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Sun Z, Liu J, Wang K, Zhang J, Liu S, Xue F. Feasibility of noninvasive near-infrared spectroscopy monitoring in predicting the prognosis of spontaneous intracerebral hemorrhage. Front Neurol 2024; 15:1406157. [PMID: 39114537 PMCID: PMC11303234 DOI: 10.3389/fneur.2024.1406157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Accepted: 07/12/2024] [Indexed: 08/10/2024] Open
Abstract
Objective This study aimed to assess the impact of multimodal monitoring on predicting the prognosis of patients with spontaneous intracerebral hemorrhage (SICH) and to examine the feasibility of using noninvasive near-infrared spectroscopy (NIRS) for monitoring clinical prognosis. Methods Clinical data of 38 patients with SICH who underwent surgery in the Department of Neurosurgery of Shaanxi Provincial People's Hospital from May 2022 to December 2022 were retrospectively analyzed. The patients were categorized into two groups based on the Glasgow Outcome Scale (GOS) 3 months after operation: poor outcome group (GOSI-III) and good outcome group (GOSIV and V). Multimodal monitoring included invasive intracranial pressure (ICP), brain temperature (BT), internal jugular venous oxygen saturation (SjvO2), and noninvasive NIRS. NIRS monitoring comprised the assessment of brain tissue oxygen saturation (StO2), blood volume index (BVI), and tissue hemoglobin index (THI). The prognostic differences between the two groups were compared. The predictive values were evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Results ICP, BT, BVI, and THI in the good prognosis group were lower than those in the poor prognosis group. The SjvO2 and StO2 in the group with a good prognosis were higher than those in the group with a poor prognosis. Conclusion The levels of ICP, BT, SjvO2, StO2, BVI, and THI reflect the changes in brain function and cerebral blood flow and significantly correlate with the prognosis of patients with SICH. NIRS monitoring has a high clinical utility in assessing the prognosis.
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Affiliation(s)
- Zhen Sun
- The Neurosurgery, Nanyang Central Hospital, Nanyang, Henan, China
| | - Jing Liu
- Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Kunpeng Wang
- The Neurosurgery, Nanyang Central Hospital, Nanyang, Henan, China
| | - Jiandang Zhang
- The Neurosurgery, Nanyang Central Hospital, Nanyang, Henan, China
| | - Sujie Liu
- The Neurosurgery, Nanyang Central Hospital, Nanyang, Henan, China
| | - Fei Xue
- Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
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Chen Y, Rivier CA, Mora SA, Torres Lopez V, Payabvash S, Sheth KN, Harloff A, Falcone GJ, Rosand J, Mayerhofer E, Anderson CD. Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan. Eur Stroke J 2024:23969873241260154. [PMID: 38880882 DOI: 10.1177/23969873241260154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2024] Open
Abstract
BACKGROUND Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework. METHODS We used the admission non-contrast CT scans from 882 patients from the Massachusetts General Hospital ICH Study for training, hyperparameter optimization, and model selection, and 146 patients from the Yale New Haven ICH Study for external validation of a deep learning model predicting functional outcome. Disability (modified Rankin scale [mRS] > 2), severe disability (mRS > 4), and dependent living status were assessed via telephone interviews after 6, 12, and 24 months. The prediction methods were evaluated by the c-index and compared with ICH score and FUNC score. RESULTS Using non-contrast CT, our deep learning model achieved higher prediction accuracy of post-ICH dependent living, disability, and severe disability by 6, 12, and 24 months (c-index 0.742 [95% CI -0.700 to 0.778], 0.712 [95% CI -0.674 to 0.752], 0.779 [95% CI -0.733 to 0.832] respectively) compared with the ICH score (c-index 0.673 [95% CI -0.662 to 0.688], 0.647 [95% CI -0.637 to 0.661] and 0.697 [95% CI -0.675 to 0.717]) and FUNC score (c-index 0.701 [95% CI- 0.698 to 0.723], 0.668 [95% CI -0.657 to 0.680] and 0.727 [95% CI -0.708 to 0.753]). In the external independent Yale-ICH cohort, similar performance metrics were obtained for disability and severe disability (c-index 0.725 [95% CI -0.673 to 0.781] and 0.747 [95% CI -0.676 to 0.807], respectively). Similar AUC of predicting each outcome at 6 months, 1 and 2 years after ICH was achieved compared with ICH score and FUNC score. CONCLUSION We developed a generalizable deep learning model to predict onset of dependent living and disability after ICH, which could help to guide treatment decisions, advise relatives in the acute setting, optimize rehabilitation strategies, and anticipate long-term care needs.
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Affiliation(s)
- Yutong Chen
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Cyprien A Rivier
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain and Mind Health, New Haven, CT, USA
| | - Samantha A Mora
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Victor Torres Lopez
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain and Mind Health, New Haven, CT, USA
| | - Sam Payabvash
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain and Mind Health, New Haven, CT, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain and Mind Health, New Haven, CT, USA
| | - Andreas Harloff
- Department of Neurology, University of Freiburg, Freiburg, Germany
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain and Mind Health, New Haven, CT, USA
| | - Jonathan Rosand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Ernst Mayerhofer
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Christopher D Anderson
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
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Ai M, Zhang H, Feng J, Chen H, Liu D, Li C, Yu F, Li C. Research advances in predicting the expansion of hypertensive intracerebral hemorrhage based on CT images: an overview. PeerJ 2024; 12:e17556. [PMID: 38860211 PMCID: PMC11164062 DOI: 10.7717/peerj.17556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 06/12/2024] Open
Abstract
Hematoma expansion (HE) is an important risk factor for death or poor prognosis in patients with hypertensive intracerebral hemorrhage (HICH). Accurately predicting the risk of HE in patients with HICH is of great clinical significance for timely intervention and improving patient prognosis. Many imaging signs reported in literatures showed the important clinical value for predicting HE. In recent years, the development of radiomics and artificial intelligence has provided new methods for HE prediction with high accuracy. Therefore, this article reviews the latest research progress in CT imaging, radiomics, and artificial intelligence of HE, in order to help identify high-risk patients for HE in clinical practice.
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Affiliation(s)
- Min Ai
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Hanghang Zhang
- Department of Breast and Thyroid Surgery, Chongqing Bishan District Maternal and Child Health Care Hospital, Chongqing, China
| | - Junbang Feng
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Hongying Chen
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Di Liu
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Chang Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Fei Yu
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
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Guo P, Zou W. Neutrophil-to-lymphocyte ratio, white blood cell, and C-reactive protein predicts poor outcome and increased mortality in intracerebral hemorrhage patients: a meta-analysis. Front Neurol 2024; 14:1288377. [PMID: 38288330 PMCID: PMC10824245 DOI: 10.3389/fneur.2023.1288377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/29/2023] [Indexed: 01/31/2024] Open
Abstract
Objective Inflammation participates in the pathology and progression of secondary brain injury after intracerebral hemorrhage (ICH). This meta-analysis intended to explore the prognostic role of inflammatory indexes, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), white blood cell (WBC), and C-reactive protein (CRP) in ICH patients. Methods Embase, PubMed, Web of Science, and Cochrane Library were searched until June 2023. Two outcomes, including poor outcome and mortality were extracted and measured. Odds ratio (OR) and 95% confidence interval (CI) were presented for outcome assessment. Results Forty-six studies with 25,928 patients were included in this meta-analysis. The high level of NLR [OR (95% CI): 1.20 (1.13-1.27), p < 0.001], WBC [OR (95% CI): 1.11 (1.02-1.21), p = 0.013], and CRP [OR (95% CI): 1.29 (1.08-1.54), p = 0.005] were related to poor outcome in ICH patients. Additionally, the high level of NLR [OR (95% CI): 1.06 (1.02-1.10), p = 0.001], WBC [OR (95% CI): 1.39 (1.16-1.66), p < 0.001], and CRP [OR (95% CI): 1.02 (1.01-1.04), p = 0.009] were correlated with increased mortality in ICH patients. Nevertheless, PLR was not associated with poor outcome [OR (95% CI): 1.00 (0.99-1.01), p = 0.749] or mortality [OR (95% CI): 1.00 (0.99-1.01), p = 0.750] in ICH patients. The total score of risk of bias assessed by Newcastle-Ottawa Scale criteria ranged from 7-9, which indicated the low risk of bias in the included studies. Publication bias was low, and stability assessed by sensitivity analysis was good. Conclusion This meta-analysis summarizes that the high level of NLR, WBC, and CRP estimates poor outcome and higher mortality in ICH patients.
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Affiliation(s)
- Peixin Guo
- Integrated Traditional Chinese and Western Medicine, Heilongjiang University of Traditional Chinese Medicine, Harbin, China
| | - Wei Zou
- Third Ward of Acupuncture Department, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
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Chu H, Li L, Qiu B, Tang Y. Editorial: Outcomes of stroke: prediction and improvement. Front Neurol 2023; 14:1256253. [PMID: 37560449 PMCID: PMC10407940 DOI: 10.3389/fneur.2023.1256253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 08/11/2023] Open
Affiliation(s)
- Heling Chu
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Longxuan Li
- Department of Neurology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bin Qiu
- School of Medicine, Yale University, New Haven, CT, United States
| | - Yuping Tang
- Department of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
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