There is worldwide geographic variation in the epidemiology of traumatic spinal cord injury (tSCI). The aim of this study was to determine whether environmental barriers, health status, and quality-of-life outcomes differ between patients with tSCI living in rural or urban settings, and whether patients move from rural to urban settings after tSCI. A cohort review of the Rick Hansen SCI Registry (RHSCIR) was undertaken from 2004 to 2012 for one province in Canada. Rural/urban setting was determined using postal codes. Outcomes data at 1 year in the community included the Short Form-36 Version 2 (SF36v2™), Life Satisfaction Questionnaire, Craig Hospital Inventory of Environmental Factors-Short Form (CHIEF-SF), Functional Independent Measure® Instrument, and SCI Health Questionnaire. Statistical methodologies used were t test, Mann-Whitney U test, and Fisher's exact or χ2 test. In the analysis, 338 RHSCIR participants were included; 65 lived in a rural setting and 273 in an urban setting. Of the original patients residing in a rural area at discharge,10 moved to an urban area by 1 year. Those who moved from a rural to urban area reported a lower SF-36v2™ Mental Component Score (MCS; p = 0.04) and a higher incidence of depression at 1 year (p = 0.04). Urban patients also reported a higher incidence of depression (p = 0.02) and a lower CHIEF-SF total score (p = 0.01) indicating fewer environmental barriers. No significant differences were found in other outcomes. Results suggest that although the patient outcomes are similar, some patients move from rural to urban settings after tSCI. Future efforts should target screening mental health problems early, especially in urban settings.
International Classification of Diseases (ICD) codes are used to document patient morbidity in administrative databases. Although administrative data are used for research purposes, the validity of the data to accurately describe clinical diagnostic information is uncertain. We compared the clinical diagnoses for spinal cord and column injuries from a longitudinal patient registry, the Rick Hansen Spinal Cord Injury Registry (RHSCIR), to the ICD-10 spinal injury codes from the Discharge Abstract Database (DAD) at one institution. There were 603 RHSCIR participants with data describing the spinal cord injury, and 341 had data on the spinal column injury. The validity of DAD data to describe spinal injuries was evaluated using the sensitivity and positive predictive values of specific ICD-10 codes; 5.3% of the spinal column injuries and 10.9% of the spinal cord injuries documented in RHSCIR were missed in data from the DAD using ICD-10 codes. The most problematic spinal column ICD-10 code was the dislocation of the cervical vertebra (S13.1); only 14.0% of the dislocations of the cervical vertebrae in RHSCIR were correctly coded in the DAD. The most problematic spinal cord injury ICD-10 code was the incomplete lesion of the lumbar spinal cord (S34.1X); 66.7% of incomplete lesions of the lumbar spinal cord in RHSCIR were correctly coded in the DAD. The validity of DAD data to code spinal injuries is variable, and cannot be reliably used to classify all types of spinal injuries. Patient registries, such as RHSCIR, should be used if accurate detailed diagnostic data are required.
Adverse events (AEs) are common during care in patients with traumatic spinal cord injury (tSCI). Increased risk of AEs is linked to patient factors including pre-existing comorbidities. Our aim was to examine the relationships between patient factors and common post-injury AEs, and identify potentially modifiable comorbidities. Adults with tSCI admitted to a Level I acute specialized spine center between 2006 and 2014 who were enrolled in the Rick Hansen SCI Registry (RHSCIR) and had AE data collected using the Spine Adverse Events Severity system were included. Patient demographic, neurological injury, and comorbidities data were obtained from RHSCIR. Potentially modifiable comorbidities were grouped into health-related conditions, substance use/withdrawal, and psychiatric conditions. Negative binomial regression and multiple logistic regression were used to model the impact of patient factors on the number of AEs experienced and the occurrence of the five previously identified common AEs, respectively. Of the 444 patients included in the study, 24.8% reported a health-related condition, 15.3% had a substance use/withdrawal condition, 8% reported having a psychiatric condition; and 79.3% experienced one or more AEs. Older age (p = 0.004) and more severe injuries (p < 0.001) were nonmodifiable independent variables significantly associated with increased AEs. The AEs experienced by patients were urinary tract infections (42.8%), pneumonia (39.2%), neuropathic pain (31.5%), delirium (18.2%), and pressure ulcers (11.0%). Risk of delirium increased in those with substance use/withdrawal; and pneumonia risk increased with psychiatric comorbidities. Opportunity exists to develop clinical algorithms that include these types of risk factors to reduce the incidence and impact of AEs.
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