Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity
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....
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Berlin Heidelberg
2023
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| Online Access: | https://hdl.handle.net/1721.1/152972 |
| _version_ | 1824458116890099712 |
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| author | Zhang, Zhongheng Chen, Lin Liu, Xiaoli Yang, Jie Huang, Jiajie Yang, Qiling Hu, Qichao Jin, Ketao Celi, Leo A. Hong, Yucai |
| author2 | Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology |
| author_facet | Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology Zhang, Zhongheng Chen, Lin Liu, Xiaoli Yang, Jie Huang, Jiajie Yang, Qiling Hu, Qichao Jin, Ketao Celi, Leo A. Hong, Yucai |
| author_sort | Zhang, Zhongheng |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:55:28Z |
| format | Article |
| id | mit-1721.1/152972 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:20:47Z |
| publishDate | 2023 |
| publisher | Springer Berlin Heidelberg |
| record_format | dspace |
| spelling | mit-1721.1/1529722025-01-04T05:39:48Z Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity Zhang, Zhongheng Chen, Lin Liu, Xiaoli Yang, Jie Huang, Jiajie Yang, Qiling Hu, Qichao Jin, Ketao Celi, Leo A. Hong, Yucai Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology 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. 2023-11-14T19:37:08Z 2023-11-14T19:37:08Z 2023-10-04 2023-11-03T04:18:40Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/152972 Zhang, Zhongheng, Chen, Lin, Liu, Xiaoli, Yang, Jie, Huang, Jiajie et al. 2023. "Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity." en https://doi.org/10.1007/s00134-023-07226-1 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer-Verlag GmbH Germany, part of Springer Nature application/pdf Springer Berlin Heidelberg Springer Berlin Heidelberg |
| spellingShingle | Zhang, Zhongheng Chen, Lin Liu, Xiaoli Yang, Jie Huang, Jiajie Yang, Qiling Hu, Qichao Jin, Ketao Celi, Leo A. Hong, Yucai Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity |
| title | Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity |
| title_full | Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity |
| title_fullStr | Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity |
| title_full_unstemmed | Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity |
| title_short | Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity |
| title_sort | exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity |
| url | https://hdl.handle.net/1721.1/152972 |
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