Delirium in postoperative nonventilated intensive care patients: risk factors and outcomes

The setting was a 12-bed SICU at a tertiary hospital in Rio de Janeiro, Brazil. After approval from the ethics committee, informed consent was obtained from the patients or their representatives before enrollment.

A total of 824 consecutive subjects admitted to the SICU between November 2005 and July 2006 were screened for inclusion in the study. Patients were included if they had an adequate level of consciousness, as determined by a Richmond Agitation-Sedation Scale (RASS) score greater than −3. Patients were excluded if one of the following factors were present: mechanical ventilation, pregnancy, age less than 18 years, inability to verbalize, considerable hearing or visual impairment, or lack of informed consent.

The RASS was used to assess sedation status and is a ten-point rating scale with four levels for agitation, five levels for sedation, and one level for calm, awake patients [15, 16]. Two trained nurses applied the CAM for delirium evaluation. Briefly, CAM evaluates the four key delirium features: (1) acute onset and fluctuating course; (2) inattention; (3) disorganized thinking; and (4) altered level of consciousness. Delirium was considered present if features 1 and 2 were present in addition to either feature 3 or 4 [14]. The CAM tool was performed twice a day during the entire SICU stay, and evaluation started on the day shift after the patient’s arrival from surgery and continued until SICU discharge.

The training program for nurses consisted of three steps. In step 1, information about delirium, including relevant literature, handouts, and CAM tools, was provided and a video about the detailed application of the CAM was shown. In step 2, one-to-one instruction at the patients’ bedside was performed. During this period, staff members were able to interact with the delirium experts regarding any issues with the screening. In step 3, patient evaluations that were completed by the delirium expert 1 week before were compared with the evaluations completed by the nurses. Diagnosis of delirium and the data collected by two nurses were compared at the same time of day for 1 week to confirm consistency between observers.

A standardized data entry form was created for the collection of the following: demographic data, major surgical diagnoses, comorbidities, laboratory results based on the computerized laboratory records necessary to calculate the APACHE II score, hemodynamic changes (fever, hypotension, hypoxemia, or need of oxygen therapy), and the use of benzodiazepines or opioids in the first 24 h of admission to the SICU. Data regarding comorbidities were collected with the anesthetic record and confirmed with family and patients. We considered comorbidities to be all medical conditions reported by the patients or their family during the pre-anesthetic interview. We considered emergency surgery when it was required in less than 24 h after surgeon evaluation. Acute Physiology and Chronic Health Evaluation (APACHE) II scores were calculated using data from the first 24 h of the SICU admission. The need to use hearing or ocular devices was investigated to be sure that the patient is able to understand the CAM interview.

Continuous variables are summarized as medians and interquartile ranges (25th and 75th percentiles), and dichotomous categorical variables are presented as proportions of frequency. The result of the CAM was evaluated as a dichotomous variable (presence or absence of delirium). Nonparametric Mann–Whitney, Kruskal-Wallis, Spearman correlation coefficients, and chi-squared tests were performed to compare the association between the clinical and demographic variables and the development of delirium. A p value < 0.05 was considered significant. The odds ratio (OR) and 95% confidence interval (CI) were described. Intensive care unit and hospital mortality were of particular interest. We performed multivariate forward logistic regression relating the APACHE II score and the presence of delirium with mortality. Univariate and multivariate logistic regression were used to identify risk factors present in the first 24 h of SICU admission associated with the development of delirium. Variables yielding p < 0.2 by univariate analysis and those considered clinically relevant were entered in the multivariate analysis to estimate the independent association of each covariate with the dependent variable. Age, type of surgery, emergency surgery, APS, benzodiazepine use, hypoxemia, and hypotension were included in the model as risk factor for delirium. Hypotension and hypoxemia was positively related with delirium occurrence in univariate analysis, but after the multivariate analysis both variables did not promote a better model considering APS simultaneously. The Hosmer-Lemeshow goodness-of-fit test was used to verify the calibration of the risk-factor-to-delirium model developed using logistic regression.

SPSS®, version 10.0 (SPSS Inc., Chicago, IL) was used for statistical analysis.

The median lengths of SICU and hospital stays were significantly increased in patients who developed delirium. Patients with delirium had higher hospital and SICU mortality rates compared with patients without delirium (Table 1).

The relative risk of hospital mortality associated with delirium and APACHE II scores (each point) was 1.367 (1.135-1.999) and 1.194 (1.097-1.299), respectively. Moreover, as shown in Figure 3, the occurrence of delirium impacted the mortality related to APACHE II score by increasing the probability of death.

The risk factors related to delirium were as follows: age (years) (OR, 1.04 [1.02-1.07]), APS (each points) (OR, 1.11 [1.04-1.2]), emergency surgery (8.05 [3.58-18.06]), the use of benzodiazepines in the first 24 h after admission (OR, 2.28 [1.04-5]), and trauma (OR, 6.16 [4.1-6.5]; Table 2).