Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury
Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury
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深度学习在脓毒症相关急性肾损伤亚表型识别中的应用
DOI：
10.2215/cjn.09330819
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发表时间：
2020-11-06
影响因子：
9.8
通讯作者：
Nadkarni, Girish N.
中科院分区：
文献类型：
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作者：
Chaudhary, Kumardeep;Vaid, Akhil;Nadkarni, Girish N.
背景和目的脓毒症相关AKI是一种异质性的临床实体。我们的目的是通过对电子健康记录中常规收集的数据进行深度学习，以确定败血症相关的AKI亚表型。设计、设置、参与者和测量我们使用了重症监护医疗信息市场III数据库，该数据库由来自美国一家三级医院重症监护病房的电子健康记录数据组成。我们纳入了b> = 18岁的脓毒症患者，这些患者在重症监护病房入院48小时内发生AKI。然后，我们使用深度学习来利用所有可用的生命体征、实验室测量和合并症来识别亚表型。结果是AKI后28天的死亡率和透析需求。结果我们确定了4001例败血症相关AM患者。我们利用2546个组合特征进行k均值聚类，确定了三个亚表型。亚表型1 1443例，亚表型2 1898例，而亚表型3 660例。与亚表型2和3相比，亚表型1出现肝脏疾病的比例最低，简化急性生理评分II得分最低。亚表型1和3患者的CKD比例相似（15%），但亚表型2患者的比例最高（21%）。与亚表型2和3相比，亚表型1的胆红素、天冬氨酸转氨酶和丙氨酸转氨酶的中位数水平较低。亚表型1患者的乳酸、乳酸脱氢酶和白细胞计数中位数也低于亚表型2和3患者。亚表型1的肌酐和BUN也低于亚表型2和3。亚表型1的透析需求最低（4%对7%[亚表型2]对26%[亚表型3]）。亚表型1患者AKI后28天死亡率最低（23% vs 35%[亚表型2]vs 49%[亚表型3]）。调整后，以亚表型1为参照，亚表型3死亡率的调整优势比为1.9（95%可信区间为1.5 ~ 2.4）。利用常规收集的实验室变量、生命体征和合并症，我们能够确定脓毒症相关AKI的三种不同亚表型，其结果不同。
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 AM. 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.