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Comparison of time series clustering methods for identifying novel subphenotypes of patients with infection

动态时间归整 中胚层 聚类分析 医学 生命体征 败血症 层次聚类 优势比 内科学 可能性 生物信息学 计算机科学 人工智能 逻辑回归 外科 生物
作者
Sivasubramanium V. Bhavani,Li Xiong,Abish Pius,Matthew W. Semler,Edward T Qian,Philip A. Verhoef,Chad Robichaux,Craig M. Coopersmith,Matthew M Churpek
出处
期刊:Journal of the American Medical Informatics Association [Oxford University Press]
卷期号:30 (6): 1158-1166 被引量:1
标识
DOI:10.1093/jamia/ocad063
摘要

Severe infection can lead to organ dysfunction and sepsis. Identifying subphenotypes of infected patients is essential for personalized management. It is unknown how different time series clustering algorithms compare in identifying these subphenotypes.Patients with suspected infection admitted between 2014 and 2019 to 4 hospitals in Emory healthcare were included, split into separate training and validation cohorts. Dynamic time warping (DTW) was applied to vital signs from the first 8 h of hospitalization, and hierarchical clustering (DTW-HC) and partition around medoids (DTW-PAM) were used to cluster patients into subphenotypes. DTW-HC, DTW-PAM, and a previously published group-based trajectory model (GBTM) were evaluated for agreement in subphenotype clusters, trajectory patterns, and subphenotype associations with clinical outcomes and treatment responses.There were 12 473 patients in training and 8256 patients in validation cohorts. DTW-HC, DTW-PAM, and GBTM models resulted in 4 consistent vitals trajectory patterns with significant agreement in clustering (71-80% agreement, P < .001): group A was hyperthermic, tachycardic, tachypneic, and hypotensive. Group B was hyperthermic, tachycardic, tachypneic, and hypertensive. Groups C and D had lower temperatures, heart rates, and respiratory rates, with group C normotensive and group D hypotensive. Group A had higher odds ratio of 30-day inpatient mortality (P < .01) and group D had significant mortality benefit from balanced crystalloids compared to saline (P < .01) in all 3 models.DTW- and GBTM-based clustering algorithms applied to vital signs in infected patients identified consistent subphenotypes with distinct clinical outcomes and treatment responses.Time series clustering with distinct computational approaches demonstrate similar performance and significant agreement in the resulting subphenotypes.
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