A Clinical Prediction Tool for MRI in Emergency Department Patients with Spinal Infection

• Published in: The western journal of emergency medicine Vol. 22; no. 5; pp. 1156 - 1166
• Main Authors: Shroyer, Steven R, Davis, William T, April, Michael D, Long, Brit, Boys, Greg, Mehta, Sumeru G, Mercaldo, Sarah F
• Format: Journal Article
• Language: English
• Published: United States University of California Digital Library - eScholarship 30-08-2021; Department of Emergency Medicine, University of California, Irvine School of Medicine; eScholarship Publishing, University of California
• Subjects: Adult; Aged; Back pain; Back Pain - diagnostic imaging; Back Pain - microbiology; C-Reactive Protein - analysis; Clinical Practice; Data collection; Decision Support Systems, Clinical; Delayed Diagnosis; Emergency medical care; Emergency Service, Hospital - statistics & numerical data; Enrollments; Fever; Hospital systems; Hospitalization - statistics & numerical data; Hospitals; Humans; Infections; Laboratories; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medicine; Middle Aged; Neck pain; Patients; Physicians; Population; Predictive Value of Tests; Prospective Studies; Sensitivity and Specificity
• ISSN: 1936-900X; 1936-9018; 1936-9018
• DOI: 10.5811/westjem.2021.5.52007

Patients with pyogenic spinal Infection (PSI) are often not diagnosed at their initial presentation, and diagnostic delay is associated with increased morbidity and medical-legal risk. We derived a decision tool to estimate the risk of spinal infection and inform magnetic resonance imaging (MRI) decisions.
We conducted a two-part prospective observational cohort study that collected variables from spine pain patients over a six-year derivation phase. We fit a multivariable regression model with logistic coefficients rounded to the nearest integer and used them for variable weighting in the final risk score. This score, SIRCH (spine infection risk calculation heuristic), uses four clinical variables to predict PSI. We calculated the statistical performance, MRI utilization, and model fit in the derivation phase. In the second phase we used the same protocol but enrolled only confirmed cases of spinal infection to assess the sensitivity of our prediction tool.
In the derivation phase, we evaluated 134 non-PSI and 40 PSI patients; median age in years was 55.5 (interquartile range [IQR] 38-70 and 51.5 (42-59), respectively. We identified four predictors for our risk score: historical risk factors; fever; progressive neurological deficit; and C-reactive protein (CRP) ≥ 50 milligrams per liter (mg/L). At a threshold SIRCH score of ≥ 3, the predictive model's sensitivity, specificity, and positive predictive value were, respectively, as follows: 100% (95% confidence interval [CI], 100-100%); 56% (95% CI, 48-64%), and 40% (95% CI, 36-46%). The area under the receiver operator curve was 0.877 (95% CI, 0.829-0.925). The SIRCH score at a threshold of ≥ 3 would prompt significantly fewer MRIs compared to using an elevated CRP (only 99/174 MRIs compared to 144/174 MRIs, P <0.001). In the second phase (49 patient disease-only cohort), the sensitivities of the SIRCH score and CRP use (laboratory standard cut-off 3.5 mg/L) were 92% (95% CI, 84-98%), and 98% (95% CI, 94-100%), respectively.
The SIRCH score provides a sensitive estimate of spinal infection risk and prompts fewer MRIs than elevated CRP (cut-off 3.5 mg/L) or clinician suspicion.