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Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity

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Abstract

Purpose

Various studies have analyzed sepsis subtypes, yet the reproducibility of such results remains unclear. This study aimed to determine the reproducibility of sepsis subtypes across multiple cohorts.

Methods

The study examined 63,547 sepsis patients from six distinct cohorts who had similar sepsis-related characteristics (vital signs, lactate, sequential organ failure assessment score, bilirubin, serum, urine output, and Glasgow coma scale). Identical cluster analysis techniques were used, employing 27 clustering schemes, and normalized mutual information (NMI), a metric ranging from 0 to 1 with higher values indicating better concordance, was employed to quantify the clustering solutions' reproducibility. Principal component analysis (PCA) was utilized to obtain the disease axis, and its uniformity across cohorts was evaluated through patterns of feature loading and correlation.

Results

The reproducibility of sepsis clustering subtypes across the various studies was modest (median NMI ranging from 0.08 to 0.54). The top-down transfer learning method (model trained on cohorts with greater severity was transferred to cohorts with lower severity score) had a higher NMI value than the bottom-up approach (median [Q1, Q3]: 0.64 [0.49, 0.78] vs. 0.23 [0.2, 0.31], p < 0.001). The reproducibility was greater when the transfer solution was performed within United States (US) cohorts. The PCA analysis revealed that the correlation pattern between variables was consistent across all cohorts, and the first two disease axes were the "shock axis" and "systemic inflammatory response syndrome (SIRS) axis."

Conclusions

Cluster analysis of sepsis patients across various cohorts showed modest reproducibility. Sepsis heterogeneity is better characterized through continuous disease axes that coexist to varying degrees within the same individual instead of mutually exclusive subtypes.

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Data availability

Datasets can be obtained by contacting the corresponding author upon a reasonable request.

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Funding

ZZ received funding from the Open Foundation of Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province (SZZD202206), Health Science and Technology Plan of Zhejiang Province (2021KY745), the Fundamental Research Funds for the Central Universities (226–2022-00148), National natural science foundation of China (82272180) and the Project of Drug Clinical Evaluate Research of Chinese Pharmaceutical Association NO.CPA-Z06-ZC-2021–004. YH received funding from the Key Research & Development project of Zhejiang Province (2021C03071).

Author information

Author notes

  1. Zhongheng Zhang and Lin Chen have contributed equally to this work and should be considered as co-first author.

Authors and Affiliations

  1. Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China

    Zhongheng Zhang, Jie Yang, Jiajie Huang & Yucai Hong

  2. Neurological Intensive Care Unit, Department of Neurosurgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China

    Lin Chen

  3. Center for Artificial Intelligence in Medicine, The General Hospital of PLA, Beijing, China

    Xiaoli Liu

  4. Department of Critical Care, The Second Affiliated Hospital of Guangzhou Medical University, No. 250 Changgang East RoadHaizhu District, Guangzhou, Guangdong, China

    Qiling Yang

  5. Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Dian Diagnostics Group Co., Ltd., Hangzhou, Zhejiang Province, China

    Qichao Hu

  6. Department of Colorectal Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, 321000, Zhejiang, China

    Ketao Jin

  7. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Leo Anthony Celi

  8. Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Leo Anthony Celi

  9. Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Leo Anthony Celi

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  1. Zhongheng Zhang
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Correspondence to Zhongheng Zhang.

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Zhang, Z., Chen, L., Liu, X. et al. Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity. Intensive Care Med 49, 1349–1359 (2023). https://doi.org/10.1007/s00134-023-07226-1

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