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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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Draganich C, Anderson D, Dornan GJ, Sevigny M, Berliner J, Charlifue S, Welch A, Smith A. Predictive modeling of ambulatory outcomes after spinal cord injury using machine learning. Spinal Cord 2024:10.1038/s41393-024-01008-2. [PMID: 38890506 DOI: 10.1038/s41393-024-01008-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/12/2024] [Accepted: 06/06/2024] [Indexed: 06/20/2024]
Abstract
STUDY DESIGN Retrospective multi-site cohort study. OBJECTIVES To develop an accurate machine learning predictive model using predictor variables from the acute rehabilitation period to determine ambulatory status in spinal cord injury (SCI) one year post injury. SETTING Model SCI System (SCIMS) database between January 2000 and May 2019. METHODS Retrospective cohort study using data that were previously collected as part of the SCI Model System (SCIMS) database. A total of 4523 patients were analyzed comparing traditional models (van Middendorp and Hicks) compared to machine learning algorithms including Elastic Net Penalized Logistic Regression (ENPLR), Gradient Boosted Machine (GBM), and Artificial Neural Networks (ANN). RESULTS Compared with GBM and ANN, ENPLR was determined to be the preferred model based on predictive accuracy metrics, calibration, and variable selection. The primary metric to judge discrimination was the area under the receiver operating characteristic curve (AUC). When compared to the van Middendorp all patients (0.916), ASIA A and D (0.951) and ASIA B and C (0.775) and Hicks all patients (0.89), ASIA A and D (0.934) and ASIA B and C (0.775), ENPLR demonstrated improved AUC for all patients (0.931), ASIA A and D (0.965) ASIA B and C (0.803). CONCLUSIONS Utilizing artificial intelligence and machine learning methods are feasible for accurately classifying outcomes in SCI and may provide improved sensitivity in identifying which individuals are less likely to ambulate and may benefit from augmentative strategies, such as neuromodulation. Future directions should include the use of additional variables to further refine these models.
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Affiliation(s)
- Christina Draganich
- University of Colorado Department of Physical Medicine and Rehabilitation, Aurora, CO, USA.
| | | | | | | | - Jeffrey Berliner
- University of Colorado Department of Physical Medicine and Rehabilitation, Aurora, CO, USA
- Craig Hospital, Englewood, CO, USA
| | | | | | - Andrew Smith
- University of Colorado Department of Physical Medicine and Rehabilitation, Aurora, CO, USA
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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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Maki S, Furuya T, Inoue T, Yunde A, Miura M, Shiratani Y, Nagashima Y, Maruyama J, Shiga Y, Inage K, Eguchi Y, Orita S, Ohtori S. Machine Learning Web Application for Predicting Functional Outcomes in Patients With Traumatic Spinal Cord Injury Following Inpatient Rehabilitation. J Neurotrauma 2024; 41:1089-1100. [PMID: 37917112 DOI: 10.1089/neu.2022.0383] [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: 11/03/2023] Open
Abstract
Accurately predicting functional outcomes in patients with spinal cord injury (SCI) helps clinicians set realistic functional recovery goals and improve the home environment after discharge. The present study aimed to develop and validate machine learning (ML) models to predict functional outcomes in patients with SCI and deploy the models within a web application. The study included data from the Japan Association of Rehabilitation Database from January 1, 1991, to December 31, 2015. Patients with SCI who were admitted to an SCI center or transferred to a participating post-acute rehabilitation hospital after receiving acute treatment were enrolled in this database. The primary outcome was functional ambulation at discharge from the rehabilitation hospital. The secondary outcome was the total motor Functional Independence Measure (FIM) score at discharge. We used binary classification models to predict whether functional ambulation was achieved, as well as regression models to predict total motor FIM scores at discharge. In the training dataset (70% random sample) using demographic characteristics and neurological and functional status as predictors, we built prediction performance matrices of multiple ML models and selected the best one for each outcome. We validated each model's predictive performance in the test dataset (the remaining 30%). Among the 4181 patients, 3827 were included in the prediction model for the total motor FIM score. The mean (standard deviation [SD]) age was 50.4 (18.7) years, and 3211 (83.9%) patients were male. There were 3122 patients included in the prediction model for functional ambulation. The CatBoost Classifier and regressor models showed the best performances in the training dataset. On the test dataset, the CatBoost Classifier had an area under the receiver operating characteristic curve of 0.8572 and an accuracy of 0.7769 for predicting functional ambulation. Likewise, the CatBoost Regressor performed well, with an R2 of 0.7859, a mean absolute error of 9.2957, and a root mean square error of 13.4846 for predicting the total motor FIM score. The final models were deployed in a web application to provide functional predictions. The application can be found at http://3.138.174.54:8501. In conclusion, our prediction models developed using ML successfully predicted functional outcomes in patients with SCI and were deployed in an open-access web application.
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Affiliation(s)
- Satoshi Maki
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | - Takeo Furuya
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Takaki Inoue
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Atsushi Yunde
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Masataka Miura
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yuki Shiratani
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yuki Nagashima
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Juntaro Maruyama
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yasuhiro Shiga
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Kazuhide Inage
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yawara Eguchi
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Sumihisa Orita
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | - Seiji Ohtori
- Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
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Lu T, Lu M, Liu H, Song D, Wang Z, Guo Y, Fang Y, Chen Q, Li T. Establishment of a prognostic model for gastric cancer patients who underwent radical gastrectomy using machine learning: a two-center study. Front Oncol 2024; 13:1282042. [PMID: 38665864 PMCID: PMC11043579 DOI: 10.3389/fonc.2023.1282042] [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: 08/23/2023] [Accepted: 12/21/2023] [Indexed: 04/28/2024] Open
Abstract
Objective Gastric cancer is a prevalent gastrointestinal malignancy worldwide. In this study, a prognostic model was developed for gastric cancer patients who underwent radical gastrectomy using machine learning, employing advanced computational techniques to investigate postoperative mortality risk factors in such patients. Methods Data of 295 patients with gastric cancer who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) between March 2016 and November 2019 were retrospectively analyzed as the training group. Additionally, 109 patients who underwent radical gastrectomy at the Department of General Surgery Affiliated to Jining First People's Hospital (Jining, China) were included for external validation. Four machine learning models, including logistic regression (LR), decision tree (DT), random forest (RF), and gradient boosting machine (GBM), were utilized. Model performance was assessed by comparing the area under the curve (AUC) for each model. An LR-based nomogram model was constructed to assess patients' clinical prognosis. Results Lasso regression identified eight associated factors: age, sex, maximum tumor diameter, nerve or vascular invasion, TNM stage, gastrectomy type, lymphocyte count, and carcinoembryonic antigen (CEA) level. The performance of these models was evaluated using the AUC. In the training group, the AUC values were 0.795, 0.759, 0.873, and 0.853 for LR, DT, RF, and GBM, respectively. In the validation group, the AUC values were 0.734, 0.708, 0.746, and 0.707 for LR, DT, RF, and GBM, respectively. The nomogram model, constructed based on LR, demonstrated excellent clinical prognostic evaluation capabilities. Conclusion Machine learning algorithms are robust performance assessment tools for evaluating the prognosis of gastric cancer patients who have undergone radical gastrectomy. The LR-based nomogram model can aid clinicians in making more reliable clinical decisions.
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Affiliation(s)
- Tong Lu
- Department of Emergency Medicine, Jining No.1 People’s Hospital, Jining, China
| | - Miao Lu
- Wuxi Mental Health Center, Wuxi, China
| | - Haonan Liu
- Department of Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Daqing Song
- Department of Emergency Medicine, Jining No.1 People’s Hospital, Jining, China
| | - Zhengzheng Wang
- Department of Gastroenterology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Yahui Guo
- Department of Gastroenterology, Xuzhou First People’s Hospital, Xuzhou, China
| | - Yu Fang
- Jiangsu Normal University, Xuzhou, China
| | - Qi Chen
- Department of Gastroenterology, Jining First People’s Hospital, Jining, China
| | - Tao Li
- Department of Emergency Medicine, Jining No.1 People’s Hospital, Jining, China
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Pozin M, Najafali D, Naik A, MacInnis B, Subbarao N, Zuckerman SL, Arnold PM. Long-term assessment of the functional independence measure in sports-related spinal cord injury. J Spinal Cord Med 2024; 47:214-228. [PMID: 36977319 PMCID: PMC10885752 DOI: 10.1080/10790268.2023.2167903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/30/2023] Open
Abstract
CONTEXT Patients with spinal cord injury (SCI) secondary to traumatic sports-related etiology potentially face loss of independence. The Functional Independence Measure (FIM) assesses the amount of assistance patients require and has shown sensitivity to changes in patient functional status post injury. OBJECTIVES We aimed to (1) examine long-term outcomes following sports-related SCI (SRSCI) using FIM scoring at the time of injury, one year, and five years post-injury, and (2) determine predictors of independence at one and five-year follow-up considering surgical and non-surgical management. Few studies have investigated the cohort analyzed in this study. METHODS The 1973-2016 National Spinal Cord Injury Model Systems (SCIMS) Database was used to develop a SRSCI cohort. The primary outcome of interest captured functional independence using a multivariate logistic regression, defined by FIM individual scores greater than or equal to six, evaluated at one and five years. RESULTS A total of 491 patients were analyzed, 60 (12%) were female, 452 (92%) underwent surgery. The cohort demographics were stratified by patients with and without spine surgery and evaluated for functional independence in FIM subcategories. Increased time spent in inpatient rehabilitation and FIM score at post-operative discharge were associated with greater likelihood of functional ability at both one and five-year follow-up. CONCLUSION Our study demonstrated that SRSCI patients are a unique subset of SCI patients for whom factors repeatedly associated with independence at one year follow-up were dissimilar to those associated with independence at five-year follow-up. Larger prospective studies should be conducted to establish guidelines for this unique subcategory of SCI patients.
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Affiliation(s)
- Michael Pozin
- Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Daniel Najafali
- Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Anant Naik
- Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Bailey MacInnis
- Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Natasha Subbarao
- Kansas City University College of Medicine, Joplin, Missouri, USA
| | - Scott L. Zuckerman
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Paul M. Arnold
- Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Department of Neurosurgery, Carle Foundation Hospital, Urbana, Illinois, USA
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Hakimjavadi R, Basiratzadeh S, Wai EK, Baddour N, Kingwell S, Michalowski W, Stratton A, Tsai E, Viktor H, Phan P. Multivariable Prediction Models for Traumatic Spinal Cord Injury: A Systematic Review. Top Spinal Cord Inj Rehabil 2024; 30:1-44. [PMID: 38433735 PMCID: PMC10906375 DOI: 10.46292/sci23-00010] [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] [Indexed: 03/05/2024]
Abstract
Background Traumatic spinal cord injuries (TSCI) greatly affect the lives of patients and their families. Prognostication may improve treatment strategies, health care resource allocation, and counseling. Multivariable clinical prediction models (CPMs) for prognosis are tools that can estimate an absolute risk or probability that an outcome will occur. Objectives We sought to systematically review the existing literature on CPMs for TSCI and critically examine the predictor selection methods used. Methods We searched MEDLINE, PubMed, Embase, Scopus, and IEEE for English peer-reviewed studies and relevant references that developed multivariable CPMs to prognosticate patient-centered outcomes in adults with TSCI. Using narrative synthesis, we summarized the characteristics of the included studies and their CPMs, focusing on the predictor selection process. Results We screened 663 titles and abstracts; of these, 21 full-text studies (2009-2020) consisting of 33 distinct CPMs were included. The data analysis domain was most commonly at a high risk of bias when assessed for methodological quality. Model presentation formats were inconsistently included with published CPMs; only two studies followed established guidelines for transparent reporting of multivariable prediction models. Authors frequently cited previous literature for their initial selection of predictors, and stepwise selection was the most frequent predictor selection method during modelling. Conclusion Prediction modelling studies for TSCI serve clinicians who counsel patients, researchers aiming to risk-stratify participants for clinical trials, and patients coping with their injury. Poor methodological rigor in data analysis, inconsistent transparent reporting, and a lack of model presentation formats are vital areas for improvement in TSCI CPM research.
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Affiliation(s)
| | | | - Eugene K. Wai
- University of Ottawa, Ottawa, Ontario, Canada
- The Ottawa Hospital, Ottawa, Ontario, Canada
| | | | - Stephen Kingwell
- University of Ottawa, Ottawa, Ontario, Canada
- The Ottawa Hospital, Ottawa, Ontario, Canada
| | | | - Alexandra Stratton
- University of Ottawa, Ottawa, Ontario, Canada
- The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Eve Tsai
- University of Ottawa, Ottawa, Ontario, Canada
- The Ottawa Hospital, Ottawa, Ontario, Canada
| | | | - Philippe Phan
- University of Ottawa, Ottawa, Ontario, Canada
- The Ottawa Hospital, Ottawa, Ontario, Canada
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Boyles RH, Alexander CM, Belsi A, Strutton PH. Are Clinical Prediction Rules Used in Spinal Cord Injury Care? A Survey of Practice. Top Spinal Cord Inj Rehabil 2024; 30:45-58. [PMID: 38433737 PMCID: PMC10906376 DOI: 10.46292/sci23-00069] [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: 03/05/2024]
Abstract
Background Accurate outcome prediction is desirable post spinal cord injury (SCI), reducing uncertainty for patients and supporting personalized treatments. Numerous attempts have been made to create clinical prediction rules that identify patients who are likely to recover function. It is unknown to what extent these rules are routinely used in clinical practice. Objectives To better understand knowledge of, and attitudes toward, clinical prediction rules amongst SCI clinicians in the United Kingdom. Methods An online survey was distributed via mailing lists of clinical special interest groups and relevant National Health Service Trusts. Respondents answered questions about their knowledge of existing clinical prediction rules and their general attitudes to using them. They also provided information about their level of experience with SCI patients. Results One hundred SCI clinicians completed the survey. The majority (71%) were unaware of clinical prediction rules for SCI; only 8% reported using them in clinical practice. Less experienced clinicians were less likely to be aware. Lack of familiarity with prediction rules was reported as being a barrier to their use. The importance of clinical expertise when making prognostic decisions was emphasized. All respondents reported interest in using clinical prediction rules in the future. Conclusion The results show widespread lack of awareness of clinical prediction rules amongst SCI clinicians in the United Kingdom. However, clinicians were positive about the potential for clinical prediction rules to support decision-making. More focus should be directed toward refining current rules and improving dissemination within the SCI community.
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Affiliation(s)
- Rowan H. Boyles
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Department of Therapies, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Caroline M. Alexander
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Department of Therapies, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Athina Belsi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Paul H. Strutton
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
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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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Lu T, Lu M, Wu D, Ding YY, Liu HN, Li TT, Song DQ. Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study. World J Gastrointest Surg 2024; 16:85-94. [PMID: 38328326 PMCID: PMC10845275 DOI: 10.4240/wjgs.v16.i1.85] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/24/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Gastric cancer is one of the most common malignant tumors in the digestive system, ranking sixth in incidence and fourth in mortality worldwide. Since 42.5% of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type, the application of imaging diagnosis is restricted. AIM To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) algorithms and to evaluate their predictive performance in clinical practice. METHODS Data of a total of 369 patients who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Seven ML models, including decision tree, random forest, support vector machine (SVM), gradient boosting machine, naive Bayes, neural network, and logistic regression, were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML models were established following ten cross-validation iterations using the training dataset, and subsequently, each model was assessed using the test dataset. The models' performance was evaluated by comparing the area under the receiver operating characteristic curve of each model. RESULTS Among the seven ML models, except for SVM, the other ones exhibited higher accuracy and reliability, and the influences of various risk factors on the models are intuitive. CONCLUSION The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer, which can aid in personalized clinical diagnosis and treatment.
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Affiliation(s)
- Tong Lu
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Miao Lu
- Wuxi Mental Health Center, Wuxi 214000, Jiangsu Province, China
| | - Dong Wu
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Yuan-Yuan Ding
- Department of Gastroenterology, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Hao-Nan Liu
- Department of Oncology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, Jiangsu Province, China
| | - Tao-Tao Li
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Da-Qing Song
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
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11
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Adida S, Legarreta AD, Hudson JS, McCarthy D, Andrews E, Shanahan R, Taori S, Lavadi RS, Buell TJ, Hamilton DK, Agarwal N, Gerszten PC. Machine Learning in Spine Surgery: A Narrative Review. Neurosurgery 2024; 94:53-64. [PMID: 37930259 DOI: 10.1227/neu.0000000000002660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/06/2023] [Indexed: 11/07/2023] Open
Abstract
Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.
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Affiliation(s)
- Samuel Adida
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Andrew D Legarreta
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Joseph S Hudson
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - David McCarthy
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Edward Andrews
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Regan Shanahan
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Suchet Taori
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Raj Swaroop Lavadi
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Thomas J Buell
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - D Kojo Hamilton
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Nitin Agarwal
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh , Pennsylvania , USA
| | - Peter C Gerszten
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
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12
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Lu T, Fang Y, Liu H, Chen C, Li T, Lu M, Song D. Comparison of Machine Learning and Logic Regression Algorithms for Predicting Lymph Node Metastasis in Patients with Gastric Cancer: A two-Center Study. Technol Cancer Res Treat 2024; 23:15330338231222331. [PMID: 38190617 PMCID: PMC10775719 DOI: 10.1177/15330338231222331] [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: 08/20/2023] [Revised: 11/01/2023] [Accepted: 11/20/2023] [Indexed: 01/10/2024] Open
Abstract
OBJECTIVES This two-center study aimed to establish a model for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) and logistic regression (LR) algorithms, and to evaluate its predictive performance in clinical practice. METHODS Data of a total of 369 patients who underwent radical gastrectomy in the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy in the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Besides, 7 ML and logistic models were developed, including decision tree, random forest, support vector machine (SVM), gradient boosting machine (GBM), naive Bayes, neural network, and LR, in order to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML model was established following 10 cross-validation iterations within the training dataset, and subsequently, each model was assessed using the test dataset. The model's performance was evaluated by comparing the area under the receiver operating characteristic curve of each model. RESULTS Compared with the traditional logistic model, among the 7 ML algorithms, except for SVM, the other models exhibited higher accuracy and reliability, and the influences of various risk factors on the model were more intuitive. CONCLUSION For the prediction of lymph node metastasis in gastric cancer patients, the ML algorithm outperformed traditional LR, and the GBM algorithm exhibited the most robust predictive capability.
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Affiliation(s)
- Tong Lu
- Department of emergency medicine, Jining No.1 People's Hospital, Jining, China
| | - Yu Fang
- Jiangsu Normal University, Xuzhou, China
| | - Haonan Liu
- Department of Oncology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Chong Chen
- Department of Gastroenterology, Xuzhou No.1 People's Hospital, Xuzhou, China
| | - Taotao Li
- Department of emergency medicine, Jining No.1 People's Hospital, Jining, China
| | - Miao Lu
- Wuxi Mental Health Center, Wuxi, China
| | - Daqing Song
- Department of emergency medicine, Jining No.1 People's Hospital, Jining, China
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13
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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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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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15
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Ma Y, Lu Q, Yuan F, Chen H. Comparison of the effectiveness of different machine learning algorithms in predicting new fractures after PKP for osteoporotic vertebral compression fractures. J Orthop Surg Res 2023; 18:62. [PMID: 36683045 PMCID: PMC9869614 DOI: 10.1186/s13018-023-03551-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND The use of machine learning has the potential to estimate the probability of a second classification event more accurately than traditional statistical methods, and few previous studies on predicting new fractures after osteoporotic vertebral compression fractures (OVCFs) have focussed on this point. The aim of this study was to explore whether several different machine learning models could produce better predictions than logistic regression models and to select an optimal model. METHODS A retrospective analysis of 529 patients who underwent percutaneous kyphoplasty (PKP) for OVCFs at our institution between June 2017 and June 2020 was performed. The patient data were used to create machine learning (including decision trees (DT), random forests (RF), support vector machines (SVM), gradient boosting machines (GBM), neural networks (NNET), and regularized discriminant analysis (RDA)) and logistic regression models (LR) to estimate the probability of new fractures occurring after surgery. The dataset was divided into a training set (75%) and a test set (25%), and machine learning models were built in the training set after ten cross-validations, after which each model was evaluated in the test set, and model performance was assessed by comparing the area under the curve (AUC) of each model. RESULTS Among the six machine learning algorithms, except that the AUC of DT [0.775 (95% CI 0.728-0.822)] was lower than that of LR [0.831 (95% CI 0.783-0.878)], RA [0.953 (95% CI 0.927-0.980)], GBM [0.941 (95% CI 0.911-0.971)], SVM [0.869 (95% CI 0.827-0.910), NNET [0.869 (95% CI 0.826-0.912)], and RDA [0.890 (95% CI 0.851-0.929)] were all better than LR. CONCLUSIONS For prediction of the probability of new fracture after PKP, machine learning algorithms outperformed logistic regression, with random forest having the strongest predictive power.
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Affiliation(s)
- Yiming Ma
- Department of Orthopaedic Surgery, Affiliated Hospital of Xuzhou Medical University, 99 Huaihai Road, Xuzhou, 221006 Jiangsu China
- Xuzhou Medical University, 209 Tongshan Road, Xuzhou, 221004 Jiangsu China
| | - Qi Lu
- Department of Orthopaedic Surgery, Affiliated Hospital of Xuzhou Medical University, 99 Huaihai Road, Xuzhou, 221006 Jiangsu China
- Xuzhou Medical University, 209 Tongshan Road, Xuzhou, 221004 Jiangsu China
| | - Feng Yuan
- Department of Orthopaedic Surgery, Affiliated Hospital of Xuzhou Medical University, 99 Huaihai Road, Xuzhou, 221006 Jiangsu China
| | - Hongliang Chen
- Department of Orthopaedic Surgery, Affiliated Hospital of Xuzhou Medical University, 99 Huaihai Road, Xuzhou, 221006 Jiangsu China
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16
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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: 1] [Impact Index Per Article: 0.5] [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: 9] [Impact Index Per Article: 4.5] [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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18
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Fan G, Yang S, Liu H, Xu N, Chen Y, He J, Su X, Pang M, Liu B, Han L, Rong L. Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury. Spine (Phila Pa 1976) 2022; 47:E390-E398. [PMID: 34690328 DOI: 10.1097/brs.0000000000004267] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
STUDY DESIGN A retrospective cohort study. OBJECTIVE The objective of the study was to develop machine-learning (ML) classifiers for predicting prolonged intensive care unit (ICU)-stay and prolonged hospital-stay for critical patients with spinal cord injury (SCI). SUMMARY OF BACKGROUND DATA Critical patients with SCI in ICU need more attention. SCI patients with prolonged stay in ICU usually occupy vast medical resources and hinder the rehabilitation deployment. METHODS A total of 1599 critical patients with SCI were included in the study and labeled with prolonged stay or normal stay. All data were extracted from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care III-IV Database. The extracted data were randomly divided into training, validation and testing (6:2:2) subdatasets. A total of 91 initial ML classifiers were developed, and the top three initial classifiers with the best performance were further stacked into an ensemble classifier with logistic regressor. The area under the curve (AUC) was the main indicator to assess the prediction performance of all classifiers. The primary predicting outcome was prolonged ICU-stay, while the secondary predicting outcome was prolonged hospital-stay. RESULTS In predicting prolonged ICU-stay, the AUC of the ensemble classifier was 0.864 ± 0.021 in the three-time five-fold cross-validation and 0.802 in the independent testing. In predicting prolonged hospital-stay, the AUC of the ensemble classifier was 0.815 ± 0.037 in the three-time five-fold cross-validation and 0.799 in the independent testing. Decision curve analysis showed the merits of the ensemble classifiers, as the curves of the top three initial classifiers varied a lot in either predicting prolonged ICU-stay or discriminating prolonged hospital-stay. CONCLUSION The ensemble classifiers successfully predict the prolonged ICU-stay and the prolonged hospital-stay, which showed a high potential of assisting physicians in managing SCI patients in ICU and make full use of medical resources.Level of Evidence: 3.
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Affiliation(s)
- Guoxin Fan
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yatsen University, Guangzhou, China
- Intelligent and Digital Surgery Innovation Center, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, China
| | - Sheng Yang
- Department of Orthopedics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Huaqing Liu
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China
| | - Ningze Xu
- Tongji University School of Medicine, Shanghai, P. R. China
| | - Yuyong Chen
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yatsen University, Guangzhou, China
- Intelligent and Digital Surgery Innovation Center, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, China
| | - Jie He
- Intelligent and Digital Surgery Innovation Center, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, China
| | - Xiuyun Su
- Intelligent and Digital Surgery Innovation Center, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, China
| | - Mao Pang
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yatsen University, Guangzhou, China
| | - Bin Liu
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yatsen University, Guangzhou, China
| | - Lanqing Han
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China
| | - Limin Rong
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yatsen University, Guangzhou, China
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Doerr SA, Weber-Levine C, Hersh AM, Awosika T, Judy B, Jin Y, Raj D, Liu A, Lubelski D, Jones CK, Sair HI, Theodore N. Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm. Neurosurg Focus 2022; 52:E5. [PMID: 35364582 DOI: 10.3171/2022.1.focus21745] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/18/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Damage to the thoracolumbar spine can confer significant morbidity and mortality. The Thoracolumbar Injury Classification and Severity Score (TLICS) is used to categorize injuries and determine patients at risk of spinal instability for whom surgical intervention is warranted. However, calculating this score can constitute a bottleneck in triaging and treating patients, as it relies on multiple imaging studies and a neurological examination. Therefore, the authors sought to develop and validate a deep learning model that can automatically categorize vertebral morphology and determine posterior ligamentous complex (PLC) integrity, two critical features of TLICS, using only CT scans. METHODS All patients who underwent neurosurgical consultation for traumatic spine injury or degenerative pathology resulting in spine injury at a single tertiary center from January 2018 to December 2019 were retrospectively evaluated for inclusion. The morphology of injury and integrity of the PLC were categorized on CT scans. A state-of-the-art object detection region-based convolutional neural network (R-CNN), Faster R-CNN, was leveraged to predict both vertebral locations and the corresponding TLICS. The network was trained with patient CT scans, manually labeled vertebral bounding boxes, TLICS morphology, and PLC annotations, thus allowing the model to output the location of vertebrae, categorize their morphology, and determine the status of PLC integrity. RESULTS A total of 111 patients were included (mean ± SD age 62 ± 20 years) with a total of 129 separate injury classifications. Vertebral localization and PLC integrity classification achieved Dice scores of 0.92 and 0.88, respectively. Binary classification between noninjured and injured morphological scores demonstrated 95.1% accuracy. TLICS morphology accuracy, the true positive rate, and positive injury mismatch classification rate were 86.3%, 76.2%, and 22.7%, respectively. Classification accuracy between no injury and suspected PLC injury was 86.8%, while true positive, false negative, and false positive rates were 90.0%, 10.0%, and 21.8%, respectively. CONCLUSIONS In this study, the authors demonstrate a novel deep learning method to automatically predict injury morphology and PLC disruption with high accuracy. This model may streamline and improve diagnostic decision support for patients with thoracolumbar spinal trauma.
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Affiliation(s)
- Sophia A Doerr
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Carly Weber-Levine
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Andrew M Hersh
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Tolulope Awosika
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Brendan Judy
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Yike Jin
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Divyaansh Raj
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Ann Liu
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Daniel Lubelski
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Craig K Jones
- 2Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore; and
| | - Haris I Sair
- 3Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Nicholas Theodore
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
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Liu WC, Ying H, Liao WJ, Li MP, Zhang Y, Luo K, Sun BL, Liu ZL, Liu JM. Using preoperative and intraoperative factors to predict the risk of surgical site infections after lumbar spinal surgery: a machine learning-based study. World Neurosurg 2022; 162:e553-e560. [DOI: 10.1016/j.wneu.2022.03.060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 03/13/2022] [Accepted: 03/14/2022] [Indexed: 10/18/2022]
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21
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Cathomen A, Sirucek L, Killeen T, Abel R, Maier D, Weidner N, Rupp R, Hothorn T, Steeves JD, Curt A, Bolliger M. Inclusive Trial Designs in Acute Spinal Cord Injuries: Prediction-Based Stratification of Clinical Walking Outcome and Projected Enrolment Frequencies. Neurorehabil Neural Repair 2022; 36:274-285. [PMID: 35164574 PMCID: PMC9003761 DOI: 10.1177/15459683221078302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background New therapeutic approaches in neurological disorders are progressing into clinical development. Past failures in translational research have underlined the critical importance of selecting appropriate inclusion criteria and primary outcomes. Narrow inclusion criteria provide sensitivity, but increase trial duration and cost to the point of infeasibility, while broader requirements amplify confounding, increasing the risk of trial failure. This dilemma is perhaps most pronounced in spinal cord injury (SCI), but applies to all neurological disorders with low frequency and/or heterogeneous clinical manifestations. Objective Stratification of homogeneous patient cohorts to enable the design of clinical trials with broad inclusion criteria. Methods Prospectively–gathered data from patients with acute cervical SCI were analysed using an unbiased recursive partitioning conditional inference tree (URP–CTREE) approach. Performance in the 6-minute walk test at 6 months after injury was classified based on standardized neurological assessments within the first 15 days of injury. Functional and neurological outcomes were tracked throughout rehabilitation up to 6 months after injury. Results URP–CTREE identified homogeneous outcome cohorts in a study group of 309 SCI patients. These cohorts were validated by an internal, yet independent, validation group of 172 patients. The study group cohorts identified demonstrated distinct recovery profiles throughout rehabilitation. The baseline characteristics of the analysed groups were compared to a reference group of 477 patients. Conclusion URP–CTREE enables inclusive trial design by revealing the distribution of outcome cohorts, discerning distinct recovery profiles and projecting potential patient enrolment by providing estimates of the relative frequencies of cohorts to improve the design of clinical trials in SCI and beyond.
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Affiliation(s)
- Adrian Cathomen
- Spinal Cord Injury Center, 31031Balgrist University Hospital, Zurich, Switzerland.,ETH Zurich, Zurich, Switzerland.,Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
| | - Laura Sirucek
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland.,Integrative Spinal Research, Department of Chiropractic Medicine, 31031Balgrist University Hospital, University of Zurich, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
| | - Tim Killeen
- Spinal Cord Injury Center, 31031Balgrist University Hospital, Zurich, Switzerland
| | - Rainer Abel
- Trauma Center Bayreuth, Bayreuth, Germany.,EMSCI Study Group
| | - Doris Maier
- EMSCI Study Group.,Trauma Center Murnau, Murnau, Germany
| | - Norbert Weidner
- EMSCI Study Group.,Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Rüdiger Rupp
- EMSCI Study Group.,Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Torsten Hothorn
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - John D Steeves
- ICORD, Blusson Spinal Cord Centre, University of British Columbia and Vancouver Coastal Health, Vancouver, BC, Canada
| | - Armin Curt
- Spinal Cord Injury Center, 31031Balgrist University Hospital, Zurich, Switzerland.,Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland.,EMSCI Study Group
| | - Marc Bolliger
- Spinal Cord Injury Center, 31031Balgrist University Hospital, Zurich, Switzerland.,Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland.,EMSCI Study Group
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22
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Hori T, Imura T, Tanaka R. Development of a clinical prediction rule for patients with cervical spinal cord injury who have difficulty in obtaining independent living. Spine J 2022; 22:321-328. [PMID: 34487911 DOI: 10.1016/j.spinee.2021.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 08/27/2021] [Accepted: 08/27/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND CONTEXT A simple and easy to use clinical prediction rule (CPR) to detect patients with a cervical spinal cord injury (SCI) who would have difficulty in obtaining independent living status is vital for providing the optimal rehabilitation and education in both care recipients and caregivers. A machine learning approach was recently applied to the field of rehabilitation and has the possibility to develop an accurate and useful CPR. PURPOSE The aim of this study was to develop and assess a CPR using a decision tree algorithm for predicting which patients with a cervical SCI would have difficulty in obtaining an independent living. STUDY DESIGN The present study was a cohort study. PATIENT SAMPLE In the present study, the data was obtained from the nationwide Japan Rehabilitation Database (JRD). The data on the SCIs was collected from 10 hospitals and the data was collected from the registries obtained between 2005 and 2015. The severity of SCI can vary, and patient prognosis differs depending on the damage site. In this study, the patients with cervical SCI were included. OUTCOME MEASURES In this study, the degree of the independent living at discharge was investigated. The degree of the independent living was classified and listed as below: independent in social, independent at home, need care at home, independent at facility, need care at facility. In this study, the independent in social and independent at home were defined as "independent," and the other situations were defined as "non-independent." METHODS We performed a classification and regression tree (CART) analysis to develop the CPR to predict whether the cervical SCI patients obtain an independent living at discharge. The area under the curve, the classification accuracy, sensitivity, specificity, and positive predictive value were used for model evaluation. RESULTS A total of 4181 patients with SCI were registered in the JRD and the CART analysis was performed for 1282 patients with the cervical SCI. The Functional Independence Measure (FIM) total score and the American Spinal Injury Association impairment scale were identified as the first and second discriminators for predicting the degree of the independence, respectively. Subsequently, the CART model identified FIM eating, the residual function level, and the FIM bed to chair transfer as next discriminators. Each parameter for evaluating the CART model were the area under the curve 0.813, the classification accuracy 78.6%, the sensitivity 80.7%, the specificity 75.1%, and the positive predictive value 84.5%. CONCLUSIONS In this study, we developed a clinically useful CPR with moderate accuracy to predict whether the cervical SCI patients obtain independent living at the discharge.
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Affiliation(s)
- Tomonari Hori
- Department of Rehabilitation, Fukuyama Rehabilitation Hospital, 2-15-41, Myojincho, Fukuyama 721-0961, Japan
| | - Takeshi Imura
- Department of Rehabilitation, Faculty of Health Sciences, Hiroshima Cosmopolitan University, 3-2-1, Otsuka-higashi, Hiroshima 731-3166, Japan; Graduate School of Humanities and Social Sciences, Hiroshima University, 1-3-2, Kagamiyama, Higashihiroshima 739-8511, Japan.
| | - Ryo Tanaka
- Graduate School of Humanities and Social Sciences, Hiroshima University, 1-3-2, Kagamiyama, Higashihiroshima 739-8511, Japan
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23
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Fallah N, Noonan VK, Waheed Z, Rivers CS, Plashkes T, Bedi M, Etminan M, Thorogood NP, Ailon T, Chan E, Dea N, Fisher C, Charest-Morin R, Paquette S, Park S, Street JT, Kwon BK, Dvorak MF. Development of a machine learning algorithm for predicting in-hospital and 1-year mortality after traumatic spinal cord injury. Spine J 2022; 22:329-336. [PMID: 34419627 DOI: 10.1016/j.spinee.2021.08.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/15/2021] [Accepted: 08/12/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND CONTEXT Current prognostic tools such as the Injury Severity Score (ISS) that predict mortality following trauma do not adequately consider the unique characteristics of traumatic spinal cord injury (tSCI). PURPOSE Our aim was to develop and validate a prognostic tool that can predict mortality following tSCI. STUDY DESIGN Retrospective review of a prospective cohort study. PATIENT SAMPLE Data was collected from 1245 persons with acute tSCI who were enrolled in the Rick Hansen Spinal Cord Injury Registry between 2004 and 2016. OUTCOME MEASURES In-hospital and 1-year mortality following tSCI. METHODS Machine learning techniques were used on patient-level data (n=849) to develop the Spinal Cord Injury Risk Score (SCIRS) that can predict mortality based on age, neurological level and completeness of injury, AOSpine classification of spinal column injury morphology, and Abbreviated Injury Scale scores. Validation of the SCIRS was performed by testing its accuracy in an independent validation cohort (n=396) and comparing its performance to the ISS, a measure which is used to predict mortality following general trauma. RESULTS For 1-year mortality prediction, the values for the Area Under the Receiver Operating Characteristic Curve (AUC) for the development cohort were 0.84 (standard deviation=0.029) for the SCIRS and 0.55 (0.041) for the ISS. For the validation cohort, AUC values were 0.86 (0.051) for the SCIRS and 0.71 (0.074) for the ISS. For in-hospital mortality, AUC values for the development cohort were 0.87 (0.028) and 0.60 (0.050) for the SCIRS and ISS, respectively. For the validation cohort, AUC values were 0.85 (0.054) for the SCIRS and 0.70 (0.079) for the ISS. CONCLUSIONS The SCIRS can predict in-hospital and 1-year mortality following tSCI more accurately than the ISS. The SCIRS can be used in research to reduce bias in estimating parameters and can help adjust for coefficients during model development. Further validation using larger sample sizes and independent datasets is needed to assess its reliability and to evaluate using it as an assessment tool to guide clinical decision-making and discussions with patients and families.
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Affiliation(s)
- Nader Fallah
- Praxis Spinal Cord Institute, 6400-818 West 10th Ave, Vancouver, British Columbia, V5Z 1M9, Canada; Division of Neurology, Department of Medicine, University of British Columbia, Koerner Pavilion, UBC Hospital, S192 - 2211 Wesbrook Mall, V6T 2B5, Vancouver, British Columbia, Canada
| | - Vanessa K Noonan
- Praxis Spinal Cord Institute, 6400-818 West 10th Ave, Vancouver, British Columbia, V5Z 1M9, Canada.
| | - Zeina Waheed
- Praxis Spinal Cord Institute, 6400-818 West 10th Ave, Vancouver, British Columbia, V5Z 1M9, Canada
| | - Carly S Rivers
- Praxis Spinal Cord Institute, 6400-818 West 10th Ave, Vancouver, British Columbia, V5Z 1M9, Canada
| | - Tova Plashkes
- Praxis Spinal Cord Institute, 6400-818 West 10th Ave, Vancouver, British Columbia, V5Z 1M9, Canada
| | - Manekta Bedi
- Praxis Spinal Cord Institute, 6400-818 West 10th Ave, Vancouver, British Columbia, V5Z 1M9, Canada
| | - Mahyar Etminan
- Department of Ophthalmology and Visual Sciences, University of British Columbia, 2329 West Mall, Vancouver, British Columbia, V6T 1Z4, Canada
| | - Nancy P Thorogood
- Praxis Spinal Cord Institute, 6400-818 West 10th Ave, Vancouver, British Columbia, V5Z 1M9, Canada
| | - Tamir Ailon
- Department of Orthopaedics, University of British Columbia, Gordon and Leslie Diamond Health Care Centre, 11th Floor - 2775 Laurel Street, Vancouver, British Columbia, Canada, V5Z 1M9
| | - Elaine Chan
- Praxis Spinal Cord Institute, 6400-818 West 10th Ave, Vancouver, British Columbia, V5Z 1M9, Canada
| | - Nicolas Dea
- Department of Orthopaedics, University of British Columbia, Gordon and Leslie Diamond Health Care Centre, 11th Floor - 2775 Laurel Street, Vancouver, British Columbia, Canada, V5Z 1M9
| | - Charles Fisher
- Department of Orthopaedics, University of British Columbia, Gordon and Leslie Diamond Health Care Centre, 11th Floor - 2775 Laurel Street, Vancouver, British Columbia, Canada, V5Z 1M9
| | - Raphaele Charest-Morin
- Department of Orthopaedics, University of British Columbia, Gordon and Leslie Diamond Health Care Centre, 11th Floor - 2775 Laurel Street, Vancouver, British Columbia, Canada, V5Z 1M9
| | - Scott Paquette
- Department of Orthopaedics, University of British Columbia, Gordon and Leslie Diamond Health Care Centre, 11th Floor - 2775 Laurel Street, Vancouver, British Columbia, Canada, V5Z 1M9
| | - SoEyun Park
- Praxis Spinal Cord Institute, 6400-818 West 10th Ave, Vancouver, British Columbia, V5Z 1M9, Canada
| | - John T Street
- Department of Orthopaedics, University of British Columbia, Gordon and Leslie Diamond Health Care Centre, 11th Floor - 2775 Laurel Street, Vancouver, British Columbia, Canada, V5Z 1M9
| | - Brian K Kwon
- Department of Orthopaedics, University of British Columbia, Gordon and Leslie Diamond Health Care Centre, 11th Floor - 2775 Laurel Street, Vancouver, British Columbia, Canada, V5Z 1M9
| | - Marcel F Dvorak
- Department of Orthopaedics, University of British Columbia, Gordon and Leslie Diamond Health Care Centre, 11th Floor - 2775 Laurel Street, Vancouver, British Columbia, Canada, V5Z 1M9
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Li W, Wang J, Liu W, Xu C, Li W, Zhang K, Su S, Li R, Hu Z, Liu Q, Lu R, Yin C. Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty. Front Public Health 2021; 9:812023. [PMID: 34957041 PMCID: PMC8702729 DOI: 10.3389/fpubh.2021.812023] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 11/17/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Bone cement leakage is a common complication of percutaneous vertebroplasty and it could be life-threatening to some extent. The aim of this study was to develop a machine learning model for predicting the risk of cement leakage in patients with osteoporotic vertebral compression fractures undergoing percutaneous vertebroplasty. Furthermore, we developed an online calculator for clinical application. Methods: This was a retrospective study including 385 patients, who had osteoporotic vertebral compression fracture disease and underwent surgery at the Department of Spine Surgery, Liuzhou People's Hospital from June 2016 to June 2018. Combing the patient's clinical characteristics variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision Tree (DT) and Multilayer perceptron (MLP), which could predict the risk of bone cement leakage. We tested the results with ten-fold cross-validation, which calculated the Area Under Curve (AUC) of the six models and selected the model with the highest AUC as the excellent performing model to build the web calculator. Results: The results showed that Injection volume of bone cement, Surgery time and Multiple vertebral fracture were all independent predictors of bone cement leakage by using multivariate logistic regression analysis in the 385 observation subjects. Furthermore, Heatmap revealed the relative proportions of the 15 clinical variables. In bone cement leakage prediction, the AUC of the six ML algorithms ranged from 0.633 to 0.898, while the RF model had an AUC of 0.898 and was used as the best performing ML Web calculator (https://share.streamlit.io/liuwencai0/pvp_leakage/main/pvp_leakage) was developed to estimate the risk of bone cement leakage that each patient undergoing vertebroplasty. Conclusion: It achieved a good prediction for the occurrence of bone cement leakage with our ML model. The Web calculator concluded based on RF model can help orthopedist to make more individual and rational clinical strategies.
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Affiliation(s)
- Wenle Li
- Department of Orthopedics, Xianyang Central Hospital, Xianyang, China
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Jiaming Wang
- Department of Orthopedics, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Wencai Liu
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chan Xu
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
- Department of Dermatology, Xianyang Central Hospital, Xianyang, China
| | - Wanying Li
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Kai Zhang
- Department of Orthopedics, Xianyang Central Hospital, Xianyang, China
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | | | - Rong Li
- The First Affiliated Hospital, Shaanxi University of Traditional Chinese Medicine, Xianyang, China
| | - Zhaohui Hu
- Department of Spine Surgery, Liuzhou People's Hospital, Liuzhou, China
| | - Qiang Liu
- Department of Orthopedics, Xianyang Central Hospital, Xianyang, China
| | - Ruogu Lu
- Department of Electronic and Computer Science, University of Southampton, Southampton, United Kingdom
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
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25
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Yang F, Guo X. Research on Rehabilitation Effect Prediction for Patients with SCI Based on Machine Learning. World Neurosurg 2021; 158:e662-e674. [PMID: 34793992 DOI: 10.1016/j.wneu.2021.11.040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Due to the complex condition of Spinal Cord Injury (SCI) patients, it is difficult to accurately calculate the Activity of Daily Living (ADL) score of discharged patients. In view of the above problem, this research proposes a prediction model of discharged ADL score based on machine learning, in order to get the rehabilitation effect of patients after rehabilitation training. METHODS Firstly, the medical records of 1231 SCI patients were collected, and the corresponding data preprocessing was carried out. Secondly, Pearson Correlation Coefficient method (PCC) was combined with feature selection method based on Random Forest (RF) to screen out six features closely related to discharged ADL score. Then RF and RF optimized by Harris Hawks Optimizer (HHO-RF) were used to predict discharged ADL score of SCI patients. The Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Coefficient of determination () were used as evaluation indicators of the model. RESULTS The prediction features selected by feature extraction were ADL score on admission, age, injury segment, injury reason, injury position, and injury degree. After 10-fold cross-validation, MAE, RMSE and of RF were 0.0875, 0.1346 and 0.7662. MAE, RMSE and of HHO-RF were 0.0821, 0.1089 and 0.8537. The prediction effect of HHO-RF has been greatly improved. CONCLUSIONS In clinical treatment, HHO-RF can accurately predict discharged ADL score and provide a reasonable direction for patients to choose rehabilitation programs.
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Affiliation(s)
- Fei Yang
- School of Artificial Intelligence and Data Science, Hebei University of Technology, No. 8 Guangrong Road, HongQiao, Tianjin 300130, China; Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Tianjin 300130, China
| | - Xin Guo
- School of Artificial Intelligence and Data Science, Hebei University of Technology, No. 8 Guangrong Road, HongQiao, Tianjin 300130, China; Qinhuangdao Institute of Rehabilitation Technical Aids, NRRA, Qinhuangdao 066000, Hebei, China; Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Tianjin 300130, China.
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26
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Engel-Haber E, Radomislensky I, Peleg K, Bodas M, Bondi M, Noy S, Zeilig G. Early Trauma Predictors of Mobility in People with Spinal Cord Injury. Spine (Phila Pa 1976) 2021; 46:E1089-E1096. [PMID: 33813583 DOI: 10.1097/brs.0000000000004053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
STUDY DESIGN A retrospective cohort study. OBJECTIVE This study aims to assess the potential value of very early trauma variables such as Abbreviated Injury Scale (AIS) and the Injury Severity Score for predicting independent ambulation following a traumatic spinal cord injury (TSCI). SUMMARY OF BACKGROUND DATA Several models for prediction of ambulation early after TSCI have been published and validated. The vast majority rely on the initial examination of American Spinal Injury Association (ASIA) impairment scale and level of injury; however, in many locations and clinical situations this examination is not feasible early after the injury. METHODS Patient characteristics, trauma data, and ASIA scores on admission to rehabilitation were collected for each of the 144 individuals in the study. Outcome measure was the indoor mobility item of the Spinal Cord Independence Measure taken upon discharge from rehabilitation. Univariate and multivariable models were created for each predictor, Odds ratios (ORs) were obtained by a multivariable logistic regression analysis, and area under the receiver operator curve was calculated for each model. RESULTS We observed a significant correlation between the trauma variables and independent ambulation upon discharge from rehabilitation. Of the early variables, the AIS for the spine region showed the strongest correlation. CONCLUSION These findings support using preliminary trauma variables for early prognostication of ambulation following a TSCI, allowing for tailored individual interventions.Level of Evidence: 3.
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Affiliation(s)
- Einat Engel-Haber
- Department of Neurological Rehabilitation, The Chaim Sheba Medical Center, Tel Hashomer, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Irina Radomislensky
- Israel National Centre for Trauma and Emergency Medicine Research, The Gertner institute for Epidemiology and Health Policy Research, The Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Kobi Peleg
- Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
- Israel National Centre for Trauma and Emergency Medicine Research, The Gertner institute for Epidemiology and Health Policy Research, The Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Moran Bodas
- Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
- Israel National Centre for Trauma and Emergency Medicine Research, The Gertner institute for Epidemiology and Health Policy Research, The Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Moshe Bondi
- Department of Neurological Rehabilitation, The Chaim Sheba Medical Center, Tel Hashomer, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Shlomo Noy
- The Chaim Sheba Medical Center, Tel Hashomer, Israel
- School of Health Professions, Ono Academic College, Kiryat Ono, Israel
| | - Gabi Zeilig
- Department of Neurological Rehabilitation, The Chaim Sheba Medical Center, Tel Hashomer, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
- School of Health Professions, Ono Academic College, Kiryat Ono, Israel
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27
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Azad TD, Ehresman J, Ahmed AK, Staartjes VE, Lubelski D, Stienen MN, Veeravagu A, Ratliff JK. Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery. Spine J 2021; 21:1610-1616. [PMID: 33065274 DOI: 10.1016/j.spinee.2020.10.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/13/2020] [Accepted: 10/07/2020] [Indexed: 02/03/2023]
Abstract
As the use of machine learning algorithms in the development of clinical prediction models has increased, researchers are becoming more aware of the deleterious effects that stem from the lack of reporting standards. One of the most obvious consequences is the insufficient reproducibility found in current prediction models. In an attempt to characterize methods to improve reproducibility and to allow for better clinical performance, we utilize a previously proposed taxonomy that separates reproducibility into 3 components: technical, statistical, and conceptual reproducibility. By following this framework, we discuss common errors that lead to poor reproducibility, highlight the importance of generalizability when evaluating a ML model's performance, and provide suggestions to optimize generalizability to ensure adequate performance. These efforts are a necessity before such models are applied to patient care.
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Affiliation(s)
- Tej D Azad
- Department of Neurosurgery, Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, MD, USA 21287
| | - Jeff Ehresman
- Department of Neurosurgery, Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, MD, USA 21287
| | - Ali Karim Ahmed
- Department of Neurosurgery, Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, MD, USA 21287
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Clinical Neuroscience Centre, University of Zurich, Switzerland; Department of Neurosurgery, University Hospital Zurich, Zurich, Switzerland
| | - Daniel Lubelski
- Department of Neurosurgery, Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, MD, USA 21287
| | - Martin N Stienen
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Clinical Neuroscience Centre, University of Zurich, Switzerland; Department of Neurosurgery, University Hospital Zurich, Zurich, Switzerland
| | - Anand Veeravagu
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - John K Ratliff
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
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SMART on FHIR in spine: integrating clinical prediction models into electronic health records for precision medicine at the point of care. Spine J 2021; 21:1649-1651. [PMID: 32599144 PMCID: PMC7762727 DOI: 10.1016/j.spinee.2020.06.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 02/03/2023]
Abstract
Recent applications of artificial intelligence have shown great promise for improving the quality and efficiency of clinical care. Numerous clinical decision support tools exist in today's electronic health records (EHRs) such as medication dosing support, order facilitators (eg, procedure specific order sets), and point of care alerts. However, less has been done to integrate artificial intelligence (AI)-enabled risk predictors into EHRs despite wide availability of validated risk prediction tools. An interoperability standard known as SMART on FHIR (substitutable medical applications and reusable technologies on fast health interoperability resources) offers a promising path forward, enabling digital innovations to be seamlessly integrated with the EHR with regard to the user interface and patient data. For the next step in progress towards the goal of learning healthcare and informatics-enabled spine surgery, we propose the application of SMART on FHIR to integrate existing and new risk predictions tools in spine surgery through an EHR add-on-application.
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The potential of prediction models of functioning remains to be fully exploited: A scoping review in the field of spinal cord injury rehabilitation. J Clin Epidemiol 2021; 139:177-190. [PMID: 34329726 DOI: 10.1016/j.jclinepi.2021.07.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/29/2021] [Accepted: 07/22/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE The study aimed to explore existing prediction models of functioning in spinal cord injury (SCI). STUDY DESIGN AND SETTING The databases PubMed, EBSCOhost CINAHL Complete, and IEEE Xplore were searched for relevant literature. The search strategy included published search filters for prediction model and impact studies, index terms and keywords for SCI, and relevant outcome measures able to assess functioning as reflected in the International Classification of Functioning, Disability and Health (ICF). The search was completed in October 2020. RESULTS We identified seven prediction model studies reporting twelve prediction models of functioning. The identified prediction models were mainly envisioned to be used for rehabilitation planning, however, also other possible applications were stated. The method predominantly used was regression analysis and the investigated predictors covered mainly the ICF-components of body functions and activities and participation, next to characteristics of the health condition and health interventions. CONCLUSION Findings suggest that the development of prediction models of functioning for use in clinical practice remains to be fully exploited. By providing a comprehensive overview of what has been done, this review informs future research on prediction models of functioning in SCI and contributes to an efficient use of research evidence.
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30
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Schading S, Emmenegger TM, Freund P. Improving Diagnostic Workup Following Traumatic Spinal Cord Injury: Advances in Biomarkers. Curr Neurol Neurosci Rep 2021; 21:49. [PMID: 34268621 PMCID: PMC8282571 DOI: 10.1007/s11910-021-01134-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE OF REVIEW Traumatic spinal cord injury (SCI) is a life-changing event with drastic implications for patients due to sensorimotor impairment and autonomous dysfunction. Current clinical evaluations focus on the assessment of injury level and severity using standardized neurological examinations. However, they fail to predict individual trajectories of recovery, which highlights the need for the development of advanced diagnostics. This narrative review identifies recent advances in the search of clinically relevant biomarkers in the field of SCI. RECENT FINDINGS Advanced neuroimaging and molecular biomarkers sensitive to the disease processes initiated by the SCI have been identified. These biomarkers range from advanced neuroimaging techniques, neurophysiological readouts, and molecular biomarkers identifying the concentrations of several proteins in blood and CSF samples. Some of these biomarkers improve current prediction models based on clinical readouts. Validation with larger patient cohorts is warranted. Several biomarkers have been identified-ranging from imaging to molecular markers-that could serve as advanced diagnostic and hence supplement current clinical assessments.
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Affiliation(s)
- Simon Schading
- Spinal Cord Injury Centre, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008, Zurich, Switzerland
| | - Tim M Emmenegger
- Spinal Cord Injury Centre, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008, Zurich, Switzerland
| | - Patrick Freund
- Spinal Cord Injury Centre, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008, Zurich, Switzerland.
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DeVries Z, Locke E, Hoda M, Moravek D, Phan K, Stratton A, Kingwell S, Wai EK, Phan P. Using a national surgical database to predict complications following posterior lumbar surgery and comparing the area under the curve and F1-score for the assessment of prognostic capability. Spine J 2021; 21:1135-1142. [PMID: 33601012 DOI: 10.1016/j.spinee.2021.02.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 01/11/2021] [Accepted: 02/11/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND With spinal surgery rates increasing in North America, models that are able to accurately predict which patients are at greater risk of developing complications are highly warranted. However, the previously published methods which have used large, multi-centre databases to develop their prediction models have relied on the receiver operator characteristics curve with the associated area under the curve (AUC) to assess their model's performance. Recently, it has been found that a precision-recall curve with the associated F1-score could provide a more realistic analysis for these models. PURPOSE To develop a logistic regression (LR) model for the prediction of complications following posterior lumbar spine surgery and to then assess for any difference in performance of the model when using the AUC versus the F1-score. STUDY DESIGN Retrospective review of a prospective cohort. PATIENT SAMPLE The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) registry was used. All patients that underwent posterior lumbar spine surgery between 2005 to 2016 with appropriate data were included. OUTCOME MEASURES Both the AUC and F1-score were utilized to assess the prognostic performance of the prediction model. METHODS In order to develop the LR model used to predict a complication during or following spine surgery, 19 variables were selected by three orthopedic spine surgeons from the NSQIP registry. Two datasets were developed for this analysis: (1) an imbalanced dataset, which was taken directly from the NSQIP registry, and (2) a down-sampled set. The purpose of the down-sampled set was to balance the data in order to evaluate whether balancing the data had an effect on model performance. The AUC and F1-score were applied to both of these datasets. RESULTS Within the NSQIP database, 52,787 spine surgery cases were identified of which only 10% of these cases had complications during surgery. Applying the LR model showed a large difference between the AUC (0.69) and the F1 score (0.075) on the imbalanced dataset. However, no major differences existed between the AUC and F1-score when the data was balanced and the LR model was reapplied (0.69 and 0.62, AUC and F1-score, respectively). CONCLUSIONS The F1-score detected a drastically lower performance for the prediction of complications when using the imbalanced data, but detected a performance similar to the AUC level when balancing techniques were utilized for the dataset. This difference is due to a low precision score when many false positive classifications are present, which is not identified when using the AUC value. This lowers the utility of the AUC score, as many of the datasets used in medicine are imbalanced. Therefore, we recommend using the F1-score on large, prospective databases when the data is imbalanced with a large amount of true negative classifications.
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Affiliation(s)
- Zachary DeVries
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9
| | - Eric Locke
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9
| | - Mohamad Hoda
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9
| | - Dita Moravek
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9; Ottawa Hospital Research Institute, Ottawa, ON, Canada K1Y 4E9
| | - Kim Phan
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9
| | - Alexandra Stratton
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9; Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, ON, Canada K1Y 4E9
| | - Stephen Kingwell
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9; Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, ON, Canada K1Y 4E9
| | - Eugene K Wai
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9; Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, ON, Canada K1Y 4E9; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada K1Y 4E9
| | - Philippe Phan
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9; Ottawa Hospital Research Institute, Ottawa, ON, Canada K1Y 4E9; Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, ON, Canada K1Y 4E9.
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Murphy MP, Brown NM. CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning? Clin Orthop Relat Res 2021; 479:1497-1505. [PMID: 33595930 PMCID: PMC8208440 DOI: 10.1097/corr.0000000000001679] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/22/2021] [Indexed: 01/31/2023]
Affiliation(s)
- Michael P Murphy
- Department of Orthopaedic Surgery and Rehabilitation, Loyola University Medical Center, Maywood, IL, USA
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Khan O, Badhiwala JH, Akbar MA, Fehlings MG. Prediction of Worse Functional Status After Surgery for Degenerative Cervical Myelopathy: A Machine Learning Approach. Neurosurgery 2021; 88:584-591. [PMID: 33289519 DOI: 10.1093/neuros/nyaa477] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 08/12/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Surgical decompression for degenerative cervical myelopathy (DCM) is one of the mainstays of treatment, with generally positive outcomes. However, some patients who undergo surgery for DCM continue to show functional decline. OBJECTIVE To use machine learning (ML) algorithms to determine predictors of worsening functional status after surgical intervention for DCM. METHODS This is a retrospective analysis of prospectively collected data. A total of 757 patients enrolled in 2 prospective AO Spine clinical studies, who underwent surgical decompression for DCM, were analyzed. The modified Japanese Orthopedic Association (mJOA) score, a marker of functional status, was obtained before and 1 yr postsurgery. The primary outcome measure was the dichotomized change in mJOA at 1 yr according to whether it was negative (worse functional status) or non-negative. After applying an 80:20 training-testing split of the dataset, we trained, optimized, and tested multiple ML algorithms to evaluate algorithm performance and determine predictors of worse mJOA at 1 yr. RESULTS The highest-performing ML algorithm was a polynomial support vector machine. This model showed good calibration and discrimination on the testing data, with an area under the receiver operating characteristic curve of 0.834 (accuracy: 74.3%, sensitivity: 88.2%, specificity: 72.4%). Important predictors of functional decline at 1 yr included initial mJOA, male gender, duration of myelopathy, and the presence of comorbidities. CONCLUSION The reasons for worse mJOA are frequently multifactorial (eg, adjacent segment degeneration, tandem lumbar stenosis, ongoing neuroinflammatory processes in the cord). This study successfully used ML to predict worse functional status after surgery for DCM and to determine associated predictors.
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Affiliation(s)
- Omar Khan
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Jetan H Badhiwala
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Muhammad A Akbar
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Michael G Fehlings
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.,Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada
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Rigot SK, Boninger ML, Ding D, McKernan G, Field-Fote EC, Hoffman J, Hibbs R, Worobey LA. Toward Improving the Prediction of Functional Ambulation After Spinal Cord Injury Though the Inclusion of Limb Accelerations During Sleep and Personal Factors. Arch Phys Med Rehabil 2021; 103:676-687.e6. [PMID: 33839107 DOI: 10.1016/j.apmr.2021.02.029] [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: 10/10/2020] [Revised: 01/21/2021] [Accepted: 02/07/2021] [Indexed: 11/02/2022]
Abstract
OBJECTIVE To determine if functional measures of ambulation can be accurately classified using clinical measures; demographics; personal, psychosocial, and environmental factors; and limb accelerations (LAs) obtained during sleep among individuals with chronic, motor incomplete spinal cord injury (SCI) in an effort to guide future, longitudinal predictions models. DESIGN Cross-sectional, 1-5 days of data collection. SETTING Community-based data collection. PARTICIPANTS Adults with chronic (>1 year), motor incomplete SCI (N=27). INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES Ambulatory ability based on the 10-m walk test (10MWT) or 6-minute walk test (6MWT) categorized as nonambulatory, household ambulator (0.01-0.44 m/s, 1-204 m), or community ambulator (>0.44 m/s, >204 m). A random forest model classified ambulatory ability using input features including clinical measures of strength, sensation, and spasticity; demographics; personal, psychosocial, and environmental factors including pain, environmental factors, health, social support, self-efficacy, resilience, and sleep quality; and LAs measured during sleep. Machine learning methods were used explicitly to avoid overfitting and minimize the possibility of biased results. RESULTS The combination of LA, clinical, and demographic features resulted in the highest classification accuracies for both functional ambulation outcomes (10MWT=70.4%, 6MWT=81.5%). Adding LAs, personal, psychosocial, and environmental factors, or both increased the accuracy of classification compared with the clinical/demographic features alone. Clinical measures of strength and sensation (especially knee flexion strength), LA measures of movement smoothness, and presence of pain and comorbidities were among the most important features selected for the models. CONCLUSIONS The addition of LA and personal, psychosocial, and environmental features increased functional ambulation classification accuracy in a population with incomplete SCI for whom improved prognosis for mobility outcomes is needed. These findings provide support for future longitudinal studies that use LA; personal, psychosocial, and environmental factors; and advanced analyses to improve clinical prediction rules for functional mobility outcomes.
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Affiliation(s)
- Stephanie K Rigot
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA; Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
| | - Michael L Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA; Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA; Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
| | - Dan Ding
- Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA
| | - Gina McKernan
- Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
| | - Edelle C Field-Fote
- Crawford Research Institute, Shepherd Center, Atlanta, GA; Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA; Program in Applied Physiology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA
| | - Jeanne Hoffman
- Department of Rehabilitation Medicine, University of Washington School of Medicine, Seattle, WA
| | - Rachel Hibbs
- Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA; Physical Therapy, University of Pittsburgh, Pittsburgh, PA
| | - Lynn A Worobey
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA; Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA; Physical Therapy, University of Pittsburgh, Pittsburgh, PA.
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Tang Y, Li Z, Yang D, Fang Y, Gao S, Liang S, Liu T. Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning. Chin Med 2021; 16:2. [PMID: 33407711 PMCID: PMC7789502 DOI: 10.1186/s13020-020-00409-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 12/10/2020] [Indexed: 11/10/2022] Open
Abstract
Background Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment. Conclusions The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.
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Affiliation(s)
- Yuqi Tang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Zechen Li
- School of Automation, Chongqing University, Chongqing, 400044, China
| | - Dongdong Yang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China.
| | - Yu Fang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Shanshan Gao
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Shan Liang
- School of Automation, Chongqing University, Chongqing, 400044, China
| | - Tao Liu
- Electronic Engineering College, Chengdu University of Information Technology, Chengdu, 610225, China
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Khan O, Badhiwala JH, Grasso G, Fehlings MG. Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care. World Neurosurg 2020; 140:512-518. [PMID: 32797983 DOI: 10.1016/j.wneu.2020.04.022] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 04/04/2020] [Indexed: 01/14/2023]
Abstract
Personalized medicine is a new paradigm of healthcare in which interventions are based on individual patient characteristics rather than on "one-size-fits-all" guidelines. As epidemiological datasets continue to burgeon in size and complexity, powerful methods such as statistical machine learning and artificial intelligence (AI) become necessary to interpret and develop prognostic models from underlying data. Through such analysis, machine learning can be used to facilitate personalized medicine via its precise predictions. Additionally, other AI tools, such as natural language processing and computer vision, can play an instrumental part in personalizing the care provided to patients with spine disease. In the present report, we discuss the current strides made in incorporating AI into research on spine disease, especially traumatic spinal cord injury and degenerative spine disease. We describe studies using AI to build accurate prognostic models, extract important information from medical reports via natural language processing, and evaluate functional status in a granular manner using computer vision. Through a case illustration, we have demonstrated how these breakthroughs can facilitate an increased role for more personalized medicine and, thus, change the landscape of spine care.
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Affiliation(s)
- Omar Khan
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Jetan H Badhiwala
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada
| | - Giovanni Grasso
- Neurosurgical Unit, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Michael G Fehlings
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada.
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Olby NJ, da Costa RC, Levine JM, Stein VM. Prognostic Factors in Canine Acute Intervertebral Disc Disease. Front Vet Sci 2020; 7:596059. [PMID: 33324703 PMCID: PMC7725764 DOI: 10.3389/fvets.2020.596059] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/12/2020] [Indexed: 12/17/2022] Open
Abstract
Knowledge of the prognosis of acute spinal cord injury is critical to provide appropriate information for clients and make the best treatment choices. Acute intervertebral disc extrusions (IVDE) are a common cause of pain and paralysis in dogs with several types of IVDE occurring. Important prognostic considerations are recovery of ambulation, return of urinary and fecal continence, resolution of pain and, on the negative side, development of progressive myelomalacia. Initial injury severity affects prognosis as does type of IVDE, particularly when considering recovery of continence. Overall, loss of deep pain perception signals a worse outcome. When considering Hansen type 1 IVDE, the prognosis is altered by the choice of surgical vs. medical therapy. Concentration of structural proteins in the plasma, as well as inflammatory mediators, creatine kinase, and myelin basic protein in the cerebrospinal fluid (CSF) can provide additional prognostic information. Finally, cross-sectional area and length of T2 hyperintensity and loss of HASTE signal on MRI have been associated with outcome. Future developments in plasma and imaging biomarkers will assist in accurate prognostication and optimization of patient management.
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Affiliation(s)
- Natasha J. Olby
- Department of Clinical Sciences, North Carolina State University College of Veterinary Medicine, Raleigh, NC, United States
| | - Ronaldo C. da Costa
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, United States
| | - Jon M. Levine
- Department of Small Animal Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Veronika M. Stein
- Department for Clinical Veterinary Medicine, Vetsuisse Faculty, University of Bern, Bern, Switzerland
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Engel-Haber E, Zeilig G, Haber S, Worobey L, Kirshblum S. The effect of age and injury severity on clinical prediction rules for ambulation among individuals with spinal cord injury. Spine J 2020; 20:1666-1675. [PMID: 32502654 DOI: 10.1016/j.spinee.2020.05.551] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 05/22/2020] [Accepted: 05/22/2020] [Indexed: 02/03/2023]
Abstract
BACKGROUND CONTEXT While several models for predicting independent ambulation early after traumatic spinal cord injury (SCI) based upon age and specific motor and sensory level findings have been published and validated, their accuracy, especially in individual American Spinal Injury Association [ASIA] Impairment Scale (AIS) classifications, has been questioned. Further, although age is widely used in prediction rules, its role and possible modifications have not been adequately evaluated until now. PURPOSE To evaluate the predictive accuracy of existing clinical prediction rules for independent ambulation among individuals at spinal cord injury model systems (SCIMS) Centers as well as the effect of modifying the age parameter from a cutoff of 65 years to 50 years. STUDY DESIGN Retrospective analysis of a longitudinal database. PATIENT SAMPLE Adult individuals with traumatic SCI. OUTCOME MEASURES The FIM locomotor score was used to assess independent walking ability at the 1-year follow-up. METHODS In all, 639 patients were enrolled in the SCIMS database between 2011 and 2015, with complete neurological examination data within 15 days following the injury and a follow-up assessment with functional independence measure (FIM) at 1-year post injury. Two previously validated logistic regression models were evaluated for their ability to predict independent walking at 1-year post injury with participants in the SCIMS database. Area under the receiver operating curve (AUC) was calculated for the individual AIS categories and for different age groups. Prediction accuracy was also calculated for a new modified LR model (with cut-off age of 50). RESULTS Overall AUC for each of the previous prediction models was found to be consistent with previous reports (0.919 and 0.904). AUCs for grouped AIS levels (A+D, B+C) were consistent with prior reports, moreover, prediction for individual AIS grades continued to reveal lower values. AUCs by different age categories showed a decline in prognostication accuracy with an increase in age, with statistically significant improvement of AUC when age-cut off was reduced to 50. CONCLUSIONS We confirmed previous results that former prediction models achieve strong prognostic accuracy by combining AIS subgroups, yet prognostication of the separate AIS groups is less accurate. Further, prognostication of persons with AIS B+C, for whom a clinical prediction model has arguably greater clinical utility, is less accurate than those with AIS A+D. Our findings emphasize that age is an important factor in prognosticating ambulation following SCI. Prediction accuracy declines for older individuals compared with younger ones. To improve prediction of independent ambulation, the age of 50 years may be a better cutoff instead of age of 65.
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Affiliation(s)
- Einat Engel-Haber
- Department of Neurological Rehabilitation, The Chaim Sheba Medical Center, Tel Hashomer, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel.
| | - Gabi Zeilig
- Department of Neurological Rehabilitation, The Chaim Sheba Medical Center, Tel Hashomer, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Simi Haber
- Department of Mathematics, Bar-Ilan University, Ramat-Gan, Israel
| | - Lynn Worobey
- Department of Physical Medicine & Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - Steven Kirshblum
- Kessler Institute for Rehabilitation, West Orange NJ, USA; Rutgers New Jersey Medical School, Newark, NJ, USA
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Inoue T, Ichikawa D, Ueno T, Cheong M, Inoue T, Whetstone WD, Endo T, Nizuma K, Tominaga T. XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury. Neurotrauma Rep 2020; 1:8-16. [PMID: 34223526 PMCID: PMC8240917 DOI: 10.1089/neur.2020.0009] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.
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Affiliation(s)
- Tomoo Inoue
- Department of Neurosurgery, National Health Organization Sendai Medical Center, Sendai, Miyagi, Japan
| | | | | | - Maxwell Cheong
- Department of Radiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Takashi Inoue
- Department of Neurosurgery, National Health Organization Sendai Medical Center, Sendai, Miyagi, Japan
| | - William D. Whetstone
- Department of Emergency Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Toshiki Endo
- Department of Neurosurgery, National Health Organization Sendai Medical Center, Sendai, Miyagi, Japan
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Kuniyasu Nizuma
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
- Department of Neurosurgical Engineering and Translational Neuroscience, Graduate School of Biomedical Engineering, Tohoku University, Sendai, Miyagi, Japan
- Department of Neurosurgical Engineering and Translational Neuroscience, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Teiji Tominaga
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
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Azimi P, Yazdanian T, Benzel EC, Aghaei HN, Azhari S, Sadeghi S, Montazeri A. A Review on the Use of Artificial Intelligence in Spinal Diseases. Asian Spine J 2020; 14:543-571. [PMID: 32326672 PMCID: PMC7435304 DOI: 10.31616/asj.2020.0147] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 04/12/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. This review aims to identify the role of ANNs in spinal diseases. Literature were searched from electronic databases of Scopus and Medline from 1993 to 2020 with English publications reported on the application of ANNs in spinal diseases. The search strategy was set as the combinations of the following keywords: “artificial neural networks,” “spine,” “back pain,” “prognosis,” “grading,” “classification,” “prediction,” “segmentation,” “biomechanics,” “deep learning,” and “imaging.” The main findings of the included studies were summarized, with an emphasis on the recent advances in spinal diseases and its application in the diagnostic and prognostic procedures. According to the search strategy, a set of 3,653 articles were retrieved from Medline and Scopus databases. After careful evaluation of the abstracts, the full texts of 89 eligible papers were further examined, of which 79 articles satisfied the inclusion criteria of this review. Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative complications, and readmission rate following spine surgery; (2) enhancement of the clinically relevant information extracted from radiographic images to predict Pfirrmann grades, Modic changes, and spinal stenosis grades on magnetic resonance images automatically; (3) prediction of outcomes in lumbar spinal stenosis, lumbar disc herniation and patient-reported outcomes in lumbar fusion surgery, and preoperative planning and intraoperative assistance; and (4) its application in the biomechanical assessment of spinal diseases. The evidence suggests that ANNs can be successfully used for optimizing the diagnosis, prognosis and outcome prediction in spinal diseases. Therefore, incorporation of ANNs into spine clinical practice may improve clinical decision making.
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Affiliation(s)
- Parisa Azimi
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Edward C Benzel
- Department of Neurosurgery, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Hossein Nayeb Aghaei
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shirzad Azhari
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sohrab Sadeghi
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Montazeri
- Mental Health Research Group, Health Metrics Research Centre, Iranian Institute for Health Sciences Research, ACECR, Tehran, Iran
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Khan O, Badhiwala JH, Wilson JRF, Jiang F, Martin AR, Fehlings MG. Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions. Neurospine 2019; 16:678-685. [PMID: 31905456 PMCID: PMC6945005 DOI: 10.14245/ns.1938390.195] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 12/15/2019] [Indexed: 12/17/2022] Open
Abstract
Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over conventional regression techniques, such as a reduced requirement for a priori knowledge on predictors and better ability to manage large datasets. Current studies have made extensive strides in employing machine learning to a greater capacity in spinal cord injury (SCI). Analyses using machine learning algorithms have been done on both traumatic SCI and nontraumatic SCI, the latter of which typically represents degenerative spine disease resulting in spinal cord compression, such as degenerative cervical myelopathy. This article is a literature review of current studies published in traumatic and nontraumatic SCI that employ machine learning for the prediction of a host of outcomes. The studies described utilize machine learning in a variety of capacities, including imaging analysis and prediction in large epidemiological data sets. We discuss the performance of these machine learning-based clinical prognostic models relative to conventional statistical prediction models. Finally, we detail the future steps needed for machine learning to become a more common modality for statistical analysis in SCI.
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Affiliation(s)
- Omar Khan
- Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Jetan H Badhiwala
- Department of Surgery, University of Toronto, Toronto, ON, Canada.,Spinal Program, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Jamie R F Wilson
- Department of Surgery, University of Toronto, Toronto, ON, Canada.,Spinal Program, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Fan Jiang
- Department of Surgery, University of Toronto, Toronto, ON, Canada.,Spinal Program, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Allan R Martin
- Spinal Program, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Michael G Fehlings
- Department of Surgery, University of Toronto, Toronto, ON, Canada.,Spinal Program, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
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Schwartz JT, Gao M, Geng EA, Mody KS, Mikhail CM, Cho SK. Applications of Machine Learning Using Electronic Medical Records in Spine Surgery. Neurospine 2019; 16:643-653. [PMID: 31905452 PMCID: PMC6945000 DOI: 10.14245/ns.1938386.193] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 12/04/2019] [Indexed: 12/15/2022] Open
Abstract
Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.
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Affiliation(s)
- John T. Schwartz
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Gao
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric A. Geng
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kush S. Mody
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Christopher M. Mikhail
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samuel K. Cho
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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