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Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
Published on: September 28, 2017
The development of a clinical decision making algorithm for detection of osteoporotic vertebral compression fracture
Matthew Roman1, Christopher Brown, William Richardson
1Department of Physical and Occupational Therapy.
Abstract:
The clinical diagnosis of an osteoporotic vertebral compression fracture (OVCF) is challenging and requires detailed assessment using comprehensive imaging methods. Further complicating matter is that the clinical sequelae associated with OVCF typically involves asymptomatic findings and variable pain patterns. The purpose of this study was to identify clinical characteristics and assessment findings that were associated with a diagnosis of OVCF. The study evaluated routine clinical findings in over 1400 subjects seen at an adult spine surgery clinic for thoracolumbar spine-related conditions within the years 2005-2009. All patients underwent a standardized clinical examination that included a self-report, observational, physical examination and imaging assessment. The diagnosis of OVCF was made after assessment of radiographic findings in sagittal alignment, vertebral body compression, and spinal canal dimensions. Data from the patient history and observational findings were then statistically analyzed and compared between those patients with a diagnosis of OVCF and those with an alternative diagnosis. Based on the results, a diagnostic support tool was created to predict the likelihood of OVCF. The most diagnostic combination included a cluster of: (1) age > 52 years; (2) no presence of leg pain; (3) body mass index ⩽ 22; (4) does not exercise regularly; and (5) female gender. A finding of two of five positive tests or less demonstrated high sensitivity of 0.95 (95% CI = 0.83-0.99) and low negative likelihood ratio of 0.16 (95% CI = 0.04-0.51), providing moderate value to rule out OVCF. Four of five yielded a positive likelihood ratio (LR+) of 9.6 (95% CI = 3.7-14.9) providing moderate value in ruling in the diagnosis of OVCF. Further validation is necessary prospectively to determine the value of these findings on a disparate sample of patients in other unique environments.
