Characteristics and Cluster Analysis of 18,030 Sepsis Patients Who Were Admitted to Thailand's Largest National Tertiary Referral Center during 2014-2020 to Identify Distinct Subtypes of Sepsis in Thai Population.

Background: This study aimed to investigate the demographic, clinical, and laboratory characteristics of sepsis patients who were admitted to our center during 2014-2020 and to employ cluster analysis, which is a type of machine learning, to identify distinct types of sepsis in Thai population.

Methods: Demographic, clinical, laboratory, medicine, and source of infection data of patients admitted to medical wards of Siriraj Hospital (Bangkok, Thailand) during 2014-2020 were collected. Sepsis was diagnosed according to the Sepsis-3 criteria. Nineteen demographic, clinical, and laboratory variables were analyzed using hierarchical clustering to identify sepsis subtypes.

Results: Of 98,359 admissions, 18,030 (18.3%) had sepsis. Respiratory tract was the most common site of infection. The mean Sequential Organ Failure Assessment (SOFA) score was 4.21 ± 2.24, and the median serum lactate level was 2.7 mmol/L [range: 0.4-27.5]. Twenty percent of admissions required vasopressor. In-hospital mortality was 19.6%. Ten sepsis subtypes were identified using hierarchical clustering. Three clusters (clusters L1-L3) were considered low risk, and seven clusters (clusters H1-H7) were considered high risk for in-hospital mortality. Cluster H1 had prominent hematologic abnormalities. Clusters H3 and H5 had younger ages and significant hepatic dysfunction. Cluster H5 had multiple organ dysfunctions, and a higher proportion of cluster H5 patients required vasopressor, mechanical ventilation, and renal replacement therapy. Cluster H6 had more respiratory tract infection and acute respiratory failure and a lower SpO₂/FiO₂ value.

Conclusions: Cluster analysis revealed 10 distinct subtypes of sepsis in Thai population. Furthermore, the study is needed to investigate the value of these sepsis subtypes in clinical practice.