Artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study
Abstract Septic shock is one of the most lethal conditions in ICU, and early risk prediction may help reduce mortality. We developed a TOPSIS-based Classification Fusion (TCF) model to predict mortality risk in septic shock patients using data from 4872 ICU patients from February 2003 to November 20...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01643-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849314853890555904 |
|---|---|
| author | Shurui Wang Xinyi Liu Shaohua Yuan Yi Bian Hong Wu Qing Ye |
| author_facet | Shurui Wang Xinyi Liu Shaohua Yuan Yi Bian Hong Wu Qing Ye |
| author_sort | Shurui Wang |
| collection | DOAJ |
| description | Abstract Septic shock is one of the most lethal conditions in ICU, and early risk prediction may help reduce mortality. We developed a TOPSIS-based Classification Fusion (TCF) model to predict mortality risk in septic shock patients using data from 4872 ICU patients from February 2003 to November 2023 across three hospitals. The model integrates seven machine learning models via the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), achieving AUCs of 0.733 in internal validation, 0.808 in the pediatric ICU, 0.662 in the respiratory ICU, with external validation AUCs of 0.784 and 0.786, respectively. It demonstrated high stability and accuracy in cross-specialty and multi-center validation. This interpretable model provides clinicians with a reliable early-warning tool for septic shock mortality risk, facilitating early intervention to reduce mortality. |
| format | Article |
| id | doaj-art-15db814cfba64fa08bedd29ac23caee6 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-15db814cfba64fa08bedd29ac23caee62025-08-20T03:52:19ZengNature Portfolionpj Digital Medicine2398-63522025-04-018111010.1038/s41746-025-01643-wArtificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective studyShurui Wang0Xinyi Liu1Shaohua Yuan2Yi Bian3Hong Wu4Qing Ye5Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologySchool of Public Health, Tongji Medical College, Huazhong University of Science and TechnologySchool of Cyber Science and Engineering, Zhengzhou UniversityDepartment of Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologySchool of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and TechnologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyAbstract Septic shock is one of the most lethal conditions in ICU, and early risk prediction may help reduce mortality. We developed a TOPSIS-based Classification Fusion (TCF) model to predict mortality risk in septic shock patients using data from 4872 ICU patients from February 2003 to November 2023 across three hospitals. The model integrates seven machine learning models via the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), achieving AUCs of 0.733 in internal validation, 0.808 in the pediatric ICU, 0.662 in the respiratory ICU, with external validation AUCs of 0.784 and 0.786, respectively. It demonstrated high stability and accuracy in cross-specialty and multi-center validation. This interpretable model provides clinicians with a reliable early-warning tool for septic shock mortality risk, facilitating early intervention to reduce mortality.https://doi.org/10.1038/s41746-025-01643-w |
| spellingShingle | Shurui Wang Xinyi Liu Shaohua Yuan Yi Bian Hong Wu Qing Ye Artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study npj Digital Medicine |
| title | Artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study |
| title_full | Artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study |
| title_fullStr | Artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study |
| title_full_unstemmed | Artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study |
| title_short | Artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study |
| title_sort | artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study |
| url | https://doi.org/10.1038/s41746-025-01643-w |
| work_keys_str_mv | AT shuruiwang artificialintelligencebasedmultispecialtymortalitypredictionmodelsforsepticshockinamulticenterretrospectivestudy AT xinyiliu artificialintelligencebasedmultispecialtymortalitypredictionmodelsforsepticshockinamulticenterretrospectivestudy AT shaohuayuan artificialintelligencebasedmultispecialtymortalitypredictionmodelsforsepticshockinamulticenterretrospectivestudy AT yibian artificialintelligencebasedmultispecialtymortalitypredictionmodelsforsepticshockinamulticenterretrospectivestudy AT hongwu artificialintelligencebasedmultispecialtymortalitypredictionmodelsforsepticshockinamulticenterretrospectivestudy AT qingye artificialintelligencebasedmultispecialtymortalitypredictionmodelsforsepticshockinamulticenterretrospectivestudy |