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Critical Care Database Comprising Patients With Infection.
Xu, Ping; Chen, Lin; Zhu, Yuanfang; Yu, Shuai; Chen, Rangui; Huang, Wenbin; Wu, Fuli; Zhang, Zhongheng.
  • Xu P; Emergency Department, Zigong Fourth People's Hospital, Zigong, China.
  • Chen L; Artificial Intelligence Key Laboratory of Sichuan Province, Zigong, China.
  • Zhu Y; Institute of Medical Big Data, Zigong Academy of Artificial Intelligence and Big Data for Medical Science, Sichuan, China.
  • Yu S; Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.
  • Chen R; Key Laboratory of Emergency and Trauma of Ministry of Education, Hainan Provincial Key Laboratory for Tropical Cardiovascular Diseases Research, The First Affiliated Hospital of Hainan Medical University, Research Unit of Island Emergency Medicine of Chinese Academy of Medical Sciences, Hainan Medic
  • Huang W; Department of Health Management Center, Zigong Fourth People's Hospital, Zigong, China.
  • Wu F; Department of Gynecology, Fushun County Maternal and Child Health Hospital, Fushun, China.
  • Zhang Z; Emergency Department, Zigong Fourth People's Hospital, Zigong, China.
Front Public Health ; 10: 852410, 2022.
Article in English | MEDLINE | ID: covidwho-1776072
ABSTRACT
Patients treated in the intensive care unit (ICU) are closely monitored and receive intensive treatment. Such aggressive monitoring and treatment will generate high-granularity data from both electronic healthcare records and nursing charts. These data not only provide infrastructure for daily clinical practice but also can help to inform clinical studies. It is technically challenging to integrate and cleanse medical data from a variety of sources. Although there are several open-access critical care databases from western countries, there is a lack of this kind of database for Chinese adult patients. We established a critical care database involving patients with infection. A large proportion of these patients have sepsis and/or septic shock. High-granularity data comprising laboratory findings, baseline characteristics, medications, international statistical classification of diseases (ICD) code, nursing charts, and follow-up results were integrated to generate a comprehensive database. The database can be utilized for a variety of clinical studies. The dataset is fully accessible at PhysioNet(https//physionet.org/content/icu-infection-zigong-fourth/1.0/).
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Databases, Factual / Sepsis / Critical Care Type of study: Cohort study / Prognostic study Limits: Adult / Humans Language: English Journal: Front Public Health Year: 2022 Document Type: Article Affiliation country: Fpubh.2022.852410

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Databases, Factual / Sepsis / Critical Care Type of study: Cohort study / Prognostic study Limits: Adult / Humans Language: English Journal: Front Public Health Year: 2022 Document Type: Article Affiliation country: Fpubh.2022.852410