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Fig. 1 | Critical Care

Fig. 1

From: Sepsis subphenotyping based on organ dysfunction trajectory

Fig. 1

Workflow of study. A The MIMIC-III dataset was used as development cohort and NMEDW, eICU, and CEDAR datasets were used as validation cohorts. Electronic health records including laboratory tests, vital signs, and medication were extracted to compute the SOFA score every 6 h during 72 h after admission to ICU. B Each patient was represented as a 72-h SOFA score trajectory. Dynamic time warping (DTW) was used to compute heterogeneous SOFA trajectory similarities and HAC was applied to identify subphenotypes based on trajectory similarities. C To re-derive subphenotypes in three validation cohorts and consider sensitivity analysis to clustering method, specifically, use another method (Group-Based Trajectory Modeling, GBTM) to generate subphenotypes. Statistical analysis were performed among subphenotypes in terms of demographic factors, laboratory tests and vital signs. D The predictive model of subphenotypes at successive time points (hours 6, 24, 36, 48, 60) after ICU admission was constructed based on a random forest classifier by using patients’ clinical data including laboratory tests, vital signs, and SOFA subscores

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