title: An algorithm to identify the development of lymphedema after breast cancer treatment.
abstract: OBJECTIVE
  Large, population-based studies are needed to better understand lymphedema, a major source of morbidity among breast cancer survivors. One challenge is identifying lymphedema in a consistent fashion. We sought to develop and validate an algorithm using Medicare claims to identify lymphedema after breast cancer surgery.
  
  
  METHODS
  From a population-based cohort of 2,597 elderly (65+) women who underwent incident breast cancer surgery in 2003 and completed annual telephone surveys through 2008, two algorithms were developed using Medicare claims from half of the cohort and validated in the remaining half. A lymphedema-positive case was defined by patient report.
  
  
  RESULTS
  A simple two ICD-9 code algorithm had 69 % sensitivity, 96 % specificity, positive predictive value >75 % if prevalence of lymphedema is >16 %, negative predictive value >90 %, and area under receiver operating characteristic curve (AUC) of 0.82 (95 % CI 0.80-0.85). A more sophisticated, multi-step algorithm utilizing diagnostic and treatment codes, logistic regression methods, and a reclassification step performed similarly to the two-code algorithm.
  
  
  CONCLUSIONS
  Given the similar performance of the two validated algorithms, the ease of implementing the simple algorithm and the fact that the simple algorithm does not include treatment codes, we recommend that this two-code algorithm be validated in and applied to other population-based breast cancer cohorts.
  
  
  CONCLUSIONS
  This validated lymphedema algorithm will facilitate the conduct of large, population-based studies in key areas (incidence rates, risk factors, prevention measures, treatment, and cost/economic analyses) that are critical to advancing our understanding and management of this challenging and debilitating chronic disease.
authors.affiliation: Division of Surgical Oncology, Medical College of Wisconsin, 9200 W. Wisconsin Avenue, Milwaukee, WI, 53226, USA, tyen@mcw.edu.
authors.forename: Tina W F
authors.initials: TW
authors.lastname: Yen
authors.forename: Purushuttom W
authors.initials: PW
authors.lastname: Laud
authors.forename: Rodney A
authors.initials: RA
authors.lastname: Sparapani
authors.forename: Jianing
authors.initials: J
authors.lastname: Li
authors.forename: Ann B
authors.initials: AB
authors.lastname: Nattinger
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000601:surgery*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008209:Lymphedema / Q000175:diagnosis* / Q000209:etiology; D006278:Medicare / Q000706:statistics & numerical data; D015995:Prevalence; D012307:Risk Factors; D012680:Sensitivity and Specificity; D017741:Survivors / Q000706:statistics & numerical data; D014481:United States