Frontiers in Aging Neuroscience (May 2022)
Development and Validation of a Clinical-Based Signature to Predict the 90-Day Functional Outcome for Spontaneous Intracerebral Hemorrhage
- Xiaoyu Huang,
- Xiaoyu Huang,
- Xiaoyu Huang,
- Xiaoyu Huang,
- Dan Wang,
- Qiaoying Zhang,
- Yaqiong Ma,
- Yaqiong Ma,
- Shenglin Li,
- Shenglin Li,
- Shenglin Li,
- Shenglin Li,
- Hui Zhao,
- Hui Zhao,
- Hui Zhao,
- Hui Zhao,
- Juan Deng,
- Juan Deng,
- Juan Deng,
- Juan Deng,
- Jingjing Yang,
- Jingjing Yang,
- Jingjing Yang,
- Jingjing Yang,
- JiaLiang Ren,
- Min Xu,
- Min Xu,
- Min Xu,
- Min Xu,
- Huaze Xi,
- Huaze Xi,
- Huaze Xi,
- Huaze Xi,
- Fukai Li,
- Fukai Li,
- Fukai Li,
- Fukai Li,
- Hongyu Zhang,
- Hongyu Zhang,
- Hongyu Zhang,
- Hongyu Zhang,
- Yijing Xie,
- Yijing Xie,
- Yijing Xie,
- Yijing Xie,
- Long Yuan,
- Long Yuan,
- Long Yuan,
- Long Yuan,
- Yucheng Hai,
- Mengying Yue,
- Qing Zhou,
- Qing Zhou,
- Qing Zhou,
- Qing Zhou,
- Junlin Zhou,
- Junlin Zhou,
- Junlin Zhou
Affiliations
- Xiaoyu Huang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Xiaoyu Huang
- Second Clinical School, Lanzhou University, Lanzhou, China
- Xiaoyu Huang
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Xiaoyu Huang
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Dan Wang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Qiaoying Zhang
- Department of Radiology, Xi’an Central Hospital, Xi’an, China
- Yaqiong Ma
- Second Clinical School, Lanzhou University, Lanzhou, China
- Yaqiong Ma
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
- Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Shenglin Li
- Second Clinical School, Lanzhou University, Lanzhou, China
- Shenglin Li
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Shenglin Li
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Hui Zhao
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Hui Zhao
- Second Clinical School, Lanzhou University, Lanzhou, China
- Hui Zhao
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Hui Zhao
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Juan Deng
- Second Clinical School, Lanzhou University, Lanzhou, China
- Juan Deng
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Juan Deng
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Jingjing Yang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Jingjing Yang
- Second Clinical School, Lanzhou University, Lanzhou, China
- Jingjing Yang
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Jingjing Yang
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- JiaLiang Ren
- GE Healthcare, Beijing, China
- Min Xu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Min Xu
- Second Clinical School, Lanzhou University, Lanzhou, China
- Min Xu
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Min Xu
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Huaze Xi
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Huaze Xi
- Second Clinical School, Lanzhou University, Lanzhou, China
- Huaze Xi
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Huaze Xi
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Fukai Li
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Fukai Li
- Second Clinical School, Lanzhou University, Lanzhou, China
- Fukai Li
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Fukai Li
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Hongyu Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Hongyu Zhang
- Second Clinical School, Lanzhou University, Lanzhou, China
- Hongyu Zhang
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Hongyu Zhang
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Yijing Xie
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Yijing Xie
- Second Clinical School, Lanzhou University, Lanzhou, China
- Yijing Xie
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Yijing Xie
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Long Yuan
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Long Yuan
- Second Clinical School, Lanzhou University, Lanzhou, China
- Long Yuan
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Long Yuan
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Yucheng Hai
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Mengying Yue
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Qing Zhou
- Second Clinical School, Lanzhou University, Lanzhou, China
- Qing Zhou
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Qing Zhou
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Junlin Zhou
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Junlin Zhou
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- DOI
- https://doi.org/10.3389/fnagi.2022.904085
- Journal volume & issue
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Vol. 14
Abstract
We aimed to develop and validate an objective and easy-to-use model for identifying patients with spontaneous intracerebral hemorrhage (ICH) who have a poor 90-day prognosis. This three-center retrospective study included a large cohort of 1,122 patients with ICH who presented within 6 h of symptom onset [training cohort, n = 835; internal validation cohort, n = 201; external validation cohort (center 2 and 3), n = 86]. We collected the patients’ baseline clinical, radiological, and laboratory data as well as the 90-day functional outcomes. Independent risk factors for prognosis were identified through univariate analysis and multivariate logistic regression analysis. A nomogram was developed to visualize the model results while a calibration curve was used to verify whether the predictive performance was satisfactorily consistent with the ideal curve. Finally, we used decision curves to assess the clinical utility of the model. At 90 days, 714 (63.6%) patients had a poor prognosis. Factors associated with prognosis included age, midline shift, intraventricular hemorrhage (IVH), subarachnoid hemorrhage (SAH), hypodensities, ICH volume, perihematomal edema (PHE) volume, temperature, systolic blood pressure, Glasgow Coma Scale (GCS) score, white blood cell (WBC), neutrophil, and neutrophil-lymphocyte ratio (NLR) (p < 0.05). Moreover, age, ICH volume, and GCS were identified as independent risk factors for prognosis. For identifying patients with poor prognosis, the model showed an area under the receiver operating characteristic curve of 0.874, 0.822, and 0.868 in the training cohort, internal validation, and external validation cohorts, respectively. The calibration curve revealed that the nomogram showed satisfactory calibration in the training and validation cohorts. Decision curve analysis showed the clinical utility of the nomogram. Taken together, the nomogram developed in this study could facilitate the individualized outcome prediction in patients with ICH.
Keywords
