Learning curve for intracranial angioplasty and stenting in single center

• Published in: Catheterization and cardiovascular interventions 2014-01, Vol.83 (1), p.E94-E100
• Main Authors: Cai, Qiankun, Li, Yongkun, Xu, Gelin, Sun, Wen, Xiong, Yunyun, Sun, Wenshan, Bao, Yuanfei, Huang, Xianjun, Zhang, Yao, Zhou, Lulu, Zhu, Wusheng, Liu, Xinfeng
• Format: Article
• Language: English
• Subjects: Aged; Angioplasty - adverse effects; Angioplasty - instrumentation; Cerebrovascular Disorders - diagnosis; Cerebrovascular Disorders - therapy; Chi-Square Distribution; China; Clinical Competence; complications; Female; Humans; intracranial angioplasty and stenting; Learning Curve; Logistic Models; Male; Middle Aged; Multivariate Analysis; Odds Ratio; Retrospective Studies; Risk Factors; risk-adjusted cumulative sum; Stents; Workload
• ISSN: 1522-1946; 1522-726X
• DOI: 10.1002/ccd.25038

Objective To identify the specific caseload to overcome learning curve effect based on data from consecutive patients treated with Intracranial Angioplasty and Stenting (IAS) in our center. Background The Stenting and Aggressive Medical Management for Preventing Recurrent Stroke and Intracranial Stenosis trial was prematurely terminated owing to the high rate of periprocedural complications in the endovascular arm. To date, there are no data available for determining the essential caseload sufficient to overcome the learning effect and perform IAS with an acceptable level of complications. Methods Between March 2004 and May 2012, 188 consecutive patients with 194 lesions who underwent IAS were analyzed retrospectively. The outcome variables used to assess the learning curve were periprocedural complications (included transient ischemic attack, ischemic stroke, vessel rupture, cerebral hyperperfusion syndrome, and vessel perforation). Multivariable logistic regression analysis was employed to illustrate the existence of learning curve effect on IAS. A risk‐adjusted cumulative sum chart was performed to identify the specific caseload to overcome learning curve effect. Results The overall rate of 30‐days periprocedural complications was 12.4% (24/194). After adjusting for case‐mix, multivariate logistic regression analysis showed that operator experience was an independent predictor for periprocedural complications. The learning curve of IAS to overcome complications in a risk‐adjusted manner was 21 cases. Conclusions Operator's level of experience significantly affected the outcome of IAS. Moreover, we observed that the amount of experience sufficient for performing IAS in our center was 21 cases. © 2013 Wiley Periodicals, Inc.