AI-based algorithm predicts serious kidney-related complications after hospitalization: Study

Florida: Serious kidney-related complications-or major adverse kidney events are common after hospitalization for various medical problems. Investigators have now developed and validated an artificial intelligence–based algorithm to predict a patient's risk of major adverse kidney events within 90 days of hospital discharge. The research will be presented at ASN Kidney Week 2022, November 3-November 6.

The scientists developed and validated their algorithm in 50,448 patients without baseline severe chronic kidney disease who were admitted to the University of Chicago between November 2008 and June 2020. The algorithm was developed using demographics, inpatient vital signs, and laboratory results. Within 90 days of discharge, 19.7% of patients developed a major adverse kidney event (acute kidney injury, chronic kidney disease, need for dialysis, or kidney-related death), and the algorithm accurately predicted these events.

"Our work needs to be validated with outside data, but it could be used to help prioritize follow-up with nephrology and primary care as well as to determine which patients should (and should not) be sent for transplant or dialysis access evaluation," said corresponding author Jay Koyner, MD. "Similarly, combining our risk score with existing literature that shows acute kidney injury increases the risk of new congestive heart failure, we could determine which patients should be seen by cardiologists."

"Similarly, combining our risk score with existing literature that shows acute kidney injury increases the risk of new congestive heart failure, we could potentially determine which patients should be seen by cardiologists."

All study participants had been treated at the University of Chicago between November 2008 and June 2020, and none had severe, chronic kidney disease at hospital admission.

Nonetheless, 19.7% of patients developed a major adverse kidney event within 90 days of discharge, which was defined as acute kidney injury, chronic kidney disease, need for dialysis, or kidney-related death.

The team developed the machine learning, gradient boosted algorithm in 70 per cent of the admissions and then applied it to the remaining 30 percent of test data.

The model was able to discriminate those test patients that developed post-hospitalization major adverse kidney events, with an area under the receiver operating characteristic curve (AUC) of 0.74.

Among the individual endpoints, it was best at identifying those patients that developed chronic kidney disease after hospitalization, with an AUC of 0.94 at 90 days and 0.92 at 1 year.