Sepsis subphenotyping based on organ dysfunction trajectory

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.

Subphenotyping heterogeneous patients with chronic critical illness to guide individualised fluid balance treatment using machine learning: a retrospective cohort study

“…reported a group of sepsis subtypes based on trajectory clustering, which revealed the alteration of their organ function levels over time. 33 Further studies exploring the evolutionary relationship between the CCI subphenotype we identified and the other acute critical illness with the related subphenotypes are warranted in the future.…”

Identification of two robust subclasses of sepsis with both prognostic and therapeutic values based on machine learning analysis

“…Transcriptomic features that classify the host immune response will contribute to the development of novel therapeutic treatments the improvement of personalized management for sepsis (27). Prediction of clinical outcomes could be well accomplished by establishing the specific classifiers, which have been validated with transcriptomic data (28)(29)(30). Therefore, the purposes of the research were to reveal the clinical subtypes using large-scale samples with sepsis.…”

Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes

“…With the learned K -dimensional topic loading vector for n -th patient θ n , we applied the hierarchical agglomerative clustering method with Euclidean distance calculation and Ward linkage criterion 38 to derive subphenotypes as patient clusters. For determining the optimal number of clusters (subphenotypes), according to Su et al 39 and Xu et al 40 , we applied the NbClust R package 41 , which includes 21 cluster indices to evaluate the quality of clusters. With the patients from the INSIGHT and OneFlorida+ CRN, 13 and 12 out of the 21 indices agreed that four is the optimal number of clusters (Supplementary Table 12 ).…”