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Construction and application of a risk prediction model for hospital-acquired pressure injury in critically ill patients based on machine learning algorithm

Construction and application of a risk prediction model for hospital-acquired pressure injury in critically ill patients based on machine learning algorithm

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200064281
Enrollment
Unknown
Registered
2022-10-01
Start date
2022-10-01
Completion date
Unknown
Last updated
2023-05-15

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

Conditions

Pressure injury

Interventions

Case series:None

Sponsors

Huzhou First People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Critically ill patients admitted to ICU and EICU; 2. Aged >= 18 years old; 3. ICU/EICU stay for more than 24 hours.

Exclusion criteria

Exclusion criteria: 1. Patients with skin diseases, such as systemic lupus erythematosus, psoriasis, etc., as well as patients with skin damage such as burns; 2. Patients with incomplete data; 4. Patients (or their family members) refused to participate in the study or withdrew halfway;

Design outcomes

Primary

MeasureTime frame
Demographic factors;comorbidity;Treatment of assessment;Nutritional status;Tissue tolerance capacity;Other factors;relevant scoring;—

Countries

China

Contacts

Public ContactYang Chaonan

Huzhou First People's Hospital

yang515358311@163.c0m+86 15131971713

Outcome results

None listed

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