Multivariable models for advanced colorectal neoplasms in screen-eligible individuals at low-to-moderate risk of colorectal cancer: towards improving colonoscopy prioritization.
Clinical prediction rule
Colonoscopy
Colorectal cancer
Multivariable prediction model
Journal
BMC gastroenterology
ISSN: 1471-230X
Titre abrégé: BMC Gastroenterol
Pays: England
ID NLM: 100968547
Informations de publication
Date de publication:
18 Oct 2021
18 Oct 2021
Historique:
received:
28
04
2021
accepted:
07
10
2021
entrez:
19
10
2021
pubmed:
20
10
2021
medline:
21
10
2021
Statut:
epublish
Résumé
Advanced colorectal neoplasms (ACNs), including colorectal cancers (CRC) and high-risk adenomas (HRA), are detected in less than 20% of persons aged 50 years or older who undergo colonoscopy. We sought to derive personalized predictive models of risk of harbouring ACNs to improve colonoscopy wait times for high-risk patients and allocation of colonoscopy resources. We characterized colonoscopy indications, neoplasia risk factors and colonoscopy findings through chart review for consecutive individuals aged 50 years or older who underwent outpatient colonoscopy at The Ottawa Hospital (Ottawa, Canada) between April 1, 2008 and March 31, 2012 for non-life threatening indications. We linked patients to population-level health administrative datasets to ascertain additional historical predictor variables and derive multivariable logistic regression models for risk of harboring ACNs at colonoscopy. We assessed model discriminatory capacity and calibration and the ability of the models to improve colonoscopy specificity while maintaining excellent sensitivity for ACN capture. We modelled 17 candidate predictors in 11,724 individuals who met eligibility criteria. The final CRC model comprised 8 variables and had a c-statistic value of 0.957 and a goodness-of-fit p-value of 0.527. Application of the models to our cohort permitted 100% sensitivity for identifying persons with CRC and > 90% sensitivity for identifying persons with HRA, while improving colonoscopy specificity for ACNs by 23.8%. Our multivariable models show excellent discriminatory capacity for persons with ACNs and could significantly increase colonoscopy specificity without overly sacrificing sensitivity. If validated, these models could allow more efficient allocation of colonoscopy resources, potentially reducing wait times for those at higher risk while deferring unnecessary colonoscopies in low-risk individuals.
Sections du résumé
BACKGROUND
BACKGROUND
Advanced colorectal neoplasms (ACNs), including colorectal cancers (CRC) and high-risk adenomas (HRA), are detected in less than 20% of persons aged 50 years or older who undergo colonoscopy. We sought to derive personalized predictive models of risk of harbouring ACNs to improve colonoscopy wait times for high-risk patients and allocation of colonoscopy resources.
METHODS
METHODS
We characterized colonoscopy indications, neoplasia risk factors and colonoscopy findings through chart review for consecutive individuals aged 50 years or older who underwent outpatient colonoscopy at The Ottawa Hospital (Ottawa, Canada) between April 1, 2008 and March 31, 2012 for non-life threatening indications. We linked patients to population-level health administrative datasets to ascertain additional historical predictor variables and derive multivariable logistic regression models for risk of harboring ACNs at colonoscopy. We assessed model discriminatory capacity and calibration and the ability of the models to improve colonoscopy specificity while maintaining excellent sensitivity for ACN capture.
RESULTS
RESULTS
We modelled 17 candidate predictors in 11,724 individuals who met eligibility criteria. The final CRC model comprised 8 variables and had a c-statistic value of 0.957 and a goodness-of-fit p-value of 0.527. Application of the models to our cohort permitted 100% sensitivity for identifying persons with CRC and > 90% sensitivity for identifying persons with HRA, while improving colonoscopy specificity for ACNs by 23.8%.
CONCLUSIONS
CONCLUSIONS
Our multivariable models show excellent discriminatory capacity for persons with ACNs and could significantly increase colonoscopy specificity without overly sacrificing sensitivity. If validated, these models could allow more efficient allocation of colonoscopy resources, potentially reducing wait times for those at higher risk while deferring unnecessary colonoscopies in low-risk individuals.
Identifiants
pubmed: 34663234
doi: 10.1186/s12876-021-01965-5
pii: 10.1186/s12876-021-01965-5
pmc: PMC8524805
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
383Informations de copyright
© 2021. The Author(s).
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