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Interpretable Multi-task Deep Learning Predicts Transfusion Requirement and Volume in Acute Type AAortic Dissection Surgery

Interpretable Multi-task Deep Learning Predicts Transfusion Requirement and Volume in Acute Type AAortic Dissection Surgery

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600116945
Enrollment
Unknown
Registered
2026-01-16
Start date
2026-01-16
Completion date
Unknown
Last updated
2026-01-27

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

Conditions

Acute Stanford type A aortic dissection

Interventions

Observation group:None

Sponsors

Southern Medical University Southern Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 82 Years

Inclusion criteria

Inclusion criteria: 1.Age >= 18 years; 2.ATAAD confirmed by computed tomography angiography or echocardiography; 3.Sun’s procedure under DHCA; 4.Availability of complete preoperative, intraoperative, and perioperative records, including fluid balance and transfusion data; 5.Patients who received treatment at the hospital from March 2020 to November 2024 and did not return for follow-up visits.

Exclusion criteria

Exclusion criteria: 1.severe hemorrhagic disorders; 2.known severe coagulation dysfunction (e.g., hereditary coagulopathy or Child–Pugh class C cirrhosis); 3.major surgery or trauma within 4 weeks before admission; 4.preoperative transfusion of blood products; 5.pregnancy or perinatal status; 6.missing DO2 data; 7.incomplete clinical records; 8.death within 24 hours postoperatively; 9.preoperative visceral malperfusion; 10.non-Sun’s procedure.

Design outcomes

Primary

MeasureTime frame
tranfusion;

Countries

China

Contacts

Public ContactXing Chen

Southern Medical University Southern Hospital

496346170@qq.com+86 20 6278 6463

Outcome results

None listed

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