Standard

Prognostication of Incidence and Severity of Ischemic Stroke in Hot Dry Climate From Environmental and Non-Environmental Predictors. / Statsenko, Yauhen; Habuza, Tetiana; Fursa, Ekaterina et al.

In: IEEE Access, Vol. 10, 2022, p. 58268-58286.

Research output: Contribution to journalArticlepeer-review

Harvard

Statsenko, Y, Habuza, T, Fursa, E, Ponomareva, A, Almansoori, TM, Zahmi, FA, Gorkom, KNV, Laver, V, Talako, T, Szolics, M, Dehdashtian, A, Koteesh, JA & Ljubisavljevic, M 2022, 'Prognostication of Incidence and Severity of Ischemic Stroke in Hot Dry Climate From Environmental and Non-Environmental Predictors', IEEE Access, vol. 10, pp. 58268-58286. https://doi.org/10.1109/ACCESS.2022.3175302

APA

Statsenko, Y., Habuza, T., Fursa, E., Ponomareva, A., Almansoori, T. M., Zahmi, F. A., Gorkom, K. N. V., Laver, V., Talako, T., Szolics, M., Dehdashtian, A., Koteesh, J. A., & Ljubisavljevic, M. (2022). Prognostication of Incidence and Severity of Ischemic Stroke in Hot Dry Climate From Environmental and Non-Environmental Predictors. IEEE Access, 10, 58268-58286. https://doi.org/10.1109/ACCESS.2022.3175302

Vancouver

Statsenko Y, Habuza T, Fursa E, Ponomareva A, Almansoori TM, Zahmi FA et al. Prognostication of Incidence and Severity of Ischemic Stroke in Hot Dry Climate From Environmental and Non-Environmental Predictors. IEEE Access. 2022;10:58268-58286. doi: 10.1109/ACCESS.2022.3175302

Author

Statsenko, Yauhen ; Habuza, Tetiana ; Fursa, Ekaterina et al. / Prognostication of Incidence and Severity of Ischemic Stroke in Hot Dry Climate From Environmental and Non-Environmental Predictors. In: IEEE Access. 2022 ; Vol. 10. pp. 58268-58286.

BibTeX

@article{5501a983e14b407eb6c4af3628c2eb73,
title = "Prognostication of Incidence and Severity of Ischemic Stroke in Hot Dry Climate From Environmental and Non-Environmental Predictors",
abstract = "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. ",
keywords = "Ethnicity, Ischemic stroke, Machine learning classification model, Middle east, Risk, Sex, Weather",
author = "Yauhen Statsenko and Tetiana Habuza and Ekaterina Fursa and Anna Ponomareva and Almansoori, {Taleb M.} and Zahmi, {Fatmah Al} and Gorkom, {Klaus Neidl Van} and Vasyl Laver and Tatsiana Talako and Miklos Szolics and Alireza Dehdashtian and Koteesh, {Jamal Al} and Milos Ljubisavljevic",
note = "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: {\textcopyright} 2013 IEEE.",
year = "2022",
doi = "10.1109/ACCESS.2022.3175302",
language = "English",
volume = "10",
pages = "58268--58286",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

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