What MEnD-AKI Revealed About AI Alerts, With Sandra Kane-Gill, PharmD, MS

More than half of alerts generated by a deep learning AKI prediction system were not actionable in the MEnD-AKI trial. Only 12% of high priority pharmacist recommendations reached physicians by phone, the intended primary route.¹

Presented at the American Society of Nephrology (ASN) Kidney Week 2026 in Denver, Colorado, by Sandra L. Kane-Gill, PharmD, MS, the data from the cluster randomized trial suggest that a well validated model may not translate into an effective bedside alert without attention to how alerts are generated and delivered. The pharmacist intervention did not significantly reduce major adverse kidney events at 30 days (MAKE30) compared with a passive alert, adding to other recent AKI alert trials that did not improve outcomes.¹

The trial used a deep learning model, a type of artificial intelligence that analyzes patterns in electronic health record data, to predict progression to stage 2 or higher acute kidney injury (AKI) within 48 hours. In prior validation, the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.82.² Alerts from the model prompted pharmacists to send medication recommendations to physicians, with the highest priority Level B alerts intended to reach physicians by phone.¹ ²

“Interestingly enough, our deep learning model performed well. It was robust. It was externally validated. It had good discrimination, but that doesn’t necessarily translate into a good alert,” said Kane-Gill, professor of pharmacy and therapeutics and associate dean of faculty affairs at the University of Pittsburgh School of Pharmacy, in an interview with HCPLive. “So understanding how the model is put in place to generate the alert is pretty important.”

Earlier studies of AKI decision support had focused on detection and passive alerts, which showed only modest effects on outcomes. MEnD-AKI tested whether pairing prediction with an active pharmacist intervention could close that gap in hospitalized adults outside the intensive care unit.¹

“What we really learned here is how to put an AI model in place at the bedside, making sure there’s a human in the loop. It went through the pharmacist, and the physician decided if they wanted to follow those recommendations. We learned a lot about how to bring this type of model to the bedside,” Kane-Gill said.

MEnD-AKI Trial Design and Primary Outcome

Investigators randomized 38 hospital service clusters across 8 hospitals to the pharmacist intervention or to usual care with a passive electronic medical record alert. The intention to treat analysis included 5939 patients. MAKE30 occurred in 50.8% of intervention patients and 42.9% of usual care patients (adjusted OR, 1.05; 95% CI, 0.82–1.35), and no secondary outcome differed significantly.¹

52.3% of AKI Alerts Were Not Actionable

Of the alerts in the intention to treat population, 52.3% were not actionable.¹ Kane-Gill pointed to the model’s reliance on structured health record data as one factor. Chronic kidney disease (CKD) was often documented in clinical notes rather than the structured medical history, so some alerts suggested AKI in patients who had CKD, she said.

Most Level B Recommendations Went by Email, Not Phone

Although phone was the intended primary route, only 12% of Level B recommendations were delivered by phone rather than email.¹ Investigators cited this reliance on asynchronous email, along with non-actionable alerts and baseline comorbidity imbalances between groups, as key limitations.

They concluded that real-time delivery and improved alert actionability are needed before the approach can be considered effective.¹ Kane-Gill said future models may benefit from incorporating unstructured data, such as clinical notes.

Editor’s Note: Disclosures for Kane-Gill include the National Institute of Diabetes and Digestive and Kidney Diseases.

References

1. Kane-Gill S, Bihorac A, Kellum JA, et al. Multihospital Electronic Decision Support for Drug-Associated AKI (MEnD-AKI): A Prospective Cluster-Randomized Clinical Trial. Abstract presented at: American Society of Nephrology Kidney Week 2026; October 21–25, 2026; Denver, CO. doi:10.1681/ASN.2026fbjd7rne
2. Stottlemyer BA, Kellum JA, Bihorac A, et al. Multi-hospital electronic decision support for drug-associated acute kidney injury (MEnD-AKI): study protocol for a randomized clinical trial. Contemp Clin Trials. 2025;157:108055. doi:10.1016/j.cct.2025.108055