TABLE 2.
Clustering Methods and Findings
| Reference | Type of Clustering Variables | Clustering Algorithm/Method | Data Imputation Method | No. of Clusters | Principle Difference Between Clusters |
|---|---|---|---|---|---|
| Geri et al (12) | 11 clinical variables: echocardiographic and hemodynamic markers | Hierarchical clustering on principal components | Iterative principal component analysis | 5 | Hemodynamic state |
| Seymour et al (13) | 29 clinical variables: demographics, vital signs, and inflammatory markers | K-means | Multiple imputation with chained equations | 4 | Inflammation and coagulation |
| Bhavani et al (15) | 1 clinical variable: body temperature | Clusters previously discovered | NA | 4 | Inflammation |
| Ding and Luo (16) | 34 clinical variables: vital signs and blood markers | Subgraph augmented nonnegative matrix factorization | Linear approximation from values from previous time intervals | 3 | Mortality |
| Gårdlund et al (17) | 46 clinical variables: demographics, vital signs, infection site, and prior history | Latent class analysis | Creation of a monotone missingness pattern using the Markov chain Monte Carlo method | 6 | Infection site and disease timeline |
| Linear regression for continuous variables/logistic regression for categorical variables | |||||
| Han et al (18) | Treatment response | Causal forests | Median imputation | 2 | Response to antibiotic delays |
| Kudo et al (19) | 6 clinical variables: coagulation markers | K-means | Random forest method | 4 | Response to thrombomodulin |
| Scicluna et al (20) | Genomic | Unsupervised consensus clustering | NA | 4 | Immunity |
| Sharafoddini et al (21) | 36 clinical variables: demographics, vital signs, blood and renal markers, interventions, and International Classification of Diseases, 9th Edition code | Hierarchical clustering and Density-Based Spatial Clustering of Application with Noise | Predictve mean matching imputation | 12 | Mortality |
| Antcliffe et al (22) | Transcriptomic | None, previously described | NA | 2 | Immunocompetence vs suppression |
| Bhavani et al (23) | 1 clinical variable: body temperature | Group-based trajectory modeling | NA | 4 | Temperature trajectories |
| Davenport et al (24) | Transcriptomic | K-means | NA | 2 | Immune response |
| Liu et al (25) | 28 clinical variables: vital signs and blood markers | Spectral clustering | NA | 4 | Risk of progression to septic shock |
| Mayhew et al (26) | 14 clinical variables: demographics and vital signs | Novel composite mixture model; PAM algorithm | No missing values | 20 | Mortality |
| Nowak et al (27) | Other: Hemodynamic measurements (stroke volume, cardiac index, systemic vascular resistance, etc.) as measured | K-means with manual identification of optimal clusters | NA | 3 | Cardiac index and systemic vascular resistance index |
| Sweeney et al (28) | Transcriptomic | The COmbined Mapping of Multiple clUsteriNg ALgorithms using K-means and PAM | NA | 3 | Inflammation, coagulopathy |
| Zhang et al (29) | Transcriptomic | K-means | Excluded missing gene symbols. | 2 | Immunosuppression |
NA = not applicable, PAM = Partition Around Medoids.