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Published on: January 28, 2020
Efficient assessment of depressive symptoms and their prognostic value in myocardial infarction patients
Johan Denollet1, Elisabeth J Martens, Otto R F Smith
1CoRPS - Center of Research on Psychology in Somatic diseases, Tilburg University, the Netherlands. denollet@uvt.nl
Background:
Identification of post-myocardial infarction (MI) depressive symptoms is a major concern and clinicians are in need of a short, easy-to-use assessment tool for this population. We evaluated the utility and prognostic power of a brief 10-item version of the Beck Depression Inventory (BDI10) in post-MI patients.
Methods:
Patients (n=416) were assessed on demographic/clinical variables and completed the BDI10, BDI-21 and STAI (anxiety symptoms) two months post-MI. The end point was a composite of cardiac death and recurrent MI. The average follow-up period was 2.7 years and follow-up data was complete for all patients.
Results:
Factor analysis of the BDI10 yielded a one-factor model. The BDI10 had good reliability (alpha=.82) and correlated highly (r=.89) with the standard BDI measure of depressive symptoms; a BDI-derived proxy measure of the BDI10 correlated .94 with the actual BDI10 score. There were 41 events attributable to cardiac death (n=24) or recurrent MI (n=20). The mean level of depressive symptoms as measured by the BDI10 was significantly higher in patients who experienced an event (4.3+/-4.4) compared with event-free patients (2.6+/-2.8); p=0.015. The BDI10 (HR:1.18; 95%CI:1.08-1.29, p<0.0001) independently predicted death/recurrent MI adjusting for disease severity. Age, BMI and cardiac history were also independent predictors of death/recurrent MI in these analyses.
Limitations:
The low number of women (22%), relatively healthy sample (mean LVEF=52%) and lack of a structured interview as gold standard for assessment of depression.
Conclusions:
The BDI10 is a brief, valid, and easy-to-use self-report measure of depressive symptoms that predicts post-MI outcomes and hence can be used clinically for risk stratification purposes, while reducing assessment burden.
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