Background: The intensive care unit (ICU) is where various medical staffs and patients with diverse diseases convene. Regardless of complexity, a delirium prediction model that can be applied conveniently would help manage delirium in the ICU. Objective: This study aimed to develop and validate a generally applicable delirium prediction model in the ICU based on simple information. Methods: A retrospective study was conducted at a single hospital. The outcome variable was defined as the occurrence of delirium within 30 days of ICU admission, and the predictors consisted of a 12 simple variables. Two models were developed through logistic regression (LR) and random forest (RF). A model with higher discriminative power based on the area under the receiver operating characteristics curve (AUROC) was selected as the final model in the validation process. Results: The model was developed using 2,588 observations (training dataset) and validated temporally with 1,109 observations (test dataset) of ICU patients. The top three influential predictors of the LR and RF models were the restraint, hospitalization through emergency room, and drainage tube. The AUROC of the LR model was 0.820 (CI 0.801–0.840) and 0.779 (CI 0.748–0.811) in the training and test datasets, respectively, and that of the RF model was 0.762 (CI 0.732–0.792) and 0.698 (0.659–0.738), respectively. The LR model showed better discriminative power (z = 4.826; P < 0.001). Conclusion: The LR model developed with brief variables showed good performance. This simplified prediction model will help screening become more accessible.
|Journal||Frontiers in Psychiatry|
|Publication status||Published - 2022 Jun 29|
Bibliographical noteFunding Information:
This work was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health and Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711138277, KMDF_PR_20200901_0143).
Copyright © 2022 Kim, Oh, Kim and Park.
All Science Journal Classification (ASJC) codes
- Psychiatry and Mental health