Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU database
Purpose: To explore the characteristics of the clinical phenotype of ARDS based on Machine Learning. Methods: This is a study on Machine Learning. Screened cases of acute respiratory distress syndrome (ARDS) in the eICU database collected basic information in the cases and clinical data on the Day 1...
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Elsevier
2024-10-01
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| author | Wei Zhang Linlin Wu Shucheng Zhang |
| author_facet | Wei Zhang Linlin Wu Shucheng Zhang |
| author_sort | Wei Zhang |
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| description | Purpose: To explore the characteristics of the clinical phenotype of ARDS based on Machine Learning. Methods: This is a study on Machine Learning. Screened cases of acute respiratory distress syndrome (ARDS) in the eICU database collected basic information in the cases and clinical data on the Day 1, Day 3, and Day 7 after the diagnosis of ARDS, respectively. Using the Calinski-Harabasz criterion, Gap Statistic, and Silhouette Coefficient, we determine the optimal clustering number k value. By the K-means cluster analysis to derive clinical phenotype, we analyzed the data collected within the first 24 h. We compared it with the survival of cases under the Berlin standard classification, and also examined the phenotypic conversion within the first 24 h, on day 3, and on day 7 after the diagnosis of ARDS. Results: We collected 5054 cases and derived three clinical phenotypes using K-means cluster analysis. Phenotype-I is characterized by fewer abnormal laboratory indicators, higher oxygen partial pressure, oxygenation index, APACHE IV score, systolic and diastolic blood pressure, and lower respiratory rate and heart rate. Phenotype-II is characterized by elevated white blood cell count, blood glucose, creatinine, temperature, heart rate, and respiratory rate. Phenotype-III is characterized by elevated age, partial pressure of carbon dioxide, bicarbonate, GCS score, albumin. The differences in ICU length of stay and in-hospital mortality were significantly different between the three phenotypes (P < 0.05), with phenotype I having the lowest in-hospital mortality (10 %) and phenotype II having the highest (31.8 %). To compare the survival analysis of ARDS patients classified by phenotype and those classified according to Berlin criteria. The results showed that the differences in survival between phenotypes were statistically significant (P < 0.05) under phenotypic classification. Conclusions: The clinical classification of ARDS based on K-means clustering analysis is beneficial for further identifying ARDS patients with different characteristics. Compared to the Berlin standard, the new clinical classification of ARDS provides a clearer display of the survival status of different types of patients, which helps to predict patient prognosis. |
| format | Article |
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| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
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| series | Heliyon |
| spelling | doaj-art-83d9f34cf9ea4d4eba2d0d8452c343a42025-08-20T02:13:59ZengElsevierHeliyon2405-84402024-10-011020e3919810.1016/j.heliyon.2024.e39198Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU databaseWei Zhang0Linlin Wu1Shucheng Zhang2Department of Critical Care Medicine, Kweichow Moutai Hospital, Renhuai City, Guizhou Province, 564500, China; Department of Critical Care Medicine, People's Hospital of Leshan, Leshan City, Sichuan Province, 614008, China; Corresponding author. People's Hospital of Leshan. 238 Baita Street, Shizhong District, Leshan City, Sichuan Province, 614008, China.Department of Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi City, Guizhou Province, 563000, ChinaDepartment of Dermatology and Venerology, Qian Foshan Hospital Affiliated to Shandong First Medical University, Jinan City, Shandong Province, 250013, China; Corresponding author. Institute: Qian Foshan Hospital Affiliated to Shandong First Medical University, 16766 Jingshi Road, Jinan City, Jinan City, Shandong Province, 2500113, China.Purpose: To explore the characteristics of the clinical phenotype of ARDS based on Machine Learning. Methods: This is a study on Machine Learning. Screened cases of acute respiratory distress syndrome (ARDS) in the eICU database collected basic information in the cases and clinical data on the Day 1, Day 3, and Day 7 after the diagnosis of ARDS, respectively. Using the Calinski-Harabasz criterion, Gap Statistic, and Silhouette Coefficient, we determine the optimal clustering number k value. By the K-means cluster analysis to derive clinical phenotype, we analyzed the data collected within the first 24 h. We compared it with the survival of cases under the Berlin standard classification, and also examined the phenotypic conversion within the first 24 h, on day 3, and on day 7 after the diagnosis of ARDS. Results: We collected 5054 cases and derived three clinical phenotypes using K-means cluster analysis. Phenotype-I is characterized by fewer abnormal laboratory indicators, higher oxygen partial pressure, oxygenation index, APACHE IV score, systolic and diastolic blood pressure, and lower respiratory rate and heart rate. Phenotype-II is characterized by elevated white blood cell count, blood glucose, creatinine, temperature, heart rate, and respiratory rate. Phenotype-III is characterized by elevated age, partial pressure of carbon dioxide, bicarbonate, GCS score, albumin. The differences in ICU length of stay and in-hospital mortality were significantly different between the three phenotypes (P < 0.05), with phenotype I having the lowest in-hospital mortality (10 %) and phenotype II having the highest (31.8 %). To compare the survival analysis of ARDS patients classified by phenotype and those classified according to Berlin criteria. The results showed that the differences in survival between phenotypes were statistically significant (P < 0.05) under phenotypic classification. Conclusions: The clinical classification of ARDS based on K-means clustering analysis is beneficial for further identifying ARDS patients with different characteristics. Compared to the Berlin standard, the new clinical classification of ARDS provides a clearer display of the survival status of different types of patients, which helps to predict patient prognosis.http://www.sciencedirect.com/science/article/pii/S2405844024152292Acute respiratory distress syndromeMachine learningK-means clustering analysisPhenotype |
| spellingShingle | Wei Zhang Linlin Wu Shucheng Zhang Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU database Heliyon Acute respiratory distress syndrome Machine learning K-means clustering analysis Phenotype |
| title | Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU database |
| title_full | Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU database |
| title_fullStr | Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU database |
| title_full_unstemmed | Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU database |
| title_short | Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU database |
| title_sort | clinical phenotype of ards based on k means cluster analysis a study from the eicu database |
| topic | Acute respiratory distress syndrome Machine learning K-means clustering analysis Phenotype |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024152292 |
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