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.

ABSTRACT:

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

SUBMITTER: Sakornsakolpat P 

PROVIDER: S-EPMC11303049 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature