Well-being trajectories in breast cancer and their predictors: A machine-learning approach.
Psychooncology
; 32(11): 1762-1770, 2023 11.
Article
in En
| MEDLINE
| ID: mdl-37830776
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 eachoutcome:
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Breast Neoplasms
Limits:
Female
/
Humans
/
Middle aged
Language:
En
Journal:
Psychooncology
Journal subject:
NEOPLASIAS
/
PSICOLOGIA
Year:
2023
Type:
Article
Affiliation country:
Greece