Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
BMC Med Inform Decis Mak. 2021 Apr 9;21(Suppl 5):95. doi: 10.1186/s12911-021-01460-7.
Sepsis is a highly lethal and heterogeneous disease. Utilization of an unsupervised method may identify novel clinical phenotypes that lead to targeted therapies and improved care.
Our objective was to derive clinically relevant sepsis phenotypes from a multivariate panel of physiological data using subgraph-augmented nonnegative matrix factorization. We utilized data from the Medical Information Mart for Intensive Care III database of patients who were admitted to the intensive care unit with sepsis. The extracted data contained patient demographics, physiological records, sequential organ failure assessment scores, and comorbidities. We applied frequent subgraph mining to extract subgraphs from physiological time series and performed nonnegative matrix factorization over the subgraphs to derive patient clusters as phenotypes. Finally, we profiled these phenotypes based on demographics, physiological patterns, disease trajectories, comorbidities and outcomes, and performed functional validation of their clinical implications.
We analyzed a cohort of 5782 patients, derived three novel phenotypes of distinct clinical characteristics and demonstrated their prognostic implications on patient outcome. Subgroup 1 included relatively less severe/deadly patients (30-day mortality, 17%) and was the smallest-in-size group (n = 1218, 21%). It was characterized by old age (mean age, 73 years), a male majority (male-to-female ratio, 59-to-41), and complex chronic conditions. Subgroup 2 included the most severe/deadliest patients (30-day mortality, 28%) and was the second-in-size group (n = 2036, 35%). It was characterized by a male majority (male-to-female ratio, 60-to-40), severe organ dysfunction or failure compounded by a wide range of comorbidities, and uniquely high incidences of coagulopathy and liver disease. Subgroup 3 included the least severe/deadly patients (30-day mortality, 10%) and was the largest group (n = 2528, 44%). It was characterized by low age (mean age, 60 years), a balanced gender ratio (male-to-female ratio, 50-to-50), the least complicated conditions, and a uniquely high incidence of neurologic disease. These phenotypes were validated to be prognostic factors of mortality for sepsis patients.
Our results suggest that these phenotypes can be used to develop targeted therapies based on phenotypic heterogeneity and algorithms designed for monitoring, validating and intervening clinical decisions for sepsis patients.
败血症是一种高度致命且异质的疾病。利用无监督方法可能会识别出新的临床表型，从而实现靶向治疗和改善护理。
我们的目标是利用多变量生理数据的子图增强非负矩阵分解从败血症患者的医疗信息集市 III 数据库中提取临床相关的败血症表型。提取的数据包含患者人口统计学信息、生理记录、序贯器官衰竭评估评分和合并症。我们应用频繁子图挖掘从生理时间序列中提取子图，并对子图进行非负矩阵分解，以将患者聚类为表型。最后，根据人口统计学、生理模式、疾病轨迹、合并症和结局对这些表型进行分析，并对其临床意义进行功能验证。
我们分析了一个包含 5782 名患者的队列，得出了三种具有不同临床特征的新型表型，并证明了它们对患者预后的预后意义。亚组 1 包括相对较轻/致死率较低的患者（30 天死亡率为 17%），且是最小的一个组（n=1218，占 21%）。其特点是年龄较大（平均年龄 73 岁），男性居多（男女比例为 59 比 41），且合并复杂的慢性疾病。亚组 2 包括最严重/致死率最高的患者（30 天死亡率为 28%），且是第二大组（n=2036，占 35%）。其特点是男性居多（男女比例为 60 比 40），严重的器官功能障碍或衰竭，并伴有广泛的合并症，独特的高凝血病和肝病发生率。亚组 3 包括最轻/致死率最低的患者（30 天死亡率为 10%），且是最大的一个组（n=2528，占 44%）。其特点是年龄较低（平均年龄 60 岁），性别比例平衡（男女比例为 50 比 50），病情最简单，独特的高神经疾病发生率。这些表型被验证为败血症患者死亡率的预后因素。
我们的结果表明，这些表型可用于根据表型异质性和针对败血症患者的监测、验证和干预临床决策的算法开发靶向治疗。
