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Prediction of complications of acute pancreatitis by machine learning models: a retrospective prospective study

Prediction of complications of acute pancreatitis by machine learning models: a retrospective prospective study

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR1800016079
Enrollment
Unknown
Registered
2018-05-09
Start date
2018-06-01
Completion date
Unknown
Last updated
2020-02-10

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

Conditions

acute pancreatitis

Interventions

Gold Standard:1. Modified Marshall score for evaluation of persistent organ failure or multiple organ failure
2. Diagnosis of intraperitoneal infection depends on infection symptoms and CT examination or Abdominal drainage culture.
Index test:Logistic&#32
regression&#32
analysis,&#32
Support&#32
vector&#32
machine&#32
and&#32
Artificial&#32
neural&#32
network

Sponsors

Daping Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: The patient meet more than 2 criteria for the diagnosis of acute pancreatitis: (1) the typical clinical symptoms of persistent abdominal pain; (2) the serum amylase and lipase were 3 times higher than the upper limit of the normal value; (3) have abdominal imaging features.

Exclusion criteria

Exclusion criteria: (1) Acute [ancreatitis during pregnancy; (2) patients with pancreatic tumor, cirrhosis or coagulopathy; (3) incomplete laboratory or imaging data.

Design outcomes

Primary

MeasureTime frame
Marshall score system;—

Countries

China

Contacts

Public ContactChen Dongfeng

Daping Hospital, Third Military Medical University

chendf1981@126.com+86 13883032812

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 16, 2026