title: A clinical prediction rule for meniscal tears in primary care: development and internal validation using a multicentre study.
abstract: BACKGROUND
  In primary care, meniscal tears are difficult to detect. A quick and easy clinical prediction rule based on patient history and a single meniscal test may help physicians to identify high-risk patients for referral for magnetic resonance imaging (MRI).
  
  
  OBJECTIVE
  The study objective was to develop and internally validate a clinical prediction rule (CPR) for the detection of meniscal tears in primary care.
  
  
  METHODS
  In a cross-sectional multicentre study, 121 participants from primary care were included if they were aged 18-65 years with knee complaints that existed for <6 months, and who were suspected to suffer from a meniscal tear.
  
  
  METHODS
  One diagnostic physical meniscal test and 14 clinical variables were considered to be predictors of MRI outcome. Using known predictors for the presence of meniscal tears, a 'quick and easy' CPR was derived.
  
  
  RESULTS
  The final CPR included the variables sex, age, weight-bearing during trauma, performing sports, effusion, warmth, discolouration, and Deep Squat test. The final model had an AUC of 0.76 (95% CI = 0.72 to 0.80). A cut-point of 150 points yielded an overall sensitivity of 86.1% and a specificity of 45.5%. For this cut-point, the positive predictive value was 55.0%, and the negative predictive value was 81.1%. A scoring system was provided including the corresponding predicted probabilities for a meniscal tear.
  
  
  CONCLUSIONS
  The CPR improved the detection of meniscal tears in primary care. Further evaluation of the CPR in new primary care patients is needed, however, to assess its usefulness.
authors.affiliation: Academic Medical Centre, University of Amsterdam, Division of Public Health, Amsterdam, the Netherlands.
authors.forename: Barbara Am
authors.initials: BA
authors.lastname: Snoeker
authors.affiliation: Academic Medical Centre, University of Amsterdam, Division of Public Health, Amsterdam, the Netherlands.
authors.forename: Aeilko H
authors.initials: AH
authors.lastname: Zwinderman
authors.affiliation: Academic Medical Centre, University of Amsterdam, Division of Public Health, Amsterdam, the Netherlands.
authors.forename: Cees
authors.initials: C
authors.lastname: Lucas
authors.affiliation: Academic Medical Centre, University of Amsterdam, Division of Public Health, Amsterdam, the Netherlands.
authors.forename: Robert
authors.initials: R
authors.lastname: Lindeboom
mesh_terms: D000328:Adult; D003430:Cross-Sectional Studies; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D007718:Knee Injuries / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D010808:Physical Examination / Q000379:methods; D011320:Primary Health Care; D012017:Referral and Consultation; D015203:Reproducibility of Results; D000070600:Tibial Meniscus Injuries / Q000473:pathology*; D016474:Weight-Bearing
keywords: clinical prediction rule; diagnosis; knee; primary health care