title: Learning curve for intracranial angioplasty and stenting in single center.
abstract: 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.
authors.affiliation: Department of Neurology, Jinling Hospital, Southern Medical University, Nanjing, Jiangsu, China.
authors.forename: Qiankun
authors.initials: Q
authors.lastname: Cai
authors.forename: Yongkun
authors.initials: Y
authors.lastname: Li
authors.forename: Gelin
authors.initials: G
authors.lastname: Xu
authors.forename: Wen
authors.initials: W
authors.lastname: Sun
authors.forename: Yunyun
authors.initials: Y
authors.lastname: Xiong
authors.forename: Wenshan
authors.initials: W
authors.lastname: Sun
authors.forename: Yuanfei
authors.initials: Y
authors.lastname: Bao
authors.forename: Xianjun
authors.initials: X
authors.lastname: Huang
authors.forename: Yao
authors.initials: Y
authors.lastname: Zhang
authors.forename: Lulu
authors.initials: L
authors.lastname: Zhou
authors.forename: Wusheng
authors.initials: W
authors.lastname: Zhu
authors.forename: Xinfeng
authors.initials: X
authors.lastname: Liu
mesh_terms: D000368:Aged; D017130:Angioplasty / Q000009:adverse effects / Q000295:instrumentation*; D002561:Cerebrovascular Disorders / Q000175:diagnosis / Q000628:therapy*; D016009:Chi-Square Distribution; D002681:China; D002983:Clinical Competence; D005260:Female; D006801:Humans; D059032:Learning Curve; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016017:Odds Ratio; D012189:Retrospective Studies; D012307:Risk Factors; D015607:Stents; D016526:Workload
keywords: complications; intracranial angioplasty and stenting; learning curve; risk-adjusted cumulative sum