EHR-Embedded Risk Score for Acute Kidney Injury
(ML-AKI Trial)
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial aims to determine if a machine learning tool can reduce kidney problems after surgery. The tool, known as the EHR-Embedded AKI Risk Score, provides doctors with a risk score for acute kidney injury (AKI) and suggests kidney protection strategies. Some doctors will receive only the risk score, while others will also receive alerts prompting them to take action. This trial includes adults undergoing non-obstetric surgery at UCSF, who are staying overnight and have specific kidney function levels. For those concerned about kidney issues after surgery, this trial may help identify better kidney protection methods. As an unphased trial, it offers a unique opportunity to contribute to innovative research that could improve kidney protection strategies for future patients.
What prior data suggests that this machine learning-derived risk score is safe for improving kidney-protective care?
Research has shown that the EHR-Embedded AKI Risk Score tool is generally safe for patients. Integrated into electronic health records, it helps identify patients at high risk for acute kidney injury (AKI) without requiring extra blood tests. This tool enables doctors to focus on patients needing more attention, potentially avoiding unnecessary treatments.
The version with Best Practice Advisory includes alerts for doctors, offering suggestions to protect kidney health. These alerts serve as recommendations, allowing doctors to choose the best action for each patient.
Oversight committees have reviewed and approved both tools to ensure safety and ethics. Studies have not reported any significant harmful effects, making these tools a promising and safe option for improving patient care.12345Why are researchers excited about this trial?
Researchers are excited about this trial because it explores the potential of using machine learning to predict acute kidney injury (AKI) risk in patients. Unlike traditional methods that rely on general clinical assessments, this trial tests an electronic health record (EHR)-embedded risk score, which is tailored to each patient's data. Additionally, one arm of the trial includes a Best Practice Advisory (BPA) that actively alerts healthcare providers about high-risk patients, suggesting a kidney-protective bundle based on KDIGO guidelines. This approach aims to provide personalized, proactive care, potentially reducing the incidence of AKI by catching risk factors early and guiding timely interventions.
What evidence suggests that this trial's treatments could be effective for reducing acute kidney injury?
This trial will evaluate the effectiveness of an EHR-Embedded AKI Risk Score in predicting acute kidney injury (AKI). Studies have shown that electronic health records (EHR) with a specialized computer program can predict AKI before it occurs. This tool identifies high-risk patients even without complete kidney health information, enabling doctors to take preventive steps sooner. Research indicates that these tools improve AKI prediction by 5% compared to traditional methods. In this trial, one arm will use the EHR-Embedded AKI Risk Score alone, while another will combine the Risk Score with a Best Practice Advisory (BPA), which provides clear guidance to doctors. This system can further increase the chances of preventing kidney damage after surgery. Overall, evidence supports using these digital tools to enhance patient care and reduce the risk of AKI.12346
Who Is on the Research Team?
Andrew Bishara, MD
Principal Investigator
University of California, San Francisco
Are You a Good Fit for This Trial?
This trial is for adults (18 or older) having non-obstetric surgery at UCSF who are expected to stay overnight and have moderately functioning kidneys (eGFR ≥15). It excludes those on chronic dialysis, kidney transplant patients, and obstetrics cases.Inclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Preoperative Assessment
Machine learning-derived AKI risk score is assessed preoperatively for high-risk patients
Intraoperative Monitoring
Monitoring of intraoperative parameters including fluid volumes, nephrotoxin exposure, and hypotension
Postoperative Monitoring
Monitoring of serum creatinine changes and other secondary outcomes from operation to postoperative day 7
Follow-up
Participants are monitored for safety and effectiveness after treatment, including dialysis requirement and hospital readmission
What Are the Treatments Tested in This Trial?
Interventions
- EHR-Embedded AKI Risk Score
- EHR-Embedded AKI Risk Score with Best Practice Advisory
Trial Overview
The study tests if showing anesthesiologists a machine learning-based risk score for acute kidney injury—either alone or with an alert—improves care and reduces kidney problems after surgery. Patients are grouped based on their anesthesiologist's assignment.
How Is the Trial Designed?
3
Treatment groups
Experimental Treatment
Active Control
The machine learning-derived AKI risk score is displayed within the electronic health record for high-risk patients, accompanied by an interruptive Best Practice Advisory (BPA) that notifies providers that the patient may benefit from a KDIGO-based kidney-protective bundle. The alert is advisory only and does not mandate clinical actions.
A machine learning-derived preoperative AKI risk score is displayed within the electronic health record for high-risk patients. A passive recommendation indicating that the patient may benefit from a KDIGO-based kidney-protective bundle is provided. The information is advisory only, and no interruptive alerts are used.
Participants receive usual perioperative care with a placeholder blank display without the machine learning-derived acute kidney injury (AKI) risk score. The clinical decision support tool remains hidden in the electronic health record, and no alerts or recommendations related to the study are shown.
Find a Clinic Near You
Who Is Running the Clinical Trial?
University of California, San Francisco
Lead Sponsor
National Institute of General Medical Sciences (NIGMS)
Collaborator
Citations
EHR-Embedded Risk Score for Acute Kidney Injury (ML- ...
* Data available in the UCSF EHR for risk scoring and outcomes. * Inpatient cases with expected overnight stay. * Baseline eGFR ≥15 mL/min ...
Electronic Health Record-Based Predictive Models for Acute ...
The AKI screening tool can be incorporated into EHR systems to identify high risk patients without serum creatinine data, enabling targeted laboratory testing.
Real-Time Acute Kidney Injury Perioperative Prediction ...
This investigator-initiated, pragmatic trial evaluates whether displaying a machine learning (ML)- derived perioperative AKI risk score-alone or paired with ...
Implementation of an Electronic Health Records–Based ...
In this study, we found that an EHR-based safe contrast limit tool using automatically derived patient data performed well in predicting CA-AKI ...
Clustering analysis of multi-site electronic health records ...
Despite its mild presentation, AKI-1 outcomes range from rapid renal recovery to prolonged kidney dysfunction. For instance, in a cohort of AKI ...
Reducing Postoperative Acute Kidney Injury: A Regional ...
With the full implementation of risk tools, alerts, and order sets in Phase 3, AKI incidence declined to 400 cases per month. Process control analysis showed ...
Unbiased Results
We believe in providing patients with all the options.
Your Data Stays Your Data
We only share your information with the clinical trials you're trying to access.
Verified Trials Only
All of our trials are run by licensed doctors, researchers, and healthcare companies.