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How Much Overtesting Is Needed to Safely Exclude a Diagnosis? A Different Perspective on Triage Testing Using Bayes' Theorem
by
Sikkens, Jonne J.
, Thijs, Abel
, Bossuyt, Patrick M.
, Beekman, Djoeke G.
, Smulders, Yvo M.
in
Algorithms
/ Angiography
/ Angiography - methods
/ Bayes Theorem
/ Bayesian analysis
/ Decision Support Techniques
/ Diagnosis
/ Diagnosis, Differential
/ Diagnostic imaging
/ Diagnostic systems
/ Embolism
/ Fibrin Fibrinogen Degradation Products - analysis
/ Health risk assessment
/ Humans
/ Medical diagnosis
/ Medical imaging
/ Medical Overuse
/ Medicine and Health Sciences
/ Models, Theoretical
/ Patients
/ People and Places
/ Physical Sciences
/ Physicians
/ Predictive Value of Tests
/ Probability
/ Pulmonary embolism
/ Pulmonary Embolism - therapy
/ Pulmonary embolisms
/ Research and analysis methods
/ Risk
/ Sensitivity and Specificity
/ Systematic review
/ Theorems
/ Tomography, Spiral Computed - methods
2016
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How Much Overtesting Is Needed to Safely Exclude a Diagnosis? A Different Perspective on Triage Testing Using Bayes' Theorem
by
Sikkens, Jonne J.
, Thijs, Abel
, Bossuyt, Patrick M.
, Beekman, Djoeke G.
, Smulders, Yvo M.
in
Algorithms
/ Angiography
/ Angiography - methods
/ Bayes Theorem
/ Bayesian analysis
/ Decision Support Techniques
/ Diagnosis
/ Diagnosis, Differential
/ Diagnostic imaging
/ Diagnostic systems
/ Embolism
/ Fibrin Fibrinogen Degradation Products - analysis
/ Health risk assessment
/ Humans
/ Medical diagnosis
/ Medical imaging
/ Medical Overuse
/ Medicine and Health Sciences
/ Models, Theoretical
/ Patients
/ People and Places
/ Physical Sciences
/ Physicians
/ Predictive Value of Tests
/ Probability
/ Pulmonary embolism
/ Pulmonary Embolism - therapy
/ Pulmonary embolisms
/ Research and analysis methods
/ Risk
/ Sensitivity and Specificity
/ Systematic review
/ Theorems
/ Tomography, Spiral Computed - methods
2016
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How Much Overtesting Is Needed to Safely Exclude a Diagnosis? A Different Perspective on Triage Testing Using Bayes' Theorem
by
Sikkens, Jonne J.
, Thijs, Abel
, Bossuyt, Patrick M.
, Beekman, Djoeke G.
, Smulders, Yvo M.
in
Algorithms
/ Angiography
/ Angiography - methods
/ Bayes Theorem
/ Bayesian analysis
/ Decision Support Techniques
/ Diagnosis
/ Diagnosis, Differential
/ Diagnostic imaging
/ Diagnostic systems
/ Embolism
/ Fibrin Fibrinogen Degradation Products - analysis
/ Health risk assessment
/ Humans
/ Medical diagnosis
/ Medical imaging
/ Medical Overuse
/ Medicine and Health Sciences
/ Models, Theoretical
/ Patients
/ People and Places
/ Physical Sciences
/ Physicians
/ Predictive Value of Tests
/ Probability
/ Pulmonary embolism
/ Pulmonary Embolism - therapy
/ Pulmonary embolisms
/ Research and analysis methods
/ Risk
/ Sensitivity and Specificity
/ Systematic review
/ Theorems
/ Tomography, Spiral Computed - methods
2016
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How Much Overtesting Is Needed to Safely Exclude a Diagnosis? A Different Perspective on Triage Testing Using Bayes' Theorem
Journal Article
How Much Overtesting Is Needed to Safely Exclude a Diagnosis? A Different Perspective on Triage Testing Using Bayes' Theorem
2016
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Overview
Ruling out disease often requires expensive or potentially harmful confirmation testing. For such testing, a less invasive triage test is often used. Intuitively, few negative confirmatory tests suggest success of this approach. However, if negative confirmation tests become too rare, too many disease cases could have been missed. It is therefore important to know how many negative tests are needed to safely exclude a diagnosis. We quantified this relationship using Bayes' theorem, and applied this to the example of pulmonary embolism (PE), for which triage is done with a Clinical Decision Rule (CDR) and D-dimer testing, and CT-angiography (CTA) is the confirmation test. For a maximum proportion of missed PEs of 1% in triage-negative patients, we calculate a 67% 'mandatory minimum' proportion of negative CTA scans. To achieve this, the proportion of patients with PE undergoing triage testing should be appropriately low, in this case no higher than 24%. Pre-test probability, triage test characteristics, the proportion of negative confirmation tests, and the number of missed diagnoses are mathematically entangled. The proportion of negative confirmation tests--not too high, but definitely not too low either--could be a quality benchmark for diagnostic processes.
Publisher
Public Library of Science,Public Library of Science (PLoS)
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