The western journal of emergency medicine · 2018
A Predictive Model Facilitates Early Recognition of Spinal Epidural Abscess in Adults.
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Artenstein Andrew W, Friderici Jennifer, Visintainer Paul
Аннотация
INTRODUCTION: Spinal epidural abscess (SEA), a highly morbid and potentially lethal deep tissue infection of the central nervous system has more than tripled in incidence over the past decade. Early recognition at the point of initial clinical presentation may prevent irreversible neurologic injury or other serious, adverse outcomes. To facilitate early recognition of SEA, we developed a predictive scoring model.
METHODS: Using data from a 10-year, retrospective, case-control study of adults presenting for care at a tertiary-care, regional, academic medical center, we used the Integrated Discrimination Improvement Index (IDI) to identify candidate discriminators and created a multivariable logistic regression model, refined based on p-value significance. We selected a cutpoint that optimized sensitivity and specificity.
RESULTS: The final multivariable logistic regression model based on five characteristics -patient age, fever and/or rigor, antimicrobial use within 30 days, back/neck pain, and injection drug use - shows excellent discrimination (AUC 0.88 [95% confidence interval {0.84, 0.92}]). We used the model's β coefficients to develop a scoring system in which a cutpoint of six correctly identifies cases 89% of the time. Bootstrapped validation measures suggest this model will perform well across samples drawn from this population.
CONCLUSION: Our predictive scoring model appears to reliably discriminate patients who require emergent spinal imaging upon clinical presentation to rule out SEA and should be used in conjunction with clinical judgment.
METHODS: Using data from a 10-year, retrospective, case-control study of adults presenting for care at a tertiary-care, regional, academic medical center, we used the Integrated Discrimination Improvement Index (IDI) to identify candidate discriminators and created a multivariable logistic regression model, refined based on p-value significance. We selected a cutpoint that optimized sensitivity and specificity.
RESULTS: The final multivariable logistic regression model based on five characteristics -patient age, fever and/or rigor, antimicrobial use within 30 days, back/neck pain, and injection drug use - shows excellent discrimination (AUC 0.88 [95% confidence interval {0.84, 0.92}]). We used the model's β coefficients to develop a scoring system in which a cutpoint of six correctly identifies cases 89% of the time. Bootstrapped validation measures suggest this model will perform well across samples drawn from this population.
CONCLUSION: Our predictive scoring model appears to reliably discriminate patients who require emergent spinal imaging upon clinical presentation to rule out SEA and should be used in conjunction with clinical judgment.