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Zhang X, Sha Z, Feng D, Wu C, Tian Y, Wang D, Wang J, Jiang R. Establishment and validation of a CT-based prediction model for the good dissolution of mild chronic subdural hematoma with atorvastatin treatment. Neuroradiology 2024; 66:1113-1122. [PMID: 38587561 DOI: 10.1007/s00234-024-03340-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 03/19/2024] [Indexed: 04/09/2024]
Abstract
PURPOSE To develop and validate a prediction model based on imaging data for the prognosis of mild chronic subdural hematoma undergoing atorvastatin treatment. METHODS We developed the prediction model utilizing data from patients diagnosed with CSDH between February 2019 and November 2021. Demographic characteristics, medical history, and hematoma characteristics in non-contrast computed tomography (NCCT) were extracted upon admission to the hospital. To reduce data dimensionality, a backward stepwise regression model was implemented to build a prognostic prediction model. We calculated the area under the receiver operating characteristic curve (AUC) of the prognostic prediction model by a tenfold cross-validation procedure. RESULTS Maximum thickness, volume, mean density, morphology, and kurtosis of the hematoma were identified as the most significant predictors of good hematoma dissolution in mild CSDH patients undergoing atorvastatin treatment. The prediction model exhibited good discrimination, with an area under the curve (AUC) of 0.82 (95% confidence interval [CI], 0.74-0.90) and good calibration (p = 0.613). The validation analysis showed the AUC of the final prognostic prediction model is 0.80 (95% CI 0.71-0.86) and it has good prediction performance. CONCLUSION The imaging data-based prediction model has demonstrated great prediction accuracy for good hematoma dissolution in mild CSDH patients undergoing atorvastatin treatment. The study results emphasize the importance of imaging data evaluation in the management of CSDH patients.
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Affiliation(s)
- Xinjie Zhang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Department of Pediatric Neurosurgery, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhuang Sha
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
| | - Dongyi Feng
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
| | - Chenrui Wu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
| | - Ye Tian
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
| | - Dong Wang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
| | - Junping Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China.
| | - Rongcai Jiang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China.
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China.
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Sha Z, Wu D, Dong S, Liu T, Wu C, Lv C, Liu M, Jiang W, Yuan J, Nie M, Gao C, Liu F, Zhang X, Jiang R. The value of computed tomography texture analysis in identifying chronic subdural hematoma patients with a good response to polytherapy. Sci Rep 2024; 14:3559. [PMID: 38347043 PMCID: PMC10861511 DOI: 10.1038/s41598-024-53376-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 01/31/2024] [Indexed: 02/15/2024] Open
Abstract
This study aimed to investigate the predictive factors of therapeutic efficacy for chronic subdural hematoma (CSDH) patients receiving atorvastatin combined with dexamethasone therapy by using clinical imaging characteristics in conjunction with computed tomography (CT) texture analysis (CTTA). Clinical imaging characteristics and CT texture parameters at admission were retrospectively investigated in 141 CSDH patients who received atorvastatin combined with dexamethasone therapy from June 2019 to December 2022. The patients were divided into a training set (n = 81) and a validation set (n = 60). Patients in the training data were divided into two groups based on the effectiveness of the treatment. Univariate and multivariate analyses were performed to assess the potential factors that could indicate the prognosis of CSDH patients in the training set. The receiver operating characteristic (ROC) curve was used to analyze the predictive efficacy of the significant factors in predicting the prognosis of CSDH patients and was validated using a validation set. The multivariate analysis showed that the hematoma density to brain parenchyma density ratio, singal min (minimum) and singal standard deviation of the pixel distribution histogram, and inhomogeneity were independent predictors for the prognosis of CSDH patients based on atorvastatin and dexamethasone therapy. The area under the ROC curve between the two groups was between 0.716 and 0.806. As determined by significant factors, the validation's accuracy range was 0.816 to 0.952. Clinical imaging characteristics in conjunction with CTTA could aid in distinguishing patients with CSDH who responded well to atorvastatin combined with dexamethasone.
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Affiliation(s)
- Zhuang Sha
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Di Wu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Shiying Dong
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Tao Liu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Chenrui Wu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Chuanxiang Lv
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Mingqi Liu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Weiwei Jiang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Jiangyuan Yuan
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Meng Nie
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Chuang Gao
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xinjie Zhang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China.
| | - Rongcai Jiang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China.
- State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China.
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Kashkoush AI, Potter T, Petitt JC, Hu S, Hunter K, Kelly ML. Novel application of the Rotterdam CT score in the prediction of intracranial hypertension following severe traumatic brain injury. J Neurosurg 2023; 138:1050-1057. [PMID: 35962965 DOI: 10.3171/2022.6.jns212921] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 06/17/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Severe traumatic brain injury (TBI) is associated with intracranial hypertension (ICHTN). The Rotterdam CT score (RS) can predict clinical outcomes following TBI, but the relationship between the RS and ICHTN is unknown. The purpose of this study was to investigate clinical and radiological factors that predict ICHTN in patients with severe TBI. METHODS The authors performed a single-center retrospective review of patients who, between 2018 and 2021, had an intracranial pressure (ICP) monitor placed following TBI. Radiological and clinical characteristics related to the TBI and ICP monitoring were collected. The main outcome of interest was ICHTN, which was a dichotomous outcome (yes or no) defined on a per-patient basis as an ICP > 22 mm Hg that persisted for at least 5 minutes and required an escalation of treatment. ICHTN included both elevated opening pressure on initial monitor placement and ICP elevations later during hospitalization. Multivariate logistic regression was performed to determine variables associated with ICHTN. Diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS Seventy patients with severe TBI and an ICP monitor were included in this study. There was a predominance of male patients (94.0%), and the mean patient age was 40 years old. Most patients (67%) had an intraparenchymal catheter placed, whereas 33% of patients had a ventriculostomy catheter placed. In the multivariate logistic regression analysis, the RS was an independent predictor of ICHTN (OR 2.0, 95% CI 1.2-3.5, p = 0.014). No instances of ICHTN were observed in patients with an RS of 2 or less and no sulcal effacement. The AUROC of the RS and sulcal effacement was higher than the AUROC of the RS alone for predicting ICHTN (0.76 vs 0.71, p = 0.003, z-test). CONCLUSIONS The RS was predictive of ICHTN in patients with severe TBI, and the diagnostic accuracy of the model was improved with the inclusion of sulcal effacement at the vertex on CT of the head. Patients with a low RS and no sulcal effacement are likely at low risk for the development of ICHTN.
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Affiliation(s)
| | - Tamia Potter
- 2Department of Neurological Surgery, Case Western Reserve University School of Medicine, MetroHealth Medical Center, Cleveland; and
| | - Jordan C Petitt
- 2Department of Neurological Surgery, Case Western Reserve University School of Medicine, MetroHealth Medical Center, Cleveland; and
| | - Song Hu
- 3Department of Radiology, Case Western Reserve University School of Medicine, MetroHealth Medical Center, Cleveland, Ohio
| | - Kyle Hunter
- 3Department of Radiology, Case Western Reserve University School of Medicine, MetroHealth Medical Center, Cleveland, Ohio
| | - Michael L Kelly
- 2Department of Neurological Surgery, Case Western Reserve University School of Medicine, MetroHealth Medical Center, Cleveland; and
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Li Y, Zhang G, Shan Y, Wu X, Liu J, Xue Y, Gao G. Non-Invasive Assessment of Intracranial Hypertension in Patients with Traumatic Brain Injury Using Computed Tomography Radiomic Features: A Pilot Study. J Neurotrauma 2023; 40:250-259. [PMID: 36097763 PMCID: PMC9902045 DOI: 10.1089/neu.2022.0277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
This study aimed to assess intracranial hypertension in patients with traumatic brain injury non-invasively using computed tomography (CT) radiomic features. Fifty patients from the primary cohort were enrolled in this study. The clinical data, pre-operative cranial CT images, and initial intracranial pressure readings were collected and used to develop a prediction model. Data of 20 patients from another hospital were used to validate the model. Clinical features including age, sex, midline shift, basilar cistern status, and ventriculocranial ratio were measured. Radiomic features-i.e., 18 first-order and 40 second-order features- were extracted from the CT images. LASSO method was used for features filtration. Multi-variate logistic regression was used to develop three prediction models with clinical (CF model), first-order (FO model), and second-order features (SO model). The SO model achieved the most robust ability to predict intracranial hypertension. Internal validation showed that the C-statistic of the model was 0.811 (95% confidence interval [CI]: 0.691-0.931) with the bootstrapping method. The Hosmer Lemeshow test and calibration curve also showed that the SO model had excellent performance. The external validation results showed a good discrimination with an area under the curve of 0.725 (95% CI: 0.500-0.951). Although the FO model was inferior to the SO model, it had better prediction ability than the CF model. The study shows that the radiomic features analysis, especially second-order features, can be used to evaluate intracranial hypertension non-invasively compared with conventional clinical features, given its potential for clinical practice and further research.
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Affiliation(s)
- Yihua Li
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guoqing Zhang
- Department of Neurosurgery, the People's Hospital of Qiannan, Guizhou, China
| | - Yingchi Shan
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiang Wu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiaqi Liu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yajun Xue
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guoyi Gao
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Impact of Intracranial Hypertension on Outcome of Severe Traumatic Brain Injury Pediatric Patients: A 15-Year Single Center Experience. Pediatr Rep 2022; 14:352-365. [PMID: 35997419 PMCID: PMC9397046 DOI: 10.3390/pediatric14030042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 08/04/2022] [Indexed: 12/04/2022] Open
Abstract
Background: Intracranial hypertension (IC-HTN) is significantly associated with higher risk for an unfavorable outcome in pediatric trauma. Intracranial pressure (ICP) monitoring is widely becoming a standard of neurocritical care for children. Methods: The present study was designed to evaluate influences of IC-HTN on clinical outcomes of pediatric TBI patients. Demographic, injury severity, radiologic characteristics were used as possible predictors of IC-HTN or of functional outcome. Results: A total of 118 pediatric intensive care unit (PICU) patients with severe TBI (sTBI) were included. Among sTBI cases, patients with GCS < 5 had significantly higher risk for IC-HTN and for mortality. Moreover, there was a statistically significant positive correlation between IC-HTN and severity scoring systems. Kaplan−Meier analysis determined a significant difference for good recovery among patients who had no ICP elevations, compared to those who had at least one episode of IC-HTN (log-rank chi-square = 11.16, p = 0.001). A multivariable predictive logistic regression analysis distinguished the ICP-monitored patients at risk for developing IC-HTN. The model finally revealed that higher ISS and Helsinki CT score increased the odds for developing IC-HTN (p < 0.05). Conclusion: The present study highlights the importance of ICP-guided clinical practices, which may lead to increasing percentages of good recovery for children.
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Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol. Diagnostics (Basel) 2022; 12:diagnostics12081965. [PMID: 36010315 PMCID: PMC9407063 DOI: 10.3390/diagnostics12081965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 11/26/2022] Open
Abstract
Purpose: Cardiovascular disease (CVD) is a major worldwide health burden. As the risk factors of CVD, hypertension, and hyperlipidemia are most mentioned. Early stage hypertension in the population with dyslipidemia is an important public health hazard. This study was the application of data-driven machine learning (ML), demonstrating complex relationships between risk factors and outcomes and promising predictive performance with vast amounts of medical data, aimed to investigate the association between dyslipidemia and the incidence of early stage hypertension in a large cohort with normal blood pressure at baseline. Methods: This study analyzed annual health screening data for 71,108 people from 2005 to 2017, including data for 27 risk-related indicators, sourced from the MJ Group, a major health screening center in Taiwan. We used five machine learning (ML) methods—stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), least absolute shrinkage and selection operator regression (Lasso), ridge regression (Ridge), and gradient boosting with categorical features support (CatBoost)—to develop a multi-stage ML algorithm-based prediction scheme and then evaluate important risk factors at the early stage of hypertension, especially for groups with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels within or out of the reference range. Results: Age, body mass index, waist circumference, waist-to-hip ratio, fasting plasma glucose, and C-reactive protein (CRP) were associated with hypertension. The hemoglobin level was also a positive contributor to blood pressure elevation and it appeared among the top three important risk factors in all LDL-C/HDL-C groups; therefore, these variables may be important in affecting blood pressure in the early stage of hypertension. A residual contribution to blood pressure elevation was found in groups with increased LDL-C. This suggests that LDL-C levels are associated with CPR levels, and that the LDL-C level may be an important factor for predicting the development of hypertension. Conclusion: The five prediction models provided similar classifications of risk factors. The results of this study show that an increase in LDL-C is more important than the start of a drop in HDL-C in health screening of sub-healthy adults. The findings of this study should be of value to health awareness raising about hypertension and further discussion and follow-up research.
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