Hospital-wide evaluation of delirium incidence in adults under 65 years of age

Development and validation of a nomogram to predict postoperative delirium in older patients after major abdominal surgery: a retrospective case-control study

BACKGROUND

Postoperative delirium is a common complication in older patients, with poor long-term outcomes. This study aimed to investigate risk factors and develop a predictive model for postoperative delirium in older patients after major abdominal surgery.

METHODS

This study retrospectively recruited 7577 patients aged ≥ 65 years who underwent major abdominal surgery between January 2014 and December 2018 in a single hospital in Beijing, China. Patients were divided into a training cohort (n = 5303) and a validation cohort (n = 2224) for univariate and multivariate logistic regression analyses and to build a nomogram. Data were collected for 43 perioperative variables, including demographics, medical history, preoperative laboratory results, imaging, and anesthesia information.

RESULTS

Age, chronic obstructive pulmonary disease, white blood cell count, glucose, total protein, creatinine, emergency surgery, and anesthesia time were associated with postoperative delirium in multivariate analysis. We developed a nomogram based on the above 8 variables. The nomogram achieved areas under the curve of 0.731 and 0.735 for the training and validation cohorts, respectively. The discriminatory ability of the nomogram was further assessed by dividing the cases into three risk groups (low-risk, nomogram score < 175; medium-risk, nomogram score 175~199; high-risk, nomogram score > 199; P < 0.001). Decision curve analysis revealed that the nomogram provided a good net clinical benefit.

CONCLUSIONS

We developed a nomogram that could predict postoperative delirium with high accuracy and stability in older patients after major abdominal surgery.

Rapid Handheld Continuous Electroencephalogram (EEG) Has the Potential to Detect Delirium in Older Adults

BACKGROUND

Delirium-related biochemical derangements lead to electrical changes that can be detected in electroencephalographic (EEG) patterns followed by behavioral signs and symptoms. Studies using limited lead EEG show a large difference between patients with and without delirium while discriminating delirium from other causes. Handheld rapid EEG devices may be capable of detecting delirium before symptom onset, thus providing an objective physiological method to detect delirium when it is most amenable to interventions.

OBJECTIVE

The aim of this study was to explore the potential for rapid EEG to detect waveform pattern changes consistent with delirium status.

METHODS

This prospective exploratory pilot study used a correlational design and mixed models to explore the relationships between handheld portable EEG data and delirium status.

RESULTS

While being under powered minimized opportunities to detect statistical differences in EEG-derived ratios using spectral density analysis, sleep-to-wake ratios tended to be higher in patients with delirium.

CONCLUSIONS

Limited lead EEG may be useful in predicting adverse outcomes and risk for delirium in older critically ill patients. Although this population is at the highest risk for mortality, delirium is not easily identified by current clinical assessments. Therefore, further investigation of limited lead EEG for delirium detection is warranted.