What this means
Octogenarian patients with colorectal cancer are a growing population with substantial heterogeneity in frailty, functional reserve, nutritional status, and comorbidity. Chronological age alone does not adequately capture this variation in perioperative risk.
A plain-English summary is on its way. Until then, this is the start of the registry's own summary.
- Status
- Opening soon
- Who can join
- Ages 80 and over
- Where
- Not listed
The science
- Registered title
- SGA-OCRC: Prediction of 90-Day Major Adverse Postoperative Outcomes in Octogenarians With Resectable Colorectal Cancer
- Trial ID
- NCT07903129 ↗
- Phase
- Not applicable
- Study type
- observational
- Conditions
- Octogenarian Patients With Non-metastatic Resectable Colorectal Cancer
- Sponsor
- Second Affiliated Hospital, Zhejiang University, School of Medicine
- Ages
- 80 Years to no maximum
- Dates
- First posted Oct 29, 2026 · updated Oct 29, 2026
Registry summary
Octogenarian patients with colorectal cancer are a growing population with substantial heterogeneity in frailty, functional reserve, nutritional status, and comorbidity. Chronological age alone does not adequately capture this variation in perioperative risk. Although geriatric assessment has been increasingly incorporated into the care of older patients with cancer, comprehensive geriatric assessment can be difficult to implement routinely in colorectal surgical practice.
The SGA-OCRC study is a prospective, multicenter observational cohort study designed to address this gap. The investigators will evaluate whether a pragmatic simplified geriatric assessment provides prognostic information beyond conventional clinical, laboratory, and tumor-related risk factors in patients aged ≥80 years with non-metastatic, resectable colorectal cancer. The study will develop and internally validate a preoperative prediction model for 90-day major adverse postoperative outcomes using contemporary prediction-model methodology. Particular attention will be given to model calibration, clinical utility, and internal validation rather than predictor selection based solely on statistical significance.
