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Machine Learning-Based Procalcitonin Model for Predicting Anastomotic Leakage After Colorectal Cancer Surgery: A Clinical Study

Machine Learning-Based Procalcitonin Model for Predicting Anastomotic Leakage After Colorectal Cancer Surgery: A Clinical Study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500107709
Enrollment
Unknown
Registered
2025-08-18
Start date
2025-09-01
Completion date
Unknown
Last updated
2025-08-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Anastomotic leakage

Interventions

Training cohort group:Patient baseline characteristics and postoperative inflammatory markers
Validation cohort group:Patient baseline characteristics and postoperative inflammatory markers

Sponsors

Fifth Affiliated Hospital, Sun Yat-Sen University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.Gender: Male or female; 2.Age >=18 years old; 3.Patients who had undergone surgical resection for colonic cancer with intestinal anastomosis. 4.Patients who were treated at The Fifth Affiliated Hospital of Sun Yat-sen University between May 2022 and June 2026; 5.Patients diagnosed with anastomotic leakage following colorectal cancer surgery according to the ACPGBI guidelines.

Exclusion criteria

Exclusion criteria: 1.Patients with active infection during the perioperative period. 2.Patients undergoing urgent surgery; 3.Patients lacking complete procalcitonin (PCT) and other inflammatory marker measurements on postoperative days 1 to 3. 4.Patients deemed ineligible for participation by the investigator;

Design outcomes

Primary

MeasureTime frame
AUC of the Prediction Model;

Secondary

MeasureTime frame
The accuracy of the model;Sensitivity;Specificity;Positive predictive value;Negative predictive value;FI score;Degree of calibration;Net income;

Countries

China

Contacts

Public ContactYonghui Su

Fifth Affiliated Hospital, Sun Yat-Sen University

suyh@mail.sysu.edu.cn+86 756 2528708

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026