Divorce after breast cancer diagnosis and its impact on quality of life

Kang, Danbee; Kim, Nayeon; Han, Gayeon; Kim, Soo-Yeon; Kim, Ho-Young; Lim, Jihyun; Kim, Hyunsoo; Shim, Sung Shine; Lee, Mangyeong; Lee, Jeong Eon; Nam, Seok Jin; Kim, Seok Won; Yu, Jonghan; Lee, Se Kyung; Cho, Juhee

doi:10.1017/s1478951521001711

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…First, most models did not fully include multidimensional factors. Although some studies included patient-centered factors such as functional impairment and psychological symptoms, they still missed key variables for the QoL of long-term survivors, such as spiritual well-being [13][14][15][16]. Second, only a few studies examined the predictors of QoL for long-term survivors.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model for Postoperative Quality of Life Among Breast Cancer Survivors Along the Survivorship Trajectory From Pretreatment to 5 Years: Machine Learning–Based Analysis

Kang¹,

Kim²,

Cho³

et al. 2023

JMIR Public Health Surveill

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Background Breast cancer is the most common cancer and the most common cause of cancer death in women. Although survival rates have improved, unmet psychosocial needs remain challenging because the quality of life (QoL) and QoL-related factors change over time. In addition, traditional statistical models have limitations in identifying factors associated with QoL over time, particularly concerning the physical, psychological, economic, spiritual, and social dimensions. Objective This study aimed to identify patient-centered factors associated with QoL among patients with breast cancer using a machine learning (ML) algorithm to analyze data collected along different survivorship trajectories. Methods The study used 2 data sets. The first data set was the cross-sectional survey data from the Breast Cancer Information Grand Round for Survivorship (BIG-S) study, which recruited consecutive breast cancer survivors who visited the outpatient breast cancer clinic at the Samsung Medical Center in Seoul, Korea, between 2018 and 2019. The second data set was the longitudinal cohort data from the Beauty Education for Distressed Breast Cancer (BEST) cohort study, which was conducted at 2 university-based cancer hospitals in Seoul, Korea, between 2011 and 2016. QoL was measured using European Organization for Research and Treatment of Cancer QoL Questionnaire Core 30 questionnaire. Feature importance was interpreted using Shapley Additive Explanations (SHAP). The final model was selected based on the highest mean area under the receiver operating characteristic curve (AUC). The analyses were performed using the Python 3.7 programming environment (Python Software Foundation). Results The study included 6265 breast cancer survivors in the training data set and 432 patients in the validation set. The mean age was 50.6 (SD 8.66) years and 46.8% (n=2004) had stage 1 cancer. In the training data set, 48.3% (n=3026) of survivors had poor QoL. The study developed ML models for QoL prediction based on 6 algorithms. Performance was good for all survival trajectories: overall (AUC 0.823), baseline (AUC 0.835), within 1 year (AUC 0.860), between 2 and 3 years (AUC 0.808), between 3 and 4 years (AUC 0.820), and between 4 and 5 years (AUC 0.826). Emotional and physical functions were the most important features before surgery and within 1 year after surgery, respectively. Fatigue was the most important feature between 1 and 4 years. Despite the survival period, hopefulness was the most influential feature on QoL. External validation of the models showed good performance with AUCs between 0.770 and 0.862. Conclusions The study identified important factors associated with QoL among breast cancer survivors across different survival trajectories. Understanding the changing trends of these factors could help to intervene more precisely and timely, and potentially prevent or alleviate QoL-related issues for patients. The good performance of our ML models in both training and external validation sets suggests the potential use of this approach in identifying patient-centered factors and improving survivorship care.

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Section: Introductionmentioning

confidence: 99%

Prediction Model for Postoperative Quality of Life Among Breast Cancer Survivors Along the Survivorship Trajectory From Pretreatment to 5 Years: Machine Learning–Based Analysis

Kang¹,

Kim²,

Cho³

et al. 2023

JMIR Public Health Surveill

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Partnership aspects in patients with advanced breast cancer

Zimmermann

2024

Onkologie

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ZusammenfassungDie psychosozialen Belastungen einer Brustkrebserkrankung betreffen nicht nur die erkrankte Frau, sondern auch die unmittelbaren Angehörigen, insbesondere die Partner. Neben individuellen Belastungen wie Ängsten, Depressivität oder Sorgen kann es auch zu dyadischen Belastungen des Paars und der Partnerschaft kommen. Insbesondere die partnerschaftliche Kommunikation stellt eine besondere Herausforderung dar. Über eigene Ängste und Sorgen oder Tod und Sterben zu sprechen, führt oft zu einer Sprachlosigkeit innerhalb eines Paars. Dies kann wiederum in einer Unzufriedenheit in der Beziehung und in fehlender partnerschaftlicher Unterstützung resultieren. Das Trennungsrisiko kann sich erhöhen. Hinzu kommen Veränderungen in der sexuellen Funktion, die durch die medizinische Behandlung, die psychische Belastung und/oder das veränderte Körperbild entstehen können. Auch hier findet häufig keine offene Kommunikation sowohl innerhalb des Paares als auch mit dem medizinischen Team statt. Eine Krebserkrankung ist ein Stressor für die Patientin, den Partner und die Partnerschaft. Daher ist ein gemeinsamer Umgang als Paar mit dem Stress durch die Erkrankung – das sog. dyadische Coping – bedeutsam für die Krankheitsbewältigung und Stärkung des „Wir-Gefühls“, welches wiederum zu einer höheren Partnerschaftszufriedenheit und einem höheren Kohäsionsgefühl beiträgt. Für die klinische Versorgung sind somit auch der Einbezug des Angehörigen und die Berücksichtigung partnerschaftlicher Aspekte sehr sinnvoll.

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