Acute Kidney InjuryML-AKINCT07604662
Real-Time Acute Kidney Injury Perioperative Prediction Clinical Trial
A study testing EHR-Embedded AKI Risk Score and EHR-Embedded AKI Risk Score with Best Practice Advisory in people with Acute Kidney Injury and Surgery Complications, run by University of California, San Francisco at 1 site in United States.
Trial summary
This investigator-initiated, pragmatic trial evaluates whether displaying a machine learning (ML)- derived perioperative AKI risk score-alone or paired with an interruptive Best/Our Practice Advisory (BPA/OPA)-improves kidney-protective care and reduces kidney injury after non-obstetric surgery at UCSF. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Acute Kidney Injury, Surgery Complications, Anesthesia
- What is being tested?
- EHR-Embedded AKI Risk Score and EHR-Embedded AKI Risk Score with Best Practice Advisory
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 25,518 participants
- Where
- 1 siteUnited States
- Study dates
- 15 Oct 2026to 15 Dec 2027
- Phase
- Interventional
- 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
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Adults ≥18 years undergoing non-obstetric surgery at UCSF.
- Inpatient cases with expected overnight stay.
- Baseline eGFR ≥15 mL/min/1.73 m².
- Managed by an attending anesthesiologist randomized to one of three arms (CRNAs/residents follow attending).
- Data available in the UCSF EHR for risk scoring and outcomes.
Who cannot join
- Obstetric procedures.
- Chronic dialysis patients.
- Kidney transplant recipients.
- Cases without baseline creatinine/eGFR or missing essential EHR elements needed for scoring/outcomes (operational exclusions).
- Outpatient procedures without expected overnight stay.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site
University of California, San Francisco
San Francisco, California, 94158
About this study
Official title: Prediction of Acute Kidney Injury (AKI) After Surgery: A Pragmatic Three-Arm Cluster-Randomized Trial
This investigator-initiated, pragmatic trial evaluates whether displaying a machine learning (ML)- derived perioperative AKI risk score-alone or paired with an interruptive Best/Our Practice Advisory (BPA/OPA)-improves kidney-protective care and reduces kidney injury after non-obstetric surgery at UCSF. Approximately 75-100 attending anesthesiologists (clusters) are randomized 1:1:1 to: (a) Control (risk score hidden), (b) Score Only (visible preoperative AKI risk probability with passive KDIGO bundle recommendation), or (c) Score + BPA (visible risk plus interruptive KDIGO prompt for high-risk patients). CRNAs/residents follow their attending' s assignment. Adult inpatients (age ≥18) with expected overnight stay and eGFR ≥15 mL/min/1.73 m² are included; obstetrics, chronic dialysis, and kidney transplant patients are excluded. The underlying preoperative model was prospectively validated at UCSF and outperforms anesthesiologist risk estimation reported in the literature. The model was reviewed and approved by the AI Oversight Committee at UCSF. Primary endpoint is the continuous change in serum creatinine (mg/dL) from baseline to POD 1-2. Secondary outcomes include KDIGO-defined AKI, adherence to bundle elements (hemodynamics, balanced fluids, nephrotoxin avoidance, glycemic control), intraoperative hypotension time, fluid volumes, nephrotoxin exposure, perioperative hyperglycemia, length of stay, unplanned ICU transfer, readmission, dialysis, and in-hospital mortality. Data are obtained from the EHR; analysts are blinded. No direct subject interaction is planned; the investigators will request a waiver of patient consent. The study aims to demonstrate that ML-enabled, workflow-embedded decision support can safely and feasibly improve guideline concordant care and decrease early postoperative kidney injury.
- Conditions
- Acute Kidney Injury, Surgery Complications, Anesthesia
- Being tested
- EHR-Embedded AKI Risk Score, EHR-Embedded AKI Risk Score with Best Practice Advisory
- Keywords
- Acute Kidney Injury, Surgical Outcomes, Machine Learning, Clinical Decision Support, Electronic Health Records
- Medical subject headings
- Acute Kidney Injury
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
EHR-Embedded AKI Risk ScoreDevice
Also called EHR-Embedded AKI Clinical Decision Support Tool
A non-adaptive, machine learning-based clinical decision support tool integrated into the electronic health record that generates a preoperative probability of acute kidney injury (AKI) using routinely collected patient data. For patients identified as high risk, the tool displays the risk estimate to anesthesia providers without an accompanying Best Practice Advisory (BPA) recommending consideration of a KDIGO-based kidney-protective bundle. The intervention is advisory only, does not mandate clinical actions, and is designed to support provider decision-making within the existing clinical workflow.
EHR-Embedded AKI Risk Score with Best Practice AdvisoryDevice
Also called EHR-Embedded AKI Clinical Decision Support Tool
A non-adaptive, machine learning-based clinical decision support tool integrated into the electronic health record that generates a preoperative probability of acute kidney injury (AKI) using routinely collected patient data. For patients identified as high risk, the tool displays the risk estimate to anesthesia providers with an accompanying Best Practice Advisory (BPA) recommending consideration of a KDIGO-based kidney-protective bundle. The intervention is advisory only, does not mandate clinical actions, and is designed to support provider decision-making within the existing clinical workflow.
| Study group | Role | What they receive |
|---|---|---|
| Control Arm | No intervention | 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. |
| Acute Kidney Injury Risk Score Only | Experimental | 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. Receives: Device: EHR-Embedded AKI Risk Score |
| Acute Kidney Injury Risk Score with Best Practice Advisory | Experimental | 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. Receives: Device: EHR-Embedded AKI Risk Score with Best Practice Advisory |
What is being measured
The primary outcome is the main question the study is designed to answer.
Post-operative Change in Creatinine
Maximum continuous change in serum creatinine (mg/dL) from baseline to post-operative day 1-2
Measured: From pre-operative baseline to 1-2 days post-operative level
Secondary outcomes (16)
Acute Kidney Injury
Acute Kidney Injury as defined by KDIGO
Measured: Operation to Post-operative Day 7
KDIGO Bundle Adherence
Measurement of provider adherence to KDIGO components
Measured: Intra-operative
Intra-Operative Time and Severity of Hypotension
Intra-Operative Time and Severity (meaning how far below the threshold) where patient is in hypotension, defined as systolic blood pressure <90 mmHg and mean arterial pressure <65 mmHg during surgery
Measured: Intra-operative
Total intra-operative intravenous fluid volume administered (mL)
Provider administration of intravenous fluids during the intra-operative period, measured in milliliters (mL). Intravenous fluids include normal saline, lactated Ringer's, Plasma-Lyte, other balanced crystalloids, and colloid solutions such as albumin.
Measured: Intra-operative
Length of Stay
Duration of patient admission in hospital in days
Measured: Operation to Post-operative Day 180
Intra-operative Hyperglycemic Events
Number of intra-operative hyperglycemic events, defined as the number of recorded blood glucose measurements exceeding 180 mg/dL.
Measured: Intra-operative
Intra-operative Nephrotoxin Exposure
Number of nephrotoxic medications administered intra-operatively and duration of intra-operative exposure
Measured: Intra-operative
In-Hospital Mortality
Patient death while admitted in the hospital
Measured: Operation to Post-operative Day 180
ICU Transfer and total time in the ICU
Any transfers to the ICU while admitted and the total time the patient spends in the ICU
Measured: Postoperative
Hospital Readmission
Readmission back to a UCSF hospital following operation
Measured: Operation to Post-operative Day 180
Dialysis Requirement
Patients requiring dialysis following surgery
Measured: Operation to Post-operative Day 180
Dilution Corrected KDIGO AKI measurement (Stage 1 or higher)
Acute kidney injury (AKI) assessed using KDIGO creatinine criteria applied to dilution-corrected postoperative serum creatinine. Creatinine is corrected for hemodilution from perioperative fluid retention using the formula:
Corrected Creatinine (mg/dL) = Measured Creatinine × (1 + Net Fluid Balance / Total Body Water)
Where:
- Net Fluid Balance (L) = Fluid inputs - urine output - blood loss - other outputs
- Total Body Water (L) = 0.6 × weight (kg) for males; 0.5 × weight (kg) for females
Measured: AKI is defined per KDIGO as corrected creatinine increase ≥0.3 mg/dL within 48 hours or ≥1.5× baseline within 7 days. This measure captures "hidden AKI" - kidney injury masked by fluid dilution that would be missed using standard uncorrected creatinine.
Total intra-operative packed red blood cells administered (units transfused)
Provider administration of packed red blood cells during the intra-operative period, measured as total units transfused.
Measured: intraoperative
Total intra-operative fresh frozen plasma administered (units transfused)
Provider administration of fresh frozen plasma during the intra-operative period, measured as total units transfused.
Measured: intraoperative
Total intra-operative platelets administered (units transfused)
Provider administration of platelets during the intra-operative period, measured as total units transfused.
Measured: intraoperative
Total intra-operative cryoprecipitate administered (units transfused)
Provider administration of cryoprecipitate during the intra-operative period, measured as total units transfused.
Measured: intraoperative
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 May 2026
- Study start15 Oct 2026 (estimated)
- Primary completion15 Oct 2027 (estimated)
- Study completion15 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Acute Kidney InjuryStudies of the same condition, open and under way.
- More trials run by University of California, San FranciscoThe 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- This is a pragmatic, single-center, three-arm, parallel-group, cluster-randomized controlled trial. Attending anesthesiologists are the unit of randomization and are assigned in a 1:1:1 ratio to one of three groups: (1) control (AKI risk score not displayed), (2) score only (visible preoperative machine learning-derived AKI risk score with passive KDIGO bundle recommendation), or (3) score plus Best Practice Advisory (visible risk score with an interruptive KDIGO-based alert for high-risk patients). All eligible surgical cases managed by a given attending anesthesiologist inherit that provider's assigned study arm. Trainees and nurse anesthetists follow the assignment of the supervising attending. The intervention is delivered within the electronic health record at the point of care. The clinical decision support tools are advisory only and do not mandate any clinical actions. There is no crossover between groups, and allocation remains fixed for the duration of the study.
- Masking
- None (open label)
- Enrolment
- 25,518 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of California, San Francisco
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute of General Medical Sciences (NIGMS)
- Responsible party
- Sponsor
- Investigators
- Andrew Bishara, MD, Principal investigator, University of California, San Francisco
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07604662
- Sponsor's protocol ID
- 22-37005
- Other IDs
- K23GM151611-03
- Acronym
- ML-AKI
- Status last verified
- May 2026
- First submitted
- 9 Apr 2026
- Last updated
- 22 May 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Publications
James MT, Dixon E, Tan Z, Mathura P, Datta I, Lall RN, Landry J, Minty EP, Samis GA, Winkelaar GB, Pannu N. Stepped-Wedge Trial of Decision Support for Acute Kidney Injury on Surgical Units. Kidney Int Rep. 2024 Jul 31;9(10):2996-3005. doi: 10.1016/j.ekir.2024.07.025. eCollection 2024 Oct.
PubMed 39430177Zarbock A, Kullmar M, Ostermann M, Lucchese G, Baig K, Cennamo A, Rajani R, McCorkell S, Arndt C, Wulf H, Irqsusi M, Monaco F, Di Prima AL, Garcia Alvarez M, Italiano S, Miralles Bagan J, Kunst G, Nair S, L'Acqua C, Hoste E, Vandenberghe W, Honore PM, Kellum JA, Forni LG, Grieshaber P, Massoth C, Weiss R, Gerss J, Wempe C, Meersch M. Prevention of Cardiac Surgery-Associated Acute Kidney Injury by Implementing the KDIGO Guidelines in High-Risk Patients Identified by Biomarkers: The PrevAKI-Multicenter Randomized Controlled Trial. Anesth Analg. 2021 Aug 1;133(2):292-302. doi: 10.1213/ANE.0000000000005458.
PubMed 33684086Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179-84. doi: 10.1159/000339789. Epub 2012 Aug 7. No abstract available.
PubMed 22890468Walsh M, Devereaux PJ, Garg AX, Kurz A, Turan A, Rodseth RN, Cywinski J, Thabane L, Sessler DI. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension. Anesthesiology. 2013 Sep;119(3):507-15. doi: 10.1097/ALN.0b013e3182a10e26.
PubMed 23835589Sun LY, Wijeysundera DN, Tait GA, Beattie WS. Association of intraoperative hypotension with acute kidney injury after elective noncardiac surgery. Anesthesiology. 2015 Sep;123(3):515-23. doi: 10.1097/ALN.0000000000000765.
PubMed 26181335Kork F, Balzer F, Spies CD, Wernecke KD, Ginde AA, Jankowski J, Eltzschig HK. Minor Postoperative Increases of Creatinine Are Associated with Higher Mortality and Longer Hospital Length of Stay in Surgical Patients. Anesthesiology. 2015 Dec;123(6):1301-11. doi: 10.1097/ALN.0000000000000891.
PubMed 26492475Fujii T, Takakura M, Taniguchi T, Tamura T, Nishiwaki K. Intraoperative hypotension affects postoperative acute kidney injury depending on the invasiveness of abdominal surgery: A retrospective cohort study. Medicine (Baltimore). 2023 Dec 1;102(48):e36465. doi: 10.1097/MD.0000000000036465.
PubMed 38050260
Study at a glance
From the registry record, last updated by the sponsor on 22 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Acute Kidney Injury (AKI) After Surgery: A Pragmatic Three-Arm Cluster-Randomized Trial
- ClinicalTrials.gov ID
- NCT07604662
- Acronym / sponsor ID
- ML-AKI / 22-37005
- Conditions
- Acute Kidney Injury, Surgery Complications, Anesthesia
- Keywords
- Acute Kidney Injury, Surgical Outcomes, Machine Learning, Clinical Decision Support, Electronic Health Records
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 25,518 participants (estimated)
- Lead sponsor
- University of California, San FranciscoUniversity, hospital or other
- Collaborators
- National Institute of General Medical Sciences (NIGMS)
- Study start
- 15 Oct 2026 (estimated)
- Primary completion
- 15 Oct 2027 (estimated)
- Study completion
- 15 Dec 2027 (estimated)
- First posted
- 22 May 2026 (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.
This investigator-initiated, pragmatic trial evaluates whether displaying a machine learning (ML)- derived perioperative AKI risk score-alone or paired with an interruptive Best/Our Practice Advisory (BPA/OPA)-improves kidney-protective care and reduces kidney injury after non-obstetric surgery at UCSF. Approximately 75-100 attending anesthesiologists (clusters) are randomized 1:1:1 to: (a) Control (risk score hidden), (b) Score Only (visible preoperative AKI risk probability with passive KDIGO bundle recommendation), or (c) Score + BPA (visible risk plus interruptive KDIGO prompt for high-risk patients). CRNAs/residents follow their attending' s assignment. Adult inpatients (age ≥18) with expected overnight stay and eGFR ≥15 mL/min/1.73 m² are included; obstetrics, chronic dialysis, and kidney transplant patients are excluded. The underlying preoperative model was prospectively validated at UCSF and outperforms anesthesiologist risk estimation reported in the literature. The model was reviewed and approved by the AI Oversight Committee at UCSF. Primary endpoint is the continuous change in serum creatinine (mg/dL) from baseline to POD 1-2. Secondary outcomes include KDIGO-defined AKI, adherence to bundle elements (hemodynamics, balanced fluids, nephrotoxin avoidance, glycemic control), intraoperative hypotension time, fluid volumes, nephrotoxin exposure, perioperative hyperglycemia, length of stay, unplanned ICU transfer, readmission, dialysis, and in-hospital mortality. Data are obtained from the EHR; analysts are blinded. No direct subject interaction is planned; the investigators will request a waiver of patient consent. The study aims to demonstrate that ML-enabled, workflow-embedded decision support can safely and feasibly improve guideline concordant care and decrease early postoperative kidney injury.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 3
- Model description
- This is a pragmatic, single-center, three-arm, parallel-group, cluster-randomized controlled trial. Attending anesthesiologists are the unit of randomization and are assigned in a 1:1:1 ratio to one of three groups: (1) control (AKI risk score not displayed), (2) score only (visible preoperative machine learning-derived AKI risk score with passive KDIGO bundle recommendation), or (3) score plus Best Practice Advisory (visible risk score with an interruptive KDIGO-based alert for high-risk patients). All eligible surgical cases managed by a given attending anesthesiologist inherit that provider's assigned study arm. Trainees and nurse anesthetists follow the assignment of the supervising attending. The intervention is delivered within the electronic health record at the point of care. The clinical decision support tools are advisory only and do not mandate any clinical actions. There is no crossover between groups, and allocation remains fixed for the duration of the study.
| Arm | Type | Receives |
|---|---|---|
| Control Arm | No intervention | –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. |
| Acute Kidney Injury Risk Score Only | Experimental | Device: EHR-Embedded AKI Risk ScoreA 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. |
| Acute Kidney Injury Risk Score with Best Practice Advisory | Experimental | Device: EHR-Embedded AKI Risk Score with Best Practice AdvisoryThe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- Adults ≥18 years undergoing non-obstetric surgery at UCSF.
- Inpatient cases with expected overnight stay.
- Baseline eGFR ≥15 mL/min/1.73 m².
- Managed by an attending anesthesiologist randomized to one of three arms (CRNAs/residents follow attending).
- Data available in the UCSF EHR for risk scoring and outcomes.
Key exclusion criteria (5 listed)
- Obstetric procedures.
- Chronic dialysis patients.
- Kidney transplant recipients.
- Cases without baseline creatinine/eGFR or missing essential EHR elements needed for scoring/outcomes (operational exclusions).
- Outpatient procedures without expected overnight stay.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| EHR-Embedded AKI Risk ScoreAlso: EHR-Embedded AKI Clinical Decision Support ToolA non-adaptive, machine learning-based clinical decision support tool integrated into the electronic health record that generates a preoperative probability of acute kidney injury (AKI) using routinely collected patient data. For patients identified as high risk, the tool displays the risk estimate to anesthesia providers without an accompanying Best Practice Advisory (BPA) recommending consideration of a KDIGO-based kidney-protective bundle. The intervention is advisory only, does not mandate clinical actions, and is designed to support provider decision-making within the existing clinical workflow. | Device | Acute Kidney Injury Risk Score Only |
| EHR-Embedded AKI Risk Score with Best Practice AdvisoryAlso: EHR-Embedded AKI Clinical Decision Support ToolA non-adaptive, machine learning-based clinical decision support tool integrated into the electronic health record that generates a preoperative probability of acute kidney injury (AKI) using routinely collected patient data. For patients identified as high risk, the tool displays the risk estimate to anesthesia providers with an accompanying Best Practice Advisory (BPA) recommending consideration of a KDIGO-based kidney-protective bundle. The intervention is advisory only, does not mandate clinical actions, and is designed to support provider decision-making within the existing clinical workflow. | Device | Acute Kidney Injury Risk Score with Best Practice Advisory |
Primary outcome
Post-operative Change in Creatinine
Time frame: From pre-operative baseline to 1-2 days post-operative level
Maximum continuous change in serum creatinine (mg/dL) from baseline to post-operative day 1-2
16 secondary outcomes
Acute Kidney Injury
Time frame: Operation to Post-operative Day 7
KDIGO Bundle Adherence
Time frame: Intra-operative
Intra-Operative Time and Severity of Hypotension
Time frame: Intra-operative
Total intra-operative intravenous fluid volume administered (mL)
Time frame: Intra-operative
Length of Stay
Time frame: Operation to Post-operative Day 180
Intra-operative Hyperglycemic Events
Time frame: Intra-operative
Intra-operative Nephrotoxin Exposure
Time frame: Intra-operative
In-Hospital Mortality
Time frame: Operation to Post-operative Day 180
ICU Transfer and total time in the ICU
Time frame: Postoperative
Hospital Readmission
Time frame: Operation to Post-operative Day 180
Dialysis Requirement
Time frame: Operation to Post-operative Day 180
Dilution Corrected KDIGO AKI measurement (Stage 1 or higher)
Time frame: AKI is defined per KDIGO as corrected creatinine increase ≥0.3 mg/dL within 48 hours or ≥1.5× baseline within 7 days. This measure captures "hidden AKI" - kidney injury masked by fluid dilution that would be missed using standard uncorrected creatinine.
Total intra-operative packed red blood cells administered (units transfused)
Time frame: intraoperative
Total intra-operative fresh frozen plasma administered (units transfused)
Time frame: intraoperative
Total intra-operative platelets administered (units transfused)
Time frame: intraoperative
Total intra-operative cryoprecipitate administered (units transfused)
Time frame: intraoperative
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 22 May 2026.
- Background: PubMed 39430177James MT, Dixon E, Tan Z, Mathura P, Datta I, Lall RN, Landry J, Minty EP, Samis GA, Winkelaar GB, Pannu N. Stepped-Wedge Trial of Decision Support for Acute Kidney Injury on Surgical Units. Kidney Int Rep. 2024 Jul 31;9(10):2996-3005. doi: 10.1016/j.ekir.2024.07.025. eCollection 2024 Oct.
- Background: PubMed 33684086Zarbock A, Kullmar M, Ostermann M, Lucchese G, Baig K, Cennamo A, Rajani R, McCorkell S, Arndt C, Wulf H, Irqsusi M, Monaco F, Di Prima AL, Garcia Alvarez M, Italiano S, Miralles Bagan J, Kunst G, Nair S, L'Acqua C, Hoste E, Vandenberghe W, Honore PM, Kellum JA, Forni LG, Grieshaber P, Massoth C, Weiss R, Gerss J, Wempe C, Meersch M. Prevention of Cardiac Surgery-Associated Acute Kidney Injury by Implementing the KDIGO Guidelines in High-Risk Patients Identified by Biomarkers: The PrevAKI-Multicenter Randomized Controlled Trial. Anesth Analg. 2021 Aug 1;133(2):292-302. doi: 10.1213/ANE.0000000000005458.
- Background: PubMed 22890468Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179-84. doi: 10.1159/000339789. Epub 2012 Aug 7. No abstract available.
- Background: PubMed 23835589Walsh M, Devereaux PJ, Garg AX, Kurz A, Turan A, Rodseth RN, Cywinski J, Thabane L, Sessler DI. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension. Anesthesiology. 2013 Sep;119(3):507-15. doi: 10.1097/ALN.0b013e3182a10e26.
- Background: PubMed 26181335Sun LY, Wijeysundera DN, Tait GA, Beattie WS. Association of intraoperative hypotension with acute kidney injury after elective noncardiac surgery. Anesthesiology. 2015 Sep;123(3):515-23. doi: 10.1097/ALN.0000000000000765.
- Background: PubMed 26492475Kork F, Balzer F, Spies CD, Wernecke KD, Ginde AA, Jankowski J, Eltzschig HK. Minor Postoperative Increases of Creatinine Are Associated with Higher Mortality and Longer Hospital Length of Stay in Surgical Patients. Anesthesiology. 2015 Dec;123(6):1301-11. doi: 10.1097/ALN.0000000000000891.
- Background: PubMed 38050260Fujii T, Takakura M, Taniguchi T, Tamura T, Nishiwaki K. Intraoperative hypotension affects postoperative acute kidney injury depending on the invasiveness of abdominal surgery: A retrospective cohort study. Medicine (Baltimore). 2023 Dec 1;102(48):e36465. doi: 10.1097/MD.0000000000036465.
- Individual participant dataSharing planned: No
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Acute Kidney Injury, Hypotension (MeSH: D007022)
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.
Andrew Bishara, MD principal investigator
University of California, San Francisco
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing EHR-Embedded AKI Risk Score and EHR-Embedded AKI Risk Score with Best Practice Advisory in people with Acute Kidney Injury and Surgery Complications, run by University of California, San Francisco at 1 site in United States.
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 5 inclusion and 5 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 22 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
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 Andrew Bishara, MD (415-502-5880, andrew.bishara@ucsf.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Post-operative Change in Creatinine (time frame: From pre-operative baseline to 1-2 days post-operative level). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07604662, which also lists 7 linked publications.
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 22 May 2026.