Identification of Predictive Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks
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Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is highly heterogeneous, thus identification of AKI sub-phenotypes can lead to an improved understanding of the disease pathophysiology and development of more targeted clinical interventions. This study used a memory network-based deep learning approach to discover predictive AKI sub-phenotypes using structured and unstructured electronic health record (EHR) data of patients before AKI diagnosis. We leveraged a real world critical care EHR corpus including 37,486 ICU stays. Our approach identified three distinct sub-phenotypes: sub-phenotype I is with an average age of 63.03 ± 17.25 years, and is characterized by mild loss of kidney excretory function (Serum Creatinne (SCr) 1.55± 0.34 mg/dL, estimated Glomerular Filtration Rate Test (eGFR) 107.65± 54.98 mL/min/1.73m^2). These patients are more likely to develop stage I AKI. Sub-phenotype II is with average age 66.81 ± 10.43 years, and was characterized by severe loss of kidney excretory function (SCr 1.96± 0.49 mg/dL, eGFR 82.19± 55.92 mL/min/1.73m^2). These patients are more likely to develop stage III AKI. Sub-phenotype III is with average age 65.07 ± 11.32 years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr 1.69± 0.32 mg/dL, eGFR 93.97± 56.53 mL/min/1.73m^2). Both SCr and eGFR are significantly different across the three sub-phenotypes with statistical testing plus postdoc analysis, and the conclusion still holds after age adjustment.
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