Cognitive Performance is Associated with Worse Prognosis in Patients with Heart Failure with Reduced Ejection Fraction
Bibliographic record
Abstract
AIMS: Heart failure (HF) is a complex clinical syndrome with multiple comorbidities. Cognitive impairment, stress, anxiety, depression, and lower quality of life are prevalent in HF. Herein, we explore the interplay between these parameters and study their value to predict major adverse cardiovascular events (MACEs) and health-related quality of life (HrQoL) in patients with HF with reduced ejection fraction using guideline recommended assessment tools. METHODS AND RESULTS: We conducted a longitudinal study using a sample of 65 patients from two hospitals. A battery of tests was applied to assess cognition [Montreal Cognitive Assessment (MoCA)], stress (Perceived Stress Scale-10), anxiety, and depression (Hospital Anxiety and Depression Scale) at baseline. MACEs were registered using clinical records. HrQoL was estimated using the Kansas City Cardiomyopathy Questionnaire (KCCQ). A descriptive statistical analysis was conducted, and multiple linear and Cox regression models conducted to determine the predictive value of neurocognitive parameters and HrQoL in MACE. Both MoCA [hazard ratio = 0.906 (0.829-0.990); P = 0.029] and KCCQ scores were predictors of MACE, but not of overall mortality. Anxiety, depression, and stress scores did not predict MACE. However, anxiety (β = -0.326; P = 0.012) and depression levels (β = -0.309; P = 0.014) were independent predictors of the KCCQ score. CONCLUSIONS: The MoCA score and HrQoL were predictors of MACE-free survival. Anxiety and depression were good predictors of HrQoL, but not of MACE-free survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".