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Abstract Background Sepsis is a heterogeneous syndrome, and the identification of clinical subphenotypes is essential. Although organ dysfunction is a defining element of sepsis, subphenotypes of differential trajectory are not well studied. We sought to identify distinct Sequential Organ Failure Assessment (SOFA) score trajectory-based subphenotypes in sepsis. Methods We created 72-h SOFA score trajectories in patients with sepsis from four diverse intensive care unit (ICU) cohorts. We then used dynamic time warping (DTW) to compute heterogeneous SOFA trajectory similarities and hierarchical agglomerative clustering (HAC) to identify trajectory-based subphenotypes. Patient characteristics were compared between subphenotypes and a random forest model was developed to predict subphenotype membership at 6 and 24 h after being admitted to the ICU. The model was tested on three validation cohorts. Sensitivity analyses were performed with alternative clustering methodologies. Results A total of 4678, 3665, 12,282, and 4804 unique sepsis patients were included in development and three validation cohorts, respectively. Four subphenotypes were identified in the development cohort: Rapidly Worsening ( n = 612, 13.1%), Delayed Worsening ( n = 960, 20.5%), Rapidly Improving ( n = 1932, 41.3%), and Delayed Improving ( n = 1174, 25.1%). Baseline characteristics, including the pattern of organ dysfunction, varied between subphenotypes. Rapidly Worsening was defined by a higher comorbidity burden, acidosis, and visceral organ dysfunction. Rapidly Improving was defined by vasopressor use without acidosis. Outcomes differed across the subphenotypes, Rapidly Worsening had the highest in-hospital mortality (28.3%, P -value < 0.001), despite a lower SOFA (mean: 4.5) at ICU admission compared to Rapidly Improving (mortality:5.5%, mean SOFA: 5.5). An overall prediction accuracy of 0.78 (95% CI, [0.77, 0.8]) was obtained at 6 h after ICU admission, which increased to 0.87 (95% CI, [0.86, 0.88]) at 24 h. Similar subphenotypes were replicated in three validation cohorts. The majority of patients with sepsis have an improving phenotype with a lower mortality risk; however, they make up over 20% of all deaths due to their larger numbers. Conclusions Four novel, clinically-defined, trajectory-based sepsis subphenotypes were identified and validated. Identifying trajectory-based subphenotypes has immediate implications for the powering and predictive enrichment of clinical trials. Understanding the pathophysiology of these differential trajectories may reveal unanticipated therapeutic targets and identify more precise populations and endpoints for clinical trials.
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Network-Based Modeling of Sepsis: Quantification and Evaluation of Simultaneity of Organ Dysfunctions
It is shown that appropriate therapeutic management at early stages of sepsis are crucial for preventing further deterioration and irreversible organ damage. Although previous studies considered the cellular and physiological responses as the components of sepsis-related predictive models, temporal connections among the responses have not been widely studied. The objective of this study is to investigate simultaneous changes in cellular and physiological responses represented by 16 clinical variables contributing to seven organ system dysfunctions in patients with sepsis to predict in-hospital mortality. Organ dysfunctions were represented by undirected weighted network models composed of: i) nodes (i.e., 16 clinical variables and three biomarkers including procalcitonin, C-reactive protein, and sedimentation rate), ii) edges (i.e., connection between pair of nodes representing simultaneous dysfunctions), and iii) weights representing the persistence of the co-occurrence of two dysfunctions. Data was collected from 13,367 adult patients (corresponding to 17,953 visits) admitted to the study hospital from July 1, 2013, to December 31, 2015. The study population were categorized based on clinical criteria representing sepsis progression to identify different subpopulations. The findings quantify the optimal window for defining the simultaneity of two dysfunctions, the network properties corresponding to different subpopulations, the discriminatory patterns of simultaneous dysfunctions among subpopulations and in-hospital mortality prediction. The results show that the level of persistence of simultaneous dysfunctions are subpopulation-specific. Insights from this study regarding optimal thresholds of the persistence and combination of simultaneous organ dysfunctions can inform policies to personalize the in-hospital mortality prediction.
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- Award ID(s):
- 1650531
- PAR ID:
- 10139699
- Date Published:
- Journal Name:
- Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics 2019
- Page Range / eLocation ID:
- 87 to 96
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3307339.3342160
