Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.
BACKGROUND AND OBJECTIVES
Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.


DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS
We used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement.


RESULTS
We identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for K-means clustering, identifying three subphenotypes. Subphenotype 1 had 1443 patients, and subphenotype 2 had 1898 patients, whereas subphenotype 3 had 660 patients. Subphenotype 1 had the lowest proportion of liver disease and lowest Simplified Acute Physiology Score II scores compared with subphenotypes 2 and 3. The proportions of patients with CKD were similar between subphenotypes 1 and 3 (15%) but highest in subphenotype 2 (21%). Subphenotype 1 had lower median bilirubin levels, aspartate aminotransferase, and alanine aminotransferase compared with subphenotypes 2 and 3. Patients in subphenotype 1 also had lower median lactate, lactate dehydrogenase, and white blood cell count than patients in subphenotypes 2 and 3. Subphenotype 1 also had lower creatinine and BUN than subphenotypes 2 and 3. Dialysis requirement was lowest in subphenotype 1 (4% versus 7% [subphenotype 2] versus 26% [subphenotype 3]). The mortality 28 days after AKI was lowest in subphenotype 1 (23% versus 35% [subphenotype 2] versus 49% [subphenotype 3]). After adjustment, the adjusted odds ratio for mortality for subphenotype 3, with subphenotype 1 as a reference, was 1.9 (95% confidence interval, 1.5 to 2.4).


CONCLUSIONS
Utilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.
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D058186:Acute Kidney Injury D000368:Aged D000410:Alanine Transaminase D001663:Bilirubin D001806:Blood Urea Nitrogen D015897:Comorbidity D003404:Creatinine D016208:Databases, Factual D000077321:Deep Learning D057286:Electronic Health Records D005260:Female D043384:Glutamyl Aminopeptidase D006801:Humans D007770:L-Lactate Dehydrogenase D019344:Lactic Acid D007958:Leukocyte Count D008107:Liver Diseases D008297:Male D008875:Middle Aged D010641:Phenotype D011379:Prognosis D006435:Renal Dialysis D018805:Sepsis D000072878:Simplified Acute Physiology Score D014481:United States
deep learning acute kidney injury AKI mortality dialysis acute renal failure subtypes