Assessment of AI Prediction Models in Prediction of Acute Kidney Injury in Critical Patients

NCT06857188 | Not Yet Recruiting

• 1 other identifier: AI role in AKI prediction
• Study Type: observational
• Target: 1,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

The assessment of AI -based prediction models in detecting AKI early in critically ill patients. Specifically, the aim is to evaluate the model's ability to predict the onset of AKI before it clinically manifests allowing for early interventions

Conditions

Acute Kidney Failure; Artificial Intelligence (AI)

Outcome Measures

Primary Outcomes (1)

• The assessment of AI -based prediction models in detecting AKI early in critically ill patients.

  assessment of the ability of the AI based model to detect AKI in critically ill patients by evaluating the model ability to predict the onset of early AKI before it is clinically manifested for early interventions . this will be done by generating an AKI risk score by the model for each patient. Outcomes are tracked and the model is updated periodically based on new patient data to improve accuracy and reliability

  1 year

Secondary Outcomes (1)

• assessment of other aspects

  1 year

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Baseline characteristics, including demographic information, comorbidities, vital signs, laboratory results, medical interventions, disease severity scores, etc. were carefully reviewed and collected. The definitions of comorbidities including congestive heart failure, peptic ulcer disease, myocardial infarction, peripheral vascular disease, diabetes, dementia, chronic pulmonary disease, rheumatic disease, cerebrovascular disease, cancer, paraplegia, liver disease, renal disease, and acquired immunedeficiency syndrome. Severe organ failure due to ineffective immune response to infection was identified as sepsis. During the first 24 h when the patient was admitted to the ICU, the average values of the patient's vital signs (heart rate, mean arterial pressure, respiration rate, body temperature, and SpO2) were measured,, and the highest value of the biochemical laboratory tests (hematocrit, hemoglobin, platelets, white blood cell, blood urea nitrogen, international normalized ratio, Scr,

You may qualify if:

• All adult (aged 18 years old and older) patients who were admitted to the ICU were included in this study.

You may not qualify if:

• patients under 18 years old
• End-stage renal disease
• Acute Kidney Injury at ICU admission
• Inability to obtain sufficient clinical data

Sponsors & Collaborators

• Assiut University: lead

Related Links

• Related Info

MeSH Terms

Conditions

Acute Kidney Injury

Condition Hierarchy (Ancestors)

Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Study Officials

• Alaa El-Dein ElMoneim Sayed, professor

  Assiut University

  STUDY DIRECTOR

Central Study Contacts

Kareem Sherif Mosabah, Assistant lecturer

CONTACT

+201002447880; kareemsherif14@gmail.com

Radwa Awad Abd El Hafez, lecturer

CONTACT

+201003797448; radwaawad@aun.edu.eg

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assisstant lecturer of critical care

Study Record Dates

• First Submitted: February 26, 2025
• First Posted: March 4, 2025
• Study Start: May 14, 2025
• Primary Completion: March 1, 2026
• Study Completion: March 1, 2026
• Last Updated: May 16, 2025

Record last verified: 2025-05