title: Derivation of level-specific reference change values (RCV) from a health screening database and optimization of their thresholds based on clinical utility.
abstract: BACKGROUND
  Reference change values (RCV) are used for judging the significance of changes between any two measurements. Based on the within-individual CV (CVI), RCV is conventionally computed as the 95% confidence limit (CL) of the changes: RCV=1.96×2CVI. $RCV = 1.96 \times \sqrt 2 C{V_I}.$ However, the appropriateness of assuming a constant CVI and using the 95% CL for RCV remains controversial.
  
  
  METHODS
  The level-specific CVI and RCV were estimated for 20 screening tests using a database composed of results from 13,545 health-screening attendees over a 17-year period, after preliminary exclusion of individuals taking medications or having unusual changes in body mass index (BMI). A rational CL for RCV was explored in reference to a clinical score for the metabolic syndrome, sMS, which was derived based on a logistic regression model consisting of tests related to metabolic syndrome. The effect of adjusting CL for the RCV on diagnostic efficacies of detecting between-year change in sMS was evaluated.
  
  
  RESULTS
  Test level dependency of CVI was apparent for some screening tests which have distributions with prominent skewing. The use of level-specific RCV was thus essential for them. The sensitivity for detecting a critical change in sMS based on the RCV set at 95%CL was extremely low in the majority of tests. However, by lowering CL stepwise from 95 to 75%, the sensitivity improved greatly without much change in specificity and positive predictive value. Loss-and-gain analysis showed that CL for RCV set around 80% gave the lowest loss, assuming a policy of reducing false negative judgment.
  
  
  CONCLUSIONS
  Level specific CVI and RCV were necessary in tests with skewed distributions. RCV using 80%-90% CL is suitable in health screening for diseases that require early intervention for changes.
authors.forename: Reo
authors.initials: R
authors.lastname: Kawano
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Ichihara
authors.forename: Takashi
authors.initials: T
authors.lastname: Wada
mesh_terms: D000328:Adult; D000368:Aged; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male; D024821:Metabolic Syndrome / Q000175:diagnosis*; D008875:Middle Aged; D012016:Reference Values; D012044:Regression Analysis