LymphedemaV1, Main, Exploration, bibRecord, 003007

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

Identifieur interne : 003007 ( Main/Exploration ); précédent : 003006; suivant : 003008

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

Auteurs : Jason M. Green ; Sowjanya Paladugu ; Xu Shuyu ; Bob R. Stewart ; Chi-Ren Shyu ; Jane M. Armer

Source :

Nursing research [ 0029-6562 ] ; 2013.

RBID : PMC:4526254

Descripteurs français

KwdFr :
- Adulte, Adulte d'âge moyen, Facteurs de risque, Facteurs temps, Femelle, Fouille de données, Humains, Lymphoedème (diagnostic), Membres (physiopathologie), Sujet âgé, Sujet âgé de 80 ans ou plus, Survivants (), Tumeurs du sein ().
MESH :
- diagnostic : Lymphoedème.
- physiopathologie : Membres.
- Adulte, Adulte d'âge moyen, Facteurs de risque, Facteurs temps, Femelle, Fouille de données, Humains, Sujet âgé, Sujet âgé de 80 ans ou plus, Survivants, Tumeurs du sein.

English descriptors

KwdEn :
- Adult, Aged, Aged, 80 and over, Breast Neoplasms (complications), Breast Neoplasms (therapy), Data Mining, Extremities (physiopathology), Female, Humans, Lymphedema (diagnosis), Middle Aged, Risk Factors, Survivors (statistics & numerical data), Time Factors.
MESH :
- complications : Breast Neoplasms.
- diagnosis : Lymphedema.
- physiopathology : Extremities.
- statistics & numerical data : Survivors.
- therapy : Breast Neoplasms.
- Adult, Aged, Aged, 80 and over, Data Mining, Female, Humans, Middle Aged, Risk Factors, Time Factors.

Abstract

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

To identify commonly occurring patterns in limb volumes 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.

Method

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; 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.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4526254

DOI: 10.1097/NNR.0b013e318283da67
PubMed: 23458909
PubMed Central: 4526254

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en"><sec id="S1"><title>Background</title>
<p id="P1">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.</p>
</sec>
<sec id="S2"><title>Objectives</title>
<p id="P2">To identify commonly occurring patterns in limb volumes 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.</p>
</sec>
<sec id="S3"><title>Method</title>
<p id="P3">A temporal data mining technique was used to identify and compare common patterns in limb volume measurements in patient subgroups of study participants (<italic>n</italic>
 = 232). Patterns were filtered initially by support and confidence values; then t-tests were used to determine statistical significance of the remaining patterns.</p>
</sec>
<sec id="S4"><title>Results</title>
<p id="P4">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.</p>
</sec>
<sec id="S5"><title>Discussion</title>
<p id="P5">The results have potential to guide clinical guidelines for assessment of latent and early-onset lymphedema.</p>
</sec>
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<name sortKey="Shuyu, Xu" sort="Shuyu, Xu" uniqKey="Shuyu X" first="Xu" last="Shuyu">Xu Shuyu</name>
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Serveur d'exploration sur le lymphœdème

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

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

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki

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