Development of a machine learning algorithm for predicting in-hospital and 1-year mortality after traumatic spinal cord injury

• Published in: The spine journal 2022-02, Vol.22 (2), p.329-336
• Main Authors: Fallah, Nader, Noonan, Vanessa K., Waheed, Zeina, Rivers, Carly S., Plashkes, Tova, Bedi, Manekta, Etminan, Mahyar, Thorogood, Nancy P., Ailon, Tamir, Chan, Elaine, Dea, Nicolas, Fisher, Charles, Charest-Morin, Raphaele, Paquette, Scott, Park, SoEyun, Street, John T., Kwon, Brian K., Dvorak, Marcel F.
• Format: Article
• Language: English
• Subjects: Algorithms; Hospitals; Humans; Machine Learning; Mortality; Prediction tool; Prospective Studies; Reproducibility of Results; Retrospective Studies; Risk score; Spinal Cord Injuries; Spinal cord injury; Traumatic spinal cord injury
• ISSN: 1529-9430; 1878-1632
• DOI: 10.1016/j.spinee.2021.08.003

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). Our aim was to develop and validate a prognostic tool that can predict mortality following tSCI. Retrospective review of a prospective cohort study. Data was collected from 1245 persons with acute tSCI who were enrolled in the Rick Hansen Spinal Cord Injury Registry between 2004 and 2016. In-hospital and 1-year mortality following tSCI. 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. 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. 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.