Integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe COVID-19
Abstract Background The rapid evolution of the COVID-19 pandemic and subsequent global immunization efforts have rendered early metabolomics studies potentially outdated, as they primarily involved non-exposed, non-vaccinated populations. This paper presents a predictive model developed from up-to-d...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-024-10402-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559806570135552 |
|---|---|
| author | Shanpeng Cui Qiuyuan Han Ran Zhang Siyao Zeng Ying Shao Yue Li Ming Li Wenhua Liu Junbo Zheng Hongliang Wang |
| author_facet | Shanpeng Cui Qiuyuan Han Ran Zhang Siyao Zeng Ying Shao Yue Li Ming Li Wenhua Liu Junbo Zheng Hongliang Wang |
| author_sort | Shanpeng Cui |
| collection | DOAJ |
| description | Abstract Background The rapid evolution of the COVID-19 pandemic and subsequent global immunization efforts have rendered early metabolomics studies potentially outdated, as they primarily involved non-exposed, non-vaccinated populations. This paper presents a predictive model developed from up-to-date metabolomics data integrated with clinical data to estimate early mortality risk in critically ill COVID-19 patients. Our study addresses the critical gap in current research by utilizing current patient samples, providing fresh insights into the pathophysiology of the disease in a partially immunized global population. Methods One hundred elderly patients with severe COVID-19 infection, including 46 survivors and 54 non-survivors, were recruited in January-February 2023 at the Second Hospital affiliated with Harbin Medical University. A predictive model within 24 h of admission was developed using blood metabolomics and clinical data. Differential metabolite analysis and other techniques were used to identify relevant characteristics. Model performance was assessed by comparing the area under the receiver operating characteristic curve (AUROC). The final prediction model was externally validated in a cohort of 50 COVID-19 elderly critically ill patients at the First Hospital affiliated with Harbin Medical University during the same period. Results Significant disparities in blood metabolomics and laboratory parameters were noted between individuals who survived and those who did not. One metabolite indicator, Itaconic acid, and four laboratory tests (LYM, IL-6, PCT, and CRP), were identified as the five variables in all four models. The external validation set demonstrated that the KNN model exhibited the highest AUC of 0.952 among the four models. When considering a 50% risk of mortality threshold, the validation set displayed a sensitivity of 0.963 and a specificity of 0.957. Conclusions The prognostic outcome of COVID-19 elderly patients is significantly influenced by the levels of Itaconic acid, LYM, IL-6, PCT, and CRP upon admission. These five indicators can be utilized to assess the mortality risk in affected individuals. |
| format | Article |
| id | doaj-art-5935f5973c794dc59fd0f85423f304b3 |
| institution | Kabale University |
| issn | 1471-2334 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj-art-5935f5973c794dc59fd0f85423f304b32025-01-05T12:09:50ZengBMCBMC Infectious Diseases1471-23342025-01-0125111110.1186/s12879-024-10402-3Integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe COVID-19Shanpeng Cui0Qiuyuan Han1Ran Zhang2Siyao Zeng3Ying Shao4Yue Li5Ming Li6Wenhua Liu7Junbo Zheng8Hongliang Wang9Department of Critical Care Medicine, Second Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, Second Affiliated Hospital of Harbin Medical UniversitySchool of Measurement-Control and Communication Engineering, Harbin University of Science and TechnologyDepartment of Critical Care Medicine, Second Affiliated Hospital of Harbin Medical UniversityInterventional vascular department, The Fourth Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, Second Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, Second Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, Second Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, Second Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, Second Affiliated Hospital of Harbin Medical UniversityAbstract Background The rapid evolution of the COVID-19 pandemic and subsequent global immunization efforts have rendered early metabolomics studies potentially outdated, as they primarily involved non-exposed, non-vaccinated populations. This paper presents a predictive model developed from up-to-date metabolomics data integrated with clinical data to estimate early mortality risk in critically ill COVID-19 patients. Our study addresses the critical gap in current research by utilizing current patient samples, providing fresh insights into the pathophysiology of the disease in a partially immunized global population. Methods One hundred elderly patients with severe COVID-19 infection, including 46 survivors and 54 non-survivors, were recruited in January-February 2023 at the Second Hospital affiliated with Harbin Medical University. A predictive model within 24 h of admission was developed using blood metabolomics and clinical data. Differential metabolite analysis and other techniques were used to identify relevant characteristics. Model performance was assessed by comparing the area under the receiver operating characteristic curve (AUROC). The final prediction model was externally validated in a cohort of 50 COVID-19 elderly critically ill patients at the First Hospital affiliated with Harbin Medical University during the same period. Results Significant disparities in blood metabolomics and laboratory parameters were noted between individuals who survived and those who did not. One metabolite indicator, Itaconic acid, and four laboratory tests (LYM, IL-6, PCT, and CRP), were identified as the five variables in all four models. The external validation set demonstrated that the KNN model exhibited the highest AUC of 0.952 among the four models. When considering a 50% risk of mortality threshold, the validation set displayed a sensitivity of 0.963 and a specificity of 0.957. Conclusions The prognostic outcome of COVID-19 elderly patients is significantly influenced by the levels of Itaconic acid, LYM, IL-6, PCT, and CRP upon admission. These five indicators can be utilized to assess the mortality risk in affected individuals.https://doi.org/10.1186/s12879-024-10402-3COVID-19MetabolomicsMachine learningMortalityPredictive model |
| spellingShingle | Shanpeng Cui Qiuyuan Han Ran Zhang Siyao Zeng Ying Shao Yue Li Ming Li Wenhua Liu Junbo Zheng Hongliang Wang Integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe COVID-19 BMC Infectious Diseases COVID-19 Metabolomics Machine learning Mortality Predictive model |
| title | Integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe COVID-19 |
| title_full | Integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe COVID-19 |
| title_fullStr | Integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe COVID-19 |
| title_full_unstemmed | Integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe COVID-19 |
| title_short | Integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe COVID-19 |
| title_sort | integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe covid 19 |
| topic | COVID-19 Metabolomics Machine learning Mortality Predictive model |
| url | https://doi.org/10.1186/s12879-024-10402-3 |
| work_keys_str_mv | AT shanpengcui integrationofmetabolomicsmethodologiesforthedevelopmentofpredictivemodelsformortalityriskinelderlypatientswithseverecovid19 AT qiuyuanhan integrationofmetabolomicsmethodologiesforthedevelopmentofpredictivemodelsformortalityriskinelderlypatientswithseverecovid19 AT ranzhang integrationofmetabolomicsmethodologiesforthedevelopmentofpredictivemodelsformortalityriskinelderlypatientswithseverecovid19 AT siyaozeng integrationofmetabolomicsmethodologiesforthedevelopmentofpredictivemodelsformortalityriskinelderlypatientswithseverecovid19 AT yingshao integrationofmetabolomicsmethodologiesforthedevelopmentofpredictivemodelsformortalityriskinelderlypatientswithseverecovid19 AT yueli integrationofmetabolomicsmethodologiesforthedevelopmentofpredictivemodelsformortalityriskinelderlypatientswithseverecovid19 AT mingli integrationofmetabolomicsmethodologiesforthedevelopmentofpredictivemodelsformortalityriskinelderlypatientswithseverecovid19 AT wenhualiu integrationofmetabolomicsmethodologiesforthedevelopmentofpredictivemodelsformortalityriskinelderlypatientswithseverecovid19 AT junbozheng integrationofmetabolomicsmethodologiesforthedevelopmentofpredictivemodelsformortalityriskinelderlypatientswithseverecovid19 AT hongliangwang integrationofmetabolomicsmethodologiesforthedevelopmentofpredictivemodelsformortalityriskinelderlypatientswithseverecovid19 |