Effects of Meteorological Conditions on the Risk of Ischemic Stroke Events in Patients Treated with Alteplase—HEWS-tPA

Sueda, Yoshimasa; Hosomi, Naohisa; Tsunematsu, Miwako; Takamatsu, Kazuhiro; Nomura, Eiichi; Torii, Tsuyoshi; Ohtsuki, Toshiho; Aoki, Shiro; Mukai, Tomoya; Nezu, Tomohisa; Kakehashi, Masayuki; Matsumoto, Masayasu

doi:10.1016/j.jstrokecerebrovasdis.2015.03.013

Cited by 12 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further exploration showed that higher temperature acted as a protective factor on HS, whereas a risk factor for IS. This may be different from other studies [ 35 , 56 ]. A possible reason for this discrepancy might be differences in the climate, population characteristics, and proportion of stroke subtypes of the studied areas.…”

Section: Discussioncontrasting

confidence: 96%

Short-Term Effect of Ambient Temperature and the Risk of Stroke: A Systematic Review and Meta-Analysis

Lian

Ruan

Liang

et al. 2015

IJERPH

124

View full text Add to dashboard Cite

Background and Purpose: The relationship between stroke and short-term temperature changes remains controversial. Therefore, we conducted a systematic review and meta-analysis to investigate the association between stroke and both high and low temperatures, and health assessment. Methods: We searched PubMed, Embase, Cochrane, China National Knowledge Infrastructure (CNKI) and Wanfang Data up to 14 September 2014. Study selection, quality assessment, and author-contractions were steps before data extraction. We converted all estimates effects into relative risk (RR) per 1 °C increase/decrease in temperature from 75th to 99th or 25th to 1st percentiles, then conducted meta-analyses to combine the ultimate RRs, and assessed health impact among the population. Results: 20 articles were included in the final analysis. The overall analysis showed a positive relationship between 1 °C change and the occurrence of major adverse cerebrovascular events (MACBE), 1.1% (95% confidence intervals (CI), 0.6 to 1.7) and 1.2% (95% CI, 0.8 to 1.6) increase for hot and cold effects separately. The same trends can be found in both effects of mortality and the cold effect for morbidity. Hot temperature acted as a protective factor of hemorrhage stroke (HS), −1.9% (95% CI, −2.8 to −0.9), however, it acted as a risk factor for ischemic stroke (IS), 1.2% (95% CI, 0.7 to 1.8). Conclusion: Short-term changes of both low and high temperature had statistically significant impacts on MACBE.

show abstract

Section: Discussioncontrasting

confidence: 96%

Short-Term Effect of Ambient Temperature and the Risk of Stroke: A Systematic Review and Meta-Analysis

Lian

Ruan

Liang

et al. 2015

IJERPH

124

View full text Add to dashboard Cite

show abstract

“…We hypothesized that meteorological conditions on the onset day and conditions on the former days may play important roles in the modulation of physical conditions. To our knowledge, our previous small sized study is the only study that has evaluated the influence of daily changes in meteorological conditions on stroke onset [13]. The results of this study suggest a higher risk for ischemic stroke events with low ambient temperature and elevated temperature on a previous day.…”

Section: Introductionmentioning

confidence: 66%

“…The thermo-hydrological index (THI, °C) was calculated using the formula as reported previously [13]. This index is an established appropriate measure for the evaluation of the effect of air temperature on health outcomes because it takes into account mean air temperature after controlling for the effect of relative humidity.…”

Section: Methodsmentioning

confidence: 99%

Various meteorological conditions exhibit both immediate and delayed influences on the risk of stroke events: The HEWS–stroke study

et al. 2017

Self Cite

View full text Add to dashboard Cite

We hypothesized that meteorological conditions on the onset day and conditions on the former days may play important roles in the modulation of physical conditions. Associations of meteorological factors and their changes in former days with stroke onset are of interest. We conducted a multicenter retrospective study to evaluate the frequency of stroke events and their interaction with meteorological conditions and their daily changes. Acute stroke patients (n = 3935, 73.5±12.4 years, 1610 females) who were admitted to 7 stroke hospitals in three restricted areas were enrolled in this study. Poisson regression models involving time-lag variables was used to compare daily rates of stroke events with mean thermo-hydrological index (THI), atmospheric pressure, and their daily changes. We divided onset days into quintiles based on the THI, atmospheric pressure, and their daily changes for the last 7 days. The frequencies of ischemic stroke significantly increased when THI varied either cooler or warmer from a previous day (extremely cooler, risk ratio (RR) 1.19, 95% confidence interval (CI) 1.05 to 1.34; extremely warmer, RR 1.16, 95% CI 1.03 to 1.31; r2 = 0.001 for the best regression, p = 0.001). Intracerebral hemorrhage frequencies significantly decreased on high-THI days (extremely high, RR 0.72, 95% CI 0.54 to 0.95; r2 = 0.013 for the best regression, p<0.001) and increased in high atmospheric pressure days (high, RR 1.31, 95% CI 1.04 to 1.65; r2 = 0.009 for the best regression, p<0.001). Additionally, even after adjusting for the THI on the onset day and its changes for the other days, intracerebral hemorrhage increased when THI got extremely cooler in 4 days prior (RR 1.33, 95% CI 1.03 to 1.71, r2 = 0.006 for the best regression, p<0.001). Various meteorological conditions may exhibit influences on stroke onset. And, when temperature cooled, there may be a possibility to show delayed influence on the frequency of intracerebral hemorrhage 4 days later.

show abstract

“…19 The thermo-hydrological index (THI, °C) was calculated using the formula; THI = Tmean -0.55 x (1-0.01 x RH) x (Tmean -14.5]) as reported previously. 20 This index is an established appropriate measure for evaluating the effect of air temperature on health outcomes because it takes into account mean air temperature after controlling for the effect of relative humidity. The distance from our hospital to the meteorological observatory was 3.8 km.…”

Section: Meteorological and Calendarial Datamentioning

confidence: 99%

Prediction Model of Deep Learning for Ambulance Transports in Kesennuma City by Meteorological Data

Watanabe¹,

Narita²,

Katsuki³

et al. 2021

OAEM

View full text Add to dashboard Cite

Purpose With the aging population in Japan, the prediction of ambulance transports is needed to save the limited medical resources. Some meteorological factors were risks of ambulance transports, but it is difficult to predict in a classically statistical way because Japan has 4 seasons. We tried to make prediction models for ambulance transports using the deep learning (DL) framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), with the meteorological and calendarial variables. Materials and Methods We retrospectively investigated the daily ambulance transports and meteorological data between 2017 and 2019. First, to confirm their association, we performed classically statistical analysis. Second, to test the DL framework’s utility for ambulance transports prediction, we made 3 prediction models for daily ambulance transports (total daily ambulance transports more than 5 or not, cardiopulmonary arrest (CPA), and trauma) using meteorological and calendarial factors and evaluated their accuracies by internal cross-validation. Results During the 1095 days of 3 years, the total ambulance transports were 5948, including 240 CPAs and 337 traumas. Cardiogenic CPA accounted for 72.3%, according to the Utstein classification. The relation between ambulance transports and meteorological parameters by polynomial curves were statistically obtained, but their r 2 s were small. On the other hand, all DL-based prediction models obtained satisfactory accuracies in the internal cross-validation. The areas under the curves obtained from each model were all over 0.947. Conclusion We could statistically make polynomial curves between the meteorological variables and the number of ambulance transport. We also preliminarily made DL-based prediction models. The DL-based prediction for daily ambulance transports would be used in the future, leading to solving the lack of medical resources in Japan.

show abstract

Effects of Meteorological Conditions on the Risk of Ischemic Stroke Events in Patients Treated with Alteplase—HEWS-tPA

Abstract: There were higher risks for ischemic stroke events associated with low ambient temperature, high atmospheric pressure, increased temperature, and varied atmospheric pressure. Also, atmospheric pressure variation 3 days before may be associated.

Cited by 12 publications

References 16 publications

Short-Term Effect of Ambient Temperature and the Risk of Stroke: A Systematic Review and Meta-Analysis

Short-Term Effect of Ambient Temperature and the Risk of Stroke: A Systematic Review and Meta-Analysis

Various meteorological conditions exhibit both immediate and delayed influences on the risk of stroke events: The HEWS–stroke study

Prediction Model of Deep Learning for Ambulance Transports in Kesennuma City by Meteorological Data

Contact Info

Product

Resources

About