Quality of ICD-10 Diagnoses for Cataract in the Danish National Patient Register

PURPOSE: To assess the quality of International Classification of Diseases (ICD)-10 diagnoses for cataract in the Danish National Patient Register, which remains unexplored. METHODS: We extracted 100 consecutive patients registered with a diagnosis of cataract (50 with H25 [senile cataract] and 50 with H26 [other cataract]). Positive predictive values (PPVs) were calculated as the proportion of diagnoses that were confirmed after manual medical record review. PPVs were calculated both overall and in prespecified subgroups. RESULTS: The median patient age was 76 years, and most were females (61%). The overall PPV for a diagnosis of cataract (H25 or H26) in the Danish National Patient Register was 95.0% (95% confidence interval [CI] 88.8-97.8%). The PPV was 90% or above across all subgroups of the H25/H26 diagnoses, across sex, incident and recurrent diagnoses, and primary position diagnosis, but lower for secondary position diagnoses (40.0%, 95% CI 5.3-85.3%). In most cases, a more specific ICD-code was available but not used in clinical practice. CONCLUSION: The positive predictive value of a cataract diagnosis in the Danish National Patient Register is high and suitable for use in epidemiological studies. Subtypes of cataract cannot be reliably identified in the register due to widespread use of unspecified cataract diagnoses.

Authors

Institutions

Publication Details

Journal
Ophthalmic Epidemiology
Published
2026-09-10
DOI
https://doi.org/10.1080/09286586.2026.2729060
Primary Topic
Connexins and lens biology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quality of ICD-10 Diagnoses for Cataract in the Danish National Patient Register

Ida Ehlers Albertsen, Thure Filskov Overvad, Marie Ørskov, Mai Skov Christiansen
Ophthalmic Epidemiology
Connexins and lens biology
article

Quality of ICD-10 Diagnoses for Cataract in the Danish National Patient Register

Ida Ehlers Albertsen, Thure Filskov Overvad, Marie Ørskov, Mai Skov Christiansen
article en

Abstract

PURPOSE: To assess the quality of International Classification of Diseases (ICD)-10 diagnoses for cataract in the Danish National Patient Register, which remains unexplored. METHODS: We extracted 100 consecutive patients registered with a diagnosis of cataract (50 with H25 [senile cataract] and 50 with H26 [other cataract]). Positive predictive values (PPVs) were calculated as the proportion of diagnoses that were confirmed after manual medical record review. PPVs were calculated both overall and in prespecified subgroups. RESULTS: The median patient age was 76 years, and most were females (61%). The overall PPV for a diagnosis of cataract (H25 or H26) in the Danish National Patient Register was 95.0% (95% confidence interval [CI] 88.8-97.8%). The PPV was 90% or above across all subgroups of the H25/H26 diagnoses, across sex, incident and recurrent diagnoses, and primary position diagnosis, but lower for secondary position diagnoses (40.0%, 95% CI 5.3-85.3%). In most cases, a more specific ICD-code was available but not used in clinical practice. CONCLUSION: The positive predictive value of a cataract diagnosis in the Danish National Patient Register is high and suitable for use in epidemiological studies. Subtypes of cataract cannot be reliably identified in the register due to widespread use of unspecified cataract diagnoses.

Ophthalmic Epidemiology
Aalborg University Hospital (DK), Odense University Hospital (DK), Western Regional Hospital (NP), Aalborg University (DK)
Good health and well-being
Openalex Percentile: Top 18%
Connexins and lens biology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.