Does Evidence Support the American Heart Association's Recommendation to Screen Patients for Depression in Cardiovascular Care? An Updated Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To systematically review evidence on depression screening in coronary heart disease (CHD) by assessing the (1) accuracy of screening tools; (2) effectiveness of treatment; and (3) effect of screening on depression outcomes. BACKGROUND: A 2008 American Heart Association (AHA) Science Advisory recommended routine depression screening in CHD. METHODS: CINAHL, Cochrane, EMBASE, ISI, MEDLINE, PsycINFO and SCOPUS databases searched through December 2, 2011; manual journal searches; reference lists; citation tracking; trial registries. Included articles (1) compared a depression screening instrument to a depression diagnosis; (2) compared depression treatment to placebo or usual care in a randomized controlled trial (RCT); or (3) assessed the effect of screening on depression outcomes in a RCT. RESULTS: There were few examples of screening tools with good sensitivity and specificity using a priori-defined cutoffs in more than one patient sample among 15 screening accuracy studies. Depression treatment with antidepressants or psychotherapy generated modest symptom reductions among post-myocardial infarction (post-MI) and stable CHD patients (N = 6; effect size = 0.20-0.38), but antidepressants did not improve symptoms more than placebo in 2 heart failure (HF) trials. Depression treatment did not improve cardiac outcomes. No RCTs investigated the effects of screening on depression outcomes. CONCLUSIONS: There is evidence that treatment of depression results in modest improvement in depressive symptoms in post-MI and stable CHD patients, although not in HF patients. There is still no evidence that routine screening for depression improves depression or cardiac outcomes. The AHA Science Advisory on depression screening should be revised to reflect this lack of evidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".