Evidence map›Paper›PMID 26838721›Full record

ArticlemBio2016

Will Regulatory and Financial Considerations Dampen Innovation in the Clinical Microbiology Laboratory?

Peter H Gilligan, Melissa B Miller

Abstract readComment
In one paragraph

Article in mBio, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Peter H GilliganClinical Microbiology-Immunology Laboratories, UNC HealthCare, Chapel Hill, North Carolina, USA Pathology and Laboratory Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA gilliganncphd@gmail.com.
Melissa B MillerClinical Microbiology-Immunology Laboratories, UNC HealthCare, Chapel Hill, North Carolina, USA Pathology and Laboratory Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over a million prosthetic joints are placed in patients in the United States annually. Of those that fail, 25% will be due to infection, with an estimated cost approaching 1 billion dollars. Despite the clinical and economic importance of these infections, the techniques for their detection are relatively insensitive. An innovative method for detecting these infections by using blood culture bottles (BCB) to culture specimens of periprosthetic tissue (PPT) was described in a recent article [T. N. Peel, et al., mBio 7(1):e01776-15, 2016, doi:10.1128/mBio.01776-15]. There are two potential stumbling blocks to the widespread implementation of this innovation. First, the FDA judges such an application of BCB as an "off-label use" and as such, a laboratory-developed test (LDT). LDTs are coming under greater scrutiny by the FDA and may require extensive, costly validation studies in laboratories that adopt this methodology. Second, the Center for Medicare and Medicaid Services has established a Hospital Acquired Condition Reduction Act under which institutions performing in the lowest quartile forfeit 1% of their Medicare reimbursement. Hospital-acquired infections are an important component of this quality metric. Although prosthetic joint infection (PJI) rates are not currently a hospital quality metric, given their cost and increasing frequency, it is reasonable to expect that they may become one. Will those with financial oversight allow an innovative technique that will require an expensive validation and may put the institution at risk for loss of CMS reimbursement?

Indexed as

HospitalsMedicareCross InfectionHumansUnited States

Identifiers

PMID26838721
PMCPMC4742713

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.