Using Temporal Mining to Examine the Development of Lymphedema in Breast Cancer Survivors

and Sowjanya Paladugu, MS, is Graduate Research Assistant, Department of Computer Science, University of Missouri, Columbia. Xu Shuyu, MS, is Graduate Research Assistant, Informatics Institute, University of Missouri, Columbia. Bob R. Stewart, EdD, is Professor Emeritus, College of Education, and Adjunct Clinical Faculty, Sinclair School of Nursing, University of Missouri, Columbia. Chi-Ren Shyu, PhD, is Professor and Director of the Informatics Institute, Department of Computer Science, Informatics Institute, University of Missouri, Columbia. Jane M. Armer, PhD, RN, FAAN, is Professor and Director of Nursing Research at Ellis Fischel Cancer Center and Director of the American Lymphedema Framework Project, Sinclair School of Nursing, University of Missouri, Columbia.
Nursing research (Impact Factor: 1.36). 03/2013; 62(2):122-129. DOI: 10.1097/NNR.0b013e318283da67
Source: PubMed


BACKGROUND:: Secondary lymphedema is a lifetime risk for breast cancer survivors and can severely affect quality of life. Early detection and treatment are crucial for successful lymphedema management. Limb volume measurements can be utilized not only to diagnose lymphedema but also to track progression of limb volume changes before lymphedema, which has the potential to provide insight into the development of this condition. OBJECTIVES:: This study aims to identify commonly occurring patterns in limb volume changes in breast cancer survivors before the development of lymphedema and to determine if there were differences in these patterns between certain patient subgroups. Furthermore, pattern differences were studied between patients who developed lymphedema quickly and those whose onset was delayed. METHODS:: A temporal data mining technique was used to identify and compare common patterns in limb volume measurements in patient subgroups of study participants (n = 232). Patterns were filtered initially by support and confidence values, and then t tests were used to determine statistical significance of the remaining patterns. RESULTS:: Higher body mass index and the presence of postoperative swelling are supported as risk factors for lymphedema. In addition, a difference in trajectory to the lymphedema state was observed. DISCUSSION:: The results have potential to guide clinical guidelines for assessment of latent and early-onset lymphedema.

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    ABSTRACT: Breast cancer-related lymphedema (LE) is one type of incurable progressive chronic disease caused by cancer treatment or surgery that damages a patient's lymphatic system. Many patients are unaware of available treatment and places to seek for help with LE. This paper introduces a framework for a mobile application package for LE patients or patients at-risk to 1) locate available trained therapists using embedded Google Map technology; 2) monitor disease progression using profile match and rules mining; and 3) acquire up-to-date LE research findings based on patient characteristics. It aims to increase patients' accessibility to available resources and improve patient's quality of life by a mHealth-driven disease management approach.
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    ABSTRACT: Purpose Approximately 20 % of patients develop lymphedema (LE) following breast cancer (BC) surgery. An evaluation of distinct trajectories of volume change may improve our ability to diagnose LE sooner. The purposes of this study were to identify subgroups of women with distinct trajectories of limb volume changes following BC surgery and to evaluate for phenotypic differences among these classes. Methods In this prospective longitudinal study, 380 women were enrolled prior to unilateral BC surgery. Upper limb bioimpedance was measured preoperatively and serially for 1 year postoperatively. Resistance ratios (RRs) were calculated. A RR of >1 indicates affected limb volume > unaffected limb volume. Latent class growth analysis (LCGA) was used to identify classes of women with distinct postoperative RR trajectories. Differences among classes were evaluated using analyses of variance and chi-square analyses. Results Three distinct classes were identified as follows: RR <0.95 (37.9 %), RR ~1.00 (46.8 %), and RR >1.05 (15.3 %). Patients in the RR >1.05 class were more likely to have diabetes (p = 0.036), were more likely to have BC on their dominant side (p < 0.001), had higher RR ratios at the preoperative and 1-month assessments (p < 0.001), and were more likely to be diagnosed with LE (p < 0.001). Conclusions LCGA is a useful analytic technique to identify subgroups of women who may be at higher risk for the development of LE, based on trajectories of limb volume change after BC surgery. Implications for Cancer Survivors Assessment of preoperative and 1-month bioimpedance RRs may allow for the earlier identification of patients who are at higher risk for the development of LE.
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