Dataset Information

Impact of the Dedicated Neurointensivists on the Outcome in Patients with Ischemic Stroke Based on the Linked Big Data for Stroke in Korea.

ABSTRACT: BACKGROUND:Neurocritical care by dedicated neurointensivists may improve outcomes of critically ill patients with severe brain injury. In this study, we aimed to validate whether neurointensive care could improve the outcome in patients with critically ill acute ischemic stroke using the linked big dataset on stroke in Korea. METHODS:We included 1,405 acute ischemic stroke patients with mechanical ventilator support in the intensive care unit after an index stroke. Patients were retrieved from linking the Clinical Research Center for Stroke Registry and the Health Insurance Review and Assessment Service data from the period between January 2007 and December 2014. The outcomes were mortality at discharge and at 3 months after an index stroke. The main outcomes were compared between the centers with and without dedicated neurointensivists. RESULTS:Among the included patients, 303 (21.6%) were admitted to the centers with dedicated neurointensivists. The patients treated by dedicated neurointensivists had significantly lower in-hospital mortality (18.3% vs. 26.8%, P = 0.002) as well as lower mortality at 3-month (38.0% vs. 49.1%, P < 0.001) than those who were treated without neurointensivists. After adjusting for confounders, a treatment without neurointensivists was independently associated with higher in-hospital mortality (odds ratio [OR], 1.59; 95% confidence intervals [CIs], 1.13-2.25; P = 0.008) and 3-month mortality (OR, 1.48; 95% CIs, 1.12-1.95; P = 0.005). CONCLUSION:Treatment by dedicated neurointensivists is associated with lower in-hospital and 3-month mortality using the linked big datasets for stroke in Korea. This finding stresses the importance of neurointensivists in treating patients with severe ischemic stroke.

SUBMITTER: Kim TJ

PROVIDER: S-EPMC7261699 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Impact of the Dedicated Neurointensivists on the Outcome in Patients with Ischemic Stroke Based on the Linked Big Data for Stroke in Korea.

Kim Tae Jung TJ Lee Ji Sung JS Yoon Jae Sun JS Oh Mi Sun MS Kim Ji Woo JW Jung Keun Hwa KH Yu Kyung Ho KH Lee Byung Chul BC Ko Sang Bae SB Yoon Byung Woo BW

Journal of Korean medical science 20200601 21

<h4>Background</h4>Neurocritical care by dedicated neurointensivists may improve outcomes of critically ill patients with severe brain injury. In this study, we aimed to validate whether neurointensive care could improve the outcome in patients with critically ill acute ischemic stroke using the linked big dataset on stroke in Korea.<h4>Methods</h4>We included 1,405 acute ischemic stroke patients with mechanical ventilator support in the intensive care unit after an index stroke. Patients were r ...[more]

PMID: 32476299

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Project description:Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.

Dataset Information

Impact of the Dedicated Neurointensivists on the Outcome in Patients with Ischemic Stroke Based on the Linked Big Data for Stroke in Korea.

Publications

Impact of the Dedicated Neurointensivists on the Outcome in Patients with Ischemic Stroke Based on the Linked Big Data for Stroke in Korea.

Similar Datasets

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