Nigerian journal of clinical practice · 2024
Multivariate Analysis and Scoring Prediction Model of Risk Factors for Atrial Fibrillation after Stroke: A Retrospective Study in Indonesia.
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Permana S A, Purwoko, Sunjoyo A, Santoso S B, Thamrin M H
Аннотация
BACKGROUND: Several studies have shown that atrial fibrillation (AF) detected after stroke (AFDAS) occurs in approximately 23.7% of patients with ischemic stroke.
AIM: This study aimed to determine the relationship between the type, location, volume, and therapy of stroke as risk factors for AF.
METHODS: This retrospective study was composed of adult patients diagnosed with stroke in the High Care Unit (HCU) of Dr. Moewardi General Hospital. The type and location of stroke, hemorrhage volume, history of medication administration, and serum low density lipoprotein-cholesterol (LDL-c) level were studied. Multivariate regression was used to determine the risk factor scoring for AF likelihood after diagnosis.
RESULTS: From 549 included patients, 262 (47.7%) had AF. The elderly (55.9%) and women (52.1%) constituted the majority of the study population. Seven variables that significantly contributed to AF incidence were ischemic stroke (AOR 4.12, CI 2.40-7.07, P < 0.001), cerebral cortex location (AOR 2.34, CI 1.35-4.06, P = 0.003), administration of the neuroprotective agent (AOR 0.24, CI 0.15-0.41, P < 0.001), history of hypertension (AOR 2.46, CI 1.09-5.56, P = 0.031), coronary heart disease (AOR 7.61, CI 3.82-15.15, P < 0.001), heart failure (AOR 2.80, CI 1.37-5.73, P = 0.005), and serum LDL-c with a cutoff level of 112 mg/dL (AOR 5.10, CI 3.04-8.57, P < 0.001). A scoring system from logistic regression analysis showed that a score of >1.7 may be interpreted as a risk factor AF.
CONCLUSION: A scoring system from the risk factors can be used to predict the probability of AFDAS.
AIM: This study aimed to determine the relationship between the type, location, volume, and therapy of stroke as risk factors for AF.
METHODS: This retrospective study was composed of adult patients diagnosed with stroke in the High Care Unit (HCU) of Dr. Moewardi General Hospital. The type and location of stroke, hemorrhage volume, history of medication administration, and serum low density lipoprotein-cholesterol (LDL-c) level were studied. Multivariate regression was used to determine the risk factor scoring for AF likelihood after diagnosis.
RESULTS: From 549 included patients, 262 (47.7%) had AF. The elderly (55.9%) and women (52.1%) constituted the majority of the study population. Seven variables that significantly contributed to AF incidence were ischemic stroke (AOR 4.12, CI 2.40-7.07, P < 0.001), cerebral cortex location (AOR 2.34, CI 1.35-4.06, P = 0.003), administration of the neuroprotective agent (AOR 0.24, CI 0.15-0.41, P < 0.001), history of hypertension (AOR 2.46, CI 1.09-5.56, P = 0.031), coronary heart disease (AOR 7.61, CI 3.82-15.15, P < 0.001), heart failure (AOR 2.80, CI 1.37-5.73, P = 0.005), and serum LDL-c with a cutoff level of 112 mg/dL (AOR 5.10, CI 3.04-8.57, P < 0.001). A scoring system from logistic regression analysis showed that a score of >1.7 may be interpreted as a risk factor AF.
CONCLUSION: A scoring system from the risk factors can be used to predict the probability of AFDAS.