Epidemiological State, Predictors of Early Mortality, and Predictive Models for Traumatic Spinal Cord Injury

Abstract

Study Design. Multi-center, retrospective cohort study. Objective. To determine the epidemiology, identify predictors of early mortality, and develop predictive models for traumatic spinal cord injury (SCI). Summary of Background Data. Despite improved initial care and management strategies, traumatic SCI remains a devastating event. Knowledge of the epidemiological state and predictive factors for mortality is important for developing strategies and counseling; however, they have not been adequately investigated, and predictive modeling regarding outcomes remains an underused modality for patients with traumatic SCI. Methods. Using a nationwide trauma registry—the Japan Trauma Data Bank—we identified adult (≥18 yrs) patients with SCI between 2004 and 2015. The endpoint was in-hospital mortality. Characteristics of each patient were described. Multivariate logistic regression analyses were performed to identify factors significantly associated with in-hospital mortality and develop a predictive model. Results. In total, 236,698 patients were registered in the database. Of the 215,835 adult patients, 8069 (3.7%) had SCI. The majority had SCI at the cervical level with falls at ground level being the primary etiology. Over the study period, median age, the proportion of cervical SCI, and the etiology of falls at ground level increased. The mortality rate was 5.6%. The following eight factors, age, sex, Glasgow Coma Scale on arrival (GCS), hypotension on arrival, bradycardia on arrival, severe head injury, Injury Severity Score (ISS), and neurological severity of SCI, were independently associated with mortality. A predictive model consisting of these variables predicted mortality with area under the receiver operating characteristic curve of 0.88 (95% confidence interval [CI], 0.86–0.90). Conclusion. Over the 12-year period, patient characteristics, etiology, and post-SCI outcomes significantly changed. We identified eight prognostic factors of early mortality. A predictive model including these factors showed excellent performance and may improve treatment strategies, healthcare resource allocation, and counseling. Level of Evidence: 3

Baseline predictors of in-hospital mortality after acute traumatic spinal cord injury: data from a level I trauma center

“…The reported mortality rate of 6.2% parallels with information from comparable monocentric studies in Canada (5.7% 15 ), Iceland (6.3% 20 ) and the United States (6.6% 7 ) and is also in the range of large nationwide epidemiological datasets (6.6%–7.5% 21 , 5.7% 22 ), although the length of stay (LOS) of our cohort is expanded compared to most other studies, as it covers acute and early chronic rehabilitation phase. Patient characteristics reflect the changing epidemiology of tSCI in high income countries with an increase of fall related injuries of patients with high age and more frequent and multiple comorbid conditions.…”

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

“…We also graphically examined the association between the SCIRS and ISS (see Supplementary Figure S5). Results showed a positive correlation between the SCIRS and ISS scores (Pearson's correlation = 0.30, p<0.05) which supported the validity of the SCIRS given that ISS is a well-known index that has previously been used in tSCI research [11,[35][36][37]. This figure also demonstrates that the SCIRS is more sensitive than ISS in measuring mortality in the datasets.…”

“…Developing a predictive model of mortality is challenging due to the complex interactions of factors that contribute to patient outcomes. Regression models based on the generalized linear model have previously been used to develop prediction tools in several clinical studies [11][12][13]38,39]. Although these models have the benefit of simplicity with readily-available and interpretable parameters, they may fail to appreciate the potential interaction and complex behaviour of variables that is often present in biological conditions [32].…”

“…It is also essential for informing discussions with patients and their families, especially when the benefits of surgical intervention are uncertain and end-of-life care is contemplated [8][9][10]. Many studies have previously identified predictive factors and algorithms for assessing mortality risk following tSCI, however, no prognostic tool specific to the tSCI patient population is readily available for use in a clinical setting [2,3,[11][12][13]. In addition to the value that a predictive tool can provide for clinical decision-making and discussions with patients and families, it can also be used in clinical research to adjust for the potential impact of specific patient and injury characteristics on a study participant's risk of mortality.…”

Regeneration of Spinal Cord Connectivity Through Stem Cell Transplantation and Biomaterial Scaffolds

“…Spinal cord injury (SCI) is a severely debilitating condition leading to neurological dysfunction, loss of independence, respiratory failure, psychological morbidities, and an increased lifelong mortality rate (Marion et al, 2017; Satkunendrarajah et al, 2018; Shibahashi et al, 2018; Wang et al, 2018b). In the United States, approximately 288,000 individuals are estimated to suffer from symptoms caused by SCI, and a recent survey showed the annual incidence of SCI is approximately 54 cases per one million people (Fehlings et al, 2018).…”