1) Adjusting the difficulty level of chronic total occlusion (CTO) percutaneous coronary intervention (PCI) cases to the skill level of the operator is important. This can be done through patient selection based on predictors of success and operator experience. 2) A team-based approach, use of new devices and techniques, individual case volumes, and proctoring can help improve CTO PCI success rates. 3) Scores like the J-CTO score and the new CL-SCORE can help predict procedural success and guide patient selection and referral. Maintaining a database is useful for monitoring outcomes and individual operator success.

1. Adjusting your level of competence to
the difficulty of a CTO
Yves Louvard, Massy, France
Euro CTO club meeting,
hotel Melia Castilla, Madrid, september 24-25, 2014

2. How to adjust ?
• Adapt the patient difficulty to the skillness of the operator =
patient selection
- predictors of success
- scores
- operator’s experience as a predictor of success
• In case of excessive difficulty
- surgery (LM, LAD) ?
- referral inside / outside the group
- proctoring

4. PCI of CTO’s and the Learning Curve
74 72
67
60
70 72
57
86
67
91
67
29
25
100
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Primary success rate (%)
%
n= 292 250 118 77 76 72 49 22 12 11 9 7 4 1
Succes rate / operator: 2004-2009
ICPS CTO Database

5. Success rates and failure predictors / years
Year n
Success
rate (%)
Previous
CABG (%)
No visible
stump (%)
Calcification
(0-3)
Lesion
length (mm)
2004 107 67.3 2.9 7.5 0.96+0.97 17.9+14.6
2005 165 67.9 4.3 16.6 1.07+1.01 18.7+13.2
2006 195 69.7 7.7 18.0 1.15+1.00 20.6+14.4
2007 178 68.5 8.4 22.9 0.92+0.98 22.7+21.2
2008 161 72.7 6.3 25.9 1.08+0.98 21.4+16.1
2009 194 73.2 8.4 32.5 0.86+1.09 23.2+19.8
ICPS CTO Database
PCI of CTO’s and the Learning Curve

6. Patient’s selection

7. Predictors of outcome

9. Tortuosity <0.001 5.45 ( 2.05-14.51)
Calcification 0.003 3.64 ( 1.56-8.48 )
Bridging collateral 0.089 0.41 ( 0.14-1.15 )
Long lesion 0.091 1.92 ( 0.90-4.07 )
Abrupt type 0.161 1.92 ( 0.77-4.80 )
Occl. Age (>3Mo) 0.409 1.68 ( 0.49-5.79 )
Side branch 0.572 0.78 ( 0.33-1.86 )
Multivariate analysis of procedural failure
Determinants of procedural failure in CTO
Period IV P value Odds ratio (95% CI)
Mitsudo et al. CCT 2002

10.  Prospective Registry started in 2004
 European CTO club definition
 Ischaemia and/or viability in the index territory
 Patients not considered for surgery as a 1st option
 14 operators, Team approach
 CK measured 12-24 hours post PCI
 Lesion characteristics defined by one operator
ICPS Registry
PCI of CTO’s and the Learning Curve
ICPS CTO Database

11. Predictors of Success / Failure of CTO PCI
n= 1000
Success predictors: multivariate
OR 95% CI p
No visible stump 0.61 0.39-0.96 0.031
Previous CABG 0.44 0.23-0.86 0.015
Calcifications (0-3) 0.78 0.65-0.94 0.009
Operator experience (1-6) 1.24 1.08-1.42 0.002
Occlusion length (mm) 0.98 0.97-0.99 0.000
Louvard, Hayashida, ESC, AHA 2010

12. Predictors of success in CTO PCI (1)
Successful PCI Univariate OR Multivariate OR
Age
(per 5 years increase)
0.96
(0.89, 0.98)
p=0.009
0.94
(0.89, 0.99)
p=0.048
Lesion length
(per 1 mm increase)
0.98
(0.97, 0.98)
p<0.001
0.97
(0.97, 0.98)
p<0.001
Calcification
(increasing thresholds of mild, mod-severe, and
massive)
0.72
(0.65, 0.80)
p<0.001
0.74
(0.66, 0.84)
p<0.001
No visible stump 0.66
(0.52, 0.85)
p=0.001
0.62
(0.46, 0.84)
p=0.002
Tapered morphology 1.58
(1.26, 1.99)
p<0.001
1.24
(0.94, 1.63)
p=0.12
Leong, Louvard, ESC, AHA 2012
N= 1508

13. Predictors of success in CTO PCI (2)
Successful PCI Univariate OR Multivariate OR
Tortuosity 0.73
(0.56, 0.97)
p=0.03
0.68
(0.50, 0.93)
p=0.015
Intraluminal channel 2.32
(1.59, 3.40)
p<0.001
1.89
(1.27, 2.83)
p=0.002
Previous MI 0.60
(0.47, 0.78)
p<0.001
0.66
(0.50, 0.87)
p=0.003
Previous CABG 0.40
(0.27, 0.58)
p<0.001
0.42
(0.28, 0.65)
p<0.001
Operator experience
(per 50 cases increase)
1.08
(1.03, 1.13)
p=0.002
1.11
(1.05, 1.18)
p=0.001
Leong, Louvard, ESC, AHA 2012
N= 1508

14. Predictors of success in CTO PCI: Lesion length
Leong, Louvard, ESC, AHA 2012

15. Higher Probability of Successful PCI in Patients With CTO of
the LAD Compared to non LAD CTO PCI: Single Center Study
of 1,819 Consecutive Procedures
Alessandrino, Louvard, TCT 2014
Non LAD-CTO-PCI OR 95%CI P value
DM 0.6979 0.5277 to 0.9230 0.0117
Length>20 mm 0.4522 0.3489 to 0.5862 <0.0001
Severe Calcification 0.3523 0.2372 to 0.5233 <0.0001
Previous CABG 0.6152 0.4048 to 0.9349 0.0229
Previous MI 0.6903 0.5090 to 0.9360 0.0171
Tapered Stump 1.7297 1.3327 to 2.2450 <0.0001
LAD-CTO-PCI OR 95%CI P value
Dyslipidemia 0.5734 0.3560 to 0.9236 0.0222
Length>20 mm 0.5542 0.3496 to 0.8785 0.0120
Severe Calcification 0.4630 0.2453 to 0.8739 0.0175
Previous CABG 0.1039 0.0403 to 0.2682 <0.0001
Previous MI 0.5801 0.3477 to 0.9677 0.0370
Intra CTO tortuosity 0.4172 0.1859 to 0.9365 0.0341
LAD-CTO-PCI had a higher procedural success rate compared to non LAD
(78.8% vs 69.7%, p<0.0001).

16. Scores

17. Morino et al. JACC Interv 2011; 4: 213-221
Wire crossing within less than 30 minutes
Predictors of success: the J-CTO score

18. PCI sucess
Morino et al. JACC Interv 2011; 4: 213-221

19. J-CTO score
%
P<0.001
Predictive value of the J-CTO score in percutaneous coronary
interventions for chronic total occlusions: observed failure rate
Failure
rate
Ferrante, Louvard, TCT 2012

20. Predictive value of the J-CTO score in PCI for CTO: Predictive value of
the J-CTO score
1.95
3.47
4.57
P=0.003
P<0.001
P<0.001
Ferrante, Louvard, TCT 2012

21. Predictive value of the J-CTO score in PCI for CTO: Predictive
value of the J-CTO score: discriminationTruepositiverate
False positive rate
AUC
0.77 (0.75-0.80) vs. 0.71 (0.69-0.74),
P< 0.001
Ferrante, Louvard, TCT 2012

22. Influence of case load on individual succes
rate

23. Learning Curve index
PCI of CTO’s and the Learning Curve
1: First 50 cases included in the study by 1 operator
2: 50 to 100
3: 100 to 150
4: 150 to 200
5: 200 to 250
6: > 250
ICPS CTO Database

24. Multi-operator success / Case volume
Operator case volume categories
1481 CTO-lesions
1260 patients
% P=0.038
Tora Leong

25. *
**
ORofsuccess
* p=0.01
* * p<0.001
† p= 0.014
2.1 (95% CI 1.2-3.7)
2.9 (95% CI 1.9-4.4)
†
J-CTO score
G. Ferrante, Y. Louvard, TCT 2012
Success rate of CTO PCI vs individual case load

26. Patient’s selection: conclusion
• Case load is a factor of success in CTO PCI:
- dedicated operators
- individual case selection
- refer / refuse too « complex » patients
- how to select patient: score, ideally including operator’s
experience
• But:
- predictors of failure are changing, scores ?
- case load influence on success rate is very unlikely to be linear
and much probably vary between individuals
- dedicated operators ?: how to traine new operators in new
centers and more difficult in same center to face the case-load
increase or operator retirement …

27. How to improve the success rate in
patients with CTO PCI ?

28. Individual
Success Rate
Team Interaction
New TechniquesNew Devices
Patient selection
PCI of CTO’s and the Learning Curve
ICPS CTO Database
Case load
Intrinsic operator
quality

29. Impact of novel guide wire “Fielder XT” on CTO
PCI success rate (ICPS, 2 operators)
Before Fielder XT After Fielder XT P
CTO n= 83 83
Fielder XT use (%) 1.2 80.7 0.000
Second attempt (%) 6.0 7.2 0.38
Procedural duration (min) 110 + 58 106 + 51 0.31
Contrast medium volume (cc) 258 + 137 290 + 147 0.08
Dissection (%) 24.7 12.2 0.02
Success rate (%) 67.5 84.3 0.005
Hayashida, Eur Heart J 2010; 31 (Suppl 1)

30. - extensive didactic training
- on-site proctorship
- routine determination of case appropriateness
- adherence to procedural safety guidelines
- 2-operator/case approach
Team approach

31. Proctoring, thanks to …
• Invited operators during local workshops (Japanese famous
operators, Euro CTO members …)
• Invited operators for live transmissions (PCR, mainly on
previously failed cases)
• « Private » proctoring for new devices ( James Spratt for
StingRay and hybrid strategy, Peter O’Kane for Laser …)

32. Database
• To individual success rate in well described lesions
• To know the procedural complications
• To help case distribution inside a group and referral outside
• To re validate the scores

33. Overall Population
Univariate Analysis
Stepwise Logistic regression
P<0.0001
Failure
(455)
Success
(1202)
p Odds Ratio 95% CI p
Hypertension, n (%) 301 (66,1) 694 (57,7) 0,0022
Previous MI n, (%) 128 (28,1) 224 (18,63) <0,0001 1,5633 1,1955 to 2,0443 0,0011
Previous PCI n, (%) 196 (43) 429 (35,7) 0,0067
Previous CABG n, (%) 58 (12,7) 66 (5,5) <0,0001 2,2809 1,5299 to 3,4005 0,0001
Multivessel CAD n, (%) 279 (61,3) 647 (53,8) 0,007
Non LAD n (%) 105 (23) 401 (33,3) <0,0001 1,5821 1,2135 to 2,0626 0,0007
Blunt Stump (%) 273 (60) 602 (50) 0,0004 1,5738 1,2444 to 1,9905 0,0002
Tortuous lesion n, (%) 84 (18,4) 167 (13,9) 0,009
Severe Calcification n, (%) 79 (17,3) 82 (6,8) <0,0001 2,9569 2,0815 to 4,2005 <0,0001
Lesion Lenght >20 mm n, (%) 214 (47) 366 (30,4) <0,0001 2,0480 1,6204 to 2,5884 <0,0001
Multivariable analysis
A Clinical And Angiographic Scoring system to predict the probability of
successful first- attempt PCI in patients with total CTO
G. Allessandrino, T. Lefevre, Y. Louvard, submitted

34. Independent Variables Odds Ratio Score
Severe Calcified lesion 2,95 + 2
Previous CABG 2,28 + 1,5
Lesion Length ≥ 20 mm 2.0 + 1,5
Previous MI 1,56 + 1
Blunt Stump 1,57 + 1
Non LAD CTO location 1,58 + 1
Independent predictive variables scored according to OR
A Clinical And Angiographic Scoring system to predict the probability of
successful first- attempt PCI in patients with total CTO
G. Allessandrino, T. Lefevre, Y. Louvard, submitted

35. Procedural success rate according to CL-SCORE value
A Clinical And Angiographic Scoring system to predict the probability of
successful first- attempt PCI in patients with total CTO
G. Allessandrino, T. Lefevre, Y. Louvard, submitted

36. Conclusions
• Individual CTO case load and experience are factors of CTO
PCI success.
• To increase the institution success rate, selection of case
based on actualized predictors and scores, selection of
operator and team interaction / proctoring are important
• Database including lesion analysis, outcome, and individual
success rate can be used to select the operators for specific
cases