Tiffany L. Tran

Limited evidence exists synthesizing the risk of acute kidney injury (AKI) associated with the concomitant administration of multiple nephrotoxic drugs, and even less examining the concept of nephrotoxic burden. The objective of this scoping review was to (1) identify definitions of nephrotoxic burden; (2) methods used to quantify (use of calculations) nephrotoxic burden; and (3) determine the association between nephrotoxic burden and AKI risk. Additionally, we assessed studies reporting the risk of AKI with the concurrent use of three or more nephrotoxic drugs. Following PRISMA guidelines, a comprehensive literature search was conducted. Observational studies in hospitalized patients were included if they assessed nephrotoxic burden or the risk of AKI with concurrent nephrotoxic drug use. Sixteen studies met the inclusion criteria. Four studies assessed nephrotoxic burden, two of which defined and quantified it, and two additional studies adopted those definitions to evaluate associations with AKI. All four reported a significant relationship between increased nephrotoxic burden and AKI risk. Twelve studies evaluated the likelihood of AKI with concurrent administration of three or more nephrotoxic drugs, with reported odds ratios ranging from 1.15 to 3.18 per additional drug. The deleterious effects of concomitant exposure to three or more nephrotoxins on the kidney are evident, stressing a need to take conscious action from a clinician and institutional perspective in the attempt to prevent AKI. Future research should incorporate drug‐specific weighting and consistent reporting standards to improve nephrotoxic burden assessment and guide clinical decision‐making to reduce AKI.

Key Points: The developed dynamic model for predicting progression to stage 2 or higher AKI using multicenter data had robust performance. Clinical decision support implementation of the developed model could help prevent AKI progression. Kinetic eGFR, nephrotoxic drug burden, and BUN were the top features and remained the same across the models and sites. Background: AKI is an abrupt decline in kidney function that occurs in about 20% of hospitalized admissions and may lead to irreversible kidney damage. Methods: We developed and externally validated deep learning models to dynamically predict progression to stage 2 or higher AKI defined by Kidney Disease Improving Global Outcomes serum creatinine criteria within the next 48 hours. We used an extensive set of predictors including demographics, admission source, comorbidities, medications, laboratory, and vitals measurements. Results: Our retrospective study includes adult noncritical care patients at the University of Pittsburgh Medical Center (UPMC; 2018–2022; n =39,755) and the University of Florida Health (UFH; 2012–2019; n =122,324). In the UFH and UPMC development cohort, the mean age was 55 and 71 and 55% ( n =47,350) and 54% ( n =15,128) were female, respectively. Stage 2 or higher AKI occurred in 3% ( n =3257) and 8% ( n =2296) of UFH and UPMC patients, respectively. Area under the receiver operating characteristic curve values with 95% confidence interval (CI) ranged between 0.77 (95% CI, 0.75 to 0.78; UPMC Model—model trained on UPMC patients) and 0.81 (95% CI, 0.79 to 0.82; UFH Model—model trained on University of Florida patients) for the UFH test cohort and between 0.79 (95% CI, 0.78 to 0.8; UFH Model) and 0.83 (95% CI, 0.82 to 0.84; UPMC Model) for the UPMC test cohort. UFH-UPMC Model achieved an area under the receiver operating characteristic curve of 0.81 (95% CI, 0.80 to 0.83) for UFH and 0.82 (95% CI, 0.81 to 0.84) for UPMC test cohorts. Kinetic eGFR, nephrotoxic drug burden, and BUN remained the features with the highest influence across the models and institutions. Conclusions: The model developed using multicenter data had robust performance, suggesting that implementation could help prevent AKI progression.