Performance of patient acuity rating by rapid response team nurses for predicting short-term prognosis

Although scoring and machine learning methods have been developed to predict patient deterioration, bedside assessment by nurses should not be overlooked. This study aimed to evaluate the performance of subjective bedside assessment of the patient by the rapid response team (RRT) nurses in predictin...

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Bibliographic Details
Published in:PloS one 2019-11, Vol.14 (11), p.e0225229-e0225229
Main Authors: Kim, Hyung-Jun, Min, Hyun-Ju, Lee, Dong-Seon, Choi, Yun-Young, Yoon, Miae, Lee, Da-Yun, Song, In-Ae, Cho, Jun Yeun, Park, Jong Sun, Cho, Young-Jae, Jo, You-Hwan, Yoon, Ho Il, Lee, Jae Ho, Lee, Choon-Taek, Lee, Yeon Joo
Format: Article
Language:English
Subjects:
Acuity
Aged
Aged, 80 and over
Artificial intelligence
Cardiac arrest
Confidence intervals
Critical Care
Emergency medical care
Female
Heart
Hospital Rapid Response Team
Hospitals
Humans
Inpatient care
Intensive care
Intensive Care Units
Internal medicine
Learning algorithms
Machine learning
Male
Mathematical analysis
Medical prognosis
Medical research
Medical schools
Medicine
Medicine and Health Sciences
Middle Aged
Mortality
Nurses
Nursing
Nursing research
Patient Acuity
Patients
People and Places
Performance evaluation
Physical Sciences
Prognosis
ROC Curve
Systematic review
Teaching methods
Vital signs
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Summary:Although scoring and machine learning methods have been developed to predict patient deterioration, bedside assessment by nurses should not be overlooked. This study aimed to evaluate the performance of subjective bedside assessment of the patient by the rapid response team (RRT) nurses in predicting short-term patient deterioration. Patients noticed by RRT nurses based on the vital sign instability, abnormal laboratory results, and direct contact via phone between November 1, 2016, and December 12, 2017, were included. Five RRT nurses visited the patients according to their shifts and assessed the possibility of patient deterioration. Patient acuity rating (PAR), a scale of 1-7, was used as the tool of bedside assessment. Other scores, including the modified early warning score, VitalPAC early warning score, standardised early warning score, and cardiac arrest risk triage, were calculated afterwards. The performance of these scores in predicting mortality and/or intensive care unit admission within 1 day was compared by calculating the area under the receiver operating curve. A total of 1,426 patients were included in the study, of which 258 (18.1%) died or were admitted to the intensive care unit within 1 day. The area under the receiver operating curve of PAR was 0.87 (95% confidence interval [CI] 0.84-0.89), which was higher than those of modified early warning score (0.66, 95% CI 0.62-0.70), VitalPAC early warning score (0.69, 95% CI 0.66-0.73), standardised early warning score (0.67, 95% CI 0.63-0.70) and cardiac arrest risk triage (0.63, 95% CI 0.59-0.66) (P
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0225229