Mapping acute encounters in end-stage renal disease: a multi-scale network analysis of presenting reasons and diagnoses

Abstract Emergency department (ED) encounters among patients with end-stage renal disease (ESRD) are clinically complex and multisystemic. Conventional frequency summaries do not capture the co-occurrence structure linking presenting reasons to downstream diagnoses, limiting their utility for system-level triage and pathway design. We performed a retrospective, encounter-level multi-scale network analysis of NHAMCS-ED 2020–2022 visits with the chronic condition indicator ESRD = 1 ( n = 533). We constructed ICD-10-CM chapter and Top-50 diagnosis code co-occurrence networks and assessed symptom-to-diagnosis convergence using bipartite heatmaps. Primary edge weights were raw co-occurrence counts (n₁₁); robustness was assessed using Jaccard, cosine, ϕ, odds ratio, and relative risk, with sensitivity checks for explicit N18.6 coding and pre-pandemic 2018–2019 encounters. ESRD-indicated ED encounters showed a renal-centered architecture. The strongest chapter-level links were Genitourinary System–Symptoms/Signs not elsewhere classified (n₁₁ = 111) and Genitourinary System–Circulatory System (n₁₁ = 110). In the Top-50 network, N18.6 (end-stage renal disease) was the dominant hub, and the strongest edge was N18.6–Z99.2 (dependence on renal dialysis; n₁₁ = 64). Heatmaps showed convergence from broad symptoms to the renal axis, led by General Symptoms–N18.6 (n₁₁ = 62) and Respiratory Symptoms–N18.6 (n₁₁ = 55). Although all encounters were ESRD-indicated, N18.6 appeared in the five available diagnosis fields (DIAG1–DIAG5) in 34.0% of encounters. The renal-centered indicators were descriptively stable across periods; N18.6 and Z99.2 encounter-level proportions did not differ significantly between 2018 and 2019 and 2020–2022. ESRD-indicated ED encounters are organized around a renal-centered backbone with cross-system coupling and symptom-to-diagnosis convergence, supporting network-informed triage and pathway-design hypotheses for acute ESRD care.

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Publication Details

Journal
Network Modeling Analysis in Health Informatics and Bioinformatics
Published
2026-07-23
DOI
https://doi.org/10.1007/s13721-026-00848-7
Primary Topic
Emergency and Acute Care Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Mapping acute encounters in end-stage renal disease: a multi-scale network analysis of presenting reasons and diagnoses

X Zhang, Hairong Wang
Network Modeling Analysis in Health Informatics and Bioinformatics
Emergency and Acute Care Studies
article

Mapping acute encounters in end-stage renal disease: a multi-scale network analysis of presenting reasons and diagnoses

X Zhang, Hairong Wang
article en

Abstract

Abstract Emergency department (ED) encounters among patients with end-stage renal disease (ESRD) are clinically complex and multisystemic. Conventional frequency summaries do not capture the co-occurrence structure linking presenting reasons to downstream diagnoses, limiting their utility for system-level triage and pathway design. We performed a retrospective, encounter-level multi-scale network analysis of NHAMCS-ED 2020–2022 visits with the chronic condition indicator ESRD = 1 ( n = 533). We constructed ICD-10-CM chapter and Top-50 diagnosis code co-occurrence networks and assessed symptom-to-diagnosis convergence using bipartite heatmaps. Primary edge weights were raw co-occurrence counts (n₁₁); robustness was assessed using Jaccard, cosine, ϕ, odds ratio, and relative risk, with sensitivity checks for explicit N18.6 coding and pre-pandemic 2018–2019 encounters. ESRD-indicated ED encounters showed a renal-centered architecture. The strongest chapter-level links were Genitourinary System–Symptoms/Signs not elsewhere classified (n₁₁ = 111) and Genitourinary System–Circulatory System (n₁₁ = 110). In the Top-50 network, N18.6 (end-stage renal disease) was the dominant hub, and the strongest edge was N18.6–Z99.2 (dependence on renal dialysis; n₁₁ = 64). Heatmaps showed convergence from broad symptoms to the renal axis, led by General Symptoms–N18.6 (n₁₁ = 62) and Respiratory Symptoms–N18.6 (n₁₁ = 55). Although all encounters were ESRD-indicated, N18.6 appeared in the five available diagnosis fields (DIAG1–DIAG5) in 34.0% of encounters. The renal-centered indicators were descriptively stable across periods; N18.6 and Z99.2 encounter-level proportions did not differ significantly between 2018 and 2019 and 2020–2022. ESRD-indicated ED encounters are organized around a renal-centered backbone with cross-system coupling and symptom-to-diagnosis convergence, supporting network-informed triage and pathway-design hypotheses for acute ESRD care.

Network Modeling Analysis in Health Informatics and BioinformaticsVol. 15(1)
University of Pittsburgh (US), Carnegie Mellon University (US)
Carnegie Mellon University
Good health and well-being
Openalex Percentile: Top 7%
Emergency and Acute Care Studies
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