Artificial intelligence-assisted analysis on the association between exposure to ambient fine particulate matter and incidence of arrhythmias in outpatients of Shanghai community hospitals

Yang, Mei; Zhou, Runze; Qiu, Xiangjun; Feng, Xiang‐Fei; Sun, Jian; Wang, Qunshan; Lu, Qiuchen; Zhang, Pengpai; Liu, Bo; Li, Wei; Chen, Mu; Zhao, Yan; Mo, Bin‐Feng; Zhou, Xin; Zhang, Xi; Hua, Yingxue; Guo, Jin; Bi, Fangfang; Cao, Yajun; Feng, Ling; Shengming, Shi; Li, Yi‐Gang

doi:10.1016/j.envint.2020.105745

Cited by 23 publications

(12 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prior research using daily hospital records of AF have reported different lag patterns in association with air pollution exposure. For example, a time-series study in Shanghai reported the largest effects of PM 2.5 on AF hospitalization on lag 0-2 days, while another similar study in Beijing found the largest effect on lag 0-1 day 17,18 . Fang et al reported significant associations between six air pollutants and AF hospitalization in a time-series study on lag 0-4 day 15 .…”

Section: Discussionmentioning

confidence: 99%

Application of smart devices in investigating the effects of air pollution on atrial fibrillation onset

Liu

Tai

et al. 2023

npj Digit. Med.

View full text Add to dashboard Cite

Few studies have examined the link between short-term exposure to air pollutants and atrial fibrillation (AF) episodes. This study aims to examine the association of hourly criteria air pollutants with AF episodes. We employ a smart device-based photoplethysmography technology to screen AF from 2018 to 2021. Hourly concentrations of six criteria air pollutants are matched to the onset hour of AF for each participant. We adopt a time-stratified case-crossover design to capture the acute effects of air pollutants on AF episodes, using conditional logistic regression models. Subgroup analyses are conducted by age, gender, and season. A total of 11,906 episodes of AF are identified in 2976 participants from 288 Chinese cities. Generally, the strongest associations of air pollutants are present at lag 18–24 h, with positive and linear exposure-response relationships. For an interquartile range increase in inhalable particles, fine particles, nitrogen dioxide, and carbon monoxide, the odds ratio (OR) of AF is 1.19 [95% confidential interval (CI): 1.03, 1.37], 1.38 (95%CI: 1.14, 1.67), 1.60 (95%CI: 1.16, 2.20) and 1.48 (95%CI: 1.19, 1.84), respectively. The estimates are robust to the adjustment of co-pollutants, and they are larger in females, older people, and in cold seasons. There are insignificant associations for sulfur dioxide and ozone. This nationwide case-crossover study demonstrates robust evidence of significant associations between hourly exposure to air pollutants and the onset of AF episodes, which underscores the importance of ongoing efforts to further improve air quality as an effective target for AF prevention.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of smart devices in investigating the effects of air pollution on atrial fibrillation onset

Liu

Tai

et al. 2023

npj Digit. Med.

View full text Add to dashboard Cite

show abstract

“…All data used for this research were derived from the Siwei (Shanghai Siwei Medical, Shanghai, China) remote ECG diagnosis system. ( 12 ) As the largest ECG diagnostic system in China, the platform collected ECG data from 1,320 medical institutions across 13 provinces in China, with a volume of more than one million medical visits involving ECGs per year ( Figure 1A ). As approximately 85% of this ECG data was collected from Shanghai ( Supplementary Table 1 ), which covered 278 medical institutions from almost all administrative districts ( Figure 1B and Supplementary Table 2 ), the present study analyzes ECG data in Shanghai between January 1, 2018 and April 7, 2021.…”

Section: Methodsmentioning

confidence: 99%

ECG Utilization Patterns of Patients With Arrhythmias During COVID-19 Epidemic and Post-SARS-CoV-2 Eras in Shanghai, China

et al. 2022

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

BackgroundThe COVID-19 pandemic has led to concerns around its subsequent impact on global health.ObjectiveTo investigate the health-seeking behavior, reflected by ECG utilization patterns, of patients with non-COVID-19 diseases during and after COVID-19 epidemic.MethodsTaking advantage of the remote ECG system covering 278 medical institutions throughout Shanghai, the numbers of medical visits with ECG examinations during the lockdown (between January 23 and April 7, 2020), post-lockdown (between April 8 and December 31, 2020) and post-SARS-CoV-2 (between January 23 and April 7, 2021) periods were analyzed and compared against those during the same periods of the preceding years (2018 and 2019).ResultsCompared with the same period during pre-COVID years, the number of medical visits decreased during the lockdown (a 38% reduction), followed by a rebound post-lockdown (a 17% increase) and a fall to the baseline level in post-SARS-CoV-2 period. The number of new COVID-19 cases announced on a given day significantly correlated negatively with the numbers of medical visits during the following 7 days. Medical visit dynamics differed for various arrhythmias. Whereas medical visits for sinus bradycardia exhibited a typical decrease-rebound-fallback pattern, medical visits for atrial fibrillation did not fall during the lockdown but did exhibit a subsequent increase during the post-lockdown period. By comparison, the volume for ventricular tachycardia remained constant throughout this entire period.ConclusionThe ECG utilization patterns of patients with arrhythmias exhibited a decrease-rebound-fallback pattern following the COVID-19 lockdowns. Medical visits for diseases with more severe symptoms were less influenced by the lockdowns, showing a resilient demand for healthcare.

show abstract

“…Recently, two studies 141 , 142 paid attention to the influence of air pollution on cardiovascular systems. Yang et al .…”

Section: Other Perspectivesmentioning

confidence: 99%

“…Yang et al . 141 examined the impact of fine particulate matter pollution on the cardiovascular system and found that ambient exposure to them was linked with increased risk of arrhythmias in outpatients visiting Shanghai community hospitals, with an immediate or lag effect. Kim et al .…”

Section: Other Perspectivesmentioning

confidence: 99%

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

Olier

Ortega‐Martorell

Pieroni

et al. 2021

Cardiovascular Research

View full text Add to dashboard Cite

There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centered on applying ML for detecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modeling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centered in aims such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g., blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.

show abstract

Artificial intelligence-assisted analysis on the association between exposure to ambient fine particulate matter and incidence of arrhythmias in outpatients of Shanghai community hospitals

Cited by 23 publications

References 61 publications

Application of smart devices in investigating the effects of air pollution on atrial fibrillation onset

Application of smart devices in investigating the effects of air pollution on atrial fibrillation onset

ECG Utilization Patterns of Patients With Arrhythmias During COVID-19 Epidemic and Post-SARS-CoV-2 Eras in Shanghai, China

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

Contact Info

Product

Resources

About