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Abstract: FR-PO013

eGFR Trajectories Among Children with CKD Using a Multi-Institutional Electronic Health Record Database

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science

Authors

  • Zee, Jarcy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
  • Gluck, Caroline A., Nemours Children's Hospital Delaware, Wilmington, Delaware, United States
  • Maltenfort, Mitchell, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Goodwin Davies, Amy, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Webb, Ryan, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Higginbotham, Miranda J., The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Dicocco, Elana, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Dharnidharka, Vikas R., St Louis Children's Hospital, St Louis, Missouri, United States
  • Marsolo, Keith A., Duke University School of Medicine, Durham, North Carolina, United States
  • Verghese, Priya S., Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Illinois, United States
  • Ruebner, Rebecca, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Glenn, Dorey A., The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
  • Neu, Alicia, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Denburg, Michelle, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
Background

Progression of pediatric chronic kidney disease (CKD) is highly variable across patients and may be nonlinear over time. This study's objective was to describe eGFR trajectories among a large, unselected cohort of children with CKD.

Methods

Using electronic health records from 16 healthcare institutions within PCORnet, a computable phenotype was implemented to identify children ages 1-18 with mild to moderate CKD (two eGFRs between 30-90 mL/min/1.73m2 at least 90 days apart). Latent class mixture models were used to identify classes of eGFR trajectories from cohort entry until dialysis, transplant, or last follow-up.

Results

N=17,168 children with CKD were grouped into four eGFR trajectory classes [Figure]. Class 1 (“steep decline”) contained N=499 (2.9%) patients with mean eGFR at cohort entry of 66.4 and a steep, nonlinear decline in eGFR. Class 2 (“moderate decline”) had N=6,661 (38.8%) patients with mean entry eGFR of 59.2 and a moderate, nonlinear decline. Class 3 (“stable”) had N=7,999 (46.6%) patients with mean entry eGFR of 78.1 and little change in eGFR over time. Class 4 (“improvement”) had N=2,009 (11.7%) patients with mean entry eGFR of 79.5 and an increase in eGFR. Class 1 (steep decline) patients had more clinic visits and hospitalizations and a larger proportion on anti-hypertension medications, whereas those in Class 4 (improvement) were slightly younger.

Conclusion

Four distinct classes of eGFR trajectories were identified among children meeting criteria for CKD stage 2-3. Over half were in stable or improving eGFR subgroups, and only a small proportion exhibited steep decline. These subgroups provide a real-world, multi-institutional characterization of eGFR trajectories among children with mild-moderate CKD.

Funding

  • Other U.S. Government Support