Validated administrative data based ICD-10 algorithms for chronic conditions: A systematic review.
J Epidemiol Popul Health
; 72(4): 202744, 2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-38971056
ABSTRACT
OBJECTIVE:
This systematic review aimed to identify ICD-10 based validated algorithms for chronic conditions using health administrative data.METHODS:
A comprehensive systematic literature search using Ovid MEDLINE, Embase, PsycINFO, Web of Science and CINAHL was performed to identify studies, published between 1983 and May 2023, on validated algorithms for chronic conditions using administrative health data. Two reviewers independently screened titles and abstracts and reviewed full text of selected studies to complete data extraction. A third reviewer resolved conflicts arising at the screening or study selection stages. The primary outcome was validated studies of ICD-10 based algorithms with both sensitivity and PPV of ≥70 %. Studies with either sensitivity or PPV <70 % were included as secondary outcomes.RESULTS:
Overall, the search identified 1686 studies of which 54 met the inclusion criteria. Combining a previously published literature search, a total of 61 studies were included for data extraction. The study identified 40 chronic conditions with high validity and 22 conditions with moderate validity. The validated algorithms were based on administrative data from different countries including Canada, USA, Australia, Japan, France, South Korea, and Taiwan. The algorithms identified included several types of cancers, cardiovascular conditions, kidney diseases, gastrointestinal disorders, and peripheral vascular diseases, amongst others.CONCLUSION:
With ICD-10 prominently used across the world, this up-to-date systematic review can prove to be a helpful resource for research and surveillance initiatives using administrative health data for identifying chronic conditions.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
International Classification of Diseases
Limits:
Humans
Language:
En
Journal:
J Epidemiol Popul Health
Year:
2024
Document type:
Article
Affiliation country:
Canada