Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Prognostication of Incidence and Severity of Ischemic Stroke in Hot Dry Climate From Environmental and Non-Environmental Predictors. / Statsenko, Yauhen; Habuza, Tetiana; Fursa, Ekaterina и др.
в: IEEE Access, Том 10, 2022, стр. 58268-58286.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Prognostication of Incidence and Severity of Ischemic Stroke in Hot Dry Climate From Environmental and Non-Environmental Predictors
AU - Statsenko, Yauhen
AU - Habuza, Tetiana
AU - Fursa, Ekaterina
AU - Ponomareva, Anna
AU - Almansoori, Taleb M.
AU - Zahmi, Fatmah Al
AU - Gorkom, Klaus Neidl Van
AU - Laver, Vasyl
AU - Talako, Tatsiana
AU - Szolics, Miklos
AU - Dehdashtian, Alireza
AU - Koteesh, Jamal Al
AU - Ljubisavljevic, Milos
N1 - Funding Information: This work was supported in part by the Abu Dhabi ASPIRE under Grant AARE19-060 (PI: ML), and in part by College of Medicine and Health Sciences United Arab Emirates University Seed Grant (PI: YS). Publisher Copyright: © 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - Background: Recently, rapid fluctuations of ambient temperature were found to be associated with hospital admission for cardiovascular diseases in general and the ischemic stroke in particular. Objective: to test if climatic factors predict the incidence of stroke reliably and to study the predictive potential of risk factors for a stroke. Materials and methods: In a retrospective design, we studied 566 patients admitted to the stroke unit in 2016-2019. A distributed lag nonlinear model was used to explore immediate and delayed effects of weather and clinicodemographic risk factors on the stroke incidence. Supervised machine learning was used to build models predictive of the mRS score. We assessed model performance by calculating R2 , mean absolute error and root-mean-square error. Results and conclusions: We found a non-correlation between the weather parameters and statistics on stroke. The disparities in their trends lead us to investigate behind time effects of the environment with distributed lag models and a concordant impact of all the settings - with machine learning models. If categorized into two classes by severity and functional outcomes, the cases have few disparities in the weather parameters within a week before the stroke onset. In contrast to the groups classified by severity, the ones grouped by outcomes have a significant difference in age, nationality, the presence of background diseases and smoking status. We ranked environmental and individual risk factors by the information gain that they provide to the models. Inclusion of the weather parameters into the machine learning model predicting the mRS score provides a slight boost in performance.
AB - Background: Recently, rapid fluctuations of ambient temperature were found to be associated with hospital admission for cardiovascular diseases in general and the ischemic stroke in particular. Objective: to test if climatic factors predict the incidence of stroke reliably and to study the predictive potential of risk factors for a stroke. Materials and methods: In a retrospective design, we studied 566 patients admitted to the stroke unit in 2016-2019. A distributed lag nonlinear model was used to explore immediate and delayed effects of weather and clinicodemographic risk factors on the stroke incidence. Supervised machine learning was used to build models predictive of the mRS score. We assessed model performance by calculating R2 , mean absolute error and root-mean-square error. Results and conclusions: We found a non-correlation between the weather parameters and statistics on stroke. The disparities in their trends lead us to investigate behind time effects of the environment with distributed lag models and a concordant impact of all the settings - with machine learning models. If categorized into two classes by severity and functional outcomes, the cases have few disparities in the weather parameters within a week before the stroke onset. In contrast to the groups classified by severity, the ones grouped by outcomes have a significant difference in age, nationality, the presence of background diseases and smoking status. We ranked environmental and individual risk factors by the information gain that they provide to the models. Inclusion of the weather parameters into the machine learning model predicting the mRS score provides a slight boost in performance.
KW - Ethnicity
KW - Ischemic stroke
KW - Machine learning classification model
KW - Middle east
KW - Risk
KW - Sex
KW - Weather
UR - http://www.scopus.com/inward/record.url?scp=85130453651&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3175302
DO - 10.1109/ACCESS.2022.3175302
M3 - Article
AN - SCOPUS:85130453651
VL - 10
SP - 58268
EP - 58286
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -
ID: 36566187