Clinical Prediction Model for Screening Acute Ischemic Stroke Patients With More Than 10 Cerebral Microbleeds Article Swipe
Yifan Li
,
Haifeng Gao
,
Dongsen Zhang
,
Xuan Gao
,
Lin Lu
,
Chunqin Liu
,
Qian Li
,
Chunzhi Miao
,
Hongying Ma
,
Yongqiu Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fneur.2022.833952
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fneur.2022.833952
Background Hemorrhagic transformation is one of the most serious complications in intravenous thrombolysis. Studies show that the existence of more than 10 cerebral microbleeds is strongly associated with hemorrhagic transformation. The current study attempts to develop and validate a clinical prediction model of more than 10 cerebral microbleeds. Methods We reviewed the computed tomography markers of cerebral small vessel diseases and the basic clinical information of acute ischemic stroke patients who were investigated using susceptibility weighted imaging from 2018 to 2021. A clinical prediction model of more than 10 cerebral microbleeds was established. Discrimination, calibration, and the net benefit of the model were assessed. Finally, a validation was conducted to evaluate the accuracy and stability of the model. Results The multivariate logistic regression model showed hypertension, and some computed tomography markers (leukoaraiosis, lacunar infarctions, brain atrophy) were independent risk factors of more than 10 cerebral microbleeds. These risk factors were used for establishing the clinical prediction model. The area under the receiver operating characteristic curve (AUC) was 0.894 (95% CI: 0.870–0.919); Hosmer–Lemeshow chi-squared test yielded χ 2 = 3.946 ( P = 0.862). The clinical decision cure of the model was higher than the two extreme lines. The simplified score of the model ranged from 0 to 12. The model in the internal and external validation cohort also had good discrimination (AUC 0.902, 95% CI: 0.868–0.937; AUC 0.914, 95% CI: 0.882–0.945) and calibration ( P = 0.157, 0.247), and patients gained a net benefit from the model. Conclusions We developed and validated a simple scoring tool for acute ischemic stroke patients with more than 10 cerebral microbleeds; this tool may be beneficial for paradigm decision regarding intravenous recombinant tissue plasminogen activator therapy of acute ischemic stroke.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fneur.2022.833952
- OA Status
- gold
- Cited By
- 3
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226129832
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4226129832Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fneur.2022.833952Digital Object Identifier
- Title
-
Clinical Prediction Model for Screening Acute Ischemic Stroke Patients With More Than 10 Cerebral MicrobleedsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-07Full publication date if available
- Authors
-
Yifan Li, Haifeng Gao, Dongsen Zhang, Xuan Gao, Lin Lu, Chunqin Liu, Qian Li, Chunzhi Miao, Hongying Ma, Yongqiu LiList of authors in order
- Landing page
-
https://doi.org/10.3389/fneur.2022.833952Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fneur.2022.833952Direct OA link when available
- Concepts
-
Medicine, Logistic regression, Leukoaraiosis, Receiver operating characteristic, Internal medicine, Thrombolysis, Cardiology, Stroke (engine), Cohort, Area under the curve, Magnetic resonance imaging, Radiology, White matter, Engineering, Myocardial infarction, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
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56Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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