External Exposome Factors and Adverse Heart Failure Outcomes in the OneFlorida+ Network: Retrospective Cohort Study
Bibliographic record
Abstract
Background: Heart failure (HF) readmission rates vary across geographic regions in the United States, yet the impact of external exposome factors, such as contextual-level social determinants of health (SDoH), on adverse HF outcomes is not well understood. Objective: This study aims to examine the association between external exposome factors and the risk of HF readmission and all-cause mortality using a data-driven approach. Methods: We conducted a retrospective cohort study using electronic health record (EHR) data from the OneFlorida+ Network, including patients hospitalized for HF (HHF) from 2016 to 2022. A total of 1308 external exposome factors, covering a wide range of SDoH (eg, economic stability, education, health care access, natural and built environments, and social context), were linked to patients' EHR data based on their county-level residential location. Patients were followed for 1 year after their first HHF to capture readmission and mortality events. We applied the least absolute shrinkage and selection operator regularization to preselect candidate variables, followed by a 2-phase external exposome-wide association study using mixed-effects logistic regression to identify key factors associated with the composite outcome of 1-year HF readmission and mortality. Results: Among 63,940 patients with HF (n=30,475, 48% women; mean age 65, SD 14 years), higher maximum temperature in May was significantly associated with increased risk of the composite outcome (adjusted odds ratio [aOR] 1.04, 95% CI 1.02-1.06; P<.001). Subgroup analyses showed consistent associations across age, sex, race, socioeconomic status, and rural or urban strata. Conclusions: Using a data-driven approach, we found that elevated maximum temperature in May (late spring) was significantly associated with HF readmission and mortality in Florida. Further investigations are warranted to uncover the intricate mechanisms through which extreme heat potentially influences HF outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".