Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.

Link: https://doi.org/10.2215/CJN.09330819
Authors: Chaudhary, Kumardeep; Vaid, Akhil; Duffy, Áine; Paranjpe, Ishan; Jaladanki, Suraj; Paranjpe, Manish; Johnson, Kipp; Gokhale, Avantee; Pattharanitima, Pattharawin; Chauhan, Kinsuk; O’Hagan, Ross; Van Vleck, Tielman; Coca, Steven G; Cooper, Richard; Glicksberg, Benjamin; Bottinger, Erwin P; Chan, Lili; Nadkarni, Girish N

Abstract: 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. 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. Utilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.