Abstract 17863: Analysis of Social Determinants of Health, Burden of Treatment and Quality of Life in Patients With Heart Failure With Preserved and Reduced Ejection Fraction, a Single Center Study With Six Months Follow Up
Bibliographic record
Abstract
Introduction: Heart failure (HF) is a chronic debilitating disease with immense burden on the patient’s life. This study aims to investigate the clinical characteristics, social determinants of health (SDOH), burden of treatment (BoT), and quality of life (QoL) of patients with heart failure with preserved and reduced ejection fraction (HFpEF and HFrEF). Methods: Data from 191 patients (63 HFpEF, 128 HFrEF) were collected from February 2022 to March 2023. Validated questionnaires including SDoH, BoT and QoL were filled by the patient on admission and in 6 months as a follow up. Descriptive statistics were used to compare the demographic and clinical characteristics of HFpEF and HFrEF patients. Inferential statistics, including logistic regression, were used to analyze the associations between SDOH, BoT, QoL, and 30-day readmission rates. Results: Distribution between both groups is similar to the general population. HFpEF patients experienced more interpersonal challenges and reported greater difficulty with self-care and usual activities. HFrEF patients had higher rates of substances, alcohol, and tobacco use. Regarding readmission, HFpEF patients with medication difficulties and HFrEF patients with difficulty accessing healthcare services were more likely to be readmitted. Both HFrEF and HFpEF patients showed significant improvement in SDOH, QoL and BoT in the follow up data (Table 1). Conclusions: The study findings highlight the distinct clinical characteristics, SDOH, BoT, and QoL factors associated with HFpEF and HFrEF. These findings can contribute to targeted interventions and improved patient care. Moreover, the study emphasizes the importance of addressing social and personal factors influencing HF outcomes, aiming to reduce healthcare disparities and improve patient well-being. The results have implications for healthcare providers, policymakers, and researchers in improving the management and outcomes of heart failure patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".