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Andrade de Almeida RA, Call-Orellana F, Joaquim AF. Relationship between spinal alignment and functional disability after thoracolumbar spinal fractures: A systematic review. NORTH AMERICAN SPINE SOCIETY JOURNAL 2024; 19:100529. [PMID: 39221091 PMCID: PMC11365384 DOI: 10.1016/j.xnsj.2024.100529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 07/09/2024] [Accepted: 07/12/2024] [Indexed: 09/04/2024]
Abstract
Background Thoracolumbar spinal fractures (TLSF) can cause pain, neurological deficits, and functional disability. Operative treatments aim to preserve neurological function, improve functional status, and restore spinal alignment and stability. In this review, we evaluate the relationship between spinal alignment and functional impairment in patients with TLSF. Methods We performed a systematic review in accordance with the PRISMA guidelines to identify full-text articles that evaluate the correlation between spinal alignment and functional outcomes of TLSF. The artificial intelligence software Rayyan assisted the screening process. Functional outcomes referred to activity/disability, quality of life, and pain scores, as well as return to work metrics. Radiological assessments included were vertebral compression angle, Cobb and Gardner angles, sagittal vertical axis, pelvic incidence, and pelvic tilt. Statistical analyses were performed for the data provided by articles using the SPSS v24. Results Of 1,616 articles reviewed, 6 were included for final analysis. Only 1 study primarily addressed the effects of spinopelvic parameters and functional outcomes. Four studies correlated Cobb angles with functional outcome, while 3 others compared vertebral compression angles with functional outcomes. Outcomes were assessed using work status or a combination of VAS pain and spine score, ODI, SF-36, and RMDQ-24. Neither the analysis done within the articles, nor the one made with the raw data provided by them, showed a significant correlation between the radiological measurements assessed at time of injury and final functional outcomes. Conclusions A correlation between the assessed spinal radiological measurements assessed with the functional outcomes of TLSF was not found in this review. Further well-designed prospective studies are necessary to evaluate spinal alignment measurements in TLSF with functional outcomes.
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Affiliation(s)
| | - Francisco Call-Orellana
- Department of Neurosurgery, The Texas University MD Anderson Cancer Center, Houston, TX, United States
| | - Andrei Fernandes Joaquim
- Division of Neurosurgery, Department of Neurology, University of Campinas (Unicamp), Campinas, Sao Paulo, Brazil
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Habibi MA, Naseri Alavi SA, Soltani Farsani A, Mousavi Nasab MM, Tajabadi Z, Kobets AJ. Predicting the Outcome and Survival of Patients with Spinal Cord Injury Using Machine Learning Algorithms: A Systematic Review. World Neurosurg 2024; 188:150-160. [PMID: 38796146 DOI: 10.1016/j.wneu.2024.05.103] [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] [Received: 04/10/2024] [Accepted: 05/16/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Spinal cord injury (SCI) is a significant public health issue, leading to physical, psychological, and social complications. Machine learning (ML) algorithms have shown potential in diagnosing and predicting the functional and neurologic outcomes of subjects with SCI. ML algorithms can predict scores for SCI classification systems and accurately predict outcomes by analyzing large amounts of data. This systematic review aimed to examine the performance of ML algorithms for diagnosing and predicting the outcomes of subjects with SCI. METHODS The literature was comprehensively searched for the pertinent studies from inception to May 25, 2023. Therefore, electronic databases of PubMed, Embase, Scopus, and Web of Science were systematically searched with individual search syntax. RESULTS A total of 9424 individuals diagnosed with SCI across multiple studies were analyzed. Among the 21 studies included, 5 specifically aimed to evaluate diagnostic accuracy, while the remaining 16 focused on exploring prognostic factors or management strategies. CONCLUSIONS ML and deep learning (DL) have shown great potential in various aspects of SCI. ML and DL algorithms have been employed multiple times in predicting and diagnosing patients with SCI. While there are studies on diagnosing acute SCI using DL algorithms, further research is required in this area.
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Affiliation(s)
- Mohammad Amin Habibi
- Skull Base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Clinical Research Development Center, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran
| | | | | | | | - Zohreh Tajabadi
- Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Andrew J Kobets
- Department of Neurological Surgery, Montefiore Medical, Bronx, NY, USA
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Wang Y, Zhang J, Yuan J, Li Q, Zhang S, Wang C, Wang H, Wang L, Zhang B, Wang C, Sun Y, Lu X. Application of a novel nested ensemble algorithm in predicting motor function recovery in patients with traumatic cervical spinal cord injury. Sci Rep 2024; 14:17403. [PMID: 39075134 PMCID: PMC11286788 DOI: 10.1038/s41598-024-65755-1] [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] [Received: 03/11/2024] [Accepted: 06/24/2024] [Indexed: 07/31/2024] Open
Abstract
Traumatic cervical spinal cord injury (TCSCI) often causes varying degrees of motor dysfunction, common assessed by the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI), in association with the American Spinal Injury Association (ASIA) Impairment Scale. Accurate prediction of motor function recovery is extremely important for formulating effective diagnosis, therapeutic and rehabilitation programs. The aim of this study is to investigate the validity of a novel nested ensemble algorithm that uses the very early ASIA motor score (AMS) of ISNCSCI examination to predict motor function recovery 6 months after injury in TCSCI patients. This retrospective study included complete data of 315 TCSCI patients. The dataset consisting of the first AMS at ≤ 24 h post-injury and follow-up AMS at 6 months post-injury was divided into a training set (80%) and a test set (20%). The nested ensemble algorithm was established in a two-stage manner. Support Vector Classification (SVC), Adaboost, Weak-learner and Dummy were used in the first stage, and Adaboost was selected as second-stage model. The prediction results of the first stage models were uploaded into second-stage model to obtain the final prediction results. The model performance was evaluated using precision, recall, accuracy, F1 score, and confusion matrix. The nested ensemble algorithm was applied to predict motor function recovery of TCSCI, achieving an accuracy of 80.6%, a F1 score of 80.6%, and balancing sensitivity and specificity. The confusion matrix showed few false-negative rate, which has crucial practical implications for prognostic prediction of TCSCI. This novel nested ensemble algorithm, simply based on very early AMS, provides a useful tool for predicting motor function recovery 6 months after TCSCI, which is graded in gradients that progressively improve the accuracy and reliability of the prediction, demonstrating a strong potential of ensemble learning to personalize and optimize the rehabilitation and care of TCSCI patients.
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Affiliation(s)
- Yijin Wang
- North Sichuan Medical College, No. 234 Fuljiang Road, Shunqing District, Nanchong, 637100, Sichuan, People's Republic of China
- Department of Orthopedic Surgery, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People's Republic of China
| | - Jianjun Zhang
- North Sichuan Medical College, No. 234 Fuljiang Road, Shunqing District, Nanchong, 637100, Sichuan, People's Republic of China
- Department of Orthopedic Surgery, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People's Republic of China
| | - Jincan Yuan
- Department of Orthopedic Surgery, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People's Republic of China
| | - Qingyuan Li
- North Sichuan Medical College, No. 234 Fuljiang Road, Shunqing District, Nanchong, 637100, Sichuan, People's Republic of China
| | - Shiyu Zhang
- UCSI University, No. 1, Jalan UCSI, UCSI Heights, 56000, Cheras, Kuala Lumpur, Malaysia
| | - Chenfeng Wang
- Zhejiang University, No. 866 Yuhangtang Road, Xihu District, Hangzhou, 310058, Zhejiang, People's Republic of China
| | - Haibing Wang
- Department of Orthopedic Surgery, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People's Republic of China
| | - Liang Wang
- Department of Orthopedic Surgery, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People's Republic of China
| | - Bangke Zhang
- Department of Orthopedic Surgery, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People's Republic of China
| | - Can Wang
- North Sichuan Medical College, No. 234 Fuljiang Road, Shunqing District, Nanchong, 637100, Sichuan, People's Republic of China
- Department of Orthopedic Surgery, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People's Republic of China
| | - Yuling Sun
- Department of Orthopedic Surgery, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People's Republic of China.
| | - Xuhua Lu
- Department of Orthopedic Surgery, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People's Republic of China.
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Serpanou I, Sakellari E, Fradelos EC, Galanis P, Tzavella F, Sapountzi-Krepia D, Zyga S. The Association Between Spirituality, Self-Esteem, Anxiety and Depression: A Comparative Exploratory Study Between People with a Spinal Cord Injury and Healthy Subjects in Greece. JOURNAL OF RELIGION AND HEALTH 2024:10.1007/s10943-024-02092-7. [PMID: 39030422 DOI: 10.1007/s10943-024-02092-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/10/2024] [Indexed: 07/21/2024]
Abstract
The purpose of this study was to evaluate spirituality, religiosity, self-esteem, depression, and stress in people with spinal cord injuries. The Royal Free Questionnaire for Spiritual and Religious Beliefs, the Rosenberg Self-Esteem Scale, the Center for Epidemiologic Studies Depression Scale, and the Spielberger State-Trait Anxiety Scale were all designed for the current study to collect demographic and injury-related information. In the study, 88 patients with traumatic SCI and 88 healthy subjects were included. Half of the participants and healthy subjects were women, while the mean age of the healthy subjects was 42.8 years and that of the subjects was 42.4 years. The majority of participants were Greek (97.2%), while 44.1% were married or with a partner and 43.2% were single. The mean depression and stress score was higher in patients than in healthy subjects, and the mean score of self-esteem was lower in patients than healthy subjects. Multivariable linear regression model showed that patients who believed more that a spiritual force or power outside of ourselves can influence what happens in everyday life had less depression and stress. People with spinal cord injuries experience extreme stress, anxiety, and depression. Spirituality and religiosity can play a significant role in their recuperation, rehabilitation, and adaptation.
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Affiliation(s)
- Ismini Serpanou
- Department of Physiotherapy, Pammakaristos General Hospital, Athens, Greece
| | - Evanthia Sakellari
- Department of Public and Community Health, University of West Attica, Athens, Greece
| | | | - Petros Galanis
- Faculty of Nursing, Center for Health Services Management and Evaluation, National and Kapodistrian University of Athens, Athens, Greece
| | - Foteini Tzavella
- Department of Nursing, University of Peloponnese, Tripoli, Greece
| | | | - Sofia Zyga
- Department of Nursing, University of Peloponnese, Tripoli, Greece
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Yoo HJ, Koo B, Yong CW, Lee KS. Prediction of gait recovery using machine learning algorithms in patients with spinal cord injury. Medicine (Baltimore) 2024; 103:e38286. [PMID: 38847729 PMCID: PMC11155515 DOI: 10.1097/md.0000000000038286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/26/2024] [Indexed: 06/10/2024] Open
Abstract
With advances in artificial intelligence, machine learning (ML) has been widely applied to predict functional outcomes in clinical medicine. However, there has been no attempt to predict walking ability after spinal cord injury (SCI) based on ML. In this situation, the main purpose of this study was to predict gait recovery after SCI at discharge from an acute rehabilitation facility using various ML algorithms. In addition, we explored important variables that were related to the prognosis. Finally, we attempted to suggest an ML-based decision support system (DSS) for predicting gait recovery after SCI. Data were collected retrospectively from patients with SCI admitted to an acute rehabilitation facility between June 2008 to December 2021. Linear regression analysis and ML algorithms (random forest [RF], decision tree [DT], and support vector machine) were used to predict the functional ambulation category at the time of discharge (FAC_DC) in patients with traumatic or non-traumatic SCI (n = 353). The independent variables were age, sex, duration of acute care and rehabilitation, comorbidities, neurological information entered into the International Standards for Neurological Classification of SCI worksheet, and somatosensory-evoked potentials at the time of admission to the acute rehabilitation facility. In addition, the importance of variables and DT-based DSS for FAC_DC was analyzed. As a result, RF and DT accurately predicted the FAC_DC measured by the root mean squared error. The root mean squared error of RF and the DT were 1.09 and 1.24 for all participants, 1.20 and 1.06 for those with trauma, and 1.12 and 1.03 for those with non-trauma, respectively. In the analysis of important variables, the initial FAC was found to be the most influential factor in all groups. In addition, we could provide a simple DSS based on strong predictors such as the initial FAC, American Spinal Injury Association Impairment Scale grades, and neurological level of injury. In conclusion, we provide that ML can accurately predict gait recovery after SCI for the first time. By focusing on important variables and DSS, we can guide early prognosis and establish personalized rehabilitation strategies in acute rehabilitation hospitals.
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Affiliation(s)
- Hyun-Joon Yoo
- Korea University Research Institute for Medical Bigdata Science, Korea University College of Medicine, Seoul, Republic of Korea
| | - Bummo Koo
- School of Health and Environmental Science, Korea University College of Health Science, Seoul, Republic of Korea
| | - Chan-woo Yong
- School of Health and Environmental Science, Korea University College of Health Science, Seoul, Republic of Korea
| | - Kwang-Sig Lee
- AI Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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Kim Y, Lim M, Kim SY, Kim TU, Lee SJ, Bok SK, Park S, Han Y, Jung HY, Hyun JK. Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients. J Clin Med 2024; 13:990. [PMID: 38398304 PMCID: PMC10889422 DOI: 10.3390/jcm13040990] [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] [Received: 12/19/2023] [Revised: 01/19/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
(1) Background: Pressure ulcers (PUs) substantially impact the quality of life of spinal cord injury (SCI) patients and require prompt intervention. This study used machine learning (ML) techniques to develop advanced predictive models for the occurrence of PUs in patients with SCI. (2) Methods: By analyzing the medical records of 539 patients with SCI, we observed a 35% incidence of PUs during hospitalization. Our analysis included 139 variables, including baseline characteristics, neurological status (International Standards for Neurological Classification of Spinal Cord Injury [ISNCSCI]), functional ability (Korean version of the Modified Barthel Index [K-MBI] and Functional Independence Measure [FIM]), and laboratory data. We used a variety of ML methods-a graph neural network (GNN), a deep neural network (DNN), a linear support vector machine (SVM_linear), a support vector machine with radial basis function kernel (SVM_RBF), K-nearest neighbors (KNN), a random forest (RF), and logistic regression (LR)-focusing on an integrative analysis of laboratory, neurological, and functional data. (3) Results: The SVM_linear algorithm using these composite data showed superior predictive ability (area under the receiver operating characteristic curve (AUC) = 0.904, accuracy = 0.944), as demonstrated by a 5-fold cross-validation. The critical discriminators of PU development were identified based on limb functional status and laboratory markers of inflammation. External validation highlighted the challenges of model generalization and provided a direction for future research. (4) Conclusions: Our study highlights the importance of a comprehensive, multidimensional data approach for the effective prediction of PUs in patients with SCI, especially in the acute and subacute phases. The proposed ML models show potential for the early detection and prevention of PUs, thus contributing substantially to improving patient care in clinical settings.
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Affiliation(s)
- Yuna Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Myungeun Lim
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Seo Young Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Tae Uk Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Seong Jae Lee
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Soo-Kyung Bok
- Department of Rehabilitation Medicine, College of Medicine, Chungnam National University, Daejeon 35015, Republic of Korea;
| | - Soojun Park
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Youngwoong Han
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Ho-Youl Jung
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Jung Keun Hyun
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
- Department of Nanobiomedical Science and BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan 31116, Republic of Korea
- Institute of Tissue Regeneration Engineering, Dankook University, Cheonan 31116, Republic of Korea
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Karabacak M, Jagtiani P, Margetis K. The Predictive Abilities of Machine Learning Algorithms in Patients with Thoracolumbar Spinal Cord Injuries. World Neurosurg 2024; 182:e67-e90. [PMID: 38030070 DOI: 10.1016/j.wneu.2023.11.043] [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] [Received: 09/01/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 12/01/2023]
Abstract
OBJECTIVES The goal of this study is to implement machine learning (ML) algorithms to predict mortality, non-home discharge, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with thoracolumbar spinal cord injury, while creating a publicly accessible online tool. METHODS The American College of Surgeons Trauma Quality Program database was used to identify patients with thoracolumbar spinal cord injury. Feature selection was performed with the Least Absolute Shrinkage and Selection Operator algorithm. Five ML algorithms, including TabPFN, TabNet, XGBoost, LightGBM, and Random Forest, were used along with the Optuna optimization library for hyperparameter tuning. RESULTS A total of 147,819 patients were included in the analysis. For each outcome, we determined the best model for deployment in our web application based on the area under the receiver operating characteristic (AUROC) values. The top performing algorithms were as follows: LightGBM for mortality with an AUROC of 0.885, TabPFN for non-home discharge with an AUROC of 0.801, LightGBM for prolonged LOS with an AUROC of 0.673, Random Forest for prolonged ICU-LOS with an AUROC of 0.664, and LightGBM for major complications with an AUROC of 0.73. CONCLUSIONS ML models demonstrate good predictive ability for in-hospital mortality and non-home discharge, fair predictive ability for major complications and prolonged ICU-LOS, but poor predictive ability for prolonged LOS. We have developed a web application that allows these models to be accessed.
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Affiliation(s)
- Mert Karabacak
- Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA
| | - Pemla Jagtiani
- School of Medicine, SUNY Downstate Health Sciences University, New York, New York, USA
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Fallah N, Noonan VK, Thorogood NP, Kwon BK, Kopp MA, Schwab JM. Effect of body mass index on survival after spinal cord injury. Front Neurol 2024; 14:1269030. [PMID: 38344110 PMCID: PMC10853461 DOI: 10.3389/fneur.2023.1269030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/27/2023] [Indexed: 05/12/2024] Open
Abstract
Introduction Increased mortality after acute and chronic spinal cord injury (SCI) remains a challenge and mandates a better understanding of the factors contributing to survival in these patients. This study investigated whether body mass index (BMI) measured after acute traumatic SCI is associated with a change in mortality. Methods A prospective longitudinal cohort study was conducted with 742 patients who were admitted to the Acute Spine Unit of the Vancouver General Hospital between 2004 and 2016 with a traumatic SCI. An investigation of the association between BMI on admission and long-term mortality was conducted using classification and regression tree (CART) and generalized additive models (spline curves) from acute care up to 7.7 years after SCI (chronic phase). Multivariable models were adjusted for (i) demographic factors (e.g., age, sex, and Charlson Comorbidity Index) and (ii) injury characteristics (e.g., neurological level and severity and Injury Severity Score). Results After the exclusion of incomplete datasets (n = 602), 643 patients were analyzed, of whom 102 (18.5%) died during a period up to 7.7 years after SCI. CART identified three distinct mortality risk groups: (i) BMI: > 30.5 kg/m2, (ii) 17.5-30.5 kg/m2, and (iii) < 17.5 kg/m2. Mortality was lowest in the high BMI group (BMI > 30.5 kg/m2), followed by the middle-weight group (17.5-30.5 kg/m2), and was highest in the underweight group (BMI < 17.5 kg/m2). High BMI had a mild protective effect against mortality after SCI (hazard ratio 0.28, 95% CI: 0.09-0.88, p = 0.029), concordant with a modest "obesity paradox". Moreover, being underweight at admission was a significant risk factor for mortality up to 7.7 years after SCI (hazard ratio 5.5, 95% CI: 2.34-13.17, p < 0.001). Discussion Mortality risk (1 month to 7.7 years after SCI) was associated with differences in BMI at admission. Further research is needed to better understand the underlying mechanisms. Given an established association of BMI with metabolic determinants, these results may suggest unknown neuro-metabolic pathways that are crucial for patient survival.
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Affiliation(s)
- Nader Fallah
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Vanessa K. Noonan
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
| | - Nancy P. Thorogood
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
| | - Brian K. Kwon
- Department of Orthopaedics, Vancouver Spine Surgery Institute, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Marcel A. Kopp
- Department of Neurology and Experimental Neurology, Clinical and Experimental Spinal Cord Injury Research, Charité – Universitätsmedizin Berlin, Berlin, Germany
- QUEST-Center for Transforming Biomedical Research, Berlin Institute of Health, Berlin, Germany
| | - Jan M. Schwab
- Department of Neurology and Experimental Neurology, Clinical and Experimental Spinal Cord Injury Research, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology, Spinal Cord Injury Division, The Ohio State University, Wexner Medical Center, Columbus, OH, United States
- Belford Center for Spinal Cord Injury, Departments of Physical Medicine and Rehabilitation and Neuroscience, The Ohio State University, Wexner Medical Center, Columbus, OH, United States
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Hakimjavadi R, Hong HA, Fallah N, Humphreys S, Kingwell S, Stratton A, Tsai E, Wai EK, Walden K, Noonan VK, Phan P. Enabling knowledge translation: implementation of a web-based tool for independent walking prediction after traumatic spinal cord injury. Front Neurol 2023; 14:1219307. [PMID: 38116110 PMCID: PMC10728823 DOI: 10.3389/fneur.2023.1219307] [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: 06/06/2023] [Accepted: 11/13/2023] [Indexed: 12/21/2023] Open
Abstract
Introduction Several clinical prediction rules (CPRs) have been published, but few are easily accessible or convenient for clinicians to use in practice. We aimed to develop, implement, and describe the process of building a web-based CPR for predicting independent walking 1-year after a traumatic spinal cord injury (TSCI). Methods Using the published and validated CPR, a front-end web application called "Ambulation" was built using HyperText Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. A survey was created using QualtricsXM Software to gather insights on the application's usability and user experience. Website activity was monitored using Google Analytics. Ambulation was developed with a core team of seven clinicians and researchers. To refine the app's content, website design, and utility, 20 professionals from different disciplines, including persons with lived experience, were consulted. Results After 11 revisions, Ambulation was uploaded onto a unique web domain and launched (www.ambulation.ca) as a pilot with 30 clinicians (surgeons, physiatrists, and physiotherapists). The website consists of five web pages: Home, Calculation, Team, Contact, and Privacy Policy. Responses from the user survey (n = 6) were positive and provided insight into the usability of the tool and its clinical utility (e.g., helpful in discharge planning and rehabilitation), and the overall face validity of the CPR. Since its public release on February 7, 2022, to February 28, 2023, Ambulation had 594 total users, 565 (95.1%) new users, 26 (4.4%) returning users, 363 (61.1%) engaged sessions (i.e., the number of sessions that lasted 10 seconds/longer, had one/more conversion events e.g., performing the calculation, or two/more page or screen views), and the majority of the users originating from the United States (39.9%) and Canada (38.2%). Discussion Ambulation is a CPR for predicting independent walking 1-year after TSCI and it can assist frontline clinicians with clinical decision-making (e.g., time to surgery or rehabilitation plan), patient education and goal setting soon after injury. This tool is an example of adapting a validated CPR for independent walking into an easily accessible and usable web-based tool for use in clinical practice. This study may help inform how other CPRs can be adopted into clinical practice.
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Affiliation(s)
| | - Heather A. Hong
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
| | - Nader Fallah
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
- Division of Neurology, Department of Medicine, Faculty of Medicine, The University of British Columbia, UBC Hospital, Vancouver, BC, Canada
| | - Suzanne Humphreys
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
| | - Stephen Kingwell
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Alexandra Stratton
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Eve Tsai
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Eugene K. Wai
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Kristen Walden
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
| | - Vanessa K. Noonan
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Philippe Phan
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
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Karabacak M, Margetis K. Precision medicine for traumatic cervical spinal cord injuries: accessible and interpretable machine learning models to predict individualized in-hospital outcomes. Spine J 2023; 23:1750-1763. [PMID: 37619871 DOI: 10.1016/j.spinee.2023.08.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 06/28/2023] [Accepted: 08/13/2023] [Indexed: 08/26/2023]
Abstract
BACKGROUND CONTEXT A traumatic spinal cord injury (SCI) can cause temporary or permanent motor and sensory impairment, leading to serious short and long-term consequences that can result in significant morbidity and mortality. The cervical spine is the most commonly affected area, accounting for about 60% of all traumatic SCI cases. PURPOSE This study aims to employ machine learning (ML) algorithms to predict various outcomes, such as in-hospital mortality, nonhome discharges, extended length of stay (LOS), extended length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with cervical SCI (cSCI). STUDY DESIGN Our study was a retrospective machine learning classification study aiming to predict the outcomes of interest, which were binary categorical variables, in patients diagnosed with cSCI. PATIENT SAMPLE The data for this study were obtained from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database, which was queried to identify patients who suffered from cSCI between 2019 and 2021. OUTCOME MEASURES The outcomes of interest of our study were in-hospital mortality, nonhome discharges, prolonged LOS, prolonged ICU-LOS, and major complications. The study evaluated the models' performance using both graphical and numerical methods. The receiver operating characteristic (ROC) and precision-recall curves (PRC) were used to assess model performance graphically. Numerical evaluation metrics included AUROC, balanced accuracy, weighted area under PRC (AUPRC), weighted precision, and weighted recall. METHODS The study employed data from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database to identify patients with cSCI. Four ML algorithms, namely XGBoost, LightGBM, CatBoost, and Random Forest, were utilized to develop predictive models. The most effective models were then incorporated into a publicly available web application designed to forecast the outcomes of interest. RESULTS There were 71,661 patients included in the analysis for the outcome mortality, 67,331 for the outcome nonhome discharges, 76,782 for the outcome prolonged LOS, 26,615 for the outcome prolonged ICU-LOS, and 72,132 for the outcome major complications. The algorithms exhibited an AUROC value range of 0.78 to 0.839 for in-hospital mortality, 0.806 to 0.815 for nonhome discharges, 0.679 to 0.742 for prolonged LOS, 0.666 to 0.682 for prolonged ICU-LOS, and 0.637 to 0.704 for major complications. An open access web application was developed as part of the study, which can generate predictions for individual patients based on their characteristics. CONCLUSIONS Our study suggests that ML models can be valuable in assessing risk for patients with cervical cSCI and may have considerable potential for predicting outcomes during hospitalization. ML models demonstrated good predictive ability for in-hospital mortality and nonhome discharges, fair predictive ability for prolonged LOS, but poor predictive ability for prolonged ICU-LOS and major complications. Along with these promising results, the development of a user-friendly web application that facilitates the integration of these models into clinical practice is a significant contribution of this study. The product of this study may have significant implications in clinical settings to personalize care, anticipate outcomes, facilitate shared decision making and informed consent processes for cSCI patients.
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Affiliation(s)
- Mert Karabacak
- Department of Neurosurgery, Mount Sinai Health System, 1468 Madison (Ave), New York, 10029 NY, USA
| | - Konstantinos Margetis
- Department of Neurosurgery, Mount Sinai Health System, 1468 Madison (Ave), New York, 10029 NY, USA
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11
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Kelly-Hedrik M, Abd-El-Barr MM, Aarabi B, Curt A, Howley SP, Harrop JS, Kirshblum S, Neal CJ, Noonan V, Park C, Ugiliweneza B, Tator C, Toups EG, Fehlings MG, Williamson T, Guest JD. Importance of Prospective Registries and Clinical Research Networks in the Evolution of Spinal Cord Injury Care. J Neurotrauma 2023; 40:1834-1848. [PMID: 36576020 DOI: 10.1089/neu.2022.0450] [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: 12/29/2022] Open
Abstract
Only 100 years ago, traumatic spinal cord injury (SCI) was commonly lethal. Today, most people who sustain SCI survive with continual efforts to improve their quality of life and neurological outcomes. SCI epidemiology is changing as preventative interventions reduce injuries in younger individuals, and there is an increased incidence of incomplete injuries in aging populations. Early treatment has become more intensive with decompressive surgery and proactive interventions to improve spinal cord perfusion. Accurate data, including specialized outcome measures, are crucial to understanding the impact of epidemiological and treatment trends. Dedicated SCI clinical research and data networks and registries have been established in the United States, Canada, Europe, and several other countries. We review four registry networks: the North American Clinical Trials Network (NACTN) SCI Registry, the National Spinal Cord Injury Model Systems (SCIMS) Database, the Rick Hansen SCI Registry (RHSCIR), and the European Multi-Center Study about Spinal Cord Injury (EMSCI). We compare the registries' focuses, data platforms, advanced analytics use, and impacts. We also describe how registries' data can be combined with electronic health records (EHRs) or shared using federated analysis to protect registrants' identities. These registries have identified changes in epidemiology, recovery patterns, complication incidence, and the impact of practice changes such as early decompression. They've also revealed latent disease-modifying factors, helped develop clinical trial stratification models, and served as matched control groups in clinical trials. Advancing SCI clinical science for personalized medicine requires advanced analytical techniques, including machine learning, counterfactual analysis, and the creation of digital twins. Registries and other data sources help drive innovation in SCI clinical science.
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Affiliation(s)
| | | | - Bizhan Aarabi
- University of Maryland School of Medicine, Maryland, USA
| | - Armin Curt
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
| | - Susan P Howley
- Christopher & Dana Reeve Foundation, Short Hills, New Jersey, USA
| | - James S Harrop
- Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Steven Kirshblum
- Rutgers New Jersey Medical School, Newark, New Jersey, USA
- Kessler Institute for Rehabilitation, West Orange, New Jersey, USA
- Kessler Foundation, West Orange, New Jersey, USA
| | - Christopher J Neal
- Division of Neurosurgery, Walter Reed National Military Medical Center, Bethesda, Maryland, USA
| | - Vanessa Noonan
- Praxis Spinal Cord Institute, Vancouver, British Columbia, Canada
| | - Christine Park
- Duke University School of Medicine, Durham, North Carolina, USA
| | | | - Charles Tator
- Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Elizabeth G Toups
- Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas, USA
| | - Michael G Fehlings
- Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Theresa Williamson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - James D Guest
- Neurological Surgery and The Miami Project to Cure Paralysis, University of Miami, Miami, USA
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12
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Shakil H, Jaja BNR, Zhang PF, Jaffe RH, Malhotra AK, Harrington EM, Wijeysundera DN, Wilson JR, Witiw CD. Assessment of the incremental prognostic value from the modified frailty index-5 in complete traumatic cervical spinal cord injury. Sci Rep 2023; 13:7578. [PMID: 37165004 PMCID: PMC10172291 DOI: 10.1038/s41598-023-34708-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 05/05/2023] [Indexed: 05/12/2023] Open
Abstract
Frailty, as measured by the modified frailty index-5 (mFI-5), and older age are associated with increased mortality in the setting of spinal cord injury (SCI). However, there is limited evidence demonstrating an incremental prognostic value derived from patient mFI-5. We conducted a retrospective cohort study to evaluate in-hospital mortality among adult complete cervical SCI patients at participating centers of the Trauma Quality Improvement Program from 2010 to 2018. Logistic regression was used to model in-hospital mortality, and the area under the receiver operating characteristic curve (AUROC) of regression models with age, mFI-5, or age with mFI-5 was used to compare the prognostic value of each model. 4733 patients were eligible. We found that both age (80 y versus 60 y: OR 3.59 95% CI [2.82 4.56], P < 0.001) and mFI-5 (score ≥ 2 versus < 2: OR 1.53 95% CI [1.19 1.97], P < 0.001) had statistically significant associations with in-hospital mortality. There was no significant difference in the AUROC of a model including age and mFI-5 when compared to a model including age without mFI-5 (95% CI Δ AUROC [- 8.72 × 10-4 0.82], P = 0.199). Both models were superior to a model including mFI-5 without age (95% CI Δ AUROC [0.06 0.09], P < 0.001). Our findings suggest that mFI-5 provides minimal incremental prognostic value over age with respect to in-hospital mortality for patients complete cervical SCI.
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Affiliation(s)
- Husain Shakil
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, M5T1P5, Canada
- St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, M5B1T8, Canada
- Division of Neurosurgery, Department of Surgery, St. Michael's Hospital, Toronto, M5B1W8, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, M5T1P8, Canada
| | - Blessing N R Jaja
- St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, M5B1T8, Canada
| | - Peng F Zhang
- St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, M5B1T8, Canada
| | - Rachael H Jaffe
- St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, M5B1T8, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, M5T1P8, Canada
| | - Armaan K Malhotra
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, M5T1P5, Canada
- St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, M5B1T8, Canada
- Division of Neurosurgery, Department of Surgery, St. Michael's Hospital, Toronto, M5B1W8, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, M5T1P8, Canada
| | - Erin M Harrington
- St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, M5B1T8, Canada
| | - Duminda N Wijeysundera
- St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, M5B1T8, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, M5T1P8, Canada
- Department of Anesthesia, St. Michael's Hospital, Toronto, M5B1W8, Canada
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, M5T1P8, Canada
| | - Jefferson R Wilson
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, M5T1P5, Canada
- St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, M5B1T8, Canada
- Division of Neurosurgery, Department of Surgery, St. Michael's Hospital, Toronto, M5B1W8, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, M5T1P8, Canada
| | - Christopher D Witiw
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, M5T1P5, Canada.
- St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, M5B1T8, Canada.
- Division of Neurosurgery, Department of Surgery, St. Michael's Hospital, Toronto, M5B1W8, Canada.
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, M5T1P8, Canada.
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13
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Gopinath N. Artificial intelligence and neuroscience: An update on fascinating relationships. Process Biochem 2023. [DOI: 10.1016/j.procbio.2022.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Efficacy of a machine learning-based approach in predicting neurological prognosis of cervical spinal cord injury patients following urgent surgery within 24 h after injury. J Clin Neurosci 2023; 107:150-156. [PMID: 36376152 DOI: 10.1016/j.jocn.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/12/2022] [Accepted: 11/05/2022] [Indexed: 11/13/2022]
Abstract
We aimed to develop a machine learning (ML) model for predicting the neurological outcomes of cervical spinal cord injury (CSCI). We retrospectively analyzed 135 patients with CSCI who underwent surgery within 24 h after injury. Patients were assessed with the American Spinal Injury Association Impairment Scale (AIS; grades A to E) 6 months after injury. A total of 34 features extracted from demographic variables, surgical factors, laboratory variables, neurological status, and radiological findings were analyzed. The ML model was created using Light GBM, XGBoost, and CatBoost. We evaluated Shapley Additive Explanations (SHAP) values to determine the variables that contributed most to the prediction models. We constructed multiclass prediction models for the five AIS grades and binary classification models to predict more than one-grade improvement in AIS 6 months after injury. Of the ML models used, CatBoost showed the highest accuracy (0.800) for the prediction of AIS grade and the highest AUC (0.90) for predicting improvement in AIS. AIS grade at admission, intramedullary hemorrhage, longitudinal extent of intramedullary T2 hyperintensity, and HbA1c were identified as important features for these prediction models. The ML models successfully predicted neurological outcomes 6 months after injury following urgent surgery in patients with CSCI.
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Fehlings MG, Pedro K, Hejrati N. Management of Acute Spinal Cord Injury: Where Have We Been? Where Are We Now? Where Are We Going? J Neurotrauma 2022; 39:1591-1602. [PMID: 35686453 DOI: 10.1089/neu.2022.0009] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- Michael G Fehlings
- Division of Genetics and Development, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada.,Institute of Medical Science, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.,Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Karlo Pedro
- Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Nader Hejrati
- Division of Genetics and Development, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada.,Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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Dietz N, Vaitheesh Jaganathan, Alkin V, Mettille J, Boakye M, Drazin D. Machine learning in clinical diagnosis, prognostication, and management of acute traumatic spinal cord injury (SCI): A systematic review. J Clin Orthop Trauma 2022; 35:102046. [PMID: 36425281 PMCID: PMC9678757 DOI: 10.1016/j.jcot.2022.102046] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/23/2022] [Accepted: 10/18/2022] [Indexed: 11/19/2022] Open
Abstract
Background Machine learning has been applied to improve diagnosis and prognostication of acute traumatic spinal cord injury. We investigate potential for clinical integration of machine learning in this patient population to navigate variability in injury and recovery. Materials and methods We performed a systematic review using PRISMA guidelines through PubMed database to identify studies that use machine learning algorithms for clinical application toward improvements in diagnosis, management, and predictive modeling. Results Of the 132 records identified, a total of 13 articles met inclusion criteria and were included in final analysis. Of the 13 articles, 5 focused on diagnostic accuracy and 8 were related to prognostication or management of traumatic spinal cord injury. Across studies, 1983 patients with spinal cord injury were evaluated with most classifying as ASIA C or D. Retrospective designs were used in 10 of 13 studies and 3 were prospective. Studies focused on MRI evaluation and segmentation for diagnostic accuracy and prognostication, investigation of mean arterial pressure in acute care and intraoperative settings, prediction of ambulatory and functional ability, chronic complication prevention, and psychological quality of life assessments. Decision tree, random forests (RF), support vector machines (SVM), hierarchical cluster tree analysis (HCTA), artificial neural networks (ANN), convolutional neural networks (CNN) machine learning subtypes were used. Conclusions Machine learning represents a platform technology with clinical application in traumatic spinal cord injury diagnosis, prognostication, management, rehabilitation, and risk prevention of chronic complications and mental illness. SVM models showed improved accuracy when compared to other ML subtypes surveyed. Inherent variability across patients with SCI offers unique opportunity for ML and personalized medicine to drive desired outcomes and assess risks in this patient population.
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Affiliation(s)
- Nicholas Dietz
- Department of Neurosurgery, University of Louisville, 200 Abraham Flexner Hwy, Louisville, KY, 40202, USA
| | - Vaitheesh Jaganathan
- Department of Neurosurgery, University of Louisville, 200 Abraham Flexner Hwy, Louisville, KY, 40202, USA
| | | | - Jersey Mettille
- Department of Anesthesia, University of Louisville, Louisville, KY, USA
| | - Maxwell Boakye
- Department of Neurosurgery, University of Louisville, 200 Abraham Flexner Hwy, Louisville, KY, 40202, USA
| | - Doniel Drazin
- Department of Neurosurgery, Providence Regional Medical Center Everett, Everett, WA, USA
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17
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Wilton A. Risk Factors for Postoperative Complications and In-Hospital Mortality Following Surgery for Cervical Spinal Cord Injury. Cureus 2022; 14:e31960. [DOI: 10.7759/cureus.31960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/27/2022] [Indexed: 11/29/2022] Open
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18
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Determining the short-term neurological prognosis for acute cervical spinal cord injury using machine learning. J Clin Neurosci 2022; 96:74-79. [PMID: 34998207 DOI: 10.1016/j.jocn.2021.11.037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/25/2021] [Accepted: 11/30/2021] [Indexed: 11/22/2022]
Abstract
It is challenging to predict neurological outcomes of acute spinal cord injury (SCI) considering issues such as spinal shock and injury heterogeneity. Deep learning-based radiomics (DLR) were developed to quantify the radiographic characteristics automatically using a convolutional neural network (CNN), and to potentially allow the prognostic stratification of patients. We aimed to determine the functional prognosis of patients with cervical SCI using machine learning approach based on MRI and to assess the ability to predict the neurological outcomes. We retrospectively analyzed the medical records of SCI patients (n=215) who had undergone MRI and had an American Spinal cord Injury Association Impairment Scale (AIS) assessment at 1 month after injury, enrolled with a total of 294 MR images. Sagittal T2-weighted MR images were used for the CNN training and validation. The deep learning framework TensorFlow was used to construct the CNN architecture. After we calculated the probability of the AIS grade using the DLR, we built the identification model based upon the random forest using 3 features: the probability of each AIS grade obtained by the DLR method, age, and the initial AIS grade at admission. We performed a statistical evaluation between the actual and predicted AIS. The accuracy, precision, recall and f1 score of the ensemble model based on the DLR and RF were 0.714, 0.590, 0.565 and 0.567, respectively. The present study demonstrates that prediction of the short-term neurological outcomes for acute cervical spinal cord injury based on MRI using machine learning is feasible.
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