Abstract
We identified mortality-, age-, and sex-associated differences in relation to reference intervals (RIs) for laboratory tests in population-wide data from nearly 2 million hospital patients in Denmark and comprising more than 300 million measurements. A low-parameter mathematical wave-based modification method was developed to adjust for dietary and environment influences during the year. The resulting mathematical fit allowed for improved association rates between re-classified abnormal laboratory tests, patient diagnoses, and mortality. The study highlights the need for seasonally modified RIs and presents an approach that has the potential to reduce over- and underdiagnosis, affecting both physician-patient interactions and electronic health record research as a whole.
Original language | English |
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Article number | 100778 |
Journal | Patterns |
Volume | 4 |
Issue number | 8 |
Number of pages | 14 |
DOIs | |
Publication status | Published - 2023 |
Bibliographical note
Publisher Copyright:© 2023 The Author(s)
Keywords
- DSML3: Development/pre-production: Data science output has been rolled out/validated across multiple domains/problems
- health data science
- hospital laboratory tests
- mortality
- reference intervals
- seasonality