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A new classification for emergency critically ill patients and analysis of their adverse events during intrahospital transport: A cluster analysis
被引:0
|作者:
An, Ying
[1
]
Cao, Kai
[2
]
Li, Fei
[1
]
Lu, Qi
[3
]
Guan, Ya-Mei
[3
]
Lu, Zhen-Hui
[4
]
Wang, Ai-Ping
[3
]
Tian, Zi-Rong
[1
]
机构:
[1] Capital Med Univ, Beijing Tongren Hosp, Nursing Dept, 1 Dongjiaomin Lane, Beijing 100730, Peoples R China
[2] Capital Med Univ, Beijing Tongren Hosp, Beijing Inst Ophthalmol, Beijing, Peoples R China
[3] Capital Med Univ, Beijing Tongren Hosp, Emergency Dept, Beijing, Peoples R China
[4] Capital Med Univ, Beijing Tongren Hosp, Intens Care Unit, Beijing, Peoples R China
关键词:
cluster analysis;
critical patients;
intrahospital transport;
patient safety;
OUTCOMES;
SCORE;
D O I:
10.1111/nicc.13099
中图分类号:
R47 [护理学];
学科分类号:
1011 ;
摘要:
Background: Critical patients may experience various adverse events during transportation within hospitals. Therefore, quickly evaluating and classifying patients before transporting them from the emergency department and focusing on managing high-risk patients are critical. At present, no unified classification method exists; all the current approaches are subjective. Aims: To ensure transportation safety, we conducted a cluster analysis of critically ill patients transferred from the emergency department to the intensive care unit. Study DesignSingle-centre cohort study. This study was conducted at a comprehensive first-class teaching hospital in Beijing. Convenience sampling and continuous enrolment were employed. We collected data from 1 January 2019, to 31 December 2021. All patients were transferred from the emergency department to the intensive care unit, and cluster analysis was conducted using five variables. Results: A total of 584 patients were grouped into three clusters. Cluster 1 (high systolic blood pressure group) included 208 (35.6%) patients. Cluster 2 (high heart rate and low blood oxygen group) included 55 (9.4%) patients. Cluster 3 (normal group) included the remaining 321 (55%) patients. The oxygen saturation levels of all the patients were lower after transport, and the proportion of adverse events (61.8%) was the highest in Cluster 2 (p < .05). Conclusions: This study utilized data on five important vital signs from a cluster analysis to explore possible patient classifications and provide a reference for ensuring transportation safety. Relevance to Clinical Practice: Before transferring patients, we should classify them and implement targeted care. Changes in blood oxygen levels in all patients should be considered, with a focus on the occurrence of adverse events during transportation among patients with high heart rates and low blood oxygen levels.
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