Use of hospital big data to optimize and personalize laboratory test interpretation with an application.
Big data
Optimization
Personalization
Platelets
Precision medicine
Reference population
Journal
Clinica chimica acta; international journal of clinical chemistry
ISSN: 1873-3492
Titre abrégé: Clin Chim Acta
Pays: Netherlands
ID NLM: 1302422
Informations de publication
Date de publication:
06 Jun 2024
06 Jun 2024
Historique:
received:
27
10
2023
revised:
29
04
2024
accepted:
03
06
2024
medline:
9
6
2024
pubmed:
9
6
2024
entrez:
8
6
2024
Statut:
aheadofprint
Résumé
In laboratory medicine, test results are generally interpreted with 95% reference intervals but correlations between laboratory tests are usually ignored. We aimed to use hospital big data to optimize and personalize laboratory data interpretation, focusing on platelet count. Laboratory tests were extracted from the hospital database and exploited by an algorithmic stepwise procedure. For any given laboratory test Y, an "optimized and personalized reference population" was defined by keeping only patients whose laboratory values for all Y-correlated tests fell within their own usual reference intervals, and by partitioning groups by individual-specific variables like sex and age category. The method was applied to platelet count. Laboratory data were recorded for 28,082 individuals. At the end of the algorithmic process, seven correlated laboratory tests were chosen, resulting in a reference sample of 159 platelet counts. A new 95 % reference interval was constructed [152-334 × 10 This method offers new perspectives in laboratory data interpretation, especially in patient screening and longitudinal follow-up.
Sections du résumé
BACKGROUND AND AIMS
OBJECTIVE
In laboratory medicine, test results are generally interpreted with 95% reference intervals but correlations between laboratory tests are usually ignored. We aimed to use hospital big data to optimize and personalize laboratory data interpretation, focusing on platelet count.
MATERIAL AND METHODS
METHODS
Laboratory tests were extracted from the hospital database and exploited by an algorithmic stepwise procedure. For any given laboratory test Y, an "optimized and personalized reference population" was defined by keeping only patients whose laboratory values for all Y-correlated tests fell within their own usual reference intervals, and by partitioning groups by individual-specific variables like sex and age category. The method was applied to platelet count.
RESULTS
RESULTS
Laboratory data were recorded for 28,082 individuals. At the end of the algorithmic process, seven correlated laboratory tests were chosen, resulting in a reference sample of 159 platelet counts. A new 95 % reference interval was constructed [152-334 × 10
CONCLUSION
CONCLUSIONS
This method offers new perspectives in laboratory data interpretation, especially in patient screening and longitudinal follow-up.
Identifiants
pubmed: 38851476
pii: S0009-8981(24)02015-1
doi: 10.1016/j.cca.2024.119763
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
119763Informations de copyright
Copyright © 2024 BIO LOGBOOK. Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.