In Journal of nursing management ; h5-index 43.0
AIM : This study aimed to develop a patient classification system that stratifies patients admitted to the intensive care unit based on their disease severity and care needs.
BACKGROUND : Classifying patients into homogenous groups based on clinical characteristics can optimize nursing care. However, an objective method for determining such groups remains unclear.
METHODS : Predictors representing disease severity and nursing workload were considered. Patients were clustered into subgroups with different characteristics based on the results of a clustering algorithm. A patient classification system was developed using a partial least squares regression model.
RESULTS : Data of 300 patients were analyzed. Cluster analysis identified three subgroups of critically patients with different levels of clinical trajectories. Except for blood potassium levels (P=0.29), the subgroups were significantly different according to disease severity and nursing workload. The predicted value ranges of the regression model for Classes A, B, and C were <1.44, 1.44-2.03, and >2.03. The model was shown to have good fit and satisfactory prediction efficiency using 200 permutation tests.
CONCLUSIONS : Classifying patients based on disease severity and care needs enables the development of tailored nursing programs for each subgroup.
IMPLICATIONS FOR NURSING MANAGEMENT : The patient classification system can help nurse managers identify homogeneous patient groups and further improve the management of critically ill patients.
An Ran, Chang Guang-Ming, Fan Yu-Ying, Ji Ling-Ling, Wang Xiao-Hui, Hong Su
clustering analysis, critical care, intensive care unit, machine learning