Relationship of cardiometabolic disease risk factors with age and spinal cord injury duration

Developing and Evaluating Data Infrastructure and Implementation Tools to Support Cardiometabolic Disease Indicator Data Collection

Background

Assessment of aerobic exercise (AE) and lipid profiles among individuals with spinal cord injury or disease (SCI/D) is critical for cardiometabolic disease (CMD) risk estimation.

Objectives

To utilize an artificial intelligence (AI) tool for extracting indicator data and education tools to enable routine CMD indicator data collection in inpatient/outpatient settings, and to describe and evaluate the recall of AE levels and lipid profile assessment completion rates across care settings among adults with subacute and chronic SCI/D.

Methods

A cross-sectional convenience sample of patients affiliated with University Health Network's SCI/D rehabilitation program and outpatients affiliated with SCI Ontario participated. The SCI-HIGH CMD intermediary outcome (IO) and final outcome (FO) indicator surveys were administered, using an AI tool to extract responses. Practice gaps were prospectively identified, and implementation tools were created to address gaps. Univariate and bivariate descriptive analyses were used.

Results

The AI tool had <2% error rate for data extraction. Adults with SCI/D (n = 251; 124 IO, mean age 61; 127 FO, mean age 55; p = .004) completed the surveys. Fourteen percent of inpatients versus 48% of outpatients reported being taught AE. Fifteen percent of inpatients and 51% of outpatients recalled a lipid assessment (p < .01). Algorithms and education tools were developed to address identified knowledge gaps in patient AE and lipid assessments.

Conclusion

Compelling CMD health service gaps warrant immediate attention to achieve AE and lipid assessment guideline adherence. AI indicator extraction paired with implementation tools may facilitate indicator deployment and modify CMD risk.

The Diagnosis and Management of Cardiometabolic Risk and Cardiometabolic Syndrome after Spinal Cord Injury

Individuals with spinal cord injuries (SCI) commonly present with component risk factors for cardiometabolic risk and combined risk factors for cardiometabolic syndrome (CMS). These primary risk factors include obesity, dyslipidemia, dysglycemia/insulin resistance, and hypertension. Commonly referred to as “silent killers”, cardiometabolic risk and CMS increase the threat of cardiovascular disease, a leading cause of death after SCI. This narrative review will examine current data and the etiopathogenesis of cardiometabolic risk, CMS, and cardiovascular disease associated with SCI, focusing on pivotal research on cardiometabolic sequelae from the last five years. The review will also provide current diagnosis and surveillance criteria for cardiometabolic disorders after SCI, a novel obesity classification system based on percent total body fat, and lifestyle management strategies to improve cardiometabolic health.