Abstract 16403: Trajectories of Depression and Anxiety Symptoms are Predictive of Physical Health-related Quality of Life, Mortality, and Hospital Admission at 1-Year Among Patients With Heart Failure
Bibliographic record
Abstract
Background: Little is known about the symptom trajectories of depression and anxiety in heart failure (HF) patients and how these symptoms influence health outcomes over time. Objective: The goal of this study was to describe unique trajectories of depression and anxiety symptoms in HF patients over 12 months and determine whether changes in these symptoms predict subsequent physical health-related quality of life (P-HRQOL) and event-free survival. Method: The study sample consisted of 597 patients with HF enrolled as part of a larger longitudinal study at 8 sites from across the United States. The Patient Health Questionnaire-9, the Brief Symptom Inventory Anxiety subscale, and the Minnesota Living with Heart Failure Questionnaire Physical Subscale were used to assess depression, anxiety, and P-HRQOL, respectively. Latent growth mixture modeling was used to identify distinct trajectories of change in depression and anxiety. Results: Three trajectories of depression and three trajectories of anxiety were identified (entropy = 0.919). Trajectory changes in depressive symptoms were labeled as getting better (81.1%), bad and getting slightly worse (13.9%), and bad and getting much worse (5.0%). Trajectory changes in anxiety were labeled as getting better (66.7%), stable with slight improvement (22.1%), and getting much worse (11.2%). The trajectories of depression labeled as bad and getting slightly worse and bad and getting much worse were predictive of a poorer P-HRQOL at one-year than the trajectory which showed improvement (β 11.6, p<0.001 and β 15.6, p<0.001, respectively). Changes in depressive symptoms labeled as bad and getting slightly worse was also a significant predictor of event-free survival (HR = 2.17, p<0.001). The two anxiety trajectories which showed stable, slight improvement and much worse anxiety symptoms over 12-months were significant predictors of worse P-HRQOL (β 12.0, p<0.001 and β 18.7, p<0.001, respectively). The trajectory of anxiety symptoms labeled getting much worse was a significant predictor of event-free survival (HR = 2.18, p<0.001). Conclusion: We identified three distinct trajectories for both depression and anxiety symptoms, some of which were predictive of P-HRQOL and event-free survival at one year.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".