title: Tailoring the implementation of new biomarkers based on their added predictive value in subgroups of individuals.
abstract: BACKGROUND
  The value of new biomarkers or imaging tests, when added to a prediction model, is currently evaluated using reclassification measures, such as the net reclassification improvement (NRI). However, these measures only provide an estimate of improved reclassification at population level. We present a straightforward approach to characterize subgroups of reclassified individuals in order to tailor implementation of a new prediction model to individuals expected to benefit from it.
  
  
  METHODS
  In a large Dutch population cohort (n = 21,992) we classified individuals to low (< 5%) and high (≥ 5%) fatal cardiovascular disease risk by the Framingham risk score (FRS) and reclassified them based on the systematic coronary risk evaluation (SCORE). Subsequently, we characterized the reclassified individuals and, in case of heterogeneity, applied cluster analysis to identify and characterize subgroups. These characterizations were used to select individuals expected to benefit from implementation of SCORE.
  
  
  RESULTS
  Reclassification after applying SCORE in all individuals resulted in an NRI of 5.00% (95% CI [-0.53%; 11.50%]) within the events, 0.06% (95% CI [-0.08%; 0.22%]) within the nonevents, and a total NRI of 0.051 (95% CI [-0.004; 0.116]). Among the correctly downward reclassified individuals cluster analysis identified three subgroups. Using the characterizations of the typically correctly reclassified individuals, implementing SCORE only in individuals expected to benefit (n = 2,707,12.3%) improved the NRI to 5.32% (95% CI [-0.13%; 12.06%]) within the events, 0.24% (95% CI [0.10%; 0.36%]) within the nonevents, and a total NRI of 0.055 (95% CI [0.001; 0.123]). Overall, the risk levels for individuals reclassified by tailored implementation of SCORE were more accurate.
  
  
  CONCLUSIONS
  In our empirical example the presented approach successfully characterized subgroups of reclassified individuals that could be used to improve reclassification and reduce implementation burden. In particular when newly added biomarkers or imaging tests are costly or burdensome such a tailored implementation strategy may save resources and improve (cost-)effectiveness.
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands.
authors.forename: A
authors.initials: A
authors.lastname: van Giessen
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands.
authors.forename: K G M
authors.initials: KG
authors.lastname: Moons
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands; National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
authors.forename: G A
authors.initials: GA
authors.lastname: de Wit
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands; National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
authors.forename: W M M
authors.initials: WM
authors.lastname: Verschuren
authors.affiliation: National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
authors.forename: J M A
authors.initials: JM
authors.lastname: Boer
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands.
authors.forename: H
authors.initials: H
authors.lastname: Koffijberg
mesh_terms: D015415:Biomarkers; D002318:Cardiovascular Diseases / Q000175:diagnosis*; D016000:Cluster Analysis; D006801:Humans; D018570:Risk Assessment; D012307:Risk Factors
chemical_list: D015415:Biomarkers