Huan Fan1, Meihua Ji1, Jie Huang2, Peng Yue1, Xin Yang3, Chunli Wang4, Wu Ying5. 1. School of Nursing, Capital Medical University, Beijing, China. 2. Beijing Jishuitan Hospital，Capital Medical University, Beijing, China. 3. Xuanwu Hospital, Capital Medical University, Beijing, China. 4. Beijing Children's Hospital, Capital Medical University, Beijing, China. 5. School of Nursing, Capital Medical University, Beijing, China. Electronic address: helenywu@vip.163.com.
Abstract
BACKGROUND:Delirium is one of the most common cognitive complications among patients admitted to the intensive care units (ICU). OBJECTIVE: To develop and validate a DYNAmic deliriuM predICtion rule for ICU patients (DYNAMIC-ICU) and to stratify patients into different risk levels among patients in various types of ICUs. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: A total of 560 (median age of 66 years, 62.5% male) consecutively enrolled patients from four ICUs were included in the study. The patients were randomly assigned into either the derivation (n = 336, 60%) or the validation (n = 224, 40%) cohort by stratified randomization based on delirium/non-delirium and types of ICU. METHODS: The simplified Chinese version of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) was used to assess delirium until patients were discharged from the ICUs. Potential predisposing, disease-related, and iatrogenic and environmental risk factors as well as data on patients' outcomes were collected prospectively. RESULTS: Of the enrolled patients, 20.2% and 20.5% developed delirium in the derivation and validation cohorts, respectively. Predisposing factors (history of chronic diseases, hearing deficits), disease-related factors (infection, higher APACHE II scores at admission), and iatrogenic and environmental factors (the use of sedatives and analgesics, indwelling catheter, and sleep disturbance) were identified as independent predictors of delirium. Points were assigned to each predictor according to their odds ratio to create a prediction rule which was internally validated based on total scores and by bootstrapping (AUCs of 0.907 [95% CI 0. 871 -0.944], 0.888 [95% CI 0.845-0.932], and 0.874 [95% CI 0.828-0.920]), respectively. The total score of the DYNAMIC-ICU ranged from 0 to 33 and patients were divided into low risk (0-9), moderate risk (10-17), high risk (18-33) groups in developing delirium according to their total score with incidence of delirium at 2.8%, 16.8% and 75.9% in the derivation group, respectively. The DYNAMIC-ICU and its performance of risk level stratification were further validated in the validation cohort (AUC = 0.900 [95% CI 0.858-0.941]). The all-cause mortality was increased and the length of hospital stay was prolonged dramatically with the increase of delirium risk levels in both derivation (p = 0.034, p < 0.001) and validation cohorts (p < 0.001, p < 0.001). CONCLUSIONS: Seven predictors for ICU delirium were identified to create DYNAMIC-ICU, which could well stratify ICU patients into three different delirium risk levels, tailor risk level changes, and predict in-hospital outcomes by a dynamic assessment approach.
RCT Entities:
BACKGROUND:Delirium is one of the most common cognitive complications among patients admitted to the intensive care units (ICU). OBJECTIVE: To develop and validate a DYNAmic deliriuM predICtion rule for ICU patients (DYNAMIC-ICU) and to stratify patients into different risk levels among patients in various types of ICUs. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: A total of 560 (median age of 66 years, 62.5% male) consecutively enrolled patients from four ICUs were included in the study. The patients were randomly assigned into either the derivation (n = 336, 60%) or the validation (n = 224, 40%) cohort by stratified randomization based on delirium/non-delirium and types of ICU. METHODS: The simplified Chinese version of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) was used to assess delirium until patients were discharged from the ICUs. Potential predisposing, disease-related, and iatrogenic and environmental risk factors as well as data on patients' outcomes were collected prospectively. RESULTS: Of the enrolled patients, 20.2% and 20.5% developed delirium in the derivation and validation cohorts, respectively. Predisposing factors (history of chronic diseases, hearing deficits), disease-related factors (infection, higher APACHE II scores at admission), and iatrogenic and environmental factors (the use of sedatives and analgesics, indwelling catheter, and sleep disturbance) were identified as independent predictors of delirium. Points were assigned to each predictor according to their odds ratio to create a prediction rule which was internally validated based on total scores and by bootstrapping (AUCs of 0.907 [95% CI 0. 871 -0.944], 0.888 [95% CI 0.845-0.932], and 0.874 [95% CI 0.828-0.920]), respectively. The total score of the DYNAMIC-ICU ranged from 0 to 33 and patients were divided into low risk (0-9), moderate risk (10-17), high risk (18-33) groups in developing delirium according to their total score with incidence of delirium at 2.8%, 16.8% and 75.9% in the derivation group, respectively. The DYNAMIC-ICU and its performance of risk level stratification were further validated in the validation cohort (AUC = 0.900 [95% CI 0.858-0.941]). The all-cause mortality was increased and the length of hospital stay was prolonged dramatically with the increase of delirium risk levels in both derivation (p = 0.034, p < 0.001) and validation cohorts (p < 0.001, p < 0.001). CONCLUSIONS: Seven predictors for ICU delirium were identified to create DYNAMIC-ICU, which could well stratify ICU patients into three different delirium risk levels, tailor risk level changes, and predict in-hospital outcomes by a dynamic assessment approach.
Authors: Honoria Ocagli; Daniele Bottigliengo; Giulia Lorenzoni; Danila Azzolina; Aslihan S Acar; Silvia Sorgato; Lucia Stivanello; Mario Degan; Dario Gregori Journal: Int J Environ Res Public Health Date: 2021-07-02 Impact factor: 3.390