical, procedural, and
outcome information is prospectively collected for all patients. The institutional review board approved the prospective
collection of our TAVI database (i.e., registry coronary cath
Rabin database - ReCoRD TAVI institutional registry).
For the present analysis, we included only patients with
device success and without any major and/or debilitating
complications as defined by the Valve Academic Research
Consortium (VARC)-2,14 leaving only patients with an uncomplicated, optimal TAVI. The primary end point of the
current study was procedural futility, defined as a composite
of 1-year mortality, stroke, lack of functional-class improvement (by New York Heart Association [NYHA] class), and
repeat (?2) admissions, both occurring more than 1 month
after the procedure. A prediction model was then derived and
validated. We planned to use the logistic model?s formula and
to provide an accessible calculator (similar to the way the
American College of Cardiology-STS TAVI In-Hospital Mortality Risk is calculated8) instead of a conversion to a point
score (i.e. like CHA2DS2-VASc) to prevent loss of accuracy while maintaining ease of use.
Continuous normally distributed variables are presented
as means � standard deviations and compared using the Student
t test. Ordinal and/or non-normally distributed continuous variables are presented by the median and the interquartile range
and compared using the Wilcoxon rank-sum test. Normality
was assessed using the Shapiro-Wilk test and a visual inspection of quantile-quantile plots. Categorical variables are
compared using the chi-square test.
The logistic regression model was developed by examining all possible variable subsets, including clinically plausible
interactions. Continuous variables were assessed for nonlinearity and categorized accordingly. We used 10-fold crossvalidation repeated 5 times for the validation of the results:
we avoided the use of the split-set approach as resampling
such as cross-validation has been shown to be more appropriate for our expected sample size.15 Analysis was performed
with R16 with several appropriate packages17,18
Results
In November 2008 to November 2016, a total of 543 consecutive patients underwent TAVI in our center and were
considered for inclusion. Of the 543 patients, we excluded
patients with procedural-related complications as follows: 7
due to death and 6 due to stroke within 1 month, 29 due to
postoperative renal failure, 13 due to significant (?2+)
paravalvular leak, 13 due to major or life-threatening vascular complication, 25 patients in whom valve success was
not achieved, and 15 patients with more than one of the previously mentioned exclusion criteria, leaving 435 patients
(80%) who formed the study group. The patient characteristics of both groups (included and excluded) are presented
in the supplementary.
The patients? mean age was 83 (interquartile range 79 to
86) years, and 43% were men. The mean STS score was 6.58%
(�74). At 1 year, 66 of the 435 patients (15%) experienced
the adverse futility-related outcome. Of those, 26 (6%) were
due to death, 9 (2%) were due to nonfatal stroke, 12 (3%)
were due to NYHA class lack of improvement, and 20 (5%)
were due to repeat hospitalizations (causes are detailed in
Supplementary Figure S1). The characteristics of all patients and the outcomes are presented in Table 1. Using the
STS score as a single predictor yielded a low area under the
curve (AUC) (0.48) and was not significantly associated with
futility (odds ratio 1.01, 95% confidence interval 0.81 to 1.41).
The prediction models were developed as described. The distribution of the outcome did not differ significantly by the
year TAVI was performed, and the year of TAVI was not significantly associated with the outcome in the regression model.
Both the hemoglobin level and the preprocedural aortic valve
(AV) mean gradient showed a nonlinear behavior; they were
converted to appropriate categorical variables. The logistic
regression final model included diabetes, diastolic dysfunction (any grade, per echo evaluation), the need for diuretics,
the AV mean gradient from 35 to 50 mm Hg, the creatinine
level, the baseline NYHA class of <3, and a hemoglobin level
<10 and >15, all associated with increased risk. As the mean
and the peak gradient were highly correlated, only the mean
gradient was considered. The model is presented in Table 2,
with corresponding points for each variable. The AUC for the
model was 0.73, which was reduced to 0.71 after crossvalidation (Figure 1). The performance of the logistic
regression model by the separation of raw probabilities to percentile bins is presented in Supplementary Figure S2, which
shows appropriate response and higher rates of futility with
a higher-risk percentage as produced by the model. Using a
single cutoff of 45% of the probability, the model produced
a sensitivity of 20% and a specificity of 97%.
We then further divided the model into 3 groups with raw
probabilities cut at 10% and 40%. This produced groups with
6%, 19%, and 59% futility corresponding with low, medium,
and high risk of futility. The high-risk group consisted of 4%
of the patients who underwent successful, uncomplicated TAVI,
and the low-risk group consisted of 43% of these patients.
We also applied the model on the original patient group (543
patients). The model AUC remained at 0.7, with a similar
prevalence of futility in the group (6%, 21%, and 52%). For
patients in the high-risk group, only 72% achieved a perfect
procedure, whereas in the low-risk group, the prevalence was
higher (84%). A web application or calculator utilizing the
risk model was made available.19
Discussion
In the present study, 15% of the patients experienced the
composite outcome and are believed to represent the group
of ?perfect but yet futile TAVI.? The model developed showed
a fair performance with high specificity. Using 3-tier risk categories, the model assigns high risk to 4% of the patients
(almost a third of ultimately futile patients) with at least 59%
of futility. In contrast, 45% of the patients are identified as
low risk (6%) of futility.
One of the study?s strengths is in its attention to a clinical dilemma. As caregivers, the heart team confronts difficult
ARTICLE IN PRESS
Valvular Heart Disease/TAVI Futility Risk Model
3
Table 1
Patients characteristics
Variable
Age (year)
Men
Weight (kg)
Height (m)
BMI (kg/m2)
Diabetes mellitus
Obstructive lung disease
Previous stroke
Dyslipidemia*
Hypertension?
Pacemaker
Atrial fibrillation
Frailty
Previous malignancy
Smoker
Need for diuretics
Previous myocardial Infarction
Recent hospitalization?
Previous need for PCI
Previous CABG
Previous valvular surgery
Pre-procedural PCI
Base NYHA class
2
3
4
Syncope
Dyspnea
Chest pain
STS score
Mean aortic valve gradient (mmHg)
Peak aortic valve gradient (mmHg)
Aortic valve area (cm2)
Aortic insufficiency grade
None
Mild or Moderate
Severe
Mitral insufficiency grade
None
Mild
Moderate
Moderate to severe
Diastolic dysfunction
None
Grade 1
Grade 2?3
Systolic dysfunction
None
Mild, mild to moderate
Moderate to severe, severe
Left bundle branch block in baseline ECG
Hemoglobin (mg/dl)
Platelets (K/micl)
Creatinine (mg/dl)
Albumin (g/dl)
Transfemoral access
Overall
Futile
p
No
Yes
(n = 435)
(n = 369)
(n = 66)
83 [79, 86]
186 (43%)
72.09 � 14.08
1.62 � 0.09
27.37 � 4.73
149 (34%)
85 (19%)
76 (17%)
371 (85%)
400 (92%)
33 (7%)
127 (29%)
68 (16%)
60 (14%)
44 (10%)
244 (56%)
32 (7%)
209 (48%)
127 (29%)
78 (18%)
40 (9%)
71 (16%)
83 [80, 86]
155 (42%)
72.01 � 14.12
1.62 � 0.09
27.37 � 4.77
116 (31%)
68 (18%)
61 (16%)
314 (85%)
338 (92%)
29 (8%)
108 (29%)
59 (16%)
48 (13%)
34 (9%)
200 (54%)
26 (7%)
174 (47%)
106 (29%)
70 (19%)
33 (9%)
59 (16%)
82 [77, 85]
31 (47%)
72.58 � 13.97
1.63 � 0.10
27.38 � 4.58
33 (50%)
17 (26%)
15 (23%)
57 (86%)
62 (94%)
4 (6%)
19 (29%)
9 (14%)
12 (18%)
10 (15%)
44 (67%)
6 (9%)
35 (53%)
21 (32%)
8 (12%)
7 (11%)
12 (18%)
43 (10%)
252 (58%)
140 (32%)
43 (10%)
402 (92%)
77 (18%)
6.6 � 4.74
49 [40, 59]
77 [65, 90]
0.60 [0.50, 0.75]
33 (9%)
219 (59%)
117 (32%)
38 (10%)
340 (92%)
65 (18%)
6.5 � 4.6
49 [40, 59]
77 [65, 91]
0.60 [0.50, 0.72]
10 (15%)
33 (50%)
23 (35%)
5 (8%)
62 (94%)
12 (18%)
6.8 � 5.6
46 [38, 56]
74 [66, 86]
0.70 [0.60, 0.80]
123 (28%)
260 (60%)
52 (12%)
104 (28%)
219 (59%)
46 (12%)
19 (28%)
41 (62%)
6 (9%)
76 (17%)
203 (47%)
146 (34%)
10 (2%)
63 (17%)
174 (47%)
123 (33%)
9 (2%)
13 (20%)
29 (44%)
23 (35%)
1 (1%)
170 (39%)
240 (55%)
25 (6%)
152 (41%)
198 (54%)
19 (5%)
18 (27%)
42 (64%)
6 (9%)
0.09
0.54
0.76
0.59
0.98
0.005
0.22
0.30
0.79
0.69
0.61
1
0.76
0.26
0.15
0.08
0.74
0.46
0.72
0.24
0.84
0.79
0.20
0.65
0.80
1
0.63
0.31
0.30
0.01
0.74
0.90
0.07
335 (77%)
60 (14%)
40 (9%)
53 (12%)
11.90 � 1.63
197 [158, 248]
0.99 [0.80, 1.30]
4.0 [3.7, 4.3]
406 (93%)
286 (77%)
52 (14%)
31 (8%)
43 (12%)
11.97 � 1.54
197 [159, 248]
0.97 [0.80, 1.29]
4.0 [3.7, 4.3]
346 (94%)
0.39
49 (74%)
8 (12%)
9 (14%)
10 (15%)
0.55
11.48 � 1.98
0.02
195 [148, 237]
0.34
1.10 [0.85, 1.50]
0.01
4.1 [3.7, 4.2]
0.87
60 (91%)
0.56
(continued on next page)
ARTICLE IN PRESS
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The American Journal of Cardiology (www.ajconline.org)
Table 1
(continued)
Variable
Overall
Urgent procedure
Valve in valve�
Porcelain aorta
Futile
p
No
Yes
(n = 435)
(n = 369)
(n = 66)
31 (7.1)
29 (7%)
25 (6%)
23 (6.2)
25 (6%)
21 (6%)
8 (12%)
4 (6%)
4 (6%)
0.15
1
1
Values are mean � SD, median [IQR], or n (%).
* Defined as elevated LDL levels or low HDL levels, or on statin treatment.
?
Blood pressure over 140/90 or on hypertensive agents.
?
6 months prior to the procedure.
�
Defined as transcatheter heart valve implantation into a degenerative surgical bioprosthesis.
Table 2
Logistic regression model variables
Hemoglobin < 10 or > 15 (mg/dl)
NYHA functional class < III
Diabetes mellitus
Diastolic Dysfunction
Mean aortic valve gradient between 35 to 50 (mmHg)
Need for Diuretics
Creatinine (mg/dl)
Odds ratio
95% CI
SE
p-value
Points*
2.86
2.7
2.14
2.10
2.04
1.75
1.40
1.45?5.55
1.13?6.25
1.22?3.76
1.13?3.87
1.16?3.59
0.96?3.19
1.05?1.86
0.34
0.44
0.29
0.31
0.29
0.31
0.15
0.002
0.02
0.01
0.02
0.01
0.07
0.02
10
9
7
7
7
5
3
CI = confidence interval; SE = standard error; NYHA = New York Heart Association.
* Sum of points 14 correspond to 10% probability, 21 points to 20% probability, 27 to 30%, 31 to 40%, 35 to 50%, 39 to 60%, and 43 points to 70%.
Figure 1. Receiver operating curves of the original model (derivation) and after validation.
ARTICLE IN PRESS
Valvular Heart Disease/TAVI Futility Risk Model
decision making. Especially tough is to deny a potentially
therapeutic procedure from patients in need of remedy. The
ultimate desire to ?do something? may bias the caregivers from
making the best choice. Using an objective risk calculator that
separates TAVI procedural success from subsequent patient?s outcomes may assist in the decision-making process
by indicating that a particular patient may not fully benefit
from TAVI, independent of how successful the procedure is.
The model can also assist in clarifying the predicted gap
between the patients? expectations and the projected clinical reality after TAVI. As the use of TAVI continues to spread,
the need to define and articulate the anticipated patients? health
benefit grows accordingly. The model can also assist in clarifying the predicted gap between patients? expectations and
the projected clinical reality after TAVI. The study and model
also stresses that a significant portion of patients with AS have
co-morbidities and their symptomatic status is complex?a
specific patient might not enjoy TAVI if he or she is anemic
and transaortic gradients are not high. The study has high specificity, which is desired in this clinical situation (less false
positives), but the trade-off is lower sensitivity.
The study has several limitations. It is single centered, which
might hinder generalizability. Although the resultant model
parameters such as AUC were fair (0.71 after validation), a
study with a larger sample size might have been able to include
more variables and be more accurate. Naturally, the results
of the present study should be externally validated. We chose
a specific combination of outcomes; which outcomes are
important when measuring the TAVI outcome is up for
debate.20 A different choice or addition of outcome measures, such as quality of life assessment,10,21,22 might expose
different variables and modify the defined risk. We chose to
include the NYHA class, which is partially subjective, and
rehospitalizations, which can mean a different burden to different patients. These variables should be carefully considered
before applying this score for patients.
In the present study, the portion of patients experiencing
a perfect procedure outcome (80%) and a futile 1-year outcome
(15%) might be an optimistic estimation, as adverse procedural outcomes from other reports ranged from a quarter to
a third.1,6,23 In the assessment of poor predictors from the Placement of Aortic Transcatheter Valves (PARTNER) trial,10 poor
outcome was identified in 33%. In the PARTNER 2A trial,3,4
the rate of death, stroke, or rehospitalization was 27%, whereas
in the Surgical Replacement and Transcatheter Aortic Valve
Implantation (SURTAVI) trial, the rate was 17%.4 In a metaanalysis of TAVI randomized controlled trials and matched
trials, all-cause, 1-year mortality reached 13%.24 Higher prevalence would mean a higher positive predictive value for the
model. The model components somewhat differ from TAVI
risk factors previously described; they might stress the difference between procedural risk factors and factors associated
with long-term risk-benefit unrelated to periprocedural events.
For instance, our own institutional experience found an association with frailty13; others have reported that albumin is
associated with an increased risk9,25; in FRANCE2, factors
such as age, body mass index, and emergent procedure were
included,7 and in Arnold et al,10 oxygen-dependent lung disease
was included. These were examined but, because they did
not contribute to the model, were excluded. Arnold et al
also examined the performance on the mini-mental exam
5
and the 6-minute walk test, which were not captured in our
patients.
The issue of risk stratification in TAVI can be defined in
2 broad aspects.26,27 On one hand, we want to make sure that
TAVI-eligible patients will not be denied the procedure due
to perceived ?unfitness.? In contrast, some patients might
undergo the procedure with its risks and costs but will not
enjoy its benefits. The challenge is to define and then predict
the two. The model described can aid in the heart-team decision process. For ease of use, we supply the prediction
score through a web application.19 The approach of taking
periprocedural complications out of the equation represents
a novel one. The findings of the present study will need external validation.
In summary, our prediction score could provide important insight and can aid in identifying patients who may
experience futility after successful TAVI, having hazards that
may outweigh the benefit of the procedure.
Disclosures
The authors have no conflicts of interest to disclose.
Supplementary Data
Supplementary data associated with this article can be
found, in the online version, https://doi.org/10.1016/
j.amjcard.2017.09.007.
1. Leon MB, Smith CR, Mack M, Miller DC, Moses JW, Svensson LG,
Tuzcu EM, Webb JG, Fontana GP, Makkar RR, Brown DL, Block PC,
Guyton RA, Pichard AD, Bavaria JE, Herrmann HC, Douglas PS, Petersen JL, Akin JJ, Anderson WN, Wang D, Pocock S, PARTNER Trial
Investigators. Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery. N Engl J Med 2010;363:1597?
1607.
2. Adams DH, Popma JJ, Reardon MJ, Yakubov SJ, Coselli JS, Deeb GM,
Gleason TG, Buchbinder M, Hermiller JJ, Kleiman NS, Chetcuti S, Heiser
J, Merhi W, Zorn G, Tadros P, Robinson N, Petrossian G, Hughes GC,
Harrison JK, Conte J, Maini B, Mumtaz M, Chenoweth S, Oh JK.
Transcatheter aortic-valve replacement with a self-expanding prosthesis. N Engl J Med 2014;370:1790?1798.
3. Leon MB, Smith CR, Mack MJ, Makkar RR, Svensson LG, Kodali SK,
Thourani VH, Tuzcu EM, Miller DC, Herrmann HC, Doshi D, Cohen
DJ, Pichard AD, Kapadia S, Dewey T, Babaliaros V, Szeto WY, Williams MR, Kereiakes D, Zajarias A, Greason KL, Whisenant BK, Hodson
RW, Moses JW, Trento A, Brown DL, Fearon WF, Pibarot P, Hahn RT,
Jaber WA, Anderson WN, Alu MC, Webb JG. Transcatheter or surgical aortic-valve replacement in intermediate-risk patients. N Engl J Med
2016;374:1609?1620.
4. Reardon MJ, Van Mieghem NM, Popma JJ, Kleiman NS, S鴑dergaard
L, Mumtaz M, Adams DH, Deeb GM, Maini B, Gada H, Chetcuti S,
Gleason T, Heiser J, Lange R, Merhi W, Oh JK, Olsen PS, Piazza N,
Williams M, Windecker S, Yakubov SJ, Grube E, Makkar R, Lee JS,
Conte J, Vang E, Nguyen H, Chang Y, Mugglin AS, Serruys PWJC,
Kappetein AP. Surgical or transcatheter aortic-valve replacement in
intermediate-risk patients. N Engl J Med 2017;376:1321?1331.
5. Grover FL, Vemulapalli S, Carroll JD, Edwards FH, Mack MJ, Thourani
VH, Brindis RG, Shahian DM, Ruiz CE, Jacobs JP, Hanzel G, Bavaria
JE, Tuzcu EM, Peterson ED, Fitzgerald S, Kourtis M, Michaels J,
Christensen B, Seward WF, Hewitt K, Holmes DR, Registry ST. 2016
Annual Report of The Society of Thoracic Surgeons/American College
of Cardiology Transcatheter Valve Therapy Registry. J Am Coll Cardiol
2017;69:1215?1230.
6. Reynolds MR, Magnuson EA, Wang K, Thourani VH, Williams M,
Zajarias A, Rihal CS, Brown DL, Smith CR, Leon MB, Cohen DJ,
PARTNER Trial Investigators. Health-related quality of life after
transcatheter or surgical aortic valve replacement in high-risk patients
ARTICLE IN PRESS
6
The American Journal of Cardiology (www.ajconline.org)
7.
8.
9.
10.
11.
12.
13.
14.
15.
with severe aortic stenosis: results from the PARTNER (Placement of
AoRTic TraNscathetER Valve) Trial (Cohort A). J Am Coll Cardiol
2012;60:548?558.
Iung B, Laou閚an C, Himbert D, Eltchaninoff H, Chevreul K, DonzeauGouge P, Fajadet J, Leprince P, Leguerrier A, Li鑦re M, Prat A, Teiger
E, Laskar M, Vahanian A, Gilard M, FRANCE 2 Investigators. Predictive
factors of early mortality after transcatheter aortic valve implantation:
individual risk assessment using a simple score. Heart 2014;100:1016?
1023.
Edwards FH, Cohen DJ, O?Brien SM, Peterson ED, Mack MJ, Shahian
DM, Grover FL, Tuzcu EM, Thourani VH, Carroll J, Brennan JM, Brindis
RG, Rumsfeld J, Holmes DR, Steering Committee of the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve
Therapy Registry. Development and validation of a risk prediction model
for in-hospital mortality after transcatheter aortic valve replacement. JAMA
Cardiol 2016;1:46?52.
Hermiller JB, Yakubov SJ, Reardon MJ, Deeb GM, Adams DH, Afilalo
J, Huang J, Popma JJ, CoreValve United States Clinical Investigators.
Predicting early and late mortality after transcatheter aortic valve replacement. J Am Coll Cardiol 2016;68:343?352.
Arnold SV, Reynolds MR, Lei Y, Magnuson EA, Kirtane AJ, Kodali
SK, Zajarias A, Thourani VH, Green P, Rod閟-Cabau J, Beohar N, Mack
MJ, Leon MB, Cohen DJ, PARTNER Investigators. Predictors of poor
outcomes after transcatheter aortic valve replacement: results from the
PARTNER (Placement of Aortic Transcatheter Valve) trial. Circulation 2014;129:2682?2690.
Otto CM, Kumbhani DJ, Alexander KP, Calhoon JH, Desai MY, Kaul
S, Lee JC, Ruiz CE, Vassileva CM. 2017 ACC expert consensus decision pathway for transcatheter aortic valve replacement in the management
of adults with aortic stenosis: a report of the American College of Cardiology Task Force on Clinical Expert Consensus Documents. J Am Coll
Cardiol 2017;69:1313?1346.
Kirkpatrick JN, Hauptman PJ, Swetz KM, Blume ED, Gauvreau K,
Maurer M, Goodlin SJ. Palliative care for patients with end-stage cardiovascular disease and devices: a report from the Palliative Care Working
Group of the Geriatrics Section of the American College of Cardiology. JAMA Intern Med 2016;176:1017?1019.
Levi A, Landes U, Assali AR, Orvin K, Sharony R, Vaknin-Assa H,
Hamdan A, Shapira Y, Schwartzenberg S, Codner P, Shaul AA, Vaturi
M, Gutstein A, Sagie A, Kornowski R. Long-term outcomes of 560 consecutive patients treated with transcatheter aortic valve implantation and
propensity score-matched analysis of early- versus new-generation valves.
Am J Cardiol 2017;119:1821?1831.
Kappetein AP, Head SJ, G閚閞eux P, Piazza N, Mieghem NM, van
Blackstone EH, Brott TG, Cohen DJ, Cutlip DE, Es G-A, van Hahn RT,
Kirtane AJ, Krucoff MW, Kodali S, Mack MJ, Mehran R, Rod閟Cabau J, Vranckx P, Webb JG, Windecker S, Serruys PW, Leon MB,
Valve Academic Research Consortium (VARC)-2. Updated standardized endpoint definitions for transcatheter aortic valve implantation: the
Valve Academic Research Consortium-2 consensus document (VARC2). Eur J Cardiothorac Surg 2012;42:S45?S60.
Steyerberg EW, Harrell FE, Borsboom GJ, Eijkemans MJ, Vergouwe
Y, Habbema JD. Internal validation of predictive models: efficiency of
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
some procedures for logistic regression analysis. J Clin Epidemiol
2001;54:774?781.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2015.
Available at: https://www.R-project.org/. Accessed on March 1, 2017.
Harrel FEJ. rms: regression modeling strategies; 2016. Available at: http://
CRAN.R-project.org/package=rms. Accessed on January 1, 2017.
Kuhn M, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T,
Mayer Z, Kenkel B, Team the RC, Benesty M, Lescarbeau R, Ziem A,
Scrucca L, Tang Y, Candan C, Hunt T, Wing J. caret: classification and
regression training; 2016. Available at: https://CRAN.R-project.org/
package=caret. Accessed on January 1, 2017.
Zusman O. TAVI futility risk model. Available at: https://
rabinmedicalcenter.shinyapps.io/TAVI_futility_score/. Accessed on July
20, 2017.
Arnold SV, Spertus JA, Lei Y, Green P, Kirtane AJ, Kapadia S, Thourani
VH, Herrmann HC, Beohar N, Zajarias A, Mack MJ, Leon MB, Cohen
DJ. How to define a poor outcome after transcatheter aortic valve replacement: conceptual framework and empirical observations from the
placement of aortic transcatheter valve (PARTNER) trial. Circ Cardiovasc
Qual Outcomes 2013;6:591?597.
Arnold SV, Reynolds MR, Wang K, Magnuson EA, Baron SJ,
Chinnakondepalli KM, Reardon MJ, Tadros PN, Zorn GL, Maini B,
Mumtaz MA, Brown JM, Kipperman RM, Adams DH, Popma JJ,
Cohen DJ, CoreValve US Pivotal Trial Investigators. Health status after
transcatheter or surgical aortic valve replacement in patients with severe
aortic stenosis at increased surgical risk: results from the CoreValve US
pivotal trial. JACC Cardiovasc Interv 2015;8:1207?1217.
Green CP, Porter CB, Bresnahan DR, Spertus JA. Development and evaluation of the Kansas City Cardiomyopathy Questionnaire: a new health
status measure for heart failure. J Am Coll Cardiol 2000;35:1245?
1255.
Smith CR, Leon MB, Mack MJ, Miller DC, Moses JW, Svensson LG,
Tuzcu EM, Webb JG, Fontana GP, Makkar RR, Williams M, Dewey
T, Kapadia S, Babaliaros V, Thourani VH, Corso P, Pichard AD, Bavaria
JE, Herrmann HC, Akin JJ, Anderson WN, Wang D, Pocock SJ,
PARTNER Trial Investigators. Transcatheter versus surgical aorticvalve replacement in high-risk patients. N Engl J Med 2011;364:2187?
2198.
Gargiulo G, Capodanno D, Tamburino C, Trimarco B, Esposito G.
Transcatheter aortic valve implantation versus surgical aortic valve replacement. Ann Intern Med 2017;166:606.
Yamamoto M, Shimura T, Kano S, Kagase A, Kodama A, Sago M,
Tsunaki T, Koyama Y, Tada N, Yamanaka F, Naganuma T, Araki M,
Shirai S, Watanabe Y, Hayashida K. Prognostic value of hypoalbuminemia
after transcatheter aortic valve implantation (from the Japanese Multicenter OCEAN-TAVI Registry). Am J Cardiol 2017;119:770?777.
Puri R, Iung B, Cohen DJ, Rod閟-Cabau J. TAVI or No TAVI: identifying patients unlikely to benefit from transcatheter aortic valve
implantation. Eur Heart J 2016;37:2217?2225.
Lindman BR, Alexander KP, O?Gara PT, Afilalo J. Futility, benefit, and
transcatheter aortic valve replacement. JACC Cardiovasc Interv
2014;7:707?716.
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