Development of an intervention aimed at the prevention and treatment of chronic pain in breast cancer survivors: An intervention mapping approach

Slaghmuylder, Yaël; Pype, Peter; Hecke, Ann Van; Lauwerier, Emelien

doi:10.1016/j.pec.2022.107618

Cited by 4 publications

(3 citation statements)

References 81 publications

(119 reference statements)

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“…The results of this machine learning clustering align with previous studies, underlining that these factors significantly impact the risk of lymphedema development [10,33,68,69]. Thus, recent research emphasizes that precise identifications of individual risk factors should be integrated into routine clinical practice to optimize a patient-centered approach targeting BCRL prevention [18]. In contrast, significant differences in terms of BMI were identified only in the BCRL patients, suggesting that the machine learning model might consider the body composition in the patient's clusterization less important.…”

Section: Discussionsupporting

confidence: 80%

“…In another work [10], LVI was described as an indicator of BCRL. The number of lymph nodes with metastasis (NR METASTATIC LN) was already identified as a highly influential factor in BCRL onset [18,52], together with weight variations and obesity measured by BMI [53,54]. Evidence that secondary lymphedema is aggravated by hormone therapy was argued in [55]; indeed, during the present investigation, HR DRUG resulted in another distinguishing factor between patients of the two clusters.…”

Section: Discussionsupporting

confidence: 48%

“…It is characterized by localized tissue swelling associated with fluid retention related to surgical procedures and/or radiotherapy in breast cancer (BC) patients [14]. BCRL might often lead to psychophysical frailty with detrimental consequences on work, career, and Health-Related Quality of Life (HR-QoL) [15][16][17][18]. Despite the disabling consequences of BCRL being widely documented, few guidelines are currently available, and the optimal management of BCRL is still challenging [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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Algorithm-Based Risk Identification in Patients with Breast Cancer-Related Lymphedema: A Cross-Sectional Study

Nascimben

Lippi

Sire

et al. 2023

Cancers

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Background: Breast cancer-related lymphedema (BCRL) could be one consequence of breast cancer (BC). Although several risk factors have been identified, a predictive algorithm still needs to be made available to determine the patient’s risk from an ensemble of clinical variables. Therefore, this study aimed to characterize the risk of BCRL by investigating the characteristics of autogenerated clusters of patients. Methods: The dataset under analysis was a multi-centric data collection of twenty-three clinical features from patients undergoing axillary dissection for BC and presenting BCRL or not. The patients’ variables were initially analyzed separately in two low-dimensional embeddings. Afterward, the two models were merged in a bi-dimensional prognostic map, with patients categorized into three clusters using a Gaussian mixture model. Results: The prognostic map represented the medical records of 294 women (mean age: 59.823±12.879 years) grouped into three clusters with a different proportion of subjects affected by BCRL (probability that a patient with BCRL belonged to Cluster A: 5.71%; Cluster B: 71.42%; Cluster C: 22.86%). The investigation evaluated intra- and inter-cluster factors and identified a subset of clinical variables meaningful in determining cluster membership and significantly associated with BCRL biological hazard. Conclusions: The results of this study provide potential insight for precise risk assessment of patients affected by BCRL, with implications in prevention strategies, for instance, focusing the resources on identifying patients at higher risk.

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