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

INTRODUCTION: 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 ima...

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Autores principales: Shroyer, Steven R., Davis, William T., April, Michael D., Long, Brit, Boys, Greg, Mehta, Sumeru G., Mercaldo, Sarah F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Department of Emergency Medicine, University of California, Irvine School of Medicine 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463051/
https://www.ncbi.nlm.nih.gov/pubmed/34546893
http://dx.doi.org/10.5811/westjem.2021.5.52007
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author Shroyer, Steven R.
Davis, William T.
April, Michael D.
Long, Brit
Boys, Greg
Mehta, Sumeru G.
Mercaldo, Sarah F.
author_facet Shroyer, Steven R.
Davis, William T.
April, Michael D.
Long, Brit
Boys, Greg
Mehta, Sumeru G.
Mercaldo, Sarah F.
author_sort Shroyer, Steven R.
collection PubMed
description INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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spelling pubmed-84630512021-10-01 A Clinical Prediction Tool for MRI in Emergency Department Patients with Spinal Infection Shroyer, Steven R. Davis, William T. April, Michael D. Long, Brit Boys, Greg Mehta, Sumeru G. Mercaldo, Sarah F. West J Emerg Med Clinical Practice INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. Department of Emergency Medicine, University of California, Irvine School of Medicine 2021-09 2021-08-30 /pmc/articles/PMC8463051/ /pubmed/34546893 http://dx.doi.org/10.5811/westjem.2021.5.52007 Text en Copyright: © 2021 Shroyer et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) License. See: http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/)
spellingShingle Clinical Practice
Shroyer, Steven R.
Davis, William T.
April, Michael D.
Long, Brit
Boys, Greg
Mehta, Sumeru G.
Mercaldo, Sarah F.
A Clinical Prediction Tool for MRI in Emergency Department Patients with Spinal Infection
title A Clinical Prediction Tool for MRI in Emergency Department Patients with Spinal Infection
title_full A Clinical Prediction Tool for MRI in Emergency Department Patients with Spinal Infection
title_fullStr A Clinical Prediction Tool for MRI in Emergency Department Patients with Spinal Infection
title_full_unstemmed A Clinical Prediction Tool for MRI in Emergency Department Patients with Spinal Infection
title_short A Clinical Prediction Tool for MRI in Emergency Department Patients with Spinal Infection
title_sort clinical prediction tool for mri in emergency department patients with spinal infection
topic Clinical Practice
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463051/
https://www.ncbi.nlm.nih.gov/pubmed/34546893
http://dx.doi.org/10.5811/westjem.2021.5.52007
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