Abstract
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$ \pm 17.25 $ years, and is characterized by mild loss of kidney excretory function (Serum Creatinne (SCr) $1.55\pm 0.34$ mg/dL, estimated Glomerular Filtration Rate Test (eGFR) $107.65\pm 54.98$ mL/min/1.73$m^2$). These patients are more likely to develop stage I AKI. Sub-phenotype II is with average age 66.81$ \pm 10.43 $ years, and was characterized by severe loss of kidney excretory function (SCr $1.96\pm 0.49$ mg/dL, eGFR $82.19\pm 55.92$ mL/min/1.73$m^2$). These patients are more likely to develop stage III AKI. Sub-phenotype III is with average age 65.07$ \pm 11.32 $ years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr $1.69\pm 0.32$ mg/dL, eGFR $93.97\pm 56.53$ mL/min/1.73$m^2$). 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.
Abstract (translated)
急性肾损伤（AKI）是一种常见的临床综合征，其特点是肾排泄功能迅速丧失，加重了大量住院患者其他疾病的临床严重程度。准确的早期AKI预测可以及时进行干预和治疗。然而，AKI具有高度的异质性，因此，对AKI亚型的鉴定可以提高对疾病病理生理学的理解，并发展更具针对性的临床干预措施。本研究采用基于记忆网络的深度学习方法，利用AKI诊断前患者的结构化和非结构化电子健康记录（ehr）数据发现预测性AKI亚型。我们利用了一个现实世界的紧急护理EHR语料库，包括37486个ICU住院。我们的方法确定了三个不同的亚表型：亚表型I的平均年龄为63.03美元 PM 17.25美元年，其特点是轻度肾排泄功能丧失（Serum Creatinne（SCR）1.55美元 PM 0.34美元mg/dL，估计肾小球滤过率试验（EGFR）107.65美元/PM 54.98美元/分钟/ 1.73美元M^ 2美元）。这些患者更可能发展为I期AKI。亚型II平均年龄66.81$pm 10.43$岁，其特征是肾排泄功能严重丧失（scr$1.96pm 0.49$mg/dl，egfr$82.19pm 55.92$ml/min/1.73$m^2$）。这些患者更有可能发生第三阶段AKI。亚型III平均年龄65.07$pm 11.32$岁，表现为肾排泄功能中度丧失，因此更可能发展为II期AKI（scr$1.69pm 0.32$mg/dl，egfr$93.97pm 56.53$ml/min/1.73$m^2$）。三种亚型的scr和egfr在统计学检验和博士后分析上均存在显著差异，年龄调整后的结论仍然有效。
URL
https://arxiv.org/abs/1904.04990
