Pollution atmosphérique et troubles du rythme cardiaque : une étude rétrospective
Bibliographic record
Abstract
Selon de nombreuses études, une exposition à la pollution atmosphérique augmenterait la morbi-mortalité cardiovasculaire. En effet, la fréquence des troubles du rythme cardiaque dans la population wallonne est très importante. L’objectif de cette étude est de vérifier l’hypothèse d’un lien entre troubles du rythme mesurés par des holters cardiaques et les données issues d’appareils mesurant la concentration des polluants atmosphériques. Les données de santé ont été obtenues via le centre de cardiologie de l’hôpital Erasme. Il s’agit d’un recueil de données rétrospectives sur les 2 à 5 dernières années. Les données environnementales sont : PM2.5, PM10, NO2, O3 et la température. Les modèles statistiques se sont basés sur des analyses « cas-croisé ». Une association entre les PM10 et le nombre d’ESA a été observée. Une augmentation de 10 μg/m3 de PM10 augmente de 20 % le nombre d’ESA (p = 0,040). Le nombre d’ESA augmente avec l’âge (63 % d’ESA en plus lorsque l’âge augmente de 10 ans). Un antécédent d’intervention diminue aussi le nombre d’ESA (−35 %), ce même phénomène est constaté pour les porteurs de pacemaker. Une association plus forte est observée entre le NO2 et l’ESA avec un OR de 1,37 (p = 0,027) dans le modèle final. Aucune association significative n’a été observée entre les effets des polluants et les ESVs. Nos analyses reprennent les effets des différents polluants sur les troubles du rythme, les effets ajustés pour les traitements et les comorbidités. Ils ouvrent la porte à d’autres études plus fines basés sur des mesures individuelles. According to many studies, exposure to air pollution increases cardiovascular morbidity and mortality. It has also been shown that the frequency of heart rhythm disorders in Region wallonne is very high. The objective of this study is to test the hypothesis of a link between rhythm disorders measured by cardiac holters and data from devices measuring the concentration of air pollutants present in ambient air. The health data were obtained via the Erasme hospital's cardiology center. This is a retrospective data collection over the last 2 to 5 years. The environmental data are: PM2.5, PM10, NO2, O3 and temperature. The statistical models were based on “cross-case” analyses. An association between PM10 and the number of ESAs was observed. An increase of 10 μg/m3 of PM10 increases the number of ESAs by 20% (P = 0.040). The number of ESAs increases with age (63% more ESAs when age increases by 10 years). A history of intervention also decreases the number of ESAs (−35%), the same phenomenon is observed for pacemaker wearers (−66%). The strongest association observed between NO2 and ESA with an OR of 1.37 (P = 0.027) in the final model. No significant association was observed between the effects of air pollution and VPCs. Our analyses resume the effects of the different pollutants on rhythm disorders, the effects adjusted for treatment and co-morbidities. They open the door to other more refined studies based on individual measurements.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".