OBJECTIVES: To design a model that could accurately predict eventual lumbar disc surgery in the patient initially presenting with clinical findings of nerve root compression. METHODS: Prospective study in 183 patients with nerve root compression sampled from a primary care population. All patients underwent a standardised history, physical examination, and MRI. Surgery carried out in the next 6 months was recorded. Models were constructed to predict whether patients eventually received surgery. RESULTS: Two models were constructed. Reduced model A was based on baseline findings, only, and contained six variables. Model B incorporated change over time as well and contained 10 variables. The area under the curve (in a receiver operating characteristic) for these models was 0.86 and 0.92, respectively. It was shown that at a probability cut off of 0.60, model B predicted eventual surgery with a sensitivity of 57% and a specificity of 100%. CONCLUSIONS: Given the requirement of a high specificity, eventual operation could be adequately predicted in a sample of 183 patients with clinical nerve root compression. The application of the model in patients with nerve root compression might lead to earlier operation in a subset of patients resulting in a reduction of duration of illness and associated indirect costs. An important prerequisite for future application would be the validation of the prediction rule in another population.
OBJECTIVES: To design a model that could accurately predict eventual lumbar disc surgery in the patient initially presenting with clinical findings of nerve root compression. METHODS: Prospective study in 183 patients with nerve root compression sampled from a primary care population. All patients underwent a standardised history, physical examination, and MRI. Surgery carried out in the next 6 months was recorded. Models were constructed to predict whether patients eventually received surgery. RESULTS: Two models were constructed. Reduced model A was based on baseline findings, only, and contained six variables. Model B incorporated change over time as well and contained 10 variables. The area under the curve (in a receiver operating characteristic) for these models was 0.86 and 0.92, respectively. It was shown that at a probability cut off of 0.60, model B predicted eventual surgery with a sensitivity of 57% and a specificity of 100%. CONCLUSIONS: Given the requirement of a high specificity, eventual operation could be adequately predicted in a sample of 183 patients with clinical nerve root compression. The application of the model in patients with nerve root compression might lead to earlier operation in a subset of patients resulting in a reduction of duration of illness and associated indirect costs. An important prerequisite for future application would be the validation of the prediction rule in another population.