Well‐being trajectories in breast cancer and their predictors: A machine‐learning approach.
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| Title: | Well‐being trajectories in breast cancer and their predictors: A machine‐learning approach. |
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| Authors: | Karademas, Evangelos C. (AUTHOR), Mylona, Eugenia (AUTHOR), Mazzocco, Ketti (AUTHOR), Pat‐Horenczyk, Ruth (AUTHOR), Sousa, Berta (AUTHOR), Oliveira‐Maia, Albino J. (AUTHOR), Oliveira, Jose (AUTHOR), Roziner, Ilan (AUTHOR), Stamatakos, Georgios (AUTHOR), Cardoso, Fatima (AUTHOR), Kondylakis, Haridimos (AUTHOR), Kolokotroni, Eleni (AUTHOR), Kourou, Konstantina (AUTHOR), Lemos, Raquel (AUTHOR), Manica, Isabel (AUTHOR), Manikis, George (AUTHOR), Marzorati, Chiara (AUTHOR), Mattson, Johanna (AUTHOR), Travado, Luzia (AUTHOR), Tziraki‐Segal, Chariklia (AUTHOR) |
| Source: | Psycho-Oncology. Nov2023, Vol. 32 Issue 11, p1762-1770. 9p. |
| Subjects: | Machine learning, Well-being, Breast cancer, Psychological factors, Disease progression |
| Abstract: | Objective: This study aimed to describe distinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months following a breast cancer diagnosis, and identify the medical, socio‐demographic, lifestyle, and psychological factors that predict these trajectories. Methods: 474 females (mean age = 55.79 years) were enrolled in the first weeks after surgery or biopsy. Data from seven assessment points over 18 months, at 3‐month intervals, were used. The two outcomes were assessed at all points. Potential predictors were assessed at baseline and the first follow‐up. Machine‐Learning techniques were used to detect latent patterns of change and identify the most important predictors. Results: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well‐being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune‐related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories. Conclusions: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine‐learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well‐being. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Objective: This study aimed to describe distinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months following a breast cancer diagnosis, and identify the medical, socio‐demographic, lifestyle, and psychological factors that predict these trajectories. Methods: 474 females (mean age = 55.79 years) were enrolled in the first weeks after surgery or biopsy. Data from seven assessment points over 18 months, at 3‐month intervals, were used. The two outcomes were assessed at all points. Potential predictors were assessed at baseline and the first follow‐up. Machine‐Learning techniques were used to detect latent patterns of change and identify the most important predictors. Results: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well‐being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune‐related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories. Conclusions: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine‐learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well‐being. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10579249 |
| DOI: | 10.1002/pon.6230 |
