title: Medical decision making in patients with knee pain, meniscal tear, and osteoarthritis.
abstract: OBJECTIVE
  Meniscal tears and osteoarthritis (OA) frequently coexist, but to our knowledge, no data exist to identify who will benefit from arthroscopic partial meniscectomy (APM) versus nonoperative management. Our objective was to evaluate the capability of preoperative information to predict APM outcomes in OA.
  
  
  METHODS
  Using a mathematical model and published data, we combined 2 clinical (mechanical symptoms and pain pattern) and 2 magnetic resonance imaging (tear type and bone marrow lesions) indicators into 36 possible combinations and ranked each combination according to the likelihood of having primarily tear- versus OA-related pain in individuals ages 45-65 years with knee pain, OA, and meniscal tears. By considering alternative thresholds for performing APM, we identified the cutoff rank that maximized the overall population International Knee Documentation Committee (IKDC) score (0-100 scale).
  
  
  RESULTS
  Rank 1 (e.g., displaced tear, locking, increased pain, no bone marrow lesions) represented the highest likelihood of APM benefit; rank 36 (e.g., oblique tear, no mechanical symptoms, static pain, severe bone marrow lesions) represented the lowest likelihood of APM benefit. Indeterminate middle ranks included individuals with mixed findings (i.e., 2 findings consistent with high and 2 with low likelihood of APM benefit). APM thresholds between ranks 17 and 23 resulted in >82% of the population receiving treatment producing the greatest possible IKDC improvement, with mean incremental gains in IKDC score of >24 points. Findings were robust across a broad range of indicator assumptions, but were sensitive to outcome assumptions.
  
  
  CONCLUSIONS
  Among individuals with degenerative meniscal tears and OA, easily obtainable clinical information can differentiate those who are more likely to benefit from APM.
authors.affiliation: Section of Rheumatology, Department of Internal Medicine, Yale University School of Medicine, 300 Cedar Street, Room TAC S541, PO Box 208031, New Haven, CT 06520-8031, USA. lisa.suter@yale.edu
authors.forename: Lisa G
authors.initials: LG
authors.lastname: Suter
authors.forename: Liana
authors.initials: L
authors.lastname: Fraenkel
authors.forename: Elena
authors.initials: E
authors.lastname: Losina
authors.forename: Jeffrey N
authors.initials: JN
authors.lastname: Katz
authors.forename: Andreas H
authors.initials: AH
authors.lastname: Gomoll
authors.forename: A David
authors.initials: AD
authors.lastname: Paltiel
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D003657:Decision Making; D006801:Humans; D007718:Knee Injuries / Q000453:epidemiology / Q000473:pathology / Q000601:surgery*; D008279:Magnetic Resonance Imaging; D008592:Menisci, Tibial / Q000473:pathology / Q000601:surgery*; D008875:Middle Aged; D008954:Models, Biological; D020370:Osteoarthritis, Knee / Q000453:epidemiology / Q000473:pathology / Q000601:surgery*; D010146:Pain / Q000453:epidemiology / Q000473:pathology / Q000601:surgery; D011237:Predictive Value of Tests; D011300:Preoperative Care; D015995:Prevalence; D012680:Sensitivity and Specificity; D000070600:Tibial Meniscus Injuries; D016896:Treatment Outcome