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Zhu G, Ozkara BB, Chen H, Zhou B, Jiang B, Ding VY, Wintermark M. Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study. Neuroradiol J 2024; 37:74-83. [PMID: 37921691 PMCID: PMC10863571 DOI: 10.1177/19714009231212364] [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: 11/04/2023] Open
Abstract
PURPOSE We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients. METHODS In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables. RESULTS Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3's hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis. CONCLUSION Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.
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Affiliation(s)
- Guangming Zhu
- Department of Neurology, The University of Arizona, USA
| | - Burak B Ozkara
- Department of Neuroradiology, MD Anderson Cancer Center, USA
| | - Hui Chen
- Department of Neuroradiology, MD Anderson Cancer Center, USA
| | - Bo Zhou
- Neuroradiology Division, Department of Radiology, Stanford University, USA
| | - Bin Jiang
- Neuroradiology Division, Department of Radiology, Stanford University, USA
| | - Victoria Y Ding
- Quantitative Sciences Unit, Department of Medicine, Stanford University, USA
| | - Max Wintermark
- Department of Neuroradiology, MD Anderson Cancer Center, USA
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Terabe ML, Massago M, Iora PH, Hernandes Rocha TA, de Souza JVP, Huo L, Massago M, Senda DM, Kobayashi EM, Vissoci JR, Staton CA, de Andrade L. Applicability of machine learning technique in the screening of patients with mild traumatic brain injury. PLoS One 2023; 18:e0290721. [PMID: 37616279 PMCID: PMC10449130 DOI: 10.1371/journal.pone.0290721] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As such, this study aims to evaluate the applicability of a machine learning (ML) technique in the screening of patients with mild TBI in the Regional University Hospital of Maringá, Paraná state, Brazil. This is an observational, descriptive, cross-sectional, and retrospective study using ML technique to develop a protocol that predicts which patients with an initial diagnosis of mild TBI should be recommended for a head CT. Among the tested models, he linear extreme gradient boosting was the best algorithm, with the highest sensitivity (0.70 ± 0.06). Our predictive model can assist in the screening of mild TBI patients, assisting health professionals to manage the resource utilization, and improve the quality and safety of patient care.
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Affiliation(s)
- Miriam Leiko Terabe
- Postgraduate Program in Management, Technology and Innovation in Urgency and Emergency, State University of Maringa, Maringa, Parana, Brazil
| | - Miyoko Massago
- Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil
| | - Pedro Henrique Iora
- Department of Medicine, State University of Maringa, Maringa, Parana, Brazil
| | | | - João Vitor Perez de Souza
- Postgraduate Program in Biosciences and Physiopathology, State University of Maringa, Maringa, Parana, Brazil
| | - Lily Huo
- Duke Global Health Institute, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Mamoru Massago
- Postgraduate Program in Computer Sciences, State University of Maringa, Maringa, Parana, Brazil
| | - Dalton Makoto Senda
- Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil
| | | | - João Ricardo Vissoci
- Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil
- Duke Global Health Institute, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Catherine Ann Staton
- Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil
- Duke Global Health Institute, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Luciano de Andrade
- Postgraduate Program in Management, Technology and Innovation in Urgency and Emergency, State University of Maringa, Maringa, Parana, Brazil
- Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil
- Department of Medicine, State University of Maringa, Maringa, Parana, Brazil
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Ramlakhan SL, Saatchi R, Sabir L, Ventour D, Shobayo O, Hughes R, Singh Y. Building artificial intelligence and machine learning models : a primer for emergency physicians. Emerg Med J 2022; 39:e1. [PMID: 35241439 DOI: 10.1136/emermed-2022-212379] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 02/14/2022] [Indexed: 12/23/2022]
Abstract
There has been a rise in the number of studies relating to the role of artificial intelligence (AI) in healthcare. Its potential in Emergency Medicine (EM) has been explored in recent years with operational, predictive, diagnostic and prognostic emergency department (ED) implementations being developed. For EM researchers building models de novo, collaborative working with data scientists is invaluable throughout the process. Synergism and understanding between domain (EM) and data experts increases the likelihood of realising a successful real-world model. Our linked manuscript provided a conceptual framework (including a glossary of AI terms) to support clinicians in interpreting AI research. The aim of this paper is to supplement that framework by exploring the key issues for clinicians and researchers to consider in the process of developing an AI model.
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Affiliation(s)
- Shammi L Ramlakhan
- Emergency Department, Sheffield Children's NHS Foundation Trust, Sheffield, UK
| | - Reza Saatchi
- Electronics & Computer Engineering Research Institute, Sheffield Hallam University, Sheffield, UK
| | - Lisa Sabir
- Emergency Department, Sheffield Children's NHS Foundation Trust, Sheffield, UK
| | - Dale Ventour
- Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago
| | - Olamilekan Shobayo
- Electronics & Computer Engineering Research Institute, Sheffield Hallam University, Sheffield, UK
| | - Ruby Hughes
- Advanced Forming Research Centre, University of Strathclyde, Sheffield, UK
| | - Yardesh Singh
- Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago
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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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