Abstract P26: Depression Recognition and Disease-Specific Health Status in AMI Patients: Insights from the TRIUMPH Registry
Bibliographic record
Abstract
Background: Recognizing depression in acute myocardial infarction (AMI) offers an opportunity to treat this burdensome comorbidity, and could improve the risk stratification of AMI patients. Whether depression recognition is associated with health status after AMI is unknown. Methods: In the 24-center prospective TRIUMPH study, 4062 patients completed the Patient Health Questionnaire-9 (PHQ-9) during their index AMI admission and the Seattle Angina Questionnaire (SAQ) for quality of life (QOL) and angina frequency during the index admission and at 1-year. Patients were defined as depressed based on a PHQ-9 score ≥10, and depression was defined as recognized if the treating team documented any of the following in the patients' chart: depression diagnosis at discharge; anti-depressant medications administered at discharge; or referral for counseling. We examined the association between depression recognition and impaired quality of life (SAQ QOL score <75) and angina (SAQ angina frequency score <100) at 1 year using multivariable Poisson regression analyses adjusted for demographics, AMI severity, risk factors, and baseline quality of life, or angina frequency, as appropriate. Results: Of 4062 patients, 3303 (81.3%) were not depressed, 528 (13.0%) had unrecognized depression, and 231 (5.7%) had recognized depression. Patients with unrecognized depression were just as likely to have increased risk of 1-year adverse QOL and angina, as compared to patients with recognized depression. (Table) Conclusion: Depression in AMI is a comorbidity that is frequently missed in routine care. Regardless of being recognized, however, depression is associated with adverse 1-year AMI-specific health status. Although depression recognition has the potential to identify patients at risk of persistent angina and poorer QOL that might benefit from more aggressive treatment of their coronary disease, recognition in itself will not be sufficient to optimize their outcomes. The Association Between Depression Recognition Groups and 1-Year Impaired Quality of Life/Angina 1-Year AMI-Specific Health Status Unadjusted Adjusted * N (%) RR (95% CI) P-Value RR (95% CI) P-Value Impaired SAQ QOL Non Depressed 369 (17.6%) Reference Reference Recognized Depression 51 (38.1%) 2.31 (1.81-2.93) <.0001 1.70 (1.31-2.20) <.0001 Unrecognized Depression 103 (34.4%) 1.97 (1.64-2.36) <.0001 1.54 (1.32-1.80) <.0001 Angina Non Depressed 436 (20.6%) Reference Reference Recognized Depression 53 (39.3%) 1.98 (1.57-2.49) <.0001 1.51 (1.18-1.95) 0.046 Unrecognized Depression 97 (32.3%) 1.54 (1.31-1.80) <.0001 1.32 (1.14-1.53) 0.074 Abbreviations: AMI, acute myocardial infarction; SAQ, Seattle Angina Questionnaire; QOL, quality of life. * Covariates in the multivariable model included: age, sex, race, education, marital status, insurance status, Killip class, left ventricular systolic function, chronic heart failure, diabetes mellitus, hypercholesterolemia, hypertension, prior PCI, prior CABG, prior MI, prior CVA/TIA, current smoking, BMI, family history of CAD, ST-elevation AMI, ischemic symptoms on arrival, cancer, chronic lung disease, renal failure, and peripheral arterial disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".