Age-stratified risk profiles for emergency colorectal cancer resection: A machine-learning analysis
Young and older adults need different strategies to prevent emergency colorectal cancer surgery—insurance access matters most for younger patients.
This large US national database analysis (n=510,135) applies CART machine learning to reveal fundamentally different mechanisms driving emergency CRC surgery across age groups — access barriers in younger patients vs clinical complexity in older adults. The findings argue against one-size-fits-all ECCR reduction strategies and support age-tailored interventions addressing insurance coverage gaps.
What the study was
- Study design
- Retrospective cohort (multivariable logistic regression + CART machine learning)
- Population
- Adults with colorectal cancer resection admissions in the US National Inpatient Sample (NIS), 2018-2022
- Sample size
- 510135
- Category
- Public Health
- Maturity
- Validated
- Journal
- Journal of Gastrointestinal Surgery
Why it surfaced
Large national cohort with health equity implications. ML-derived age-specific risk phenotypes for emergency CRC surgery provide actionable targets (insurance access in younger adults) and signal that CRC prevention programs should stratify by age and socioeconomic factors, not just clinical risk.
A plain-language summary of published research — not medical advice. Talk to a clinician about your own care.