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An algorithm to identify the development of lymphedema after breast cancer treatment

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Abstract

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.

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Acknowledgments

This research was supported by a career development award and supplement to Dr. Yen from the National Cancer Institute (K07CA125586, K07CA125586-03S1) and two research grants from the National Cancer Institute to Dr. Nattinger (R01CA81379, R01CA127648). The content does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

Conflict of interest

All authors (Tina Yen, Purushuttom Laud, Rodney Sparapani, Jianing Li, and Ann Nattinger) declare that they have no conflicts of interest.

Integrity of research and reporting

This study was approved by the Medical College of Wisconsin’s institutional review board. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.

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Authors and Affiliations

  1. Division of Surgical Oncology, Medical College of Wisconsin, 9200 W. Wisconsin Avenue, Milwaukee, WI, 53226, USA

    Tina W. F. Yen

  2. Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA

    Purushuttom W. Laud, Rodney A. Sparapani & Jianing Li

  3. Department of Medicine, and Center for Patient Care and Outcomes Research, Medical College of Wisconsin, Milwaukee, WI, USA

    Tina W. F. Yen, Purushuttom W. Laud, Rodney A. Sparapani, Jianing Li & Ann B. Nattinger

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  1. Tina W. F. Yen
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Correspondence to Tina W. F. Yen.

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Yen, T.W.F., Laud, P.W., Sparapani, R.A. et al. An algorithm to identify the development of lymphedema after breast cancer treatment. J Cancer Surviv 9, 161–171 (2015). https://doi.org/10.1007/s11764-014-0393-z

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  • DOI: https://doi.org/10.1007/s11764-014-0393-z

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