AI Alerts Did Not Reduce Kidney Events, With Sandra Kane-Gill, PharmD, MS

A pharmacist intervention prompted by a deep learning clinical decision support system (CDSS) did not significantly reduce major adverse kidney events at 30 days (MAKE30) among hospitalized patients outside the intensive care unit with or at risk of drug-associated .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 MEnD-AKI cluster randomized trial show that pairing predictive alerts with pharmacist review did not change kidney outcomes compared with a passive alert. Investigators concluded that real-time delivery and more actionable alerts are needed before the approach can be considered effective.¹

Medications are a leading cause of AKI, with almost 30% of cases attributed to a drug.² CDSS tools have improved AKI detection, but passive electronic alerts alone have shown only modest effects on outcomes.¹

“As we know, nephrotoxins are a common cause of acute kidney injury, but they’re also a modifiable risk factor for acute kidney injury,” 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. “About two thirds of drugs are excreted by the kidney, and so when a patient has reduced kidney function, that drug accumulates, and there’s a potential for adverse effects.”

The trial tested whether adding a predictive deep learning model and an active pharmacist intervention to existing alerts could improve the prevention and management of drug-associated AKI in hospitalized adults outside the ICU.¹ Previous work in this population had largely focused on passive alerts.²

“We chose that model because prior work indicated that having a dedicated clinician to respond to medication management for patients with kidney dysfunction was a preferred method, and hopefully an effective method,” Kane-Gill said. “In addition, those attending physicians involved received a lot of alerts already, and they didn’t want additional alerts sent their way.”

How MEnD-AKI Paired Deep Learning Alerts With Pharmacists

Investigators randomized 38 hospital service clusters across 8 hospitals to a pharmacist intervention prompted by the machine learning CDSS or to an electronic medical record passive alert (usual care).¹ The CDSS used a deep learning model, a type of artificial intelligence that analyzes patterns in electronic health record data, to continuously predict each patient’s risk of progressing to stage 2 or higher AKI within 48 hours.²

Tiered Level A and Level B alerts led pharmacists to send recommendations to physicians via telemedicine. The intervention was delivered at the physician level, while outcomes were assessed at the patient level using de-identified data.¹

The primary endpoint was MAKE30, a composite of death, new kidney replacement therapy, or final serum creatinine ≥150% of reference.² Secondary endpoints included AKI progression, AKI intensity, and nephrotoxin burden.¹

No Reduction in Major Adverse Kidney Events at 30 Days

Of 698 enrolled physicians, 480 (40.0% female) cared for a patient with a qualifying alert. The intention to treat analysis included 5939 patients: 3841 in the intervention group and 2098 in usual care.¹

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). No secondary outcome differed significantly between groups.¹

The groups were not balanced at baseline. Intervention patients were older and had more chronic kidney disease (CKD), congestive heart failure, diabetes, hypercholesterolemia, and hypertension. Usual care patients had more anemia, malignancy, and trauma (all P <.001).¹

Investigators cited reliance on asynchronous email over real-time phone delivery, a high proportion of non-actionable alerts, and the baseline comorbidity imbalances as key limitations.¹

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