title: Predictive models for arteriovenous fistula maturation.
abstract: OBJECTIVE
  Haemodialysis (HD) is a lifeline therapy for patients with end-stage renal disease (ESRD). A critical factor in the survival of renal dialysis patients is the surgical creation of vascular access, and international guidelines recommend arteriovenous fistulas (AVF) as the gold standard of vascular access for haemodialysis. Despite this, AVFs have been associated with high failure rates. Although risk factors for AVF failure have been identified, their utility for predicting AVF failure through predictive models remains unclear. The objectives of this review are to systematically and critically assess the methodology and reporting of studies developing prognostic predictive models for AVF outcomes and assess them for suitability in clinical practice.
  
  
  METHODS
  Electronic databases were searched for studies reporting prognostic predictive models for AVF outcomes. Dual review was conducted to identify studies that reported on the development or validation of a model constructed to predict AVF outcome following creation. Data were extracted on study characteristics, risk predictors, statistical methodology, model type, as well as validation process.
  
  
  RESULTS
  We included four different studies reporting five different predictive models. Parameters identified that were common to all scoring system were age and cardiovascular disease.
  
  
  CONCLUSIONS
  This review has found a small number of predictive models in vascular access. The disparity between each study limits the development of a unified predictive model.
authors: Department of Renal Surgery, University Hospital Birmingham, Birmingham - UK.
authors: Julien
authors: J
authors: Al Shakarchi
authors: Department of Renal Surgery, University Hospital Birmingham, Birmingham - UK.
authors: Damian
authors: D
authors: McGrogan
authors: Centre for Health Informatics, University of Manchester, Manchester - UK.
authors: Sabine
authors: S
authors: Van der Veer
authors: Centre for Health Informatics, University of Manchester, Manchester - UK.
authors: Matthew
authors: M
authors: Sperrin
authors: Department of Renal Surgery, University Hospital Birmingham, Birmingham - UK.
authors: Nicholas
authors: N
authors: Inston
mesh_terms: D001166:Arteriovenous Shunt, Surgical / Q000009:adverse effects; D003661:Decision Support Techniques; D006083:Graft Occlusion, Vascular / Q000209:etiology / Q000503:physiopathology; D006801:Humans; D007676:Kidney Failure, Chronic / Q000175:diagnosis / Q000628:therapy*; D015233:Models, Statistical; D018579:Patient Selection; D011237:Predictive Value of Tests; D006435:Renal Dialysis; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome; D014654:Vascular Patency