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Machine learning model based on clinical data and radiological features for predicting hematoma expansion or rebleeding after decompressive craniectomy in traumatic brain injury patients:a retrospective cohort study

Machine learning model based on clinical data and radiological features for predicting hematoma expansion or rebleeding after decompressive craniectomy in traumatic brain injury patients:a retrospective cohort study

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400086517
Enrollment
Unknown
Registered
2024-07-04
Start date
2024-07-04
Completion date
Unknown
Last updated
2024-07-08

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

Conditions

Traumatic brain injury

Interventions

Not applicable:None

Sponsors

Yixing People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: (1) Age=18 years; (2) Emergency decompressive craniectomy (DC) required after admission.

Exclusion criteria

Exclusion criteria: (1) Diffuse brain swelling caused by hypoxia or hypotension; (2) Significant coagulopathy; (3) Unstable vital signs rendering the patient unable to tolerate surgery; (4) Severe concomitant injuries in other locations or presence of serious underlying diseases; (5) Rebleeding at the surgical site; (6) Incomplete clinical data.

Design outcomes

Primary

MeasureTime frame
Hematoma expansion or rebleeding after decompressive craniectomy;Receiver operating characteristic curve;Area under the ROC curve;Positive predictive value;Negative predictive value;—

Countries

China

Contacts

Public ContactDa Wu

Yixing People's Hospital

wudabrain@163.com+86 138 6149 3111

Outcome results

None listed

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