TY - JOUR

T1 - Risk stratification and prediction of severity of hemorrhagic stroke in dry desert climate - A retrospective cohort study in eastern region of Abu Dhabi Emirate

AU - Statsenko, Yauhen

AU - Fursa, Ekaterina

AU - Laver, Vasyl

AU - Altakarli, Nourah

AU - Almansoori, Taleb

AU - Al Zahmi, Fatmah

AU - Gorkom, Klaus

AU - Sheek-Hussein, Mohamud

AU - Ponomareva, Anna

AU - Simiyu, Gillian

AU - Smetanina, Darya

AU - Ljubisavljevic, Milos

AU - Szolics, Miklos

AU - Al Koteesh, Jamal

PY - 2021/10

Y1 - 2021/10

N2 - Background and aims: Previous studies on the association between etiological factors and hemorrhagic stroke (HS) yielded inconsistent results. A proper risk stratification requires a multivariative analysis of predictors including clinical risk factors, ethnicity, age, sex, weather. We aimed to stratify a risk of moderate and high severity of HS in desert climate. Methods: For analysis, we used a large public hospital's stroke registry (4 years; 160 cases) and meteorological data acquisitions from Al-Ain city station, UAE. To elucidate associations between multiple weather parameters, demographic, clinical risk factors and HS incidence we calculated Pearson's correlation coefficients and constructed barplots that represented regional circannual weather changes and HS morbidity rates. We also examined the immediate and delayed effects of multiple weather parameters and daily changes on HS incidence by building distributed lag nonlinear models. To study an interaction of climatic and clinical risk factors with HS severity alone or in combination, we constructed ML models predicting the stoke severity (NIHSS >4 or ≤ 4). Results: HS incidence is associated significantly (p < 0.05) with changes in temperature, humidex, atmosphere pressure and relative humidity. The highest risk of HS is observed on day four after the weather event. The models that combine demographic and clinical factors in association with weather-related parameters showed the best performance to predict NIHSS severity with 87.5% sensitivity, 89% specificity. Conclusions: Accurate risk stratification of HS is possible with the employment of AI-algorithms that combine demographic, clinical, and weather-related parameters. Proposed predictive models may optimize stroke management practices.

AB - Background and aims: Previous studies on the association between etiological factors and hemorrhagic stroke (HS) yielded inconsistent results. A proper risk stratification requires a multivariative analysis of predictors including clinical risk factors, ethnicity, age, sex, weather. We aimed to stratify a risk of moderate and high severity of HS in desert climate. Methods: For analysis, we used a large public hospital's stroke registry (4 years; 160 cases) and meteorological data acquisitions from Al-Ain city station, UAE. To elucidate associations between multiple weather parameters, demographic, clinical risk factors and HS incidence we calculated Pearson's correlation coefficients and constructed barplots that represented regional circannual weather changes and HS morbidity rates. We also examined the immediate and delayed effects of multiple weather parameters and daily changes on HS incidence by building distributed lag nonlinear models. To study an interaction of climatic and clinical risk factors with HS severity alone or in combination, we constructed ML models predicting the stoke severity (NIHSS >4 or ≤ 4). Results: HS incidence is associated significantly (p < 0.05) with changes in temperature, humidex, atmosphere pressure and relative humidity. The highest risk of HS is observed on day four after the weather event. The models that combine demographic and clinical factors in association with weather-related parameters showed the best performance to predict NIHSS severity with 87.5% sensitivity, 89% specificity. Conclusions: Accurate risk stratification of HS is possible with the employment of AI-algorithms that combine demographic, clinical, and weather-related parameters. Proposed predictive models may optimize stroke management practices.

UR - https://www.mendeley.com/catalogue/b06d43a2-f257-3d98-8a70-cbe908f21d15/

U2 - 10.1016/j.jns.2021.117760

DO - 10.1016/j.jns.2021.117760

M3 - Meeting Abstract

VL - 429

JO - Journal of the neurological sciences

JF - Journal of the neurological sciences

SN - 0022-510X

ER -