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Establishment of a Machine Learning-Based Predictive Model for Sepsis-Associated Acute Kidney Injury and a Cohort Study on Traditional Chinese Medicine Syndrome Features

Establishment of a Machine Learning-Based Predictive Model for Sepsis-Associated Acute Kidney Injury and a Cohort Study on Traditional Chinese Medicine Syndrome Features

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ITMCTR
Registry ID
ITMCTR2026001701
Enrollment
Unknown
Registered
2026-07-06
Start date
2026-01-01
Completion date
Unknown
Last updated
2026-08-03

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

Conditions

Sepsis

Interventions

Sepsis patients who meet the inclusion and exclusion criteria:None

Sponsors

Shanghai University of Traditional Chinese Medicine Affiliated Longhua Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: (1) First admission to ICU; (2) 18 years old 24 hours; (4) Meets diagnostic criteria for sepsis.

Exclusion criteria

Exclusion criteria: (1) Patients who died or discontinued treatment, abandoned treatment, or were discharged against medical advice within 24 hours of admission; (2) Patients with missing individual data exceeding 30%; (3) Patients with a history of acute kidney injury (AKI) or chronic kidney dysfunction, or those already undergoing renal replacement therapy (RRT) at admission; (4) Patients with primary diseases that severely impact survival, including acute myocardial infarction, acute cerebral infarction, and metastatic malignant tumors.

Design outcomes

Primary

MeasureTime frame
The incidence rate of sepsis-associated acute kidney injury.;

Secondary

MeasureTime frame
Hospital Mortality Rate;28-day all-cause mortality rate;

Countries

China

Contacts

Public ContactShen Yicheng

Shanghai University of Traditional Chinese Medicine Affiliated Longhua Hospital

syclhyy@163.com15050331708

Outcome results

None listed

Source: ITMCTR (via WHO ICTRP) · Data processed: Aug 10, 2026