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Some patients exhibit septic symptoms following laparoscopic surgery, leading to a poor prognosis. Effective clinical subphenotyping is critical for guiding tailored therapeutic strategies in these cases. By identifying predisposing factors for postoperative sepsis, clinicians can implement targeted interventions, potentially improving outcomes. This study outlines a workflow for the subphenotype methodology in the context of laparoscopic surgery, along with its practical application. This study utilized data routinely available in clinical case systems, enhancing the applicability of our findings. The data included vital signs, such as respiratory rate, and laboratory measures, such as blood sodium levels. The process of categorizing clinical routine data involved technical complexities. A correlation heatmap was used to visually depict the relationships between variables. Ordering points were used to identify the clustering structure and combined with Consensus K clustering methods to determine the optimal categorization. Our study highlighted the intricacies of identifying clinical subphenotypes following laparoscopic surgery, and could thus serve as a valuable resource for clinicians and researchers seeking to explore disease heterogeneity in clinical settings. By simplifying complex methodologies, we aimed to bridge the gap between technical expertise and clinical application, fostering an environment where professional medical knowledge is effectively utilized in subphenotyping research. This tutorial could primarily serve as a guide for beginners. A variety of clustering approaches were explored, and each step in the process contributed to a comprehensive understanding of clinical subphenotypes.
Yang et al. (Thu,) studied this question.