Development and validation of risk-stratification delirium prediction model for critically ill patients

Abstract

The objective is to develop a model based on risk stratification to predict delirium among adult critically ill patients and whether early intervention could be provided for high-risk patients, which could reduce the incidence of delirium.We designed a prospective, observational, single-center study. We examined 11 factors, including age, APACHE-II score, coma, emergency operation, mechanical ventilation (MV), multiple trauma, metabolic acidosis, history of hypertension, delirium and dementia, and application of Dexmedetomidine Hydrochloride. Confusion assessment method for the intensive care unit (CAM-ICU) was performed to screen patients during their ICU stay. Multivariate logistic regression analysis was used to develop the model, and we assessed the predictive ability of the model by using the area under the receiver operating characteristics curve (AUROC).From May 17, 2016 to September 25, 2016, 681 consecutive patients were screened, 61 of whom were excluded. The most frequent reason for exclusion was sustained coma 30 (4.4%), followed by a length of stay in the ICU < 24 hours 18 (2.6%) and delirium before ICU admission 13 (1.9%). Among the remaining 620 patients (including 162 nervous system disease patients), 160 patients (25.8%) developed delirium, and 64 (39.5%) had nervous system disease. The mean age was 55 ± 18 years old, the mean APACHE-II score was 16 ± 4, and 49.2% of them were male. Spearman analysis of nervous system disease and incidence of delirium showed that the correlation coefficient was 0.186 (P < .01). We constructed a prediction model that included 11 risk factors. The AUROC was 0.78 (95% CI 0.72–0.83).We developed the model using 11 related factors to predict delirium in critically ill patients and further determined that prophylaxis with Dexmedetomidine Hydrochloride in delirious ICU patients was beneficial. Patients who suffer from nervous system disease are at a higher incidence of delirium, and corresponding measures should be used for prevention.Trial registration: ChiCTR-OOC-16008535.

Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study

“…This is partly consistent with a study concluding that diabetic patients are more prone to POD 27. There are some other studies28 29 that have shown associations of ICU-POD with diabetes, acid–base imbalance, coma and hypertension. Nevertheless, to our knowledge, this is the first analysis to report on associations among all of those five risk elements together, including the POSSUM score for the first time ever, in the development of ICU-POD.…”

“…The model contained nine candidate predictors: Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (POSSUM) score, sex, history of coma (history of transitional conscious disturbance before admission, discovered when doctors were collecting the patient’s medical history), history of hypertension (systolic blood pressure ≥140 mm Hg, with or without diastolic pressure ≥90 mm Hg; or simple hypertension with systolic blood pressure ≥140 mm Hg only diagnosed previously before admission by clinicians and had recorded in the medical record), history of diabetes (diagnosed before admission by clinicians and recorded in the medical record), mechanical ventilation, history of alcohol dependence (based on International Statistical Classification of Diseases and Related Health problems (10th Revision) (ICD-10)22 and diagnosed by clinicians), acid–base imbalance (including metabolic acidosis, metabolic alkalosis, respiratory acidosis and respiratory alkalosis, identified by the first blood gas analysis) and length of operation. For each candidate predictor, at least 5–10 patients with delirium were required based on the sample size formula for modeling23; as the minimum incidence of ICU-POD is 27.6%,3 we used this value. With an anticipated delirium incidence of 27.6% and an expected attrition of 10%–20%, we aimed to enrol at least 1960 patients (9×5 × (1+0.2) / 0.276).…”

Perceptions, attitudes, and current practices regards delirium in China

“…As reported by multiple studies, the incidence rate of delirium is 20% to 80% in the ICU setting. [ 1 , 3 , 8 , 11 , 19 – 26 ] Chen et al [ 27 ] reported an incidence rate of 25.8% of delirium in their ICU in China. Similarily, Su et al [ 28 ] found that incidence of postoperative delirium was 23% in patients after noncardiac surgery.…”

“…CAM-ICU and ICDSC were the most commonly used assessment tools with great reliability and validity. [ 1 , 27 , 28 ] This survey revealed that ∼16% of participants reported the use of CAM-ICU, and another 16% reported the use of ICDSC. In contrast, 34.46% of the respondents stated that they monitored delirium with multiple assessment tools.…”

Development and validation of delirium prediction model for critically ill adults parameterized to ICU admission acuity

“…Three prediction models have been validated to predict the risk for ICU delirium among critically ill delirium-free adults. The PRE-DELERIC [12] and the E-PRE-DELERIC [13] have been externally validated for use with both the ICDSC and the CAM-ICU, and the Lanzhou model [14] has been internally validated for use with the CAM-ICU. Another model, the dynamic Acute Brain Dysfunction-prediction model (ABD-pm) [15], has been internally validated using the CAM-ICU to predict next day status (i.e., normal, delirium, coma, ICU discharge or ICU death) among critically ill adults including those with delirium at time of ICU admission.…”