Abstract/Text: No clinical diagnostic support tool can help identify patients with LSS. Simple diagnostic tool may improve the accuracy of the diagnosis of LSS. The aim of this study was to develop a simple clinical diagnostic tool that may help physicians to diagnose LSS in patients with lower leg symptoms. Patients with pain or numbness of the lower legs were prospectively enrolled. The diagnosis of LSS by experienced orthopedic specialists was the outcome measure. Multivariable logistic regression analysis identified factors that predicted LSS; a simple clinical prediction rule was developed by assigning a risk score to each item based on the estimated beta-coefficients. From December 2002 to December 2004, 104 orthopedic physicians from 22 clinics and 50 hospitals evaluated 468 patients. Two items of physical examination, three items of patients' symptom, and five items of physical examination were included in the final scoring system as a result of multiple logistic regression analysis. The sum of the risk scores for each patient ranged from -2 to 16. The Hosmer-Lemeshow statistic was 11.30 (P = 0.1851); the area under the ROC curve was 0.918. The clinical diagnostic support tool had a sensitivity of 92.8% and a specificity of 72.0%. The prevalence of LSS was 6.3% in the bottom quartile of the risk score (-2 to 5) and 99.0% in the top quartile (12 to 16). We developed a simple clinical diagnostic support tool to identify patients with LSS. Further studies are needed to validate this tool in primary care settings.

Abstract/Text: BACKGROUND: There is no validated gold-standard diagnostic support tool for LSS, and therefore an accurate diagnosis depends on clinical assessment. Assessment of the diagnostic value of the history of the patient requires an evaluation of the differences and overlap of symptoms of the radicular and cauda equina types; however, no tool is available for evaluation of the LSS category. We attempted to develop a self-administered, self-reported history questionnaire as a diagnostic support tool for LSS using a clinical epidemiological approach. The aim of the present study was to use this tool to assess the diagnostic value of the history of the patient for categorization of LSS.
METHODS: The initial derivation study included 137 patients with LSS and 97 with lumbar disc herniation who successfully recovered following surgical treatment. The LSS patients were categorized into radicular and cauda equina types based on history, physical examinations, and MRI. Predictive factors for overlapping symptoms between the two types and for cauda equina symptoms in LSS were derived by univariate analysis. A self-administered, self-reported history questionnaire (SSHQ) was developed based on these findings. A prospective derivation study was then performed in a series of 115 patients with LSS who completed the SSHQ before surgery. All these patients recovered following surgical treatment. The sensitivity of the SSHQ was calculated and clinical prediction rules for LSS were developed. A validation study was subsequently performed on 250 outpatients who complained of lower back pain with or without leg symptoms. The sensitivity and specificity of the SSHQ were calculated, and the test-retest reliability over two weeks was investigated in 217 patients whose symptoms remained unchanged.
RESULTS: The key predictive factors for overlapping symptoms between the two categories of LSS were age > 50, lower-extremity pain or numbness, increased pain when walking, increased pain when standing, and relief of symptoms on bending forward (odds ratio > or = 2, p < 0.05). The key predictive factors for cauda equina type symptoms were numbness around the buttocks, walking almost causes urination, a burning sensation around the buttocks, numbness in the soles of both feet, numbness in both legs, and numbness without pain (odds ratio > or = 2, p < 0.05). The sensitivity and specificity of the SSHQ were 84% and 78%, respectively, in the validation data set. The area under the receiver operating characteristic curve was 0.797 in the derivation set and 0.782 in the validation data set. In the test-retest analysis, the intraclass correlation coefficient for the first and second tests was 85%.
CONCLUSION: A new self-administered, self-reported history questionnaire was developed successfully as a diagnostic support tool for LSS.