Development and Validation of The Post-RT LARS Prediction Model (PORTLARS)
- Conditions
- Rectal CancerLow Anterior Resection SyndromeRadiotherapy
- 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
- Curative low anterior resection for nonmetastatic rectal cancer
- Preoperative radiotherapy
- 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
Group Intervention Description Development Questionnaire A primary cohort of eligible patients from the Sixth Affiliated Hospital of Sun Yat-sen University is used for model derivation. Internal cross-validation Questionnaire A cohort of consecutive patients from the Sixth Affiliated Hospital of Sun Yat-sen University is used for internal cross-validation. External validation Questionnaire An independent cohort of eligible patients from other hospitals is used for external validation.
- Primary Outcome Measures
Name Time Method 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
Name Time Method 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.Specificity Over 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.Sensitivity Over 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