Evidence map›Paper›PMID 42364021›Full record

ArticlePediatric nephrology (Berlin, Germany)2026

Pediatric clinician perspectives on clinical decision support tools for chronic kidney disease risk after preterm birth.

Keia Sanderson, Christine E Kistler, Marissa Velarde, Mary E Grewe, T Michael OʼShea, Jennifer E Flythe

Abstract read
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In one paragraph

Article in Pediatric nephrology (Berlin, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Keia SandersonDivision of Nephrology and Hypertension, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA. keia_sanderson@med.unc.edu.ORCID http://orcid.org/0000-0001-7759-4220
Christine E KistlerDivision of Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Marissa VelardeNorth Carolina Translational and Clinical Sciences Institute, University of North Carolina, Chapel Hill, NC, USA.
Mary E GreweNorth Carolina Translational and Clinical Sciences Institute, University of North Carolina, Chapel Hill, NC, USA.
T Michael OʼSheaDepartment of Pediatrics, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
Jennifer E FlytheDivision of Nephrology and Hypertension, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA.

Funding

North Carolina Translational and Clinical Sciences Institute (NC TraCS)UM1TR004406 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI NICHOLAS J SHAHEEN · 2023 to 2026
$37.5M
Machine Learning Risk Prediction of Kidney Disease After Extremely Preterm BirthK23DK131289 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SANDERSON, KEIA · 2023 to 2025
$602k
NCATS NIH HHS UM1TR004406NIDDK NIH HHS K23DK131289
6 · The paper itself

Abstract

backgroundPreterm birth affects approximately 10% of U.S. births, and children born preterm face twice the lifetime risk of chronic kidney disease (CKD). Despite this, kidney health surveillance after preterm birth is uncommon. Although clinical decision support (CDS) tools are widely used in pediatric practice, none address CKD risk stratification after preterm birth. This study assessed pediatric clinician perspectives on facilitators and barriers to CDS tool use, in general and for pediatric CKD risk stratification.

methodsWe conducted a qualitative descriptive study using semistructured interviews with neonatologists, general pediatricians, and pediatric nephrologists in the United States (December 2023-April 2024). Interviews were conducted by video or teleconference, digitally recorded, and professionally transcribed. Thematic analysis followed COREQ guidelines, and sampling continued until thematic saturation was confirmed.

resultsTwenty-five pediatric clinicians participated (44% neonatologists, 44% general pediatricians, and 12% nephrologists; median age 39 years, 76% female, 52% White, 88% non-Hispanic, and 80% academic practice). Clinicians reported strong preferences for CDS tools that efficiently support workflows, integrate with the electronic health record (EHR), and provide actionable recommendations with caregiver education. Key concerns included unintended consequences such as false reassurance, over-referral to nephrology, and care burden for families with limited subspecialty access. All participants endorsed the need for a pediatric CKD risk stratification tool.

conclusionsPediatric clinicians prefer EHR-integrated, evidence-based, family-centered CDS tools to guide CKD risk identification after preterm birth. These findings represent an important step toward developing a pediatric kidney disease risk stratification CDS tool.

Indexed as

Chronic kidney diseaseClinical decision support toolsPediatricsPreterm birthRisk stratification

Identifiers

What Socratic holds

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Registered trials

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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 Socratic graph.