Prediction of gait recovery using machine learning algorithms in patients with spinal cord injury

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.

Figure 1.

Flowchart of the data enrollment. A total of 353 patients were finally included on the following process.

Figure 2.

Decision support system of all patients with spinal cord injury. Initial FAC is the key variable on the decision support system. FAC = functional ambulation categories.

Figure 3.

Decision support system of patients with traumatic spinal cord injury. Initial FAC is the key variable on the decision support system. FAC = functional ambulation categories.

Figure 4.

Decision support system of patients with non-traumatic spinal cord injury. Please note that if the AIS grade was D or E and the NLI was at the lumbar level, the patients had the possibility of recovering gait function to FAC 3, even if they were FAC 0 at the time of admission. AIS = American Spinal Injury Association Impairment Scale, FAC = functional ambulation categories, NLI = neurological level of injury.