An algorithm to identify the development of lymphedema after breast cancer treatment. J Cancer Surviv 2015 Jun;9(2):161-71
Date
09/05/2014Pubmed ID
25187004Pubmed Central ID
PMC4362809DOI
10.1007/s11764-014-0393-zScopus ID
2-s2.0-84930009058 (requires institutional sign-in at Scopus site) 6 CitationsAbstract
PURPOSE: 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.
IMPLICATIONS FOR CANCER SURVIVORS: 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.
Author List
Yen TW, Laud PW, Sparapani RA, Li J, Nattinger ABAuthors
Purushottam W. Laud PhD Adjunct Professor in the Data Science Institute department at Medical College of WisconsinAnn B. Nattinger MD, MPH Associate Provost, Professor in the Medicine department at Medical College of Wisconsin
Rodney Sparapani PhD Associate Professor in the Data Science Institute department at Medical College of Wisconsin
Tina W F Yen MD, MS Professor in the Surgery department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
AgedAged, 80 and over
Algorithms
Breast Neoplasms
Cohort Studies
Female
Humans
Lymphedema
Medicare
Prevalence
Risk Factors
Sensitivity and Specificity
Survivors
United States
