title: Evaluation of the performance of existing non-laboratory based cardiovascular risk assessment algorithms.
abstract: BACKGROUND
  The high burden and rising incidence of cardiovascular disease (CVD) in resource constrained countries necessitates implementation of robust and pragmatic primary and secondary prevention strategies. Many current CVD management guidelines recommend absolute cardiovascular (CV) risk assessment as a clinically sound guide to preventive and treatment strategies. Development of non-laboratory based cardiovascular risk assessment algorithms enable absolute risk assessment in resource constrained countries.The objective of this review is to evaluate the performance of existing non-laboratory based CV risk assessment algorithms using the benchmarks for clinically useful CV risk assessment algorithms outlined by Cooney and colleagues.
  
  
  METHODS
  A literature search to identify non-laboratory based risk prediction algorithms was performed in MEDLINE, CINAHL, Ovid Premier Nursing Journals Plus, and PubMed databases. The identified algorithms were evaluated using the benchmarks for clinically useful cardiovascular risk assessment algorithms outlined by Cooney and colleagues.
  
  
  RESULTS
  Five non-laboratory based CV risk assessment algorithms were identified. The Gaziano and Framingham algorithms met the criteria for appropriateness of statistical methods used to derive the algorithms and endpoints. The Swedish Consultation, Framingham and Gaziano algorithms demonstrated good discrimination in derivation datasets. Only the Gaziano algorithm was externally validated where it had optimal discrimination. The Gaziano and WHO algorithms had chart formats which made them simple and user friendly for clinical application.
  
  
  CONCLUSIONS
  Both the Gaziano and Framingham non-laboratory based algorithms met most of the criteria outlined by Cooney and colleagues. External validation of the algorithms in diverse samples is needed to ascertain their performance and applicability to different populations and to enhance clinicians' confidence in them.
authors.affiliation: College of Nursing and Health Sciences, University of Massachusetts, Boston, USA. Jacob.Kariuki@umb.edu.
authors.forename: Jacob K
authors.initials: JK
authors.lastname: Kariuki
authors.forename: Eileen M
authors.initials: EM
authors.lastname: Stuart-Shor
authors.forename: Suzanne G
authors.initials: SG
authors.lastname: Leveille
authors.forename: Laura L
authors.initials: LL
authors.lastname: Hayman
mesh_terms: D000465:Algorithms; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000453:epidemiology*; D006801:Humans; D015203:Reproducibility of Results; D018570:Risk Assessment