title: External model validation of binary clinical risk prediction models in cardiovascular and thoracic surgery.
abstract: Clinical risk-prediction models serve an important role in healthcare. They are used for clinical decision-making and measuring the performance of healthcare providers. To establish confidence in a model, external model validation is imperative. When designing such an external model validation study, thought must be given to patient selection, risk factor and outcome definitions, missing data, and the transparent reporting of the analysis. In addition, there are a number of statistical methods available for external model validation. Execution of a rigorous external validation study rests in proper study design, application of suitable statistical methods, and transparent reporting.
authors.affiliation: Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom. Electronic address: graeme.hickey@liverpool.ac.uk.
authors.forename: Graeme L
authors.initials: GL
authors.lastname: Hickey
authors.affiliation: Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio.
authors.forename: Eugene H
authors.initials: EH
authors.lastname: Blackstone
mesh_terms: D006348:Cardiac Surgical Procedures / Q000009:adverse effects / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D006801:Humans; D011237:Predictive Value of Tests; D012107:Research Design; D018570:Risk Assessment; D012307:Risk Factors; D019616:Thoracic Surgical Procedures / Q000009:adverse effects* / Q000706:statistics & numerical data; D054928:Validation Studies as Topic; D014656:Vascular Surgical Procedures / Q000009:adverse effects* / Q000706:statistics & numerical data
keywords: calibration; discrimination; external validation; risk prediction model; statistics