Retrospective and prospective study for personalized prediction of life-threatening complications in surgery using machine learning from multimodal process data

Retrospective and prospective study for personalized prediction of life-threatening complications in surgery using machine learning from multimodal process data - SurgOmics

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00035187

Enrollment

15000

Registered

2024-10-09

Start date

2021-05-11

Completion date

Unknown

Last updated

2025-04-07

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

Conditions

C15 C25

Interventions

Group 1: Patients with esophageal or pancreatic disease undergoing esophageal or pancreatic surgery at one of the participating surgical centers

Eligibility

Sex/Gender

All

Age

18 Years to 99 Years

Inclusion criteria

Inclusion criteria: (1) Patients with diseases of the esophagus or pancreas who undergo esophageal or pancreatic surgery at one of the participating surgical centers (2) Patients who have received one of the above-mentioned operations at one of the participating surgical centers since 01.01.2001

Exclusion criteria

Exclusion criteria: Patient incapable of giving consent

Design outcomes

Primary

Measure	Time frame
The main objective of this observational study is to build a cross-center database of retrospective and prospective patient data and use it for data-driven decision support in esophageal and pancreatic surgery using artificial intelligence.	—

Secondary

Measure	Time frame
1. identification of prognostically relevant parameters with regard to treatment decisions for esophageal and pancreatic diseases 2. development of data-driven decision support for the tumor board for gastrointestinal tumors and consultation hours for patients with esophageal or pancreatic tumors based on AI algorithms 3. development of intraoperative context-sensitive decision support 4. development of a data-driven therapy recommendation after esophageal or pancreatic surgery for intensive and normal care units	—

Measure

Time frame

1. identification of prognostically relevant parameters with regard to treatment decisions for esophageal and pancreatic diseases 2. development of data-driven decision support for the tumor board for gastrointestinal tumors and consultation hours for patients with esophageal or pancreatic tumors based on AI algorithms 3. development of intraoperative context-sensitive decision support 4. development of a data-driven therapy recommendation after esophageal or pancreatic surgery for intensive and normal care units

—

Countries

Germany

Contacts

Public ContactMartin Wagner

Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden

Martin.Wagner@ukdd.de+49 35145818283

Outcome results

None listed

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