Effects of Extreme Humidity and Heat on Ventricular Arrhythmia Risk in Patients With Cardiac Devices
Bibliographic record
Abstract
Background: Climate change is increasing the frequency of high heat and high humidity days. Whether these conditions can trigger ventricular arrhythmias [ventricular tachycardia/ventricular fibrillation, VT/VF] in susceptible persons is unknown. Objectives: The purpose of this study was to determine the relationship between warm-season weather conditions and risk of VT/VF in individuals with pacemakers and defibrillators. Methods: Baseline clinical and device data from 5,944 patients in North Carolina (2010-2021) were linked to daily weather data geocoded to individuals' residential addresses. Associations between extreme humidity, temperature, and VT/VF overall and by patient, community, and built environment factors were estimated using a case time-series design with distributed lag nonlinear models, adjusting for temporal trends and individual factors. Results: VT/VF events occurred on 4,486 of the 484,988 person-days. Extreme humidity (95th percentile: 90% relative humidity) increased odds of VT/VF in the 7 days following exposure (aOR 1.23 [95% CI: 1.00-1.51]). Humidity-associated VT/VF risk was highest among those who were male (aOR: 1.38 [95% CI: 1.08-1.76]), age 67 to 75 years (aOR: 1.65 [95% CI: 1.16-2.35]) with coronary artery disease (aOR: 1.79 [95% CI: 1.25-2.57]), heart failure (aOR: 1.72 [95% CI: 1.2-2.46]), diabetes (aOR: 3.01 [95% CI: 1.99-4.56]), hypertension (aOR: 2.06 [95% CI: 1.48-2.88]), and prior myocardial infarction (aOR: 1.75 [95% CI: 1.23-2.48]). Communities with high socioeconomic deprivation (aOR: 1.83 [95% CI: 1.28-2.62]), high income inequality (aOR: 1.56 [95% CI: 1.19-2.04]), and urban areas with less greenspace (aOR: 1.29 [95% CI: 0.93-1.78]) also had increased VT/VF risk. High temperatures were not associated with VT/VF. Conclusions: In patients with preexisting cardiovascular disease, exposure to extreme humidity increased VT/VF risk, especially among vulnerable individuals, disadvantaged communities, and urban areas with less green space. These findings emphasize the need for policies that address environmental risks in susceptible individuals and communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".