The contribution of potentially modifiable risk factors to acute ischemic stroke burden - Comparing young and older adults

Libruder, Carmit; Ram, Amit; Hershkovitz, Yael; Karolinsky, Dolev; Tanné, David; Bornstein, Natan M.; Zucker, Inbar

doi:10.1016/j.ypmed.2021.106933

Cited by 4 publications

(5 citation statements)

References 29 publications

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“…The data collected in this study are the first processed with machine learning techniques, to the best of our knowledge, on the stroke code worldwide. Our study reveals data association patterns using this approach, some novel and others confirming previously existing patterns with other statistical and data management techniques (9,10) .…”

Section: Discussionsupporting

confidence: 84%

“…With the SMOTE resampling technique, the 100% confidence reached for the association of high NIHSS (>20.4) and involvement of the carotid and basilar arteries, with a dire prognosis (exitus), was striking. These results could be extrapolated to previous studies with other assessment methods in which high NIHSS are always associated with a worse prognosis (1,9,10) . Along with this, it is known that the medial cerebral are statistically the most affected arteries and with the worst NIHSS, with some evolutions being considered malignant strokes.…”

Section: Discussionmentioning

confidence: 52%

“…Clinical decision making is essential in stroke, not only at the moment of the triage by the nurse, but also in the previous detection and control of risk factors (4) . Today, it is well known that the main risk factors for stroke include hypertension, diabetes mellitus, lipid metabolism alterations, arrhythmias (especially atrial fibrillation), and cardiovascular diseases (3,9) . These five diseases are considered "the five (not four!)…”

Section: Discussionmentioning

confidence: 99%

“…90% of acute ischemic strokes can be attributed to modifiable risk factors, and approximately 75% are due to hypertension, lipid metabolism alterations, diabetes, arrhythmias, and behavioral factors such as smoking, poor diet, and poor physical activity. Achieving control of risk factors could prevent more than three-quarters of the global stroke burden (9) . It is well known that several clinical indicators, such us the National Institute of Health Stroke Scale (NIHSS) strongly predicts outcome after stroke (10) .…”

Section: Introductionmentioning

confidence: 99%

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Predictors of the post-stroke status in the discharge from the hospital. Importance in nursing

Vico¹,

Sánchez

Mesonero

et al. 2023

Enf Global

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Nurses are often asked to predict factors that influence post-stroke outcome by the patient and family. Many studies have been carried out in order to determine the factors that influence the neurological status of the post-stroke patient at the moment of the discharge from the hospital. However, machine learning techniques have not been used for this purpose. Therefore, with the objective of obtaining association rules of neurological prognosis, a double analysis, both clinical and with machine learning techniques of the possible associations of factors that influence the neurological status of the post-stroke patients has been carried out. The Apriori algorithm detected several association rules with high confidence (≥ 95%), from which the following pattern: In patients in the age range of 50-80 years, the association of a NIHSS between 11 and 15 points (intermediate/low NIHSS), along with thrombectomy, leads to recovery ad integrum at discharge. With the SMOTE resampling technique, the 100% confidence was reached for the association of high NIHSS (>20) and involvement of the carotid and basilar arteries, with a dire prognosis (exitus). These rules confirm, for the first time with machine learning, the importance of the association of some predictors, in the post-stroke prognosis. The knowledge by the nurses of these association rules can successfully improve stroke outcome. In addition, the role of nurses in education programs that teach knowledge of risk factors and stroke prognosis becomes essential. A menudo, por parte del paciente y de la familia, se solicita a los profesionales de enfermería que predigan los factores que influyen en el estado post-ictus. Se han realizado numerosos estudios para determinar los factores que influyen en el estado neurológico post-ictus en el momento del alta hospitalaria. Sin embargo, las técnicas de aprendizaje automático no se han utilizado para este propósito. Con el objetivo de obtener reglas de asociación del pronóstico neurológico, se ha llevado a cabo un doble análisis, tanto clínico como con técnicas de aprendizaje automático, de las posibles asociaciones de factores que influyen en el estado neurológico de los pacientes post-ictus. El algoritmo Apriori detectó varias reglas de asociación con alta confianza (≥ 95%), con el siguiente patrón: En pacientes en el rango de edad de 50-80 años, la asociación de un NIHSS entre 11 y 15 puntos (NIHSS intermedio/bajo), junto con la trombectomía, conduce a la recuperación ad integrum al alta. Con la técnica de remuestreo SMOTE, se alcanzó el 100% de confianza para la asociación de NIHSS elevado (>20) y afectación de las arterias carótida y basilar, con pronóstico nefasto (exitus). Estas reglas confirman, por primera vez con aprendizaje automático, la importancia de la asociación de algunos predictores, en el pronóstico post-ictus. El conocimiento por parte de las enfermeras de estas reglas puede mejorar los resultados del ictus. Adicionalmente, el papel de la enfermería en los programas de educación sobre los factores de riesgo, y pronóstico de un ictus se torna imprescindible.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predictors of the post-stroke status in the discharge from the hospital. Importance in nursing

Vico¹,

Sánchez

Mesonero

et al. 2023

Enf Global

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“…The modifiable risk factors are responsible for 90% of the global burden of stroke [ 14 , 20 ] with hypertension being the major causal risk factor for the incidence and outcome of stroke [ 20 ]. These established risk factors account for a larger part of stroke both in young and older adults with some degree of variations [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Knowledge of stroke risk factors and warning symptoms among adults with type 2 diabetes in Addis Ababa, Ethiopia, 2021: an institution-Based cross-sectional study

Getu

Aga

Badada

et al. 2023

BMC Cardiovasc Disord

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Background Stroke is a global public health concern with type 2 diabetes being one of the common risk factors. Knowledge of stroke risk factors and warning symptoms among type 2 diabetes patients is largely unknown in developing countries like Ethiopia. The inability to recognize stroke warning symptoms accurately is an important cause of delay in seeking medical attention. We investigated knowledge of stroke risk factors and warning symptoms among adults with type 2 diabetes and the factors associated with these variables. Methods This was an institution-based cross-sectional study. We enrolled 470 adult type 2 diabetes patients using a systematic random sampling method from four government-managed hospitals in Addis Ababa from 1 to 30 February 2021. The Stroke Recognition Questionnaire was adapted to measure the knowledge of stroke risk factors and warning symptoms. Sociodemographic characteristics, source of information, and reaction to stroke were also measured. Data were analyzed using SPSS version 25 and linear regression analysis was used to determine factors independently associated with knowledge of stroke risk factors and warning symptoms. Result The mean age of the participants was 50.6 ± 12.9 years. The mean score of knowledge of stroke risk factors and warning symptoms was 67.2% and 63.9%, respectively. Higher educational level (B = 2.007, 95% CI = 1.101, 2.914, P < 0.001), knowing someone diagnosed with stroke (B = 3.328, 95% CI = 2.734, 3.922, P < 0.001), and living with others (B = 2.28, 95% CI = 1.606, 2.954, P < 0.001) were independently associated with knowledge of stroke risk factors. Younger age (B = − 0.021, 95% CI= -0.038, 0.005, P = 0.01), higher educational level (B = 1.873, 95% CI = 1.017, 2.730, P < 0.001), and knowing someone diagnosed with stroke (B = 3.64, 95% CI = 3.079, 4.200, P < 0.001) were independently associated with knowledge of warning symptoms of stroke. Conclusion The mean score of knowledge of stroke risk factors and warning symptoms was 67.2% and 63.9%, respectively. Younger age, higher educational level, and living with other people are predictors of better knowledge of stroke risk factors and warning symptoms in this study. Future educational interventions should target type 2 diabetes adults with advancing age and the involvement of their family members.

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Combined polygenic scores for ischemic stroke risk factors aid risk assessment of ischemic stroke

Huang,

Joshi,

Shi

et al. 2024

International Journal of Cardiology

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The contribution of potentially modifiable risk factors to acute ischemic stroke burden - Comparing young and older adults

Cited by 4 publications

References 29 publications

Predictors of the post-stroke status in the discharge from the hospital. Importance in nursing

Predictors of the post-stroke status in the discharge from the hospital. Importance in nursing

Knowledge of stroke risk factors and warning symptoms among adults with type 2 diabetes in Addis Ababa, Ethiopia, 2021: an institution-Based cross-sectional study

Combined polygenic scores for ischemic stroke risk factors aid risk assessment of ischemic stroke

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