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"Huede" AI Aided AKI Prediction Software, Acura AKI, uses machine learning algorithms to predict the risk of AKI within the next 24 hours and provide a ranking of feature importance. By using Acura AKI, physicians can assess the risk of AKI, focusing on high-risk patients to provide care decisions. This study will be conducted in a prospective randomized clinical trial in adult ICUs, implementing the Acura AKI system for predicting AKI. The study aims to determine whether early prediction and intervention using the Acura AKI system can improve the outcomes of critically ill patients with adverse kidney conditions. The study endpoint is to evaluate the cost-effectiveness of using Acura AKI, including the incidence of AKI, dialysis rates, mortality rates, length of hospital stay, and treatment costs.
"Huede" AI Aided AKI Prediction Software, Acura AKI, uses machine learning algorithms to predict the risk of AKI within the next 24 hours. It has undergone cross-hospital validation at four medical centers in Taiwan (Taichung Veterans General Hospital, Mackay Memorial Hospital, National Cheng Kung University Hospital, and Kaohsiung Medical University Hospital), successfully obtaining invention patents in Taiwan and the United States, as well as receiving a software medical device license from the Taiwan Food and Drug Administration. Acura AKI is installed on the hospital's servers, where it processes patient physiological data, laboratory parameters, and medication information to infer the risk of AKI occurring within 24 hours. It also provides a ranking of feature importance. By using Acura AKI, physicians can assess the risk of AKI, focusing on high-risk patients to provide care decisions.
This study will be conducted in a prospective randomized clinical trial in adult ICUs, implementing the Acura AKI system for predicting AKI. In the intervention group with Acura AKI system, physicians will be proactively notified via sending alarm message when Acura AKI identifies a high-risk patient population. After receiving alarm message, physicians and pharmacists will provide feedback and recommendations, including blood pressure, fluid management, infusion options, medication adjustment suggestions, and dialysis recommendations. The study aims to determine whether early prediction and intervention using the Acura AKI system can improve the outcomes of critically ill patients with adverse kidney conditions. Additionally, the researchers will collect 20ml of urine from Acura AKI identified patients to test for urinary biomarkers predictive of AKI then verify the performance of Acura AKI with these urinary biomarkers. The study endpoint is to evaluate the cost-effectiveness of using Acura AKI, including the incidence of AKI, dialysis rates, mortality rates, length of hospital stay, and treatment costs.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| With Acura AKI | Experimental | The group with Acura AKI will receive the Acura AKI software, which identifies high-risk AKI patients and sends alert messages to nephrologists and ICU pharmacists. Upon receiving the alert, they will make treatment suggestions. |
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| Without Acura AKI | No Intervention | The group without Acura AKI will be managed based on standard medical procedures. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Acura AKI | Device | When the AI algorithm (Acura AKI) identifies a high-risk AKI patient, nephrologists and ICU pharmacists will receive an alert message. Upon receiving the alert, they will review the patient's electronic health record and make treatment suggestions based on AKI bundle care protocols. They will also coordinate with the patient's primary care team to ensure that the recommendations are implemented |
| Measure | Description | Time Frame |
|---|---|---|
| Acute kidney injury (AKI) incidence | Acute kidney injury (AKI) incidence in ICU. AKI is defined by an increase in KDIGO creatinine stage. | Assessed from time of randomization to time of AKI occurrence (within 7 days post randomization) |
| Measure | Description | Time Frame |
|---|---|---|
| Percentage of recommendations implemented by the primary care team. | Upon receiving the alert message from the AI algorithm (Acura AKI), nephrologists and ICU pharmacists will review the patient's electronic health record and make treatment suggestions based on AKI bundle care protocols. They will also coordinate with the patient's primary care team to ensure that the recommendations are implemented |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Chun-Te Huang | Contact | +8864-23592525 | 3169 | huangchunte@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Chun-Te Huang | Taichung Veterans General Hospital (TCVGH) | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Taichung Veterans General Hospital (TCVGH) | Recruiting | Taichung | Taiwan |
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| ID | Term |
|---|---|
| D058186 | Acute Kidney Injury |
| ID | Term |
|---|---|
| D051437 | Renal Insufficiency |
| D007674 | Kidney Diseases |
| D014570 | Urologic Diseases |
| D052776 | Female Urogenital Diseases |
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| 24 hours after Randomization |
| Dialysis rate | Dialysis is defined by the receipt of hemodialysis, continuous renal replacement therapy or peritoneal dialysis. Isolated ultrafiltration treatments (for the purpose of volume removal) will not be included. | Assessed from time of randomization to time of receipt of inpatient dialysis (within 14 days post randomization) |
| Mortality rate | Mortality will be determined from hospital administrative records | Assessed from time of randomization to date of death from any cause, within 14 days of randomization |
| Length of hospital stay | The total days of hospitalization stay | Assessed from time of randomization to date of hospital discharge, assessed up to 30 days |
| Change in treatment costs |
| Assessed from time of randomization to 60 days post hospital discharge date, accessed up to 90 days |
| Long term dialysis | Receive regular dialysis | From hospital discharge date up to 90 days post discharge date |
| D005261 |
| Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D052801 | Male Urogenital Diseases |