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.

IF 1.8 Q3 CRITICAL CARE MEDICINE
Critical Care Research and Practice Pub Date : 2024-07-30 eCollection Date: 2024-01-01 DOI:10.1155/2024/6699274
Phuwanat Sakornsakolpat, Surat Tongyoo, Chairat Permpikul
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引用次数: 0

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 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.

对 2014-2020 年间泰国最大的国家三级转诊中心收治的 18030 名败血症患者进行特征和聚类分析,以确定泰国人群中败血症的不同亚型。
研究背景本研究旨在调查 2014-2020 年期间本中心收治的败血症患者的人口统计学、临床和实验室特征,并采用聚类分析(一种机器学习方法)来识别泰国人群中不同类型的败血症:方法:收集了2014-2020年间入住西里拉吉医院(泰国曼谷)内科病房的患者的人口统计学、临床、实验室、药物和感染源数据。根据败血症-3标准诊断败血症。通过分层聚类分析了19个人口统计学、临床和实验室变量,以确定败血症亚型:结果:在 98,359 例入院患者中,18,030 例(18.3%)患有败血症。呼吸道是最常见的感染部位。序贯器官衰竭评估(SOFA)的平均评分为 4.21 ± 2.24,血清乳酸水平中位数为 2.7 mmol/L [范围:0.4-27.5]。20%的入院患者需要使用血管加压素。院内死亡率为 19.6%。通过分层聚类,确定了十种败血症亚型。其中三个群组(群组 L1-L3)被认为是低风险群组,七个群组(群组 H1-H7)被认为是院内死亡率高风险群组。群组 H1 有明显的血液学异常。组群 H3 和 H5 年龄较小,肝功能明显异常。H5组有多器官功能障碍,需要血管加压、机械通气和肾脏替代治疗的H5组患者比例较高。H6组有更多的呼吸道感染和急性呼吸衰竭,SpO2/FiO2值较低:结论:聚类分析揭示了泰国人群败血症的 10 个不同亚型。此外,还需要研究这些败血症亚型在临床实践中的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Critical Care Research and Practice
Critical Care Research and Practice CRITICAL CARE MEDICINE-
CiteScore
3.60
自引率
0.00%
发文量
34
审稿时长
14 weeks
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