Spinal Cord Injury AIS Predictions Using Machine Learning

Kapoor, Dipti; Xu, Clark

doi:10.1523/eneuro.0149-22.2022

Cited by 16 publications

(8 citation statements)

References 18 publications

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“…Despite the large heterogeneity expected in SCI patients, few studies [15, 33, 42, 62] used larger sample sizes beyond 2000 subjects. It is worth noting the study by Kapoor D., et al [33], comprising 20790 individuals. It bench-marked different machine learning models (ridge classifier, support vector machine, elastic net, logistic regression, ensemble model, convolutional neural network, random forest, and naive Bayes) on data from the National Spinal Cord Injury Statistical Center (NSCISC) database to predict AIS scores at hospital discharge.…”

Section: Resultsmentioning

confidence: 99%

“…In line with the focus on computational architectures comprising limited parameterization, the majority (74.2%) of studies were conducted in small patient cohorts of less than 500 subjects. Despite the large heterogeneity expected in SCI patients, few studies [15, 33, 42, 62] used larger sample sizes beyond 2000 subjects. It is worth noting the study by Kapoor D., et al [33], comprising 20790 individuals.…”

Section: Resultsmentioning

confidence: 99%

Section: Prediction Model Architecture Choicesmentioning

confidence: 99%

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Data-driven prediction of spinal cord injury recovery: an exploration of current status and future perspectives

Håkansson,

Tuci,

Bolliger

et al. 2024

Preprint

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Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery outcomes varying widely among individuals. Machine learning (ML) is a promising approach to enhance the prediction of recovery trajectories, but its integration into clinical practice requires a thorough understanding of its efficacy and applicability. We systematically reviewed the current literature on data-driven models of SCI recovery prediction. The included studies were evaluated based on a range of criteria assessing the approach, implementation, input data preferences, and the clinical outcomes aimed to forecast. We observe a tendency to utilize routinely acquired data, such as International Standards for Neurological Classification of SCI (ISNCSCI), imaging, and demographics, for the prediction of functional outcomes derived from the Spinal Cord Independence Measure (SCIM) III and Functional Independence Measure (FIM) scores with a focus on motor ability. Although there has been an increasing interest in data-driven studies over time, traditional machine learning architectures, such as linear regression and tree-based approaches, remained the overwhelmingly popular choices for implementation. This implies ample opportunities for exploring architectures addressing the challenges of predicting SCI recovery, including techniques for learning from limited longitudinal data, improving generalizability, and enhancing reproducibility. We conclude with a perspective, highlighting possible future directions for data-driven SCI recovery prediction and drawing parallels to other application fields in terms of diverse data types (imaging, tabular, sequential, multimodal), data challenges (limited, missing, longitudinal data), and algorithmic needs (causal inference, robustness)

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Data-driven prediction of spinal cord injury recovery: an exploration of current status and future perspectives

Håkansson,

Tuci,

Bolliger

et al. 2024

Preprint

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“…In our quest to improve outcomes for patients with SCI, our study's application of ML techniques marks a shift from traditional areas of focus, including neurological and functional outcomes [30][31][32][33][34][35], to the proactive prevention of PUs. These prevalent yet preventable complications have a profound impact on the recovery and quality of life of patients with SCI [36].…”

Section: Discussionmentioning

confidence: 99%

Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients

Kim,

Lim,

Kim

et al. 2024

JCM

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(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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“…Currently, there are ongoing efforts to search for simple and accessible prognostic predictors of TSCI in order to optimize therapeutic decisions. The AIS grade has been widely used as the most acceptable predictor of prognostic after TSCI (17,18). In our study, it was also con rmed that admission TSCI injury severity (AIS grade) was an independent signi cant predictor of prognosis for TSCI patients.…”

Section: Discussionmentioning

confidence: 99%

Serum glucose/potassium ratio as a clinical risk factor for predicting the severity and prognosis of acute traumatic spinal cord injury

Zhou,

Liu,

Wang

et al. 2023

Preprint

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Objective Acute traumatic spinal cord injury (TSCI) can lead to poor prognosis, and clinical biomarkers have not been established to predict poor prognosis. This study aimed to determine the significance of serum glucose/potassium ratio (GPR) in evaluating TSCI severity and predicting prognosis. Methods An analysis of 520 clinical records of acute TSCI patients from January 2012 to June 2022 was conducted by the authors. The relationship between serum GPR and The American Spinal Injury Association Impairment Scale (AIS) grade 6-month post-trauma prognosis in addition to admission AIS grade was analyzed. In order to evaluate the discriminatory ability, a receiver operating characteristic curve was used. All methods were performed in accordance with the relevant guidelines and regulations. Results Based on the initial assessment of AIS grade, 256 (49.2%) patients were categorized into the severe TSCI group (AIS A-B), and there was a significant correlation between the severe TSCI group and serum GPR (p < 0.001). Serum GPR was reduced in an AIS grade-dependent manner (R=-0.540, p < 0.001). 262 (50.4%) patients were classified as poor prognosis according to the AIS grade at discharge. Serum GPR was also reduced in AIS grade in a discharge-dependent manner (R = -0.599, p < 0.001). It was significantly higher in the poor prognosis group compared to the good prognosis group (p < 0.001 ). Poor prognosis was significantly associated with sex(p = 0.009), severity of TSCI (p < 0.001), location of TSCI (p < 0.001), Surgical decompression(P < 0.018), body temperature (p < 0.001), heart rate (p < 0.001), systolic arterial pressure (SAP) (p < 0.001), diastolic arterial pressure (DAP) (p < 0.001), serum GPR (p < 0.001), serum glucose (p < 0.001), serum potassium (p < 0.001) and white blood cell (WBC) count (p = 0.003). Multivariate logistic regression analysis showed a significant correlation between poor prognosis and serum GPR (p = 0.023). The ROC analysis showed that the area under the curve (AUC) of serum GPR as a poor predictor of prognosis in TSCI patients was 0.842 (95% CI 0.808–0.875). Conclusion There was a significant relationship between serum GPR and admission injury severity and 6-month prognosis of acute TSCI patients. The serum GPR can be used to assess the severity of acute TSCI and predict the prognosis .

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Spinal Cord Injury AIS Predictions Using Machine Learning

Cited by 16 publications

References 18 publications

Data-driven prediction of spinal cord injury recovery: an exploration of current status and future perspectives

Data-driven prediction of spinal cord injury recovery: an exploration of current status and future perspectives

Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients

Serum glucose/potassium ratio as a clinical risk factor for predicting the severity and prognosis of acute traumatic spinal cord injury

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