Well‐being trajectories in breast cancer and their predictors: A machine‐learning approach

Karademas, Evangelos; Mylona, Eugenia; Mazzocco, Ketti; Pat‐Horenczyk, Ruth; Sousa, Berta; Oliveira‐Maia, Albino J.; Oliveira, Jose; Roziner, Ilan; Stamatakos, Georgios; Cardoso, Fatima; Kondylakis, Haridimos; Kolokotroni, Eleni; Kourou, Konstantina; Lemos, Raquel; Manica, Isabel; Manikis, George; Marzorati, Chiara; Mattson, Johanna; Travado, Luzia; Tziraki‐Segal, Chariklia; Fotiadis, Dimitris; Poikonen‐Saksela, Paula; Simos, Panagiotis

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Authors

Oliveira‐Maia, Albino J.

Abstract(s)

Objective:This study aimed to described istinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months followinga breast cancer diagnosis,and identify the medical, socio-demographic,lifestyle, and psychologica lfactors that predict these trajectories.Methods:474 females (mean age=55.79 years) were enrolled in the first weeksafter surgery or biopsy. Data from seven assessmentpoints over 18 months, at 3-month intervals,were used. The two outcomeswere assessedat 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

Keywords

Breast cancer Cancêr Oncology trajectories Trajectory predictor

URI

http://hdl.handle.net/10400.12/9672

Citation

Karademas, E. C., Mylona, E., Kondylakis, H., Kourou, K., Manikis, G., Fotiadis, D., Simos, P., Mazzocco, K., Marzorati, C., Pat-Horenczyk, R., Sousa, B., Cardoso, F., Oliveira-Maia, A. J., Oliveira, J., Lemos, R., Manica, I., Travado, L., Roziner, I., Stamatakos, G., … Tziraki-Segal, C. (2023). Well-being trajectories in breast cancer and their predictors: A machine-learning approach. Psycho-Oncology, 32(11), 1762–1770. https://doi.org/10.1002/pon.6230

Publisher

John Wiley and Sons Ltd

DOI

10.1002/pon.6230

Collections

PSAU - Artigos em revistas internacionais

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