Evidence map›Paper›PMID 30387908›Full record

ArticleClinical transplantation2018

Reliability of deceased-donor procurement kidney biopsy images uploaded in United Network for Organ Sharing.

Sherry G Mansour, Isaac E Hall, Peter P Reese, Yaqi Jia, Heather Thiessen-Philbrook, Gilbert Moeckel, Francis L Weng, Monica P Revelo, Mazdak A Khalighi, Anshu Trivedi and 3 more

Abstract readMulticenter Study
In one paragraph

Article in Clinical transplantation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Sherry G MansourProgram of Applied Translational Research, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut.
Isaac E HallDivision of Nephrology, Hypertension and Renal Transplantation, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah.ORCID 0000-0003-0885-8450
Peter P ReeseRenal-Electrolyte and Hypertension Division, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.ORCID 0000-0003-1440-069X
Yaqi JiaProgram of Applied Translational Research, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut.
Heather Thiessen-PhilbrookProgram of Applied Translational Research, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut.
Gilbert MoeckelDivision of Pathology, Yale University School of Medicine, New Haven, Connecticut.
Francis L WengSaint Barnabas Medical Center, Livingston, New Jersey.
Monica P ReveloDepartment of Pathology and Laboratory Medicine, University of Utah School of Medicine, Salt Lake City, Utah.
Mazdak A KhalighiDepartment of Pathology and Laboratory Medicine, University of Utah School of Medicine, Salt Lake City, Utah.
Anshu TrivediDivision of Pathology, Hartford Hospital, Hartford, Connecticut.
Mona D DoshiUniversity of Michigan, Ann Arbor, Michigan.
Bernd SchröppelSection of Nephrology, University Hospital, Ulm, Germany.
Chirag R ParikhDivision of Nephrology, School of Medicine, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0001-9051-7385

Funding

Renal Physiology and Phenotyping CoreP30DK079310 · NIDDK · YALE UNIVERSITY · PI PARIKH, CHIRAG R · 2008 to 2022
$16.8M
Novel Kidney Injury Tools in Deceased Organ Donation to Predict Graft OutcomesR01DK093770 · NIDDK · YALE UNIVERSITY · PI Chirag R Parikh · 2012 to 2026
$9.5M
PATHOPHYSIOLOGY OF ACUTE AND CHRONIC RENAL DISEASET32DK007276 · NIDDK · YALE UNIVERSITY · PI ARONSON, PETER S., CANTLEY, LLOYD G · 1986 to 2022
$9.3M
Mentoring Program for Translational and Patient Oriented Research in AKIK24DK090203 · NIDDK · YALE UNIVERSITY · PI PARIKH, CHIRAG R · 2012 to 2016
$896k
NIDDK NIH HHS K24 DK090203NIDDK NIH HHS P30 DK079310NIDDK NIH HHS R01 DK093770NIDDK NIH HHS T32 DK007276
6 · The paper itself

Abstract

Prior studies demonstrate poor agreement among pathologists' interpretation of kidney biopsy slides. Reliability of representative images of these slides uploaded to the United Network of Organ Sharing (UNOS) web portal for clinician review has not been studied. We hypothesized high agreement among pathologists' image interpretation, since static images eliminate variation induced by viewing different areas of movable slides. To test our hypothesis, we compared the assessments of UNOS-uploaded images recorded in standardized forms by three pathologists. We selected 100 image sets, each having at least two images from kidneys of deceased donors. Weighted Cohen's kappa was used for inter-rater agreement. Mean (SD) donor age was 50 (13). Acute tubular injury had kappas of 0.12, 0.14, and 0.19; arteriolar hyalinosis 0.16, 0.27, and 0.38; interstitial inflammation 0.30, 0.33, and 0.49; interstitial fibrosis 0.28, 0.32, and 0.67; arterial intimal fibrosis 0.34, 0.42, and 0.59; tubular atrophy 0.35, 0.41, and 0.52; glomeruli thrombi 0.32, 0.53, and 0.85; and global glomerulosclerosis 0.68, 0.70, and 0.77. Pathologists' agreement demonstrated kappas of 0.12 to 0.77. The lower values raise concern about the reliability of using images. Although further research is needed to understand how uploaded images are used clinically, the field may consider higher-quality standards for biopsy photomicrographs.

Indexed as

Databases, FactualBiopsyFemaleHumansImage Processing, Computer-AssistedKidneyKidney TransplantationMaleMiddle AgedReproducibility of ResultsTissue and Organ ProcurementTissue BanksTissue Donorsagreementbiopsydeceased donorimageskappapathologistsrenaltransplant

Identifiers

PMID30387908
PMCPMC6317379

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.