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. 2021 Jul 12;25:243. doi: 10.1186/s13054-021-03682-7

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© The Author(s) 2021

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Classes of septic shock. A Optimal number of clusters by K-means clustering. The statistics were scaled for better visualization. Statistics such as CCC, CH and KL showed the highest value for five clusters; lower value of DB indicates better fit which also supports the existence of five clusters. Some statistics utilize elbow point to identify the best number of clusters such as Hartigan, Marriot and TraceW. These statistics consistently indicated an elbow joint at 5-class. B Metrics for choosing the best number of classes for FMM. C Class membership transition over days 0, 1, 2, 3 and 7. It is noted that most patients in class 2 (critical class) on day 0 would transition to other classes, indicating improvement in these critical cases. Patients who transitioned to class 2 were more likely to die. D PCA showing that the five classes can be visually separated by the first three principal components. One point represents one sample (one data point per patient day). E Characteristics of the five classes identified by FMM. All numeric values were scaled (i.e., centered on mean and divided by standard deviation) for better visualization on the vertical axis. Seventeen variables were used for FMM training, but 29 variables are displayed to give a comprehensive clinical characteristics for these classes. Class 1 is the largest class over all study days with all variables in average value (the baseline class). Class 2 is characterized by poor tissue perfusion and multiple organ failure and can be called the critical class. Class 3 is characterized by highest serum creatinine and metabolic acidosis and can be called renal dysfunction class. Class 4 is characterized by the highest PaCO2 (60; IQR 50–77 mmHg) and low PF ratio (169; IQR 118–232 mmHg) and can be designated as respiratory failure class. Class 5 is characterized by young age, low mortality and well-preserved renal function and can be considered as the mild class. FMM finite mixture modeling, CCC cubic clustering criterion, CH Calinski and Harabasz index, DB Davies and Bouldin index, KL Krzanowski and Lai index. ****< 0.001