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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; 62:446-453. [PMID: 38890506 DOI: 10.1038/s41393-024-01008-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [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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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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3
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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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Pavese C, Kessler TM. Prediction of Lower Urinary Tract, Sexual, and Bowel Function, and Autonomic Dysreflexia after Spinal Cord Injury. Biomedicines 2023; 11:1644. [PMID: 37371739 DOI: 10.3390/biomedicines11061644] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/02/2023] [Accepted: 06/04/2023] [Indexed: 06/29/2023] Open
Abstract
Spinal cord injury (SCI) produces damage to the somatic and autonomic pathways that regulate lower urinary tract, sexual, and bowel function, and increases the risk of autonomic dysreflexia. The recovery of these functions has a high impact on health, functioning, and quality of life and is set as the utmost priority by patients. The application of reliable models to predict lower urinary tract, sexual, and bowel function, and autonomic dysreflexia is important for guiding counseling, rehabilitation, and social reintegration. Moreover, a reliable prediction is essential for designing future clinical trials to optimize patients' allocation to different treatment groups. To date, reliable and simple algorithms are available to predict lower urinary tract and bowel outcomes after traumatic and ischemic SCI. Previous studies identified a few risk factors to develop autonomic dysreflexia, albeit a model for prediction still lacks. On the other hand, there is an urgent need for a model to predict the evolution of sexual function. The aim of this review is to examine the available knowledge and models for the prediction of lower urinary tract, sexual, and bowel function, and autonomic dysreflexia after SCI, and critically discuss the research priorities in these fields.
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Affiliation(s)
- Chiara Pavese
- Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
- Istituti Clinici Scientifici Maugeri IRCCS, Neurorehabilitation and Spinal Unit of Pavia Institute, 27100 Pavia, Italy
| | - Thomas M Kessler
- Department of Neuro-Urology, Balgrist University Hospital, University of Zürich, 8008 Zürich, Switzerland
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García-Rudolph A, Wright M, García L, Sauri J, Cegarra B, Tormos JM, Opisso E. Long-term prediction of functional independence using adjusted and unadjusted single items of the functional independence measure (FIM) at discharge from rehabilitation. J Spinal Cord Med 2023:1-12. [PMID: 36913541 DOI: 10.1080/10790268.2023.2183326] [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] [Indexed: 03/15/2023] Open
Abstract
CONTEXT Being able to survive in the long-term independently is of concern to patients with spinal cord injury (SCI), their relatives, and to those providing or planning health care, especially at rehabilitation discharge. Most previous studies have attempted to predict functional dependency in activities of daily living within one year after injury. OBJECTIVES (1) build 18 different predictive models, each model using one FIM (Functional Independence Measure) item, assessed at discharge, as independent predictor of total FIM score at chronic phase (3-6 years post-injury) (2) build three different predictive models, using in each model an item from a different FIM domain with the highest predictive power obtained in objective (1) to predict "good" functional independence at chronic phase and (3) adjust the 3 models from objective (2) with known confounding factors. METHODS This observational study included 461 patients admitted to rehabilitation between 2009 and 2019. We applied regression models to predict total FIM score and "good" functional independence (FIM motor score ≥ 65) reporting adjusted R2, odds ratios, ROC-AUC (95% CI) tested using 10-fold cross-validation. RESULTS The top three predictors, each from a different FIM domain, were Toilet (adjusted R2 = 0.53, Transfers domain), Toileting (adjusted R2 = 0.46, Self-care domain), and Bowel (adjusted R2 = 0.35, Sphincter control domain). These three items were also predictors of "good" functional independence (AUC: 0.84-0.87) and their predictive power increased (AUC: 0.88-0.93) when adjusted by age, paraplegia, time since injury, and length of stay. CONCLUSIONS Discharge FIM items accurately predict long-term functional independence.
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Affiliation(s)
- Alejandro García-Rudolph
- Department of Research and Innovation, Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain.,Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain.,Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Mark Wright
- Department of Research and Innovation, Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain.,Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain.,Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Loreto García
- Department of Research and Innovation, Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain.,Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain.,Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Joan Sauri
- Department of Research and Innovation, Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain.,Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain.,Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Blanca Cegarra
- Department of Research and Innovation, Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain.,Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain.,Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Josep Maria Tormos
- Department of Research and Innovation, Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain.,Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain.,Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Eloy Opisso
- Department of Research and Innovation, Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain.,Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain.,Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Barcelona, Spain
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Khalili H, Rismani M, Nematollahi MA, Masoudi MS, Asadollahi A, Taheri R, Pourmontaseri H, Valibeygi A, Roshanzamir M, Alizadehsani R, Niakan A, Andishgar A, Islam SMS, Acharya UR. Prognosis prediction in traumatic brain injury patients using machine learning algorithms. Sci Rep 2023; 13:960. [PMID: 36653412 PMCID: PMC9849475 DOI: 10.1038/s41598-023-28188-w] [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: 08/01/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term.
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Affiliation(s)
- Hosseinali Khalili
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Maziyar Rismani
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | | | - Mohammad Sadegh Masoudi
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Arefeh Asadollahi
- Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
| | - Reza Taheri
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Hossein Pourmontaseri
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran.,Bitab Knowledge Enterprise, Fasa University of Medical Sciences, Fasa, Iran
| | - Adib Valibeygi
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, 74617-81189, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Amin Niakan
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Aref Andishgar
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia.,Cardiovascular Division, The George Institute for Global Health, Newtown, Australia.,Sydney Medical School, University of Sydney, Camperdown, Australia
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.,Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan
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Lampart P, Schäppi L, Langewitz WA, Rubinelli S, Sigrist-Nix D, Scheel-Sailer A. Health care professionals’ experiences with goal setting during initial rehabilitation after newly acquired spinal cord injury/ disorder – a qualitative focus group study. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:982321. [PMID: 36189066 PMCID: PMC9397958 DOI: 10.3389/fresc.2022.982321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022]
Abstract
IntroductionGoal setting (GS) is an important aspect of initial spinal cord injury/ disorder (SCI/D) rehabilitation. However, because expected outcomes are individual and often difficult to determine, GS is not straightforward. The aim of this study was to explore the health care professionals' (HCP's) experiences with and perspectives on the goal-setting process (GSP) during initial SCI/D rehabilitation.MethodFive semi-structured focus groups (FG) (22 purposively sampled HCP, mostly in leadership positions, six different professions). The FG were transcribed verbatim. We analyzed the transcripts for qualitative content analysis following Braun and Clarke (2013).ResultsHCP described GS-influencing aspects at the macro, meso and micro levels. At the macro level, participants spoke about restrictions imposed by health insurers or difficulties in planning the post-inpatient setting. Regarding the meso level, HCP spoke of institutional structures and culture that facilitated the GSP. At the micro level, knowledge of the diagnosis, expected outcomes, and individual patient characteristics were mentioned as important to the rehabilitation process. It was important for HCP to be patient and empathetic, to endure negative emotions, to accept that patients need time to adjust to their new situation, and to ensure that they do not lose hope. Open communication and interprofessional collaboration helped overcome barriers in the GSP.DiscussionThis paper shows the complex relationship between external (e.g., health insurers), emotional, and communication aspects. It calls for a comprehensive approach to optimizing the GSP, so that patients' experiences can be fully considered as a basis to identify the most appropriate care pathway.
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Affiliation(s)
- Patricia Lampart
- Swiss Paraplegic Center, Nottwil, Switzerland
- Department of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland
| | - Livia Schäppi
- Swiss Paraplegic Center, Nottwil, Switzerland
- Department of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland
| | | | - Sara Rubinelli
- Department of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland
- Swiss Paraplegic Research, Nottwil, Switzerland
| | | | - Anke Scheel-Sailer
- Swiss Paraplegic Center, Nottwil, Switzerland
- Department of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland
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