Clinical Phenotype Identification and Validation of Patients with Sepsis in the Intensive Care Unit

GONG Chao; YU Na; CHEN Haoran

doi:10.12290/xhyxzz.2024-0353

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GONG Chao, YU Na, CHEN Haoran. Clinical Phenotype Identification and Validation of Patients with Sepsis in the Intensive Care Unit[J]. Medical Journal of Peking Union Medical College Hospital. DOI: 10.12290/xhyxzz.2024-0353

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Clinical Phenotype Identification and Validation of Patients with Sepsis in the Intensive Care Unit

1 Departmentof Emergency, State Key Laboratory for Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China;
2 Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming 650118, China;
3 Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China

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Received Date: May 21, 2024
Accepted Date: September 24, 2024
Available Online: January 07, 2025

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Objective To identify sepsis phenotypes and facilitate precision medicine. Methods We applied unsupervised machine learning algorithms (K-means clustering and hierarchical clustering) to identify the phenotypes of sepsis patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database， based on 89 clinical features including demographic characteristics， laboratory indicators and treatment measures on the first day in ICU. Then， supervised machine learning algorithms (lightweight gradient augmenter) were used for the prediction of the patient's phenotypes ， and further combined with SHAP (Shapely Additive eXplanations) for the identification of important features ； Finally ， traditional statistical methods were used to validate the differences in clinical characteristics and clinical outcomes between the phenotypes. Results We identified three phenotypes in 22,517 sepsis patients. The phenotype 1 patient population had the highest risk of death (28-day mortality of 46.4%)， dominated by abnormal renal function and elevated disease severity scores， while the phenotype 3 patient population had the lowest risk of death (28-day mortality of 11.2%)， and it had the best neurological function score. Using interpretable machine learning， we identified six features (all the worst value on the first day) including anion gap， blood urea nitrogen， creatinine， Glasgow Coma Scale score， prothrombin time， and Sequential Organ Failure Assessment score showed good performance in phenotypic identification (AUC≥0.89) and phenotypic prognostic prediction (AUC≥0.74). The mortality risk of phenotype 3 patients was the lowest at 28 days， 60 days， 90 days， and 1 year after ICU discharge (HR<1). Conclusion Using machine learning methods， we successfully identified 3 clinical phenotypes of sepsis patients with different clinical characteristics and prognosis and screened out six key clinical features， which is expected to play an important role in the phenotype classification and prognostic assessment of sepsis and is conducive to individualized treatment.
- sepsis,
- phenotypic recognition,
- machine learning,
- precision medicine

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Clinical Phenotype Identification and Validation of Patients with Sepsis in the Intensive Care Unit

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