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J Am Soc Nephrol. 2019 Dec;30(12):2427-2435. doi: 10.1681/ASN.2019040365. Epub 2019 Nov 15.

Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research.

Author information

1
Division of Nephrology, denburgm@Email.chop.edu.
2
Department of Pediatrics and.
3
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
4
Center for Pediatric Clinical Effectiveness.
5
Applied Clinical Research Center, and.
6
Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
7
Renal Section, Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado.
8
Division of Nephrology, Department of Pediatrics, Seattle Children's Hospital, University of Washington, Seattle, Washington.
9
Division of Nephrology, Department of Pediatrics, St. Louis Children's Hospital, Washington University in St. Louis, St. Louis, Missouri.
10
Division of Nephrology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio.
11
Division of Nephrology, Department of Pediatrics, Nationwide Children's Hospital, The Ohio State University, Columbus, Ohio.
12
Division of Nephrology, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts.
13
Division of Nephrology, Nemours/Alfred I. DuPont Hospital for Children, Wilmington, Delaware; and.
14
Division of Nephrology.
15
Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor, Michigan.

Abstract

BACKGROUND:

The rarity of pediatric glomerular disease makes it difficult to identify sufficient numbers of participants for clinical trials. This leaves limited data to guide improvements in care for these patients.

METHODS:

The authors developed and tested an electronic health record (EHR) algorithm to identify children with glomerular disease. We used EHR data from 231 patients with glomerular disorders at a single center to develop a computerized algorithm comprising diagnosis, kidney biopsy, and transplant procedure codes. The algorithm was tested using PEDSnet, a national network of eight children's hospitals with data on >6.5 million children. Patients with three or more nephrologist encounters (n=55,560) not meeting the computable phenotype definition of glomerular disease were defined as nonglomerular cases. A reviewer blinded to case status used a standardized form to review random samples of cases (n=800) and nonglomerular cases (n=798).

RESULTS:

The final algorithm consisted of two or more diagnosis codes from a qualifying list or one diagnosis code and a pretransplant biopsy. Performance characteristics among the population with three or more nephrology encounters were sensitivity, 96% (95% CI, 94% to 97%); specificity, 93% (95% CI, 91% to 94%); positive predictive value (PPV), 89% (95% CI, 86% to 91%); negative predictive value, 97% (95% CI, 96% to 98%); and area under the receiver operating characteristics curve, 94% (95% CI, 93% to 95%). Requiring that the sum of nephrotic syndrome diagnosis codes exceed that of glomerulonephritis codes identified children with nephrotic syndrome or biopsy-based minimal change nephropathy, FSGS, or membranous nephropathy, with 94% sensitivity and 92% PPV. The algorithm identified 6657 children with glomerular disease across PEDSnet, ≥50% of whom were seen within 18 months.

CONCLUSIONS:

The authors developed an EHR-based algorithm and demonstrated that it had excellent classification accuracy across PEDSnet. This tool may enable faster identification of cohorts of pediatric patients with glomerular disease for observational or prospective studies.

KEYWORDS:

Epidemiology and outcomes; glomerular disease; pediatric nephrology

PMID:
31732612
PMCID:
PMC6900784
[Available on 2020-12-01]
DOI:
10.1681/ASN.2019040365

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