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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
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: (R. Kornowski).
0002-9149/� 2017 Elsevier Inc. All rights reserved.
have a ?futile? outcome, even though they had a perfect procedure, and develop a prediction model and calculator to try
and identify these patients.
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
The American Journal of Cardiology (
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
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
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
Valvular Heart Disease/TAVI Futility Risk Model
Table 1
Patients characteristics
Age (year)
Weight (kg)
Height (m)
BMI (kg/m2)
Diabetes mellitus
Obstructive lung disease
Previous stroke
Atrial fibrillation
Previous malignancy
Need for diuretics
Previous myocardial Infarction
Recent hospitalization?
Previous need for PCI
Previous CABG
Previous valvular surgery
Pre-procedural PCI
Base NYHA class
Chest pain
STS score
Mean aortic valve gradient (mmHg)
Peak aortic valve gradient (mmHg)
Aortic valve area (cm2)
Aortic insufficiency grade
Mild or Moderate
Mitral insufficiency grade
Moderate to severe
Diastolic dysfunction
Grade 1
Grade 2?3
Systolic dysfunction
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
(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%)
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%)
49 (74%)
8 (12%)
9 (14%)
10 (15%)
11.48 � 1.98
195 [148, 237]
1.10 [0.85, 1.50]
4.1 [3.7, 4.2]
60 (91%)
(continued on next page)
The American Journal of Cardiology (
Table 1
Urgent procedure
Valve in valve�
Porcelain aorta
(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%)
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
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.
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
and the 6-minute walk test, which were not captured in our
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.
The authors have no conflicts of interest to disclose.
Supplementary Data
Supplementary data associated with this article can be
found, in the online version,
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