Spinal Cord Injury Research Program, Crawford Research Institute, Shepherd Center, Atlanta, GA, USA.
Department of Sociology, Georgia State University, Atlanta, GA, USA.
Spinal Cord. 2022 Oct;60(10):934-941. doi: 10.1038/s41393-022-00855-1. Epub 2022 Sep 12.
Observational.
To assess accuracy of self-reported level of injury (LOI) and severity in individuals with chronic spinal cord injury (SCI) as compared with clinical examination.
An SCI Model System Hospital.
A 20-item survey evaluated demographics, physical abilities, and self-reported injury level and severity. A decision tree algorithm used responses to categorize participants into injury severity groups. Following the survey, participants underwent clinical examination to determine current injury level and severity. Participants were later asked three questions regarding S1 sparing. Chart abstraction was utilized to obtain initial injury level and severity. Injury level and severity from self-report, decision tree, clinical exam, and chart abstraction were compared.
Twenty-eight individuals participated. Ninety-three percent correctly self-reported anatomical region of injury (ROI). Self-report of specific LOI matched current clinical LOI for 25% of participants, but matched initial LOI for 61%. Self-report of ASIA Impairment Scale (AIS) matched clinical AIS for 36%, but matched initial AIS for 46%. The injury severity decision tree was 75% accurate without, but 79% accurate with additional S1 questions. Self-report of deep anal pressure (DAP) was correct for 86% of participants, while self-report of voluntary anal contraction (VAC) was correct for 82%.
Individuals with SCI are more accurate reporting ROI than specific LOI. Self-reported injury level and severity align more closely with initial clinical examination results than current exam results. Using aggregate data from multiple questions can categorize injury severity more reliably than self-report. Using this type of decision tree may improve injury severity classification in large survey studies.
观察性研究。
评估慢性脊髓损伤（SCI）个体自我报告的损伤水平（LOI）和严重程度的准确性，与临床检查相比。
SCI 模型系统医院。
一项 20 项调查评估了人口统计学、身体能力以及自我报告的损伤水平和严重程度。决策树算法使用反应将参与者分类为损伤严重程度组。调查后，参与者接受临床检查以确定当前的损伤水平和严重程度。随后，参与者被问及三个关于 S1 保留的问题。图表提取用于获取初始损伤水平和严重程度。比较了自我报告、决策树、临床检查和图表提取的损伤水平和严重程度。
28 人参加。93%的人正确自我报告了损伤的解剖学区域（ROI）。25%的参与者自我报告的特定 LOI 与当前临床 LOI 相符，但与初始 LOI 相符的比例为 61%。自我报告的 ASIA 损伤量表（AIS）与临床 AIS 相符的比例为 36%，但与初始 AIS 相符的比例为 46%。不包括 S1 问题的情况下，损伤严重程度决策树的准确率为 75%，而包括 S1 问题的准确率为 79%。86%的参与者自我报告深部肛门压力（DAP）正确，而 82%的参与者自我报告自愿肛门收缩（VAC）正确。
SCI 个体在报告 ROI 方面比特定 LOI 更准确。自我报告的损伤水平和严重程度与初始临床检查结果更吻合，而不是当前检查结果。使用多个问题的汇总数据可以比自我报告更可靠地对损伤严重程度进行分类。使用这种类型的决策树可能会提高大型调查研究中的损伤严重程度分类。
