Author
Listed:
- Yi-Ju Tseng
(Department of Information Management, Chang Gung University, Taoyuan 33302, Taiwan
Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan 333, Taiwan
Healthy Aging Research Center, Chang Gung University, Taoyuan 33302, Taiwan)
- Ru-Fang Hu
(Department of Information Management, Chang Gung University, Taoyuan 33302, Taiwan)
- Shin-Tyng Lee
(Department of Information Management, Chang Gung University, Taoyuan 33302, Taiwan)
- Yu-Li Lin
(Department of Nursing, Chang Gung Memorial Hospital at Linkou, Taoyuan 333, Taiwan
Department of Nursing, Chang Gung University of Science and Technology, Taoyuan 33302, Taiwan)
- Chien-Lung Hsu
(Department of Information Management, Chang Gung University, Taoyuan 33302, Taiwan
Healthy Aging Research Center, Chang Gung University, Taoyuan 33302, Taiwan
Graduate Institute of Business and Management, Chang Gung University, Taoyuan 33302, Taiwan
Department of Visual Communication Design, Ming-Chi University of Technology, New Taipei City 24301, Taiwan)
- Shih-Wei Lin
(Department of Information Management, Chang Gung University, Taoyuan 33302, Taiwan
Department of Industrial Engineering and Management, Ming-Chi University of Technology, New Taipei City 24301, Taiwan
Stroke Center and Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan)
- Chia-Wei Liou
(Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 83301, Taiwan
College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan)
- Jiann-Der Lee
(College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
Department of Neurology, Chiayi Chang Gung Memorial Hospital, Chiayi 613, Taiwan)
- Tsung-I Peng
(College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
Department of Neurology, Keelung Chang Gung Memorial Hospital, Keelung 20401, Taiwan)
- Tsong-Hai Lee
(Stroke Center and Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan)
Abstract
Ischemic stroke is the most common type of stroke, and early interventional treatment is associated with favorable outcomes. In the guidelines, thrombolytic therapy using recombinant tissue-type plasminogen activator (rt-PA) is recommended for eligible patients with acute ischemic stroke. However, the risk of hemorrhagic complications limits the use of rt-PA, and the risk factors for poor treatment outcomes need to be identified. To identify the risk factors associated with in-hospital poor outcomes in patients treated with rt-PA, we analyzed the electronic medical records of patients who were diagnosed with acute ischemic stroke and treated for rt-PA at Chang Gung Memorial Hospitals from 2006 to 2016. In-hospital death, intensive care unit (ICU) stay, or prolonged hospitalization were defined as unfavorable treatment outcomes. Medical history variables and laboratory test results were considered variables of interest to determine risk factors. Among 643 eligible patients, 537 (83.5%) and 106 (16.5%) patients had favorable and poor outcomes, respectively. In the multivariable analysis, risk factors associated with poor outcomes were female gender, higher stroke severity index (SSI), higher serum glucose levels, lower mean corpuscular hemoglobin concentration (MCHC), lower platelet counts, and anemia. The risk factors found in this research could help us study the treatment strategy for ischemic stroke.
Suggested Citation
Yi-Ju Tseng & Ru-Fang Hu & Shin-Tyng Lee & Yu-Li Lin & Chien-Lung Hsu & Shih-Wei Lin & Chia-Wei Liou & Jiann-Der Lee & Tsung-I Peng & Tsong-Hai Lee, 2020.
"Risk Factors Associated with Outcomes of Recombinant Tissue Plasminogen Activator Therapy in Patients with Acute Ischemic Stroke,"
IJERPH, MDPI, vol. 17(2), pages 1-12, January.
Handle:
RePEc:gam:jijerp:v:17:y:2020:i:2:p:618-:d:310246
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Cited by:
- Chien-Lung Chan & Chi-Chang Chang, 2020.
"Big Data, Decision Models, and Public Health,"
IJERPH, MDPI, vol. 17(18), pages 1-7, September.
- Chien-Lung Chan & Chi-Chang Chang, 2022.
"Big Data, Decision Models, and Public Health,"
IJERPH, MDPI, vol. 19(14), pages 1-9, July.
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