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. 2016 Sep 15;63(6):812-7.
doi: 10.1093/cid/ciw329. Epub 2016 May 18.

Benefit-risk Evaluation for Diagnostics: A Framework (BED-FRAME)

Scott R Evans  1 , Gene Pennello  2 , Norberto Pantoja-Galicia  2 , Hongyu Jiang  3 , Andrea M Hujer  4 , Kristine M Hujer  4 , Claudia Manca  5 , Carol Hill  6 , Michael R Jacobs  4 , Liang Chen  5 , Robin Patel  7 , Barry N Kreiswirth  5 , Robert A Bonomo  4 ; Antibacterial Resistance Leadership Group
Affiliations

Benefit-risk Evaluation for Diagnostics: A Framework (BED-FRAME)

Scott R Evans et al. Clin Infect Dis. .

Abstract

The medical community needs systematic and pragmatic approaches for evaluating the benefit-risk trade-offs of diagnostics that assist in medical decision making. Benefit-Risk Evaluation of Diagnostics: A Framework (BED-FRAME) is a strategy for pragmatic evaluation of diagnostics designed to supplement traditional approaches. BED-FRAME evaluates diagnostic yield and addresses 2 key issues: (1) that diagnostic yield depends on prevalence, and (2) that different diagnostic errors carry different clinical consequences. As such, evaluating and comparing diagnostics depends on prevalence and the relative importance of potential errors. BED-FRAME provides a tool for communicating the expected clinical impact of diagnostic application and the expected trade-offs of diagnostic alternatives. BED-FRAME is a useful fundamental supplement to the standard analysis of diagnostic studies that will aid in clinical decision making.

Keywords: benefit-risk; diagnostic yield; diagnostics; pragmatism.

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Figures

Figure 1.
Figure 1.
Assuming that the molecular beacon (MB) and polymerase chain reaction/electrospray ionization mass spectrometry (PCR/ESI-MS) platforms were utilized on 12 000 annual Acinetobacter infections, the slide-rule profile plots indicate the expected diagnostic yield (the expected distribution of true susceptible [TS], true resistance [TR], false susceptible [FS], and false resistance [FR) results) as a function of the imipenem susceptibility rate.
Figure 2.
Figure 2.
If the molecular beacon (MB) and polymerase chain reaction/electrospray ionization mass spectrometry (PCR/ESI-MS) platforms were utilized on 12 000 annual Acinetobacter infections, the figure displays the expected between-platform differences in the raw numbers of (1) true susceptible (TS) diagnoses and (2) false susceptible (FS) diagnoses as a function of the susceptibility rate to illustrate between-platform trade-offs.
Figure 3.
Figure 3.
Assuming that the imipenem susceptibility rate is 40%, the figure displays the weighted accuracy (overall percentage correctly classified, adjusted for the relative importance of a false-resistant test result relative to a false-susceptible test result) as a function of the relative importance. Results are presented for 4 tests: (1) polymerase chain reaction/electrospray ionization mass spectrometry (PCR-ESI-MS); (2) molecular beacons (MB); (3) a test that always indicates resistance; and (4) a random test (equivalent to flipping a coin).
Figure 4.
Figure 4.
The figure displays the between-platform difference (molecular beacons [MB] vs polymerase chain reaction/electrospray ionization mass spectrometry [PCR/ESI-MS]) in weighted accuracy (overall percentage correctly classified, adjusted for the relative importance of a false-resistant test result relative to a false-susceptible test result) for various combinations of relative importance and imipenem susceptibility rates.
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