Acute Kidney FailureNCT06857188
Assessment of AI Prediction Models in Prediction of Acute Kidney Injury in Critical Patients
An observational study of Acute Kidney Failure and Artificial Intelligence (AI), run by Assiut University.
Trial 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 Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Acute Kidney Failure, Artificial Intelligence (AI)
- How many people
- 1,000 participants
- Where
- No sites listed yet
- Study dates
- 14 May 2025to 1 Mar 2026
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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,
Who may be able to join
- All adult (aged 18 years old and older) patients who were admitted to the ICU were included in this study.
Who cannot join
- • patients under 18 years old
- End-stage renal disease
- Acute Kidney Injury at ICU admission
- Inability to obtain sufficient clinical data
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Role of Artificial Intelligence in the Prediction of AKI in Critically Ill Patients
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
Read the detailed description
Acute kidney injury (AKI) is the most severe, common, and life-threatening complication in hospitalized patients and is associated with high morbidity and mortality rates . It has been demonstrated that AKI affects approximately 30-60% of critically ill patients, especially those in the intensive care unit (ICU) . Despite the recent advances in clinical care and dialysis technology, the occurrence of AKI in ICU patients has a mortality rate of up to 50%, which is 1.5 to 2-fold to that of ICU patients without AKI . However, if detected and managed promptly, interventions guided by established recommendations, such as those provided by KDIGO, may mitigate the risk of further deterioration in AKI patients . Therefore, identifying individuals at high risk of AKI is vital for managing critically ill patients.
Artificial intelligence (AI) and machine learning (ML) represent emerging technologies that could use large amounts of health-related data to help physicians make better clinical decisions and improve individual health outcomes. While serum creatinine (Scr) and urine output serve as diagnostic criteria for AKI, delays in their detection may occur. Therefore, early identification of patients at risk of developing AKI is crucial to create a window for preventive interventions and mitigate the risk of further deterioration. Several previous studies have developed various ML-based models to predict AKI in critically ill patients due to the potential benefits of early detection of AKI . It is critical to remove the mystery surrounding ML since doing so makes it simpler for doctors to comprehend the reasoning behind ML . In order to explain why ML makes the choices it does, a new field called Explainable AI (XAI) has emerged. Two of the most popular methods for explaining are Local Interpretable Model-Agnostic Explanation (LIME) and Shapley Additive Explanation (SHAP) . Novel interpretable approaches have been effectively utilized to explain ML models for preventing hypoxemia during surgery [10], predicting mortality in sepsis and AKI , predicting the occurrence of AKI following cardiac surgery , and predicting antibiotic resistance .
To the best of our knowledge, the reliability and robustness of explanatory techniques for detecting AKI in critically sick patients have rarely been studied. Therefore, the present study was conducted to construct an ML approach for the early prediction of AKI in ICU patients and to apply XAIs to make ML more transparent and interpretable.
- Conditions
- Acute Kidney Failure, Artificial Intelligence (AI)
- Medical subject headings
- Acute Kidney Injury
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 1 year
Secondary outcomes (1)
assessment of other aspects
assessment of clinical outcomes ( e.g, time to intervention , AKI severity , RRT use, and patient mortality ) impact on ICU (length of ICU stay)
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Mar 2025
- Study start14 May 2025 (estimated)
- Primary completion1 Mar 2026 (estimated)
- Study completion1 Mar 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Acute Kidney FailureStudies of the same condition, open and under way.
- More trials run by Assiut UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Assiut University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kareem Sherif, Assiut University
- Investigators
- Alaa El-Dein ElMoneim Sayed, professor, Study director, Assiut University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06857188
- Sponsor's protocol ID
- AI role in AKI prediction
- Status last verified
- May 2025
- First submitted
- 26 Feb 2025
- Last updated
- 16 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Role of Artificial Intelligence in the Prediction of AKI in Critically Ill Patients
- ClinicalTrials.gov ID
- NCT06857188
- Acronym / sponsor ID
- AI role in AKI prediction
- Conditions
- Acute Kidney Failure, Artificial Intelligence (AI)
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Assiut UniversityUniversity, hospital or other
- Study start
- 14 May 2025 (estimated)
- Primary completion
- 1 Mar 2026 (estimated)
- Study completion
- 1 Mar 2026 (estimated)
- First posted
- 4 Mar 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Detailed description
Acute kidney injury (AKI) is the most severe, common, and life-threatening complication in hospitalized patients and is associated with high morbidity and mortality rates . It has been demonstrated that AKI affects approximately 30-60% of critically ill patients, especially those in the intensive care unit (ICU) . Despite the recent advances in clinical care and dialysis technology, the occurrence of AKI in ICU patients has a mortality rate of up to 50%, which is 1.5 to 2-fold to that of ICU patients without AKI . However, if detected and managed promptly, interventions guided by established recommendations, such as those provided by KDIGO, may mitigate the risk of further deterioration in AKI patients . Therefore, identifying individuals at high risk of AKI is vital for managing critically ill patients.
Artificial intelligence (AI) and machine learning (ML) represent emerging technologies that could use large amounts of health-related data to help physicians make better clinical decisions and improve individual health outcomes. While serum creatinine (Scr) and urine output serve as diagnostic criteria for AKI, delays in their detection may occur. Therefore, early identification of patients at risk of developing AKI is crucial to create a window for preventive interventions and mitigate the risk of further deterioration. Several previous studies have developed various ML-based models to predict AKI in critically ill patients due to the potential benefits of early detection of AKI . It is critical to remove the mystery surrounding ML since doing so makes it simpler for doctors to comprehend the reasoning behind ML . In order to explain why ML makes the choices it does, a new field called Explainable AI (XAI) has emerged. Two of the most popular methods for explaining are Local Interpretable Model-Agnostic Explanation (LIME) and Shapley Additive Explanation (SHAP) . Novel interpretable approaches have been effectively utilized to explain ML models for preventing hypoxemia during surgery [10], predicting mortality in sepsis and AKI , predicting the occurrence of AKI following cardiac surgery , and predicting antibiotic resistance .
To the best of our knowledge, the reliability and robustness of explanatory techniques for detecting AKI in critically sick patients have rarely been studied. Therefore, the present study was conducted to construct an ML approach for the early prediction of AKI in ICU patients and to apply XAIs to make ML more transparent and interpretable.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Day
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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,
Key inclusion criteria (1 listed)
- All adult (aged 18 years old and older) patients who were admitted to the ICU were included in this study.
Key exclusion criteria (4 listed)
- • patients under 18 years old
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The assessment of AI -based prediction models in detecting AKI early in critically ill patients.
Time frame: 1 year
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 secondary outcome
assessment of other aspects
Time frame: 1 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 May 2025.
- Related InfoLink provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Alaa El-Dein ElMoneim Sayed, professor study director
Assiut University
Questions about this trial
What is this clinical trial studying?
An observational study of Acute Kidney Failure and Artificial Intelligence (AI), run by Assiut University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 16 May 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Kareem Sherif Mosabah, Assistant lecturer (+201002447880, kareemsherif14@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The assessment of AI -based prediction models in detecting AKI early in critically ill patients. (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 May 2025.