  Background: Acute kidney injury (AKI), the decline of kidney excretory
function, occurs in up to 18% of hospitalized admissions. Progression of AKI
may lead to irreversible kidney damage. Methods: This retrospective cohort
study includes adult patients admitted to a non-intensive care unit at the
University of Pittsburgh Medical Center (UPMC) (n = 46,815) and University of
Florida Health (UFH) (n = 127,202). We developed and compared deep learning and
conventional machine learning models to predict progression to Stage 2 or
higher AKI within the next 48 hours. We trained local models for each site (UFH
Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a
development cohort of patients from both sites (UFH-UPMC Model). We internally
and externally validated the models on each site and performed subgroup
analyses across sex and race. Results: Stage 2 or higher AKI occurred in 3%
(n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under
the receiver operating curve values (AUROC) for the UFH test cohort ranged
between 0.77 (UPMC Model) and 0.81 (UFH Model), while AUROC values ranged
between 0.79 (UFH Model) and 0.83 (UPMC Model) for the UPMC test cohort.
UFH-UPMC Model achieved an AUROC of 0.81 (95% confidence interval [CI] [0.80,
0.83]) for UFH and 0.82 (95% CI [0.81,0.84]) for UPMC test cohorts; an area
under the precision recall curve values (AUPRC) of 0.6 (95% CI, [0.05, 0.06])
for UFH and 0.13 (95% CI, [0.11,0.15]) for UPMC test cohorts. Kinetic estimated
glomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen
remained the top three features with the highest influence across the models
and health centers. Conclusion: Locally developed models displayed marginally
reduced discrimination when tested on another institution, while the top set of
influencing features remained the same across the models and sites.
