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Development and Validation of The Post-RT LARS Prediction Model (PORTLARS)

Completed
Conditions
Rectal Cancer
Low Anterior Resection Syndrome
Radiotherapy
Interventions
Other: Questionnaire
Registration Number
NCT05129215
Lead Sponsor
Sixth Affiliated Hospital, Sun Yat-sen University
Brief Summary

Bowel dysfunction is common after a restorative rectal cancer resection. Neoadjuvant radiotherapy is an influential factor that impairs bowel function and quality of life. However, almost half patients who have received primary surgery with preoperative radiotherapy are able to restore a good or moderate bowel function in the long term. This multicenter observational study aims to identify the risk factors of severe bowel dysfunction after rectal cancer resection and neoadjuvant radiotherapy, in accordance with the LARS score, and to build a model that predicts long-term major LARS in the early stage of follow-up. Development and validation cohorts are enrolled from tertiary hospitals in China.

Detailed Description

Not available

Recruitment & Eligibility

Status
COMPLETED
Sex
All
Target Recruitment
901
Inclusion Criteria
  • Curative low anterior resection for nonmetastatic rectal cancer
  • Preoperative radiotherapy
Exclusion Criteria
  • Death
  • Metastasis or recurrence
  • Postoperative radiotherapy
  • Cognitive disorder
  • Intestinal stoma
  • Rectal cancer resection for <12 months
  • Stoma reversal for <6 months

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Arm && Interventions
GroupInterventionDescription
DevelopmentQuestionnaireA primary cohort of eligible patients from the Sixth Affiliated Hospital of Sun Yat-sen University is used for model derivation.
Internal cross-validationQuestionnaireA cohort of consecutive patients from the Sixth Affiliated Hospital of Sun Yat-sen University is used for internal cross-validation.
External validationQuestionnaireAn independent cohort of eligible patients from other hospitals is used for external validation.
Primary Outcome Measures
NameTimeMethod
Area under the curve (AUC)Over one year after restorative rectal cancer resection

The AUC of PORTLARS in predicting major bowel dysfunction after restorative rectal cancer resection with neoadjuvant radiotherapy.

The AUC is evaluated by calculating the area under curve of receiver operating characteristics which plots the proportion of true positive cases (sensitivity) against the proportion of false positive cases (1-specificity) based on various predictive probability threshold.

Secondary Outcome Measures
NameTimeMethod
Positive prediction value (PPV)Over one year after restorative rectal cancer resection

The PPV of PORTLARS in predicting major bowel dysfunction after restorative rectal cancer resection with neoadjuvant radiotherapy.

Model PPV is evaluated by calculating the proportion of the 'actual major LARS' subjects among the total 'predicted major LARS' subjects.

SpecificityOver one year after restorative rectal cancer resection

The specificity of PORTLARS in predicting major bowel dysfunction after restorative rectal cancer resection with neoadjuvant radiotherapy.

Model specificity is evaluated by calculating the proportion of the 'predicted no/minor LARS' subjects among the total 'actual no/minor LARS' subjects.

Negative prediction value (NPV)Over one year after restorative rectal cancer resection

The NPV of PORTLARS in predicting major bowel dysfunction after restorative rectal cancer resection with neoadjuvant radiotherapy.

Model NPV is evaluated by calculating the proportion of the 'actual no/minor LARS' subjects among the total 'predicted no/minor LARS' subjects.

SensitivityOver one year after restorative rectal cancer resection

The sensitivity of PORTLARS in predicting major bowel dysfunction after restorative rectal cancer resection with neoadjuvant radiotherapy.

Model sensitivity is evaluated by calculating the proportion of the 'predicted major LARS' subjects among the total 'actual major LARS' subjects.

Trial Locations

Locations (1)

Sixth Affiliated Hospital, Sun Yat-sen University

🇨🇳

Guangzhou, Guangdong, China

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