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High-Sensitivity Cardiac Troponin Algorithms and the Value of Likelihood Ratios
Brett G Fischer1, Arthur T Evans2
1Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA. brf9036@med.cornell.edu.
Insights
Likelihood ratios, not predictive values, improve interpretation of high-sensitivity cardiac troponin (hs-cTn) tests for myocardial infarction diagnosis. This study provides a method to calculate likelihood ratios for better patient-centered decision-making.
Area of Science:
- Biomarker analysis
- Diagnostic accuracy
- Clinical decision-making
Background:
- High-sensitivity cardiac troponin (hs-cTn) assays are standard for non-ST-elevation myocardial infarction diagnosis.
- Current interpretation of hs-cTn results relies on predictive values, which have limited patient applicability.
- Assay-specific interpretation guidelines for hs-cTn vary, complicating clinical use.
Abstract:
High-sensitivity cardiac troponin (hs-cTn) is now the recommended biomarker for diagnosis of non-ST-elevation myocardial infarction, but proper interpretation varies based on the assay being used. Nearly uniformly, suggested interpretations of assay-specific hs-cTn results are based on predictive values, which are not applicable to most patients. Through application of a published hs-cTn algorithm to several patient scenarios, we will demonstrate that likelihood ratios are superior to predictive values for patient-centered test interpretation and decision-making. Furthermore, we will provide a blueprint for how to use existing published data presented with predictive values to calculate likelihood ratios. Changing the output of diagnostic accuracy studies and diagnostic algorithms from predictive values to likelihood ratios can improve patient care.
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