Improved rule-out diagnostic gain with a combined aortic dissection detection risk score and D-dimer Bayesian decision support scheme

Amado Alejandro Baez, Laila Cochon

Research output: Contribution to journalArticle

Abstract

The objective of this study was to develop a Bayesian clinical decision support mathematical model that can assist in assessing a diagnostic utility integrating the aortic dissection detection risk score (ADD-RS) combined with the diagnostic quality of D-dimer testing. Methods Our method uses the Bayes nomogram. Pretest probability scoring for the ADD-RS was obtained using their derived precalculated effects models. Sensitivity, specificity, and positive and negative likelihood ratios (LRs) for D-dimer testing were obtained by meta-analysis. Posttest probability was obtained from Bayesian statistical modeling integrating low, intermediate, and high pretest for the ADD-RS and LRs for D-dimer testing. Relative (RDG) and absolute (AADG) diagnostic gains were calculated. Results Pool meta-analysis of D-dimer data demonstrated a sensitivity of 0.97 (95% confidence interval [CI], 0.94-0.99), specificity of 0.56 (95% CI, 0.51-0.60), negative LR of 0.06 (95% CI, 0.03-0.12), and positive LR of 2.43 (95% CI, 1.89-3.12). Bayesian modeling for negative LRs demonstrated posttest probabilities scores of 0.24% for low risk (AADG = 4.06% and RDG = 94.42%), 3.4% for intermediate risk (AADG = 33.1% and RDG = 90.68%), and 7.9% for high risk (AADG = 51.3% and RDG = 86.65%). Conclusion The integration of the ADD-RS and D-dimer testing in a decision support scheme suggested rule-out diagnostic value and gains, mostly evidenced in the AADD-RS low and intermediate pretest probability categories. We propose further evaluating the use of this decision support scheme in a prospective model and as a potential triage tool for aortic dissection.

Original languageEnglish (US)
Pages (from-to)56-59
Number of pages4
JournalJournal of Critical Care
Volume37
DOIs
StatePublished - Feb 1 2017
Externally publishedYes

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Dissection
Confidence Intervals
Meta-Analysis
Clinical Decision Support Systems
Nomograms
Decision Support Techniques
Triage
fibrin fragment D
Theoretical Models
Sensitivity and Specificity
N-acetylglucosaminylasparagine

Keywords

  • Acute Care Diagnostic Collaboration
  • Aortic dissection
  • Bayesian model
  • D-dimer

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine

Cite this

Improved rule-out diagnostic gain with a combined aortic dissection detection risk score and D-dimer Bayesian decision support scheme. / Baez, Amado Alejandro; Cochon, Laila.

In: Journal of Critical Care, Vol. 37, 01.02.2017, p. 56-59.

Research output: Contribution to journalArticle

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abstract = "The objective of this study was to develop a Bayesian clinical decision support mathematical model that can assist in assessing a diagnostic utility integrating the aortic dissection detection risk score (ADD-RS) combined with the diagnostic quality of D-dimer testing. Methods Our method uses the Bayes nomogram. Pretest probability scoring for the ADD-RS was obtained using their derived precalculated effects models. Sensitivity, specificity, and positive and negative likelihood ratios (LRs) for D-dimer testing were obtained by meta-analysis. Posttest probability was obtained from Bayesian statistical modeling integrating low, intermediate, and high pretest for the ADD-RS and LRs for D-dimer testing. Relative (RDG) and absolute (AADG) diagnostic gains were calculated. Results Pool meta-analysis of D-dimer data demonstrated a sensitivity of 0.97 (95{\%} confidence interval [CI], 0.94-0.99), specificity of 0.56 (95{\%} CI, 0.51-0.60), negative LR of 0.06 (95{\%} CI, 0.03-0.12), and positive LR of 2.43 (95{\%} CI, 1.89-3.12). Bayesian modeling for negative LRs demonstrated posttest probabilities scores of 0.24{\%} for low risk (AADG = 4.06{\%} and RDG = 94.42{\%}), 3.4{\%} for intermediate risk (AADG = 33.1{\%} and RDG = 90.68{\%}), and 7.9{\%} for high risk (AADG = 51.3{\%} and RDG = 86.65{\%}). Conclusion The integration of the ADD-RS and D-dimer testing in a decision support scheme suggested rule-out diagnostic value and gains, mostly evidenced in the AADD-RS low and intermediate pretest probability categories. We propose further evaluating the use of this decision support scheme in a prospective model and as a potential triage tool for aortic dissection.",
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N2 - The objective of this study was to develop a Bayesian clinical decision support mathematical model that can assist in assessing a diagnostic utility integrating the aortic dissection detection risk score (ADD-RS) combined with the diagnostic quality of D-dimer testing. Methods Our method uses the Bayes nomogram. Pretest probability scoring for the ADD-RS was obtained using their derived precalculated effects models. Sensitivity, specificity, and positive and negative likelihood ratios (LRs) for D-dimer testing were obtained by meta-analysis. Posttest probability was obtained from Bayesian statistical modeling integrating low, intermediate, and high pretest for the ADD-RS and LRs for D-dimer testing. Relative (RDG) and absolute (AADG) diagnostic gains were calculated. Results Pool meta-analysis of D-dimer data demonstrated a sensitivity of 0.97 (95% confidence interval [CI], 0.94-0.99), specificity of 0.56 (95% CI, 0.51-0.60), negative LR of 0.06 (95% CI, 0.03-0.12), and positive LR of 2.43 (95% CI, 1.89-3.12). Bayesian modeling for negative LRs demonstrated posttest probabilities scores of 0.24% for low risk (AADG = 4.06% and RDG = 94.42%), 3.4% for intermediate risk (AADG = 33.1% and RDG = 90.68%), and 7.9% for high risk (AADG = 51.3% and RDG = 86.65%). Conclusion The integration of the ADD-RS and D-dimer testing in a decision support scheme suggested rule-out diagnostic value and gains, mostly evidenced in the AADD-RS low and intermediate pretest probability categories. We propose further evaluating the use of this decision support scheme in a prospective model and as a potential triage tool for aortic dissection.

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