The use of artificial intelligence to identify subjects with a positive FOBT predicted to be non-compliant with both colonoscopy and harbor cancer.
Artificial intelligence
Colorectal cancer
Fecal occult blood test
Machine learning
Screening
Journal
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
ISSN: 1878-3562
Titre abrégé: Dig Liver Dis
Pays: Netherlands
ID NLM: 100958385
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
11
03
2023
revised:
23
04
2023
accepted:
27
04
2023
medline:
28
8
2023
pubmed:
8
6
2023
entrez:
7
6
2023
Statut:
ppublish
Résumé
Subjects with a positive Fecal Occult Blood Test (FOBT) that are non-compliant with colonoscopy are at increased risk for colorectal cancer (CRC). Yet, in clinical practice, many remain non-compliant. To evaluate whether machine learning models (ML) can identify subjects with a positive FOBT predicted to be both non-compliant with colonoscopy within six months and harbor CRC (defined as the "target population"). We trained and validated ML models based on extensive administrative and laboratory data about subjects with a positive FOBT between 2011 and 2013 within Clalit Health that were followed for cancer diagnosis up to 2018. Out of 25,219 included subjects, 9,979(39.6%) were non-compliant with colonoscopy, and 202(0.8%) were both non-compliant and harbored cancer. Using ML, we reduced the number of subjects needed to engage from 25,219 to either 971 (3.85%) to identify 25.8%(52/202) of the target population, reducing the number needed to treat (NNT) from 124.8 to 19.4 or to 4,010(15,8%) to identify 55.0%(52/202) of the target population, NNT = 39.7. Machine learning technology may help healthcare organizations to identify subjects with a positive FOBT predicted to be both non-compliant with colonoscopy and harbor cancer from the first day of a positive FOBT with improved efficiency.
Sections du résumé
BACKGROUND
Subjects with a positive Fecal Occult Blood Test (FOBT) that are non-compliant with colonoscopy are at increased risk for colorectal cancer (CRC). Yet, in clinical practice, many remain non-compliant.
AIMS
To evaluate whether machine learning models (ML) can identify subjects with a positive FOBT predicted to be both non-compliant with colonoscopy within six months and harbor CRC (defined as the "target population").
METHODS
We trained and validated ML models based on extensive administrative and laboratory data about subjects with a positive FOBT between 2011 and 2013 within Clalit Health that were followed for cancer diagnosis up to 2018.
RESULTS
Out of 25,219 included subjects, 9,979(39.6%) were non-compliant with colonoscopy, and 202(0.8%) were both non-compliant and harbored cancer. Using ML, we reduced the number of subjects needed to engage from 25,219 to either 971 (3.85%) to identify 25.8%(52/202) of the target population, reducing the number needed to treat (NNT) from 124.8 to 19.4 or to 4,010(15,8%) to identify 55.0%(52/202) of the target population, NNT = 39.7.
CONCLUSION
Machine learning technology may help healthcare organizations to identify subjects with a positive FOBT predicted to be both non-compliant with colonoscopy and harbor cancer from the first day of a positive FOBT with improved efficiency.
Identifiants
pubmed: 37286451
pii: S1590-8658(23)00608-4
doi: 10.1016/j.dld.2023.04.027
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1253-1258Informations de copyright
Copyright © 2023 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Conflict of interest All authors declare no conflict of interest.