Critical Care Database Comprising Patients With Infection
Bibliographic Data
| ID | 22068525 |
|---|---|
| Authors | Ping Xu (0000-0001-6099-1748, Beijing Institute of Big Data Research), Lin Chen (0000-0001-5135-3998, Chinese Academy of Medical Sciences & Peking Union Medical College), Yuanfang Zhu (0000-0003-0980-9829, Zigong First People's Hospital), Shuai Yu (0009-0000-4195-5522, Fuzhou Maternity and Child Health Care Hospital), Rangui Chen (Zigong First People's Hospital), Wenbin Huang (0000-0002-5422-6481, Zigong First People's Hospital), Fuli Wu (0000-0003-1234-9974, Fuzhou Maternity and Child Health Care Hospital), Zhongheng Zhang (0000-0002-2336-5323, Chinese Academy of Medical Sciences & Peking Union Medical College, corresponding author) |
| Year | 2022 |
| Volume | 10 |
| Pages | 852410-852410 |
| Publication date | 2022-03-17 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.852410 |
| PMID | 35372245 |
| OpenAlex | W4220924718 |
| Language | EN |
| References cited | 25 |
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/ )
Database · Diagnosis code · Health care · Intensive care · Intensive care medicine · Intensive care unit · Medical emergency · Medical record · MEDLINE · Sepsis · Septic shock · Computer Science · Machine Learning in Healthcare · Medicine · Pneumonia and Respiratory Infections · Sepsis Diagnosis and Treatment · Internal Medicine
| Highly cited | No |
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