Personalized predictions of treatment outcome in patients with post-stroke depressive symptoms
Johanne C.C. Rauwenhoff, Suzanne C. Bronswijk, Frenk Peeters, Yvonne Bol, Alexander C. H. Geurts, Caroline M. van Heugten
School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.
Objective: Post-stroke depressive symptoms have a vast individual and societal impact. However, research into interventions for such symptoms show contradictory results; it is unclear what works for which patients. In addition, clinical prediction tools are lacking. This study aimed to develop a prognostic index model for treatment outcome in patients with post-stroke depressive symptoms.
Methods: Data from a randomized controlled trial (n = 61) evaluating 2 interventions for post-stroke depressive symptoms were used to predict post-treatment post-stroke depressive symptoms and participation. From 18 pre-treatment variables of patients and caregivers, predictors were selected using elastic net regression. Based on this selection, prognostic index scores (i. e. predictions) for both out-comes were computed for each individual patient.
Results: The depression model included all pre-treatment variables, explaining 44% of the variance.
The strongest predictors were: lesion location, employment, participation, comorbidities, mobility, sex, and pre-treatment depression. Six predictors of post-treatment participation were identified, explaining 51% of the variance: mobility, pre-treatment participation, age, satisfaction with participation, caregiver strain, and psychological distress of the spouse. The cross-validated prognostic index scores correlated highly with the actual outcome scores (depression: correlation = 0. 672; participation: correlation = 0. 718).
Conclusion: Post-stroke depressive symptoms form a complex and multifactorial problem. Treatment outcome is influenced by the characteristics of the stroke, the patients, and their spouses. The results show that psychological distress is probably no obstacle to attempting to improve participation. The personalized predictions (prognostic index scores) of treatment outcome show promising results, which, after further replication and validation, could aid clinicians with treatment selection.
A stroke is a very dramatic event in a person’s life. Patients may experience cognitive, emotional, and behav-ioural changes following a stroke, such as forgetfulness, mood changes, and lack of initiative. Therefore, returning to work and a busy social calendar might not be possible. One out of 3 patients who experience a stroke develops depressive symptoms. Unfortunately, these symptoms are difficult to treat. This study examined whether it is possible to predict the treatment outcome for individual stroke patients who have received psychological treatment for depressive symptoms. A statistical model was developed to predict the level of depressive symptoms and social participation for individual patients. With further development, this model could help psychologists decide which psychological treatment would be the best option for a particular patient. This might enable more patients to be provided with personalized treatment that could alleviate their depressive symptoms.
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