ArticleScientific reports2022
Identification of the robust predictor for sepsis based on clustering analysis.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- Identification and Characterization of Sepsis Phenotypes in an Indian Cohort.Critical care research and practice · 2026Article
- Deriving consensus sepsis clusters via goal-directed subgroup identification in multi-omics study.Nature communications · 2025Article
- Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning.Scientific reports · 2025Article
- Review
- Neutrophils and Anesthetic Drugs: Implications in Onco-Anesthesia.International journal of molecular sciences · 2024Review
- Exploring the use of cluster analysis to assess antibiotic stewardship in critically-ill neonates in a low resource setting.Antimicrobial resistance and infection control · 2023Article
- Classification Model for Epileptic Seizure Using Simple Postictal Laboratory Indices.Journal of clinical medicine · 2023Article
- Preoperative inflammatory biomarkers analysis in prognosis of systemic inflammatory response syndrome following percutaneous nephrolithotomy: A systematic review and meta-analysis.Arab journal of urology · 2023Review
- Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care.International journal of environmental research and public health · 2022Article
- Article
- Neutrophil to Lymphocyte Ratio: An Emerging Marker of the Relationships between the Immune System and Diseases.International journal of molecular sciences · 2022Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a life-threatening disorder with high incidence and mortality rate. However, the early detection of sepsis is challenging due to lack of specific marker and various etiology. This study aimed to identify robust risk factors for sepsis via cluster analysis. The integrative task of the automatic platform (i.e., electronic medical record) and the expert domain was performed to compile clinical and medical information for 2,490 sepsis patients and 16,916 health check-up participants. The subjects were categorized into 3 and 4 groups based on seven clinical and laboratory markers (Age, WBC, NLR, Hb, PLT, DNI, and MPXI) by K-means clustering. Logistic regression model was performed for all subjects including healthy control and sepsis patients, and cluster-specific cases, separately, to identify sepsis-related features. White blood cell (WBC), well-known parameter for sepsis, exhibited the insignificant association with the sepsis status in old age clusters (K3C3 and K4C3). Besides, NLR and DNI were the robust predictors in all subjects as well as three or four cluster-specific subjects including K3C3 or K4C3. We implemented the cluster-analysis for real-world hospital data to identify the robust predictors for sepsis, which could contribute to screen likely overlooked and potential sepsis patients (e.g., sepsis patients without WBC count elevation).
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