Predisposing factors for delirium in the surgical intensive care unit

Abstract

CI = confidence interval; DG = delirious group; HBP = hepatobiliopancreatic diseases; ICU = intensive care unit; LSH = length of stay in hospital; LSICU = length of stay in the ICU; NDG = non-delirious group; OR = odds ratio. AbstractBackground Delirium is a sign of deterioration in the homeostasis and physical status of the patient. The objective of our study was to investigate the predisposing factors for delirium in a surgical intensive care unit (ICU) setting. Method Between January 1996 and 1997, we screened prospectively 818 patients who were consecutive applicants to the general surgery service of Dicle-University Hospital and had been kept in the ICU for delirium. All patients were hospitalized either for elective or emergency services and were treated either with medication and/or surgery. Suspected cases of delirium were identified during daily interviews. The patients who had changes in the status of consciousness (n = 150) were consulted with an experienced consultation-liaison psychiatrist. The diagnosis of delirium was based on Diagnostic and Statistical Manual of Mental Disorders (revised third edition) criteria and established through psychiatric interviews. Patients were divided into two groups: the 'delirious group' (DG) (n = 90) and the 'nondelirious group' (NDG) (n = 728). During delirium, all abnormal findings related to physical conditions, laboratory features, and additional diseases were evaluated as probable risk factors of delirium. Results Of 818 patients, 386 (47.2%) were male and 432 (52.8%) were female. Delirium developed in 90 of 818 patients (11%). The cases of delirium in the DG were more frequent among male patients (63.3%) than female patients (36.7%) (χ 2 = 10.5, P = 0.001). The mean age was 48.9 ± 18.1 and 38.5 ± 13.8 years in the DG and NDG, respectively (t = 6.4, P = 0.000). Frequency of delirium is higher in the patients admitted to the Emergency Department (χ 2 = 43.6, P = 0.000). The rate of postoperative delirium was 10.9%, but there was no statistical difference related to operations between the DG and NDG (χ 2 = 0.13, P = 0.71). The length of stay in the ICU was 10.7 ± 13.9 and 5.6 ± 2.9 days in the DG and NDG, respectively (t = 0.11, P = 0.000). The length of stay in hospital was 15.6 ± 16.5 and 8.1 ± 2.7 days in the DG and NDG, respectively (t = 11.08, P = 0.000). Logistic regression was used to explore the associations between probable risk factors and delirium. Delirium was not correlated with conditions such as hypertension, hypo/hyperpotassemia, hypernatremia, hypoalbuminemia, hypo/hyperglycemia, cardiac disease, emergency admission, age, length of stay in the ICU, length of stay in hospital, and gender. It was determined that conditions such as respiratory diseases (odds ratio [OR] = 30.6, 95% confidence interval [CI] = 9.5-98.4), infections (OR = 18.0, 95% CI = 3.5-90.8), fever (OR = 14.3, 95% CI = 4.1-49.3), anemia (OR = 5.4, 95% CI = 1.6-17.8), hypotension (OR = 19.8,3), hypocalcemia (OR = 30.9, 95% CI = 5.8-163.2), hyponatremia (OR = 8.2, 95% CI = 2.5-26.4)...

Efficacy of Liaison Education and Environmental Changes on Delirium Incidence in ICU

“…It is recommended to screen patients' and careers' perspectives, and to reduce hospital costs, which can change the levels of prevention of delirium. Since the special nurses are frequently in a relationship with the patients, they play a key role in prevention, early detection, and treatment of patients with delirium (6)(7)(8)(9)(10). Although, the use of standard screening delirium patients in intensive care unit is not still common by physicians and nurses, it is necessary to screen delirium (3,(11)(12)(13).…”

Delirium Prediction using Machine Learning Models on Predictive Electronic Health Records Data

“…These factors include, but are not limited to: age, admission type, primary surgical procedure. These factors are recorded in electronic health records and have been used in the literature for delirium prediction (6, 12, 13). …”

“…EHR data are routinely collected for all patients at admission, and many studies have used them for prediction of health outcomes during and after the hospital stay (14). Previous works that have used EHR data for delirium prediction, have mainly used multivariate regression models due to their ease of interpretation and analysis (12, 13). Using logistic regression analysis, several other studies have identified important features in the preoperative dataset contributing to development of delirium, with up to 87% area under the receiver operating characteristics (ROC) curve (AUC) (12, 13, 15).…”

“…Previous works that have used EHR data for delirium prediction, have mainly used multivariate regression models due to their ease of interpretation and analysis (12, 13). Using logistic regression analysis, several other studies have identified important features in the preoperative dataset contributing to development of delirium, with up to 87% area under the receiver operating characteristics (ROC) curve (AUC) (12, 13, 15). Although promising, they mostly used additional features such as Mini-Mental State Examination score, Visual acuity, and Geriatric Depression Scale, which are not routine assessments in the hospitals.…”

“…Although promising, they mostly used additional features such as Mini-Mental State Examination score, Visual acuity, and Geriatric Depression Scale, which are not routine assessments in the hospitals. Others have been applied to specific populations such as older patients, elderly patients with hip surgery, or patients in the ICU (12, 13, 16). …”