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Changing depressive symptoms following percutaneous coronary intervention, clustering and effect on adherence - The
Thijs Versteeg1, Eveline van Montfort1, Johan Denollet1
1CoRPS - Center of Research on Psychology in Somatic diseases, Department of Medical & Clinical Psychology, Tilburg University, Tilburg, The Netherlands.
Insights
Depressive symptom changes after cardiac intervention impact treatment adherence. Understanding these symptom clusters, particularly cognitive-affective and somatic-affective, is crucial for predicting patient adherence and improving cardiac care outcomes.
Area of Science:
- Cardiology
- Psychiatry
- Health Psychology
Background:
- Depressive symptom dimensions may differentially impact cardiac prognosis.
- The interplay of changing depressive symptoms over time and its effect on disease progression remains unclear.
- This study investigates depressive symptom clustering post-percutaneous coronary intervention (PCI).
Purpose of the Study:
- To examine the clustering of changing depressive symptoms in the first 6 months after PCI.
- To assess how these change profiles influence the predictive value of depression for treatment adherence.
- To understand the dynamic relationship between depression and adherence in cardiac patients.
Main Methods:
- 219 PCI patients reported depressive symptoms and adherence at 1 and 6 months.
- Principal component analysis (PCA) analyzed individual symptom change scores.
- Multivariable linear regression predicted treatment adherence based on symptom change profiles.
Main Results:
- Four distinct depressive symptom change-factors were identified: somatic-affective, cognitive-affective (general and severe), and mixed.
- Five symptom change profiles were extracted.
- Increased cognitive-affective and somatic-affective symptoms were linked to worse general adherence in patients with increasing depressive symptoms.
Conclusions:
- Identified depressive symptom change-factors moderate the link between symptom levels and treatment adherence.
- Symptom change, not just current symptom levels, is clinically important for screening and risk prediction in cardiac patients.
- This highlights the need to consider dynamic symptom patterns in managing post-PCI patients.
Background:
Depressive symptom dimensions may have a differential effect on cardiac prognosis. It is yet unknown whether and how depressive symptoms change together over time and how this may affect disease progression. We examined the clustering of changing depressive symptoms over the first 6 months after percutaneous coronary intervention (PCI), and examined the influence of the change profile on the predictive value of depression for treatment adherence at 6 months post-PCI.
Methods:
PCI patients (N=219, age: 62±15, 20% women) reported on depressive symptoms (PHQ-9, BDI; 30 symptoms) and adherence (MOS-GAS) at 1 and 6 months post-PCI. Principal component analysis (PCA) was performed on the individual symptom change scores. Multivariable linear regression examined the role of change profiles in predicting general treatment adherence, while adjusting for demographic and clinical characteristics.
Results:
Four change-factors emerged from PCA. One somatic-affective change-factor (10 symptoms), two cognitive-affective change-factors (6 general cognitive-affective and 7 severe cognitive symptoms) and one mixed factor were identified. We extracted 5 symptom change profiles. Linear regression showed the moderating role of the change profiles. In patients reporting a net increase in depressive symptoms, higher cognitive affective symptoms (β=-.46, p=.001) and higher somatic-affective symptoms (β=-.29; p=.044) were associated with worse general adherence.
Discussion:
Four distinct depressive symptom change-factors were identified that moderated the association of somatic-affective and cognitive-affective depressive symptom levels with general treatment adherence. This is of clinical importance as not only current symptoms, but also symptom change over the preceding months may be important to consider in screening and risk prediction.
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