Improving the Diagnosis of Inherited Kidney Diseases Through Domain-Specific Databases
6 affiliations
- 1Departament d’Enginyeria de Sistemes, B2SLab, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- 2Network Biomedical Research Centre for Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- 3Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
- 4Nephrology Department, Fundació Puigvert, Barcelona, Spain
- 5Institut de Recerca Sant Pau (IR-Sant Pau), Departament de Medicina, Universitat Autònoma de Barcelona, RICORS2040 Renal, Barcelona, Spain
- 6Pediatric Nephrology and Renal Transplant Department, Hospital Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
Abstract
Introduction: The diagnostic accuracy of phenotype-matching tools for rare diseases depends not only on the algorithm but also on the quality of the disease definitions. Inherited kidney diseases (IKDs) pose a significant challenge for clinical diagnosis due to substantial phenotypic overlap and heterogeneity, but many patients remain undiagnosed despite growing access to genetic testing. This study assesses whether an expert-curated, IKD-specific database (CURATED) provides a closer phenotypic match to real patients than the Mondo Disease Ontology (MONDO), a broad, general-purpose resource.
Methods: Using a cohort of 340 real-world patients with genetically confirmed diagnoses across 8 IKD categories, we compared MONDO- and CURATED-based disease definitions across 3 independent, computational phenotype-matching tools (Phenomizer, Phrank, and Phen2Disease), which match Human Phenotype Ontology (HPO)-annotated patient phenotypes to disease profiles, using similarity scoring, diagnostic ranking, and mixed-effects models accounting for disease imbalance and interannotator variability.
Results: With CURATED definitions, similarity scores rose by 18%–30% and Discounted Cumulative Gain (DCG) improved by 15%–37% relative to MONDO, reflecting a stronger tendency across all 3 tools to place the correct diagnosis at or near the top of the ranked list. These improvements were maintained after mixed-effects modeling.
Conclusion: The quality of the underlying disease database can be as consequential as the choice of diagnostic algorithm. In this cohort, domain-specific curation improved diagnostic prioritization, suggesting that investment in high-quality, nephrology-specific phenotype resources may advance IKD diagnosis as much as further algorithmic refinement.
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BibTeX
@article{leon2026improving,
title = {{Improving the Diagnosis of Inherited Kidney Diseases Through Domain-Specific Databases}},
author = {Rafael León and Leonor Fayos de Arizón and Elizabeth R. Viera and Joshua Llano Viles and Jordi Fonollosa and Mónica Furlano and Pedro Arango and Víctor López-Báez and Roser Torra and Alexandre Perera},
journal = {Kidney International Reports},
year = {2026},
month = sep,
doi = {10.1016/j.ekir.2026.107104},
url = {https://doi.org/10.1016/j.ekir.2026.107104},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Improving the Diagnosis of Inherited Kidney Diseases Through Domain-Specific Databases
AU - Rafael León
AU - Leonor Fayos de Arizón
AU - Elizabeth R. Viera
AU - Joshua Llano Viles
AU - Jordi Fonollosa
AU - Mónica Furlano
AU - Pedro Arango
AU - Víctor López-Báez
AU - Roser Torra
AU - Alexandre Perera
PY - 2026
DA - 2026/09/25
JO - Kidney International Reports
DO - 10.1016/j.ekir.2026.107104
UR - https://doi.org/10.1016/j.ekir.2026.107104
AB - Introduction: The diagnostic accuracy of phenotype-matching tools for rare diseases depends not only on the algorithm but also on the quality of the disease definitions. Inherited kidney diseases (IKDs) pose a significant challenge for clinical diagnosis due to substantial phenotypic overlap and heterogeneity, but many patients remain undiagnosed despite growing access to genetic testing. This study assesses whether an expert-curated, IKD-specific database (CURATED) provides a closer phenotypic match to real patients than the Mondo Disease Ontology (MONDO), a broad, general-purpose resource. Methods: Using a cohort of 340 real-world patients with genetically confirmed diagnoses across 8 IKD categories, we compared MONDO- and CURATED-based disease definitions across 3 independent, computational phenotype-matching tools (Phenomizer, Phrank, and Phen2Disease), which match Human Phenotype Ontology (HPO)-annotated patient phenotypes to disease profiles, using similarity scoring, diagnostic ranking, and mixed-effects models accounting for disease imbalance and interannotator variability. Results: With CURATED definitions, similarity scores rose by 18%–30% and Discounted Cumulative Gain (DCG) improved by 15%–37% relative to MONDO, reflecting a stronger tendency across all 3 tools to place the correct diagnosis at or near the top of the ranked list. These improvements were maintained after mixed-effects modeling. Conclusion: The quality of the underlying disease database can be as consequential as the choice of diagnostic algorithm. In this cohort, domain-specific curation improved diagnostic prioritization, suggesting that investment in high-quality, nephrology-specific phenotype resources may advance IKD diagnosis as much as further algorithmic refinement.
SN - 2468-0249
ER -