Identification of the robust predictor for sepsis based on clustering analysis

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).

Neutrophil to Lymphocyte Ratio: An Emerging Marker of the Relationships between the Immune System and Diseases

“…A recent observational study demonstrated that NLR and IL-6 appeared to be independent predictors of 28-day mortality in septic patients [ 22 ]. Moreover, Jang et al conducted a clustering analysis showing that age, NLR and delta neutrophil index (DNI) were the most robust predictors for sepsis status in all subjects, as well as in cluster-specific groups [ 23 ]. These findings could contribute to suggest a strategy to screen sepsis patients without leukocytosis in the emergency care units.…”

Classification Model for Diabetic Foot, Necrotizing Fasciitis, and Osteomyelitis

“…In general, CRP is known to have a higher diagnostic accuracy for infection than that of WBC or ESR [ 20 ]. Moreover, a recent study demonstrated that the DNI and NLR are robust predictors of equivocal septic conditions using clustering analysis [ 24 ]. In contrast, the OM group exhibited significantly negative associations only with ESR and NLR.…”

“…In our opinion, the proposed prediction model in this study could be combined with the microbiological and clinical outcome profiles in well-designed prospective cohort in the future. By extension, further study based on a clustering analysis method comparing the combined model with pre-existing etiology-based NF classification (type I~IV) is feasible and promising [ 24 , 54 ]. Lastly, we did not consider other comorbidities affecting the infection status.…”

“…Subsequently, the selection of plausible predictors (also termed features) was considered a crucial task based on previous studies. In fact, several studies used a literature-search approaches to identify disease-related features or predictors [ 22 , 23 , 24 ]. Motivated by these studies, two experts in plastic surgery and family medicine reviewed the literature and yielded approximately 30 variables known to be related to NF and/or OM ( Table S1 ).…”

Exploring the use of cluster analysis to assess antibiotic stewardship in critically-ill neonates in a low resource setting

“…Cluster analysis is a machine learning technique that aims to group observations that are similar within the same cluster. It is a useful tool in infectious disease, analyzing biomarkers and predictors of neonatal sepsis, but to the best of our knowledge, no study has applied it to antibiotic stewardship in LMICs NICU setting [10,11]. The aim of this study was to identify clusters of patients switching between different antibiotic lines and to explore their potential associations with death or severe clinical presentation in terms of RD.…”