Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment
Abstract Introduction Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment decision-making. Methods For this study, com...
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2025-01-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-024-05975-1 |
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| author | Weidong Zhang Chenxi Wu Peifeng Ni Sheng Zhang Hongwei Zhang Ying Zhu Wei Hu Mengyuan Diao |
| author_facet | Weidong Zhang Chenxi Wu Peifeng Ni Sheng Zhang Hongwei Zhang Ying Zhu Wei Hu Mengyuan Diao |
| author_sort | Weidong Zhang |
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| description | Abstract Introduction Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment decision-making. Methods For this study, comprehensive data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) 2.0 database. We excluded patients under 18 years old, those not initially admitted to the intensive care unit (ICU), or treated in the ICU for less than 72 h. A total of 57 clinical parameters relevant to CA patients were selected for analysis. These included demographic data, vital signs, and laboratory parameters. After an extensive literature review and expert consultations, key factors such as temperature (T), sodium (Na), creatinine (CR), glucose (GLU), heart rate (HR), PaO2/FiO2 ratio (P/F), hemoglobin (HB), mean arterial pressure (MAP), platelets (PLT), and white blood cell count (WBC) were identified as the most significant for cluster analysis. Consensus cluster analysis was utilized to examine the mean values of these routine clinical parameters within the first 24 h post-ICU admission to categorize patient classes. Furthermore, in-hospital and 28-day mortality rates of patients across different CA subphenotypes were assessed using multivariate logistic and Cox regression analysis. Results After applying exclusion criteria, 719 CA patients were included in the study, with a median age of 67.22 years (IQR: 55.50-79.34), of whom 63.28% were male. The analysis delineated two distinct subphenotypes: Subphenotype 1 (SP1) and Subphenotype 2 (SP2). Compared to SP1, patients in SP2 exhibited significantly higher levels of P/F, HB, MAP, PLT, and Na, but lower levels of T, HR, GLU, WBC, and CR. SP2 patients had a notably higher in-hospital mortality rate compared to SP1 (53.01% for SP2 vs. 39.36% for SP1, P < 0.001). 28-day mortality decreased continuously for both subphenotypes, with a more rapid decline in SP2. These differences remained significant after adjusting for potential covariates (adjusted OR = 1.82, 95% CI: 1.26–2.64, P = 0.002; HR = 1.84, 95% CI: 1.40–2.41, P < 0.001). Conclusions The study successfully identified two distinct clinical subphenotypes of CA by analyzing routine clinical data from the first 24 h following ICU admission. SP1 was characterized by a lower rate of in-hospital and 28-day mortality when compared to SP2. This differentiation could play a crucial role in tailoring patient care, assessing prognosis, and guiding more targeted treatment strategies for CA patients. |
| format | Article |
| id | doaj-art-5b385df19ea2418e8332d6d49f776389 |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | Journal of Translational Medicine |
| spelling | doaj-art-5b385df19ea2418e8332d6d49f7763892025-01-12T12:37:49ZengBMCJournal of Translational Medicine1479-58762025-01-0123111210.1186/s12967-024-05975-1Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatmentWeidong Zhang0Chenxi Wu1Peifeng Ni2Sheng Zhang3Hongwei Zhang4Ying Zhu5Wei Hu6Mengyuan Diao7Fourth Clinical Medical College of Zhejiang Chinese Medical UniversityFourth Clinical Medical College of Zhejiang Chinese Medical UniversityZhejiang University School of MedicineDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Critical Care Medicine, Hangzhou First People’s Hospital, West Lake University School of MedicineFourth Clinical Medical College of Zhejiang Chinese Medical UniversityFourth Clinical Medical College of Zhejiang Chinese Medical UniversityFourth Clinical Medical College of Zhejiang Chinese Medical UniversityAbstract Introduction Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment decision-making. Methods For this study, comprehensive data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) 2.0 database. We excluded patients under 18 years old, those not initially admitted to the intensive care unit (ICU), or treated in the ICU for less than 72 h. A total of 57 clinical parameters relevant to CA patients were selected for analysis. These included demographic data, vital signs, and laboratory parameters. After an extensive literature review and expert consultations, key factors such as temperature (T), sodium (Na), creatinine (CR), glucose (GLU), heart rate (HR), PaO2/FiO2 ratio (P/F), hemoglobin (HB), mean arterial pressure (MAP), platelets (PLT), and white blood cell count (WBC) were identified as the most significant for cluster analysis. Consensus cluster analysis was utilized to examine the mean values of these routine clinical parameters within the first 24 h post-ICU admission to categorize patient classes. Furthermore, in-hospital and 28-day mortality rates of patients across different CA subphenotypes were assessed using multivariate logistic and Cox regression analysis. Results After applying exclusion criteria, 719 CA patients were included in the study, with a median age of 67.22 years (IQR: 55.50-79.34), of whom 63.28% were male. The analysis delineated two distinct subphenotypes: Subphenotype 1 (SP1) and Subphenotype 2 (SP2). Compared to SP1, patients in SP2 exhibited significantly higher levels of P/F, HB, MAP, PLT, and Na, but lower levels of T, HR, GLU, WBC, and CR. SP2 patients had a notably higher in-hospital mortality rate compared to SP1 (53.01% for SP2 vs. 39.36% for SP1, P < 0.001). 28-day mortality decreased continuously for both subphenotypes, with a more rapid decline in SP2. These differences remained significant after adjusting for potential covariates (adjusted OR = 1.82, 95% CI: 1.26–2.64, P = 0.002; HR = 1.84, 95% CI: 1.40–2.41, P < 0.001). Conclusions The study successfully identified two distinct clinical subphenotypes of CA by analyzing routine clinical data from the first 24 h following ICU admission. SP1 was characterized by a lower rate of in-hospital and 28-day mortality when compared to SP2. This differentiation could play a crucial role in tailoring patient care, assessing prognosis, and guiding more targeted treatment strategies for CA patients.https://doi.org/10.1186/s12967-024-05975-1Cardiac arrestMachine learningSubphenotypesPrecision medicineCritically illnessLatent class analysis |
| spellingShingle | Weidong Zhang Chenxi Wu Peifeng Ni Sheng Zhang Hongwei Zhang Ying Zhu Wei Hu Mengyuan Diao Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment Journal of Translational Medicine Cardiac arrest Machine learning Subphenotypes Precision medicine Critically illness Latent class analysis |
| title | Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment |
| title_full | Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment |
| title_fullStr | Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment |
| title_full_unstemmed | Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment |
| title_short | Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment |
| title_sort | machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment |
| topic | Cardiac arrest Machine learning Subphenotypes Precision medicine Critically illness Latent class analysis |
| url | https://doi.org/10.1186/s12967-024-05975-1 |
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