Hypertension modifies the short-term effects of temperature on morbidity of hemorrhagic stroke

Wang, Qinzhou; Gao, Cuilian; Liu, Hongen; Li, Wei; Zhao, Yuhan; Xu, Guangrun; Yan, Chaowu; Lin, Hualiang; Lang, Lingling

doi:10.1016/j.scitotenv.2017.04.159

Cited by 17 publications

(8 citation statements)

References 31 publications

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“…Scientists have assumed that the increased morbidity and mortality in cold condition are probably driven by temperature-related increase in blood pressure as more cardio-cerebrovascular disease mortality was reported during cold seasons (15)(16)(17)(18)(19). But the direct evidence has not been fully established.…”

Section: Introductionmentioning

confidence: 99%

The association of outdoor temperature with blood pressure, and its influence on future cardio-cerebrovascular disease risk in cold areas

Yu¹,

Jin

Wang³

et al. 2020

Journal of Hypertension

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Objectives To explore whether lower outdoor temperature increases cardio-cerebrovascular disease risk through regulating blood pressure and whether indoor heating in winter is beneficial to prevent cardio-cerebrovascular disease in cold areas. MethodsWe analyzed the data of 38589 participants in Harbin from the China Kadoorie Biobank (CKB) during 2004-2008, with an average of 7.14-year follow-up.Linear regression analysis was performed to estimate the relationship between outdoor temperature and blood pressure. Cox regression analysis and Logistic regression analysis were used to analyze the association of blood pressure with cardio-cerebrovascular events risk. Mediation analysis was performed to explore the role of blood pressure in the association between outdoor pressure and cardio-cerebrovascular events risk. ResultsThere was an increase of 6.7 mmHg in SBP and 2.1 mmHg in DBP for each 10℃ decrease in outdoor temperature when outdoor temperature was higher than 5℃.There was an inverse association between outdoor temperature and cardio-cerebrovascular events morbidity. The increases in blood pressure and cardio-cerebrovascular events morbidity were attenuated in months when central heating was fully provided. Participants with hypertension have higher risks of

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Section: Introductionmentioning

confidence: 99%

The association of outdoor temperature with blood pressure, and its influence on future cardio-cerebrovascular disease risk in cold areas

Yu¹,

Jin

Wang³

et al. 2020

Journal of Hypertension

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“…As the daily counts of hospital admissions generally followed a Poisson distribution (Yu et al, 2019), we applied an over-dispersed generalized additive model (GAM) to examine the short-term effect of particulate matter on stroke admissions among people with T2D (Song et al, 2018). Speci cally, covariates were included in the main model as follows: (1) natural smooth functions of temperature and relative humidity with 3 degrees of freedom (df) to control for the nonlinear confounding effects of weather factors (Wang et al, 2017a); (2) indicator variables for public holidays and day of week; and (3) a natural cubic spline function with 7 df for calendar time to exclude unmeasured long-term and seasonal trends of daily stroke admissions (Chen et al, 2018b). The exposure-response curves were plotted between particulate matter variables and daily stroke admissions of patients with T2D.…”

Section: Discussionmentioning

confidence: 99%

Acute Effect of Particulate Matter Pollution on Hospital Admissions for Stroke among Patients with type 2 Diabetes in Beijing, China, from 2014 to 2018

Liu

Guo

et al. 2021

Preprint

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Background: The health effect of particulate matter pollution on stroke has been widely examined; however, the effect among patients with comorbid type 2 diabetes (T2D) in developing countries has remained largely unknown.Methods: A time-series study was conducted to investigate the short-term effect of fine particulate matter (PM2.5) and inhalable particulate matter (PM10) on hospital admissions for stroke among patients with T2D in Beijing, China, from 2014 to 2018. An over-dispersed Poisson generalized additive model was employed to adjust for important covariates, such as weather conditions and long-term and seasonal trends. Results: A total of 159,298 (58% male) hospital admissions for stroke were reported. Linear exposure-response curves were observed for PM2.5 and PM10 in relation to stroke admissions among T2D patients. A 10 μg/m3 increase in the four-day moving average of PM2.5 and PM10 was associated with 0.14% (95% confidence interval [CI]: 0.05%-0.23%) and 0.14% (95% CI: 0.06%-0.22%) incremental increases in stroke admissions among T2D patients, respectively. A 10 μg/m3 increase in PM2.5 in the two-day moving average corresponded to a 0.72% (95% CI: 0.02%-1.42%) incremental increase in hemorrhagic stroke, and a 10 μg/m3 increase in PM10 in the four-day moving average corresponded to a 0.14% (95% CI: 0.06%-0.22%) incremental increase in ischemic stroke. Conclusions: High particulate matter might be a risk factor for stroke among patients with T2D. PM2.5 and PM10 have a linear exposure-response relationship with stroke among T2D patients. The study provided evidence of the risk of comorbid T2D and stroke due to particulate matter pollution.

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“…Hemorrhagic stroke is a devastating condition resulting in a one-year mortality greater than 50% [48]. Etiologies include hypertension, cerebral amyloid angiopathy, arteriovenous malformations, and hemorrhagic transformation following acute ischemic strokes [49][50][51][52]. A study evaluating several machine learning algorithms' ability to predict hemorrhagic transformation severity from ischemic strokes found that the Kernel spectral regression model had a predictive accuracy of 83.7% [53].…”

Section: -Strokementioning

confidence: 99%

Advances in artificial neural networks as a disease prediction tool

Mb¹,

Cl²,

Mw³

et al. 2021

J Cancer Res Ther.

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Throughout the last decade, utilization of machine learning has seen a sharp rise in fields such as computing, transportation, engineering, and medicine. Artificial neural networks (ANNs) have demonstrated increased application due to their versatility and ability to learn from large datasets. The emergence of electronic health records has propelled healthcare into an era of personalized medicine largely aided by computers. This review summarizes the current state of ANNs as a predictive tool in medicine and the downfalls of reliance on a self-adjusting computer network to make healthcare decisions. Medical ANN studies can be grouped into three categories - diagnosis, classification, and prediction, with diagnostic studies currently dominating the field. However, recent trends show prediction studies may soon outnumber the remaining categories. ANN prediction studies dominate in fields such as cardiovascular disease, neurologic disease, and osteoporosis. Neural networks consistently show higher predictive accuracy than industry standards. But several pitfalls are preventing mainstream adoption. Clinicians often rely on situational pearls to make complex healthcare decisions, ANNs often do not account for intuitive variables during their analysis. Instead, ANNs rely on incomplete patient data and ‘black box’ computing to make decisions that are not completely transparent to the end-user. This has led to ‘runaway’ networks that may ultimately make inaccurate and harmful decisions. This review emphasizes the extensive potential of machine learning in medicine and the obstacles that must be overcome to utilize its full potential.

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Hypertension modifies the short-term effects of temperature on morbidity of hemorrhagic stroke

Cited by 17 publications

References 31 publications

The association of outdoor temperature with blood pressure, and its influence on future cardio-cerebrovascular disease risk in cold areas

The association of outdoor temperature with blood pressure, and its influence on future cardio-cerebrovascular disease risk in cold areas

Acute Effect of Particulate Matter Pollution on Hospital Admissions for Stroke among Patients with type 2 Diabetes in Beijing, China, from 2014 to 2018

Advances in artificial neural networks as a disease prediction tool

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