Rationale and objective: Acute kidney injury (AKI) is a common complication among hospitalized adults, but AKI prediction and prevention among adults has proved challenging. We used machine learning to update the nephrotoxic injury negated by just-in time action (NINJA), a pediatric program that predicts nephrotoxic AKI, to improve accuracy among adults.
Study design: A retrospective cohort study.
Setting and population: Adults admitted for > 48 hours to the University of Iowa Hospital from 2017 to 2022.
Exposure: A NINJA high-nephrotoxin exposure (≥3 nephrotoxins on 1 day or intravenous aminoglycoside or vancomycin for ≥3 days).
Outcomes: AKI within 48 hours of high-nephrotoxin exposure.
Analytical approach: We collected 85 variables, including demographics, laboratory tests, vital signs, and medications. AKI was defined as a serum creatinine increase of ≥0.3 mg/dL. A gated recurrent unit (GRU)-based recurrent neural network (RNN) was trained on 85% of the data, and then tested on the remaining 15%. Model performance was evaluated with precision, recall, negative predictive value, and area under the curve. We used an artificial neural network to determine risk factor importance.
Results: There were 14,480 patients, 18,180 admissions, and 37,300 high-nephrotoxin exposure events meeting inclusion criteria. In the testing cohort, 29% of exposures developed AKI within 48 hours. The RNN-GRU model predicted AKI with a precision of 0.60, reducing the number of false alerts from 2.5 to 0.7 per AKI case. Lowest hemoglobin, lowest blood pressure, and highest white blood cell count were the most important variables in the artificial neural network model. Acyclovir, piperacillin-tazobactam, calcineurin inhibitors, and angiotensin-converting enzyme inhibitor/angiotensin receptor blockers were the most important medications.
Limitations: Clinical variables and medications were not exhaustive, drug levels or dosing were not incorporated, and Iowa's racial makeup may limit generalizability.
Conclusions: Our RNN-GRU model substantially reduced the number of false alerts for nephrotoxic AKI, which may facilitate NINJA translation to adult hospitals by providing more targeted intervention.
Keywords: Drug-induced acute kidney injury; machine learning; nephrotoxicity.
Nephrotoxic acute kidney injury (AKI) is common and can potentially be prevented through preemptive adjustments of medications, as demonstrated by the success of the nephrotoxic injury negated by just-in time action (NINJA) program in pediatric populations. Translation of NINJA to the adult population has been challenging, and major barriers include high alert volume in adults that can lead to high resource utilization and alert fatigue. To address this issue, we developed a machine learning model for nephrotoxic AKI in adults that reduced the number of false alerts per AKI event from 2.5 to 0.7, which can enhance future NINJA implementation in adults by allowing for a more targeted intervention with fewer alerts and more efficient resource utilization.
© 2024 The Authors.