Prediction Model for Freedom from TLR from a Multi-study Analysis of Long-Term Results with the Zilver PTX Drug-Eluting Peripheral Stent View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-10-06

AUTHORS

Michael D. Dake, Fabrizio Fanelli, Aaron E. Lottes, Erin E. O’Leary, Heidi Reichert, Xiaohui Jiang, Weiguo Fu, Osamu Iida, Kan Zen, Marc Schermerhorn, Thomas Zeller, Gary M. Ansel

ABSTRACT

PurposeDevelop a prediction model to determine the impact of patient and lesion factors on freedom from target lesion revascularization (ffTLR) for patients who are candidates for Zilver PTX drug-eluting stent (DES) treatment for femoropopliteal lesions.MethodsPatient factors, lesion characteristics, and TLR results from five global studies were utilized for model development. Factors potentially associated with TLR (sex, age, diabetes, hypertension, hypercholesterolemia, renal disease, smoking status, Rutherford classification, lesion length, reference vessel diameter (RVD), popliteal involvement, total occlusion, calcification severity, prior interventions, and number of runoff vessels) were analyzed in a Cox proportional hazards model. Probability of ffTLR was generated for three example patient profiles via combinations of patient and lesion factors. TLR was defined as reintervention performed for ≥ 50% diameter stenosis after recurrent clinical symptoms.ResultsThe model used records from 2227 patients. The median follow-up time was 23.9 months (range: 0.03–60.8). The Kaplan–Meier estimates for ffTLR were 90.5% through 1 year and 75.2% through 5 years. In a multivariate analysis, sex, age, Rutherford classification, lesion length, RVD, total occlusion, and prior interventions were significant factors. The example patient profiles have predicted 1-year ffTLRs of 97.4, 92.3, and 86.0% and 5-year predicted ffTLRs of 92.8, 79.5, and 64.8%. The prediction model is available as an interactive web-based tool (https://cooksfa.z13.web.core.windows.net).ConclusionsThis is the first prediction model that uses an extensive dataset to determine the impact of patient and lesion factors on ffTLR through 5 years and provides an interactive web-based tool for expected patient outcomes with the Zilver PTX DES.Clinical Trial RegistrationsZilver PTX RCT unique identifier: NCT00120406; Zilver PTX single-arm study unique identifier: NCT01094678; Zilver PTX China study unique identifier: NCT02171962; Zilver PTX US post-approval study unique identifier: NCT01901289; Zilver PTX Japan post-market surveillance study unique identifier: NCT02254837.Levels of EvidenceZilver PTX RCT: Level 2, randomized controlled trial; Single-arm study: Level 4, large case series; China study: Level 4, case series; US post-approval study: Level 4, case series Japan PMS study: Level 4, large case series. More... »

PAGES

196-206

Journal

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00270-020-02648-6

DOI

http://dx.doi.org/10.1007/s00270-020-02648-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1131465772

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/33025243


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30 schema:description PurposeDevelop a prediction model to determine the impact of patient and lesion factors on freedom from target lesion revascularization (ffTLR) for patients who are candidates for Zilver PTX drug-eluting stent (DES) treatment for femoropopliteal lesions.MethodsPatient factors, lesion characteristics, and TLR results from five global studies were utilized for model development. Factors potentially associated with TLR (sex, age, diabetes, hypertension, hypercholesterolemia, renal disease, smoking status, Rutherford classification, lesion length, reference vessel diameter (RVD), popliteal involvement, total occlusion, calcification severity, prior interventions, and number of runoff vessels) were analyzed in a Cox proportional hazards model. Probability of ffTLR was generated for three example patient profiles via combinations of patient and lesion factors. TLR was defined as reintervention performed for ≥ 50% diameter stenosis after recurrent clinical symptoms.ResultsThe model used records from 2227 patients. The median follow-up time was 23.9 months (range: 0.03–60.8). The Kaplan–Meier estimates for ffTLR were 90.5% through 1 year and 75.2% through 5 years. In a multivariate analysis, sex, age, Rutherford classification, lesion length, RVD, total occlusion, and prior interventions were significant factors. The example patient profiles have predicted 1-year ffTLRs of 97.4, 92.3, and 86.0% and 5-year predicted ffTLRs of 92.8, 79.5, and 64.8%. The prediction model is available as an interactive web-based tool (https://cooksfa.z13.web.core.windows.net).ConclusionsThis is the first prediction model that uses an extensive dataset to determine the impact of patient and lesion factors on ffTLR through 5 years and provides an interactive web-based tool for expected patient outcomes with the Zilver PTX DES.Clinical Trial RegistrationsZilver PTX RCT unique identifier: NCT00120406; Zilver PTX single-arm study unique identifier: NCT01094678; Zilver PTX China study unique identifier: NCT02171962; Zilver PTX US post-approval study unique identifier: NCT01901289; Zilver PTX Japan post-market surveillance study unique identifier: NCT02254837.Levels of EvidenceZilver PTX RCT: Level 2, randomized controlled trial; Single-arm study: Level 4, large case series; China study: Level 4, case series; US post-approval study: Level 4, case series Japan PMS study: Level 4, large case series.
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38 China studies
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40 Cox proportional hazards model
41 DES
42 Kaplan-Meier estimates
43 PMS studies
44 RCTs
45 RVD
46 ResultsThe model
47 Rutherford classification
48 TLR
49 TLRs results
50 Zilver
51 age
52 analysis
53 candidates
54 case series
55 characteristics
56 classification
57 clinical symptoms
58 combination
59 combination of patient
60 dataset
61 development
62 diameter stenosis
63 drug-eluting stent (DES) treatment
64 estimates
65 extensive dataset
66 factors
67 femoropopliteal lesions
68 first prediction model
69 follow
70 freedom
71 global study
72 hazards model
73 identifiers
74 impact
75 impact of patient
76 interactive web-based tool
77 intervention
78 large case series
79 length
80 lesion characteristics
81 lesion factors
82 lesion length
83 lesion revascularization
84 lesions
85 level 2
86 level 4
87 long-term results
88 median follow
89 model
90 model development
91 months
92 multi-study analysis
93 multivariate analysis
94 occlusion
95 outcomes
96 patient outcomes
97 patient profiles
98 patients
99 peripheral stents
100 post-approval studies
101 prediction model
102 prior interventions
103 probability
104 profile
105 proportional hazards model
106 records
107 recurrent clinical symptoms
108 reintervention
109 results
110 revascularization
111 series
112 sex
113 significant factor
114 single-arm study
115 stenosis
116 stent treatment
117 stents
118 study
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120 target lesion revascularization
121 time
122 tool
123 total occlusion
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