PREDICTION OF MASSIVE BLEEDING IN PANCREATIC SURGERY USING A MACHINE LEARNING APPROACH
- Conditions
- pancreatic ductal adenocarcinoma
- Registration Number
- JPRN-jRCT1020210001
- Lead Sponsor
- Wakiya Taiichi
- Brief Summary
This study aimed to build a model for massive intraoperative blood loss (IBL) prediction using a decision tree algorithm. Using the preoperatively available data of the patients (34 variables), we built a decision tree classification algorithm. Decision tree sensitivity was 98.5% in the training data set and 100% in the testing data set. Our findings suggested that a decision tree can provide a new potential approach to predict massive IBL in surgery for resectable pancreatic cancer.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Complete
- Sex
- All
- Target Recruitment
- 175
Patients undergoing pancreatic surgery for resectable pancreatic ductal adenocarcinoma at Hirosaki university hospital between 2007 and 2020.
Subjects must meet the following criteria to be enrolled in this study.
1. Age >20 years
2. Pathologically diagnosed with pancreatic ductal adenocarcinoma
Patients who lacking clinicopathological findings
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Intraoperative blood loss
- Secondary Outcome Measures
Name Time Method