ObjectivesWe aimed to develop and validate a postoperative delirium (POD) prediction model for patients admitted to the intensive care unit (ICU).DesignA prospective study was conducted.SettingThe study was conducted in the surgical, cardiovascular surgical and trauma surgical ICUs of an affiliated hospital of a medical university in Heilongjiang Province, China.ParticipantsThis study included 400 patients (≥18 years old) admitted to the ICU after surgery.Primary and secondary outcome measuresThe primary outcome measure was POD assessment during ICU stay.ResultsThe model was developed using 300 consecutive ICU patients and was validated using 100 patients from the same ICUs. The model was based on five risk factors: Physiological and Operative Severity Score for the enumeration of Mortality and morbidity; acid–base disturbance and history of coma, diabetes or hypertension. The model had an area under the receiver operating characteristics curve of 0.852 (95% CI 0.802 to 0.902), Youden index of 0.5789, sensitivity of 70.73% and specificity of 87.16%. The Hosmer-Lemeshow goodness of fit was 5.203 (p=0.736). At a cutoff value of 24.5%, the sensitivity and specificity were 71% and 69%, respectively.ConclusionsThe model, which used readily available data, exhibited high predictive value regarding risk of ICU-POD at admission. Use of this model may facilitate better implementation of preventive treatments and nursing measures.

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 analysed. Cluster analysis identified three subgroups of critically patients with different levels of clinical trajectories. Except for blood potassium levels (p = .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 management 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.

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