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

Michelle R. Denburg, Hanieh Razzaghi, L. Charles Bailey, Danielle E. Soranno, Ari H. Pollack, Vikas R. Dharnidharka, Mark M. Mitsnefes, William E. Smoyer, Michael J. G. Somers, Joshua J. Zaritsky, Joseph T. Flynn, Donna J. Claes, Bradley P. Dixon, Maryjane Benton, Laura H. Mariani, Christopher B. Forrest and Susan L. Furth
JASN December 2019, 30 (12) 2427-2435; DOI: https://doi.org/10.1681/ASN.2019040365
Michelle R. Denburg
Division of Nephrology,Department of Pediatrics andDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania;Center for Pediatric Clinical Effectiveness,
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Hanieh Razzaghi
Applied Clinical Research Center, and
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L. Charles Bailey
Department of Pediatrics andApplied Clinical Research Center, andDepartment of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania;
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Danielle E. Soranno
Renal Section, Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado;
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Ari H. Pollack
Division of Nephrology, Department of Pediatrics, Seattle Children’s Hospital, University of Washington, Seattle, Washington;
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Vikas R. Dharnidharka
Division of Nephrology, Department of Pediatrics, St. Louis Children’s Hospital, Washington University in St. Louis, St. Louis, Missouri;
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Mark M. Mitsnefes
Division of Nephrology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio;
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William E. Smoyer
Division of Nephrology, Department of Pediatrics, Nationwide Children’s Hospital, The Ohio State University, Columbus, Ohio;
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Michael J. G. Somers
Division of Nephrology, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts;
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Joshua J. Zaritsky
Division of Nephrology, Nemours/Alfred I. DuPont Hospital for Children, Wilmington, Delaware; and
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Joseph T. Flynn
Division of Nephrology, Department of Pediatrics, Seattle Children’s Hospital, University of Washington, Seattle, Washington;
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Donna J. Claes
Division of Nephrology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio;
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Bradley P. Dixon
Renal Section, Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado;
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Maryjane Benton
Division of Nephrology,
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Laura H. Mariani
Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor, Michigan
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Christopher B. Forrest
Department of Pediatrics andApplied Clinical Research Center, andDepartment of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania;
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Susan L. Furth
Division of Nephrology,Department of Pediatrics andDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania;
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Significance Statement

Clinical advances in glomerular disease have been stymied by the rarity of these health conditions, making identification of sufficient numbers of patients with glomerular disease for enrollment in research studies challenging, particularly in the pediatric setting. We leveraged the PEDSnet pediatric health system population of >6.5 million children to develop and evaluate a highly sensitive and specific electronic health record (EHR)–based computable phenotype algorithm to identify the largest cohort of children with glomerular disease to date. This tool for rapid cohort identification applied to a robust resource of multi-institutional longitudinal EHR data offers great potential to enhance and accelerate comparative effectiveness and health outcomes research in glomerular disease.

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.

  • glomerular disease
  • pediatric nephrology
  • Epidemiology and outcomes
  • Copyright © 2019 by the American Society of Nephrology
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Journal of the American Society of Nephrology: 30 (12)
Journal of the American Society of Nephrology
Vol. 30, Issue 12
December 2019
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Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research
Michelle R. Denburg, Hanieh Razzaghi, L. Charles Bailey, Danielle E. Soranno, Ari H. Pollack, Vikas R. Dharnidharka, Mark M. Mitsnefes, William E. Smoyer, Michael J. G. Somers, Joshua J. Zaritsky, Joseph T. Flynn, Donna J. Claes, Bradley P. Dixon, Maryjane Benton, Laura H. Mariani, Christopher B. Forrest, Susan L. Furth
JASN Dec 2019, 30 (12) 2427-2435; DOI: 10.1681/ASN.2019040365

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Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research
Michelle R. Denburg, Hanieh Razzaghi, L. Charles Bailey, Danielle E. Soranno, Ari H. Pollack, Vikas R. Dharnidharka, Mark M. Mitsnefes, William E. Smoyer, Michael J. G. Somers, Joshua J. Zaritsky, Joseph T. Flynn, Donna J. Claes, Bradley P. Dixon, Maryjane Benton, Laura H. Mariani, Christopher B. Forrest, Susan L. Furth
JASN Dec 2019, 30 (12) 2427-2435; DOI: 10.1681/ASN.2019040365
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Keywords

  • glomerular disease
  • pediatric nephrology
  • Epidemiology and outcomes

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