Evidence map›Paper›PMID 36751383›Full record

ArticleGlomerular diseases2021

The Development and Use of an EHR-Linked Database for Glomerular Disease Research and Quality Initiatives.

Richard N Eikstadt, Hailey E Desmond, Clare Lindner, Liz Yao Chen, Cheryl D Courtlandt, Susan F Massengill, Elaine S Kamil, Richard Lafayette, Anne Pesenson, Matthew Elliott and 2 more

Abstract read
In one paragraph

Article in Glomerular diseases, 2021. 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. Trial
  2. Review
  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

12 authors.

Richard N EikstadtDivision of Nephrology, Department of Pediatrics and Communicable Diseases, C.S. Mott Children's Hospital, University of Michigan, Ann Arbor, Michigan, USA.
Hailey E DesmondDivision of Nephrology, Department of Pediatrics and Communicable Diseases, C.S. Mott Children's Hospital, University of Michigan, Ann Arbor, Michigan, USA.
Clare LindnerDivision of Nephrology, Department of Pediatrics and Communicable Diseases, C.S. Mott Children's Hospital, University of Michigan, Ann Arbor, Michigan, USA.
Liz Yao ChenThe Lundquist Institute at Harbor-UCLA Medical Center, Los Angeles, California, USA.
Cheryl D CourtlandtDivision of Pediatric Nephrology, Department of Pediatrics, Levine Children's Hospital, Charlotte, North Carolina, USA.
Susan F MassengillDivision of Pediatric Nephrology, Department of Pediatrics, Levine Children's Hospital, Charlotte, North Carolina, USA.
Elaine S KamilDivision of Pediatric Nephrology, Department of Pediatrics, Cedars-Sinai Medical Center, David Geffen School of Medicine at the University of California, Los Angeles, California, USA.
Richard LafayetteDivision of Nephrology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.
Anne PesensonThe Polyclinic, Seattle, Washington, USA.
Matthew ElliottMetrolina Nephrology Associates, Charlotte, North Carolina, USA.
Patrick E GipsonDivision of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Debbie S GipsonDivision of Nephrology, Department of Pediatrics and Communicable Diseases, C.S. Mott Children's Hospital, University of Michigan, Ann Arbor, Michigan, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: The use of electronic health record (EHR) data can facilitate efficient research and quality initiatives. The imprecision of ICD-10 codes for kidney diagnoses has been an obstacle to discrete data-defined diagnoses in the EHR. This manuscript describes the Kidney Research Network (KRN) registry and database that provide an example of a prospective, real-world data glomerular disease registry for research and quality initiatives. Methods: KRN is a multicenter collaboration of patients, physicians, and scientists across diverse health-care settings with a focus on improving treatment options and outcomes for patients with glomerular disease. The registry and data warehouse amasses retrospective and prospective data including EHR, active research study, completed clinical trials, patient reported outcomes, and other relevant data. Following consent, participating sites enter the patient into KRN and provide a physician-confirmed primary kidney diagnosis. Kidney biopsy reports are redacted and uploaded. Site programmers extract local EHR data including demographics, insurance type, zip code, diagnoses, encounters, laboratories, procedures, medications, dialysis/transplant status, vitals, and vital status monthly. Participating sites transform data to conform to a common data model prior to submitting to the Data Analysis and Coordinating Center (DACC). The DACC stores and reviews each site's EHR data for quality before loading into the KRN database. Results: As of January 2021, 1,192 patients have enrolled in the registry. The database has been utilized for research, clinical trial design, clinical trial end point validation, and supported quality initiatives. The data also support a dashboard allowing enrolling sites to assist with clinical trial enrollment and population health initiatives. Conclusion: A multicenter registry using EHR data, following physician- and biopsy-confirmed glomerular disease diagnosis, can be established and used effectively for research and quality initiatives. This design provides an example which may be readily replicated for other rare or common disease endeavors.

Indexed as

Data qualityDiagnosisElectronic health recordsGlomerular diseasesGlomerulopathyNephrotic syndromeRare disease registry

Identifiers

PMID36751383
PMCPMC9677745

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.