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Oliveira AMP, De Andrade AF, Pipek LZ, Iaccarino C, Rubiano AM, Amorim RL, Teixeira MJ, Paiva WS. New perspectives on assessment and understanding of the patient with cranial bone defect: a morphometric and cerebral radiodensity assessment. Front Surg 2024; 11:1329019. [PMID: 38379817 PMCID: PMC10876786 DOI: 10.3389/fsurg.2024.1329019] [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: 10/27/2023] [Accepted: 01/24/2024] [Indexed: 02/22/2024] Open
Abstract
Background Skull defects after decompressive craniectomy (DC) cause physiological changes in brain function and patients can have neurologic symptoms after the surgery. The objective of this study is to evaluate whether there are morphometric changes in the cortical surface and radiodensity of brain tissue in patients undergoing cranioplasty and whether those variables are correlated with neurological prognosis. Methods This is a prospective cohort with 30 patients who were submitted to cranioplasty and followed for 6 months. Patients underwent simple head CT before and after cranioplasty for morphometric and cerebral radiodensity assessment. A complete neurological exam with Mini-Mental State Examination (MMSE), modified Rankin Scale, and the Barthel Index was performed to assess neurological prognosis. Results There was an improvement in all symptoms of the syndrome of the trephined, specifically for headache (p = 0.004) and intolerance changing head position (p = 0.016). Muscle strength contralateral to bone defect side also improved (p = 0.02). Midline shift of intracranial structures decreased after surgery (p = 0.004). The Anterior Distance Difference (ADif) and Posterior Distance Difference (PDif) were used to assess morphometric changes and varied significantly after surgery. PDif was weakly correlated with MMSE (p = 0.03; r = -0.4) and Barthel index (p = 0.035; r = -0.39). The ratio between the radiodensities of gray matter and white matter (GWR) was used to assess cerebral radiodensity and was also correlated with MMSE (p = 0.041; r = -0.37). Conclusion Morphological anatomy and radiodensity of the cerebral cortex can be used as a tool to assess neurological prognosis after DC.
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Affiliation(s)
- Arthur Maynart Pereira Oliveira
- Department of Neurosurgery, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil
| | - Almir Ferreira De Andrade
- Department of Neurosurgery, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil
| | - Leonardo Zumerkorn Pipek
- Department of Neurology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil
| | - Corrado Iaccarino
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Andres M. Rubiano
- Department of Neurosurgery, Universidad de Bogotá Jorge Tadeo Lozano, Bogotá, Colombia
- Centre for Neuroscience in Education, University of Cambridge, Cambridge, United Kingdom
| | - Robson Luis Amorim
- Department of Neurosurgery, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil
| | - Manoel Jacobsen Teixeira
- Department of Neurosurgery, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil
| | - Wellingson Silva Paiva
- Department of Neurosurgery, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil
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Chinese Admission Warning Strategy for Predicting the Hospital Discharge Outcome in Patients with Traumatic Brain Injury. J Clin Med 2022; 11:jcm11040974. [PMID: 35207247 PMCID: PMC8880692 DOI: 10.3390/jcm11040974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/05/2022] [Accepted: 02/09/2022] [Indexed: 02/05/2023] Open
Abstract
Objective: To develop and validate an admission warning strategy that incorporates the general emergency department indicators for predicting the hospital discharge outcome of patients with traumatic brain injury (TBI) in China. Methods: This admission warning strategy was developed in a primary cohort that consisted of 605 patients with TBI who were admitted within 6 h of injury. The least absolute shrinkage and selection operator and multivariable logistic regression analysis were used to develop the early warning strategy of selected indicators. Two sub-cohorts consisting of 180 and 107 patients with TBI were used for the external validation. Results: Indicators of the strategy included three categories: baseline characteristics, imaging and laboratory indicators. This strategy displayed good calibration and good discrimination. A high C-index was reached in the internal validation. The multicenter external validation cohort still showed good discrimination C-indices. Decision curve analysis (DCA) showed the actual needs of this strategy when the possibility threshold was 0.01 for the primary cohort, and at thresholds of 0.02–0.83 and 0.01–0.88 for the two sub-cohorts, respectively. In addition, this strategy exhibited a significant prognostic capacity compared to the traditional single predictors, and this optimization was also observed in two external validation cohorts. Conclusions: We developed and validated an admission warning strategy that can be quickly deployed in the emergency department. This strategy can be used as an ideal tool for predicting hospital discharge outcomes and providing objective evidence for early informed consent of the hospital discharge outcome to the family members of TBI patients.
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García-Pérez D, Panero-Pérez I, Eiriz Fernández C, Moreno-Gomez LM, Esteban-Sinovas O, Navarro-Main B, Gómez López PA, Castaño-León AM, Lagares A. Densitometric analysis of brain computed tomography as a new prognostic factor in patients with acute subdural hematoma. J Neurosurg 2021; 134:1940-1950. [DOI: 10.3171/2020.4.jns193445] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 04/22/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE
Acute subdural hematoma (ASDH) is a major cause of mortality and morbidity after traumatic brain injury (TBI). Surgical evacuation is the mainstay of treatment in patients with altered neurological status or significant mass effect. Nevertheless, concerns regarding surgical indication still persist. Given that clinicians often make therapeutic decisions on the basis of their prognosis assessment, to accurately evaluate the prognosis is of great significance. Unfortunately, there is a lack of specific and reliable prognostic models. In addition, the interdependence of certain well-known predictive variables usually employed to guide surgical decision-making in ASDH has been proven. Because gray matter and white matter are highly susceptible to secondary insults during the early phase after TBI, the authors aimed to assess the extent of these secondary insults with a brain parenchyma densitometric quantitative CT analysis and to evaluate its prognostic capacity.
METHODS
The authors performed a retrospective analysis among their prospectively collected cohort of patients with moderate to severe TBI. Patients with surgically evacuated, isolated, unilateral ASDH admitted between 2010 and 2017 were selected. Thirty-nine patients were included. For each patient, brain parenchyma density in Hounsfield units (HUs) was measured in 10 selected slices from the supratentorial region. In each slice, different regions of interest (ROIs), including and excluding the cortical parenchyma, were defined. The injured hemisphere, the contralateral hemisphere, and the absolute differences between them were analyzed. The outcome was evaluated using the Glasgow Outcome Scale–Extended at 1 year after TBI.
RESULTS
Fifteen patients (38.5%) had a favorable outcome. Collected demographic, clinical, and radiographic data did not show significant differences between favorable and unfavorable outcomes. In contrast, the densitometric analysis demonstrated that greater absolute differences between both hemispheres were associated with poor outcome. These differences were detected along the supratentorial region, but were greater at the high convexity level. Moreover, these HU differences were far more marked at the cortical parenchyma. It was also detected that these differences were more prone to ischemic and/or edematous insults than to hyperemic changes. Age was significantly correlated with the side-to-side HU differences in patients with unfavorable outcome.
CONCLUSIONS
The densitometric analysis is a promising prognostic tool in patients diagnosed with ASDH. The supplementary prognostic information provided by the densitometric analysis should be evaluated in future studies.
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Guo B, Wang Y, Pei L, Yu Y, Liu F, Zhang D, Wang X, Su Y, Zhang D, Zhang B, Guo H. Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (COPD) mortality using geographically and temporally weighted regression model across Xi'an during 2014-2016. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 756:143869. [PMID: 33280870 DOI: 10.1016/j.scitotenv.2020.143869] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/21/2020] [Accepted: 11/11/2020] [Indexed: 05/19/2023]
Abstract
Numerous methods have been implemented to evaluate the relationship between environmental factors and respiratory mortality. However, the previous epidemiological studies seldom considered the spatial and temporal variation of the independent variables. The present study aims to detect the relations between respiratory mortality and related affecting factors across Xi'an during 2014-2016 based on a novel geographically and temporally weighted regression model (GTWR). Meanwhile, the ordinary least square (OLS) and the geographically weighted regression (GWR) models were developed for cross-comparison. Additionally, the spatial autocorrelation and Hot Spot analysis methods were conducted to detect the spatiotemporal dynamic of respiratory mortality. Some important outcomes were obtained. Socioeconomic and environmental determinants represented significant effects on respiratory diseases. The respiratory mortality exhibited an obvious spatial correlation feature, and the respiratory diseases tend to occur in winter and rural areas of the study area. The GTWR model outperformed OLS and GWR for determining the relations between respiratory mortality and socioeconomic as well as environmental determinants. The influence degree of anthropic factors on COPD mortality was higher than natural factors, and the effects of independent variables on COPD varied timely and locally. The results can supply a scientific basis for respiratory disease controlling and health facilities planning.
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Affiliation(s)
- Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, China.
| | - Yan Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, China
| | - Lin Pei
- School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - Yan Yu
- School of Public Health, Xi'an Jiaotong University, Xi'an, China.
| | - Feng Liu
- Shaanxi Provincial Center for Disease Control and Prevention, Xi'an, China
| | - Donghai Zhang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, China
| | - Xiaoxia Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, China
| | - Yi Su
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, China
| | - Dingming Zhang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, China
| | - Bo Zhang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, China
| | - Hongjun Guo
- Weinan Central Hospital, Weinan, Shaanxi, China
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Kim YT, Kim H, Lee CH, Yoon BC, Kim JB, Choi YH, Cho WS, Oh BM, Kim DJ. Intracranial Densitometry-Augmented Machine Learning Enhances the Prognostic Value of Brain CT in Pediatric Patients With Traumatic Brain Injury: A Retrospective Pilot Study. Front Pediatr 2021; 9:750272. [PMID: 34796154 PMCID: PMC8593245 DOI: 10.3389/fped.2021.750272] [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: 07/30/2021] [Accepted: 10/07/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The inter- and intrarater variability of conventional computed tomography (CT) classification systems for evaluating the extent of ischemic-edematous insult following traumatic brain injury (TBI) may hinder the robustness of TBI prognostic models. Objective: This study aimed to employ fully automated quantitative densitometric CT parameters and a cutting-edge machine learning algorithm to construct a robust prognostic model for pediatric TBI. Methods: Fifty-eight pediatric patients with TBI who underwent brain CT were retrospectively analyzed. Intracranial densitometric information was derived from the supratentorial region as a distribution representing the proportion of Hounsfield units. Furthermore, a machine learning-based prognostic model based on gradient boosting (i.e., CatBoost) was constructed with leave-one-out cross-validation. At discharge, the outcome was assessed dichotomously with the Glasgow Outcome Scale (favorability: 1-3 vs. 4-5). In-hospital mortality, length of stay (>1 week), and need for surgery were further evaluated as alternative TBI outcome measures. Results: Densitometric parameters indicating reduced brain density due to subtle global ischemic changes were significantly different among the TBI outcome groups, except for need for surgery. The skewed intracranial densitometry of the unfavorable outcome became more distinguishable in the follow-up CT within 48 h. The prognostic model augmented by intracranial densitometric information achieved adequate AUCs for various outcome measures [favorability = 0.83 (95% CI: 0.72-0.94), in-hospital mortality = 0.91 (95% CI: 0.82-1.00), length of stay = 0.83 (95% CI: 0.72-0.94), and need for surgery = 0.71 (95% CI: 0.56-0.86)], and this model showed enhanced performance compared to the conventional CRASH-CT model. Conclusion: Densitometric parameters indicative of global ischemic changes during the acute phase of TBI are predictive of a worse outcome in pediatric patients. The robustness and predictive capacity of conventional TBI prognostic models might be significantly enhanced by incorporating densitometric parameters and machine learning techniques.
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Affiliation(s)
- Young-Tak Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Hakseung Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Choel-Hui Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Byung C Yoon
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Jung Bin Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Won-Sang Cho
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Byung-Mo Oh
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea.,National Traffic Injury Rehabilitation Hospital, Yangpyeong, South Korea
| | - Dong-Joo Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.,Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, South Korea.,Department of Artificial Intelligence, Korea University, Seoul, South Korea
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