The bispectral electroencephalography (BSEEG) method quantifies post-operative delirium-like states in young and aged male mice after head mount implantation surgery.

Delirium, a syndrome characterized by an acute change in attention, awareness, and cognition, is commonly observed in older adults, although there are few quantitative monitoring methods in the clinical setting. We developed a bispectral electroencephalography (BSEEG) method capable of detecting delirium and can quantify the severity of delirium using a novel algorithm. Pre-clinical application of this novel BSEEG method can capture a delirium-like state in mice following LPS administration. However, its application to postoperative delirium (POD) has not yet been validated in animal experiments. This study aimed to create a POD model in mice with the BSEEG method by monitoring BSEEG scores following EEG head-mount implantation surgery and throughout the recovery. We compared the BSEEG scores of C57BL/6J young (2-3 months old) with aged (18-19 months old) male mice for quantitative evaluation of POD-like states. Postoperatively, both groups displayed increased BSEEG scores and a loss of regular diurnal changes in BSEEG scores. In young mice, BSEEG scores and regular diurnal changes recovered relatively quickly to baseline by postoperative day 3. Conversely, aged mice exhibited prolonged increases in postoperative BSEEG scores and it reached steady states only after postoperative day 8. This study suggests that the BSEEG method can be utilized as a quantitative measure of POD and assess the effect of aging on recovery from POD in the pre-clinical model.

The impact of the distal ureteral diameter ratio, bladder volume at onset of vesicoureteral reflux, and/or grade in predictive models of clinical outcomes in children with vesicoureteral reflux.

PURPOSE: Vesicoureteral reflux (VUR) grade has been used as a primary factor in assessing a child's risk of clinical outcomes. Unfortunately, grade has poor inter-observer reliability. We hypothesized that more objective and reliable VCUG parameters including the distal ureteral diameter ratio (UDR) and volume at onset of VUR (Vol) may either augment or replace the current grading system to provide more reliable prediction of clinical outcomes. MATERIALS AND METHODS: Multivariate clinical outcome models were analyzed to assess the impact on predictive accuracy by the addition of voiding cystourethrogram (VCUG) data including grade, UDR, and Vol, alone or in combinations. Clinical variables from retrospective review of 841 children's records included age, gender, presentation, VUR laterality, bowel and bladder dysfunction, history of febrile urinary tract infection (UTI), and number of UTIs. The primary outcomes assessed included VUR resolution or persistence and need for operative intervention. RESULTS: Grade, UDR, and Vol were independent predictors of resolution and operative intervention. Vol increased predictive accuracy in resolution models with grade or UDR alone; however, no significant difference occurred in models with the substitution of grade with UDR. CONCLUSIONS: A more reliable classification system for VUR, with improved predictive accuracy regarding clinical outcomes, may be developed incorporating UDR and Vol. Whether VUR grade can be completely replaced by Vol and UDR measurements requires further evaluation with larger number of patients.

Mortality among patients with sepsis associated with a bispectral electroencephalography (BSEEG) score.

We have previously developed a bispectral electroencephalography (BSEEG) device, which was shown to be effective in detecting delirium and predicting patient outcomes. In this study we aimed to apply the BSEEG approach for a sepsis. This was a retrospective cohort study conducted at a single center. Sepsis-positive cases were identified based on retrospective chart review. EEG raw data and calculated BSEEG scores were obtained in the previous studies. The relationship between BSEEG scores and sepsis was analyzed, as well as the relationship among sepsis, BSEEG score, and mortality. Data were analyzed from 628 patients. The BSEEG score from the first encounter (1st BSEEG) showed a significant difference between patients with and without sepsis (p = 0.0062), although AUC was very small indicating that it is not suitable for detection purpose. Sepsis patients with high BSEEG scores showed the highest mortality, and non-sepsis patients with low BSEEG scores showed the lowest mortality. Mortality of non-sepsis patients with high BSEEG scores was as bad as that of sepsis patients with low BSEEG scores. Even adjusting for age, gender, comorbidity, and sepsis status, BSEEG remained a significant predictor of mortality (p = 0.008). These data are demonstrating its usefulness as a potential tool for identification of patients at high risk and management of sepsis.

Mortality prediction by bispectral electroencephalography among 502 patients: its role in dementia.

Complications of delirium and dementia increase mortality; however, it is difficult to diagnose delirium accurately, especially among dementia patients. The bispectral electroencephalography score can detect delirium and predict mortality in elderly patients. We aimed to develop an efficient and reliable bispectral electroencephalography device for high-throughput screening. We also hypothesized that bispectral electroencephalography score can predict mortality among dementia patients. A prospective cohort study was conducted between January 2016 and December 2018 to measure bispectral electroencephalography from elderly patients and correlate with outcomes. A total of 502 elderly (55 years old or older) patients with and without dementia were enrolled. For a replication of the utility of bispectral electroencephalography, mortalities between bispectral electroencephalography-positive and bispectral electroencephalography-negative group were compared. In addition, patients with and without dementia status were added to examine the utility of bispectral electroencephalography among dementia patients. The mortality within 180 days in the bispectral electroencephalography-positive group was higher than that of the bispectral electroencephalography-negative group in both the replication and the total cohorts. Mortality of those in the bispectral electroencephalography-positive group showed a dose-dependent increase in both cohorts. When the dementia patients showed bispectral electroencephalography positive, their mortality was significantly higher than those with dementia but who were bispectral electroencephalography-negative. Mortality within 30 days in the bispectral electroencephalography-positive group was significantly higher than that of the bispectral electroencephalography-negative group. The utility of the bispectral electroencephalography to predict mortality among large sample of 502 elderly patients was shown. The bispectral electroencephalography score can predict mortality among elderly patients in general, and even among dementia patients, as soon as 30 days.

Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium.

Current methods for screening and detecting delirium are not practical in clinical settings. We previously showed that a simplified EEG with bispectral electroencephalography (BSEEG) algorithm can detect delirium in elderly inpatients. In this study, we performed a post-hoc BSEEG data analysis using larger sample size and performed topological data analysis to improve the BSEEG method. Data from 274 subjects included in the previous study were analyzed as a 1st cohort. Subjects were enrolled at the University of Iowa Hospitals and Clinics (UIHC) between January 30, 2016, and October 30, 2017. A second cohort with 265 subjects was recruited between January 16, 2019, and August 19, 2019. The BSEEG score was calculated as a power ratio between low frequency to high frequency using our newly developed algorithm. Additionally, Topological data analysis (TDA) score was calculated by applying TDA to our EEG data. The BSEEG score and TDA score were compared between those patients with delirium and without delirium. Among the 274 subjects from the first cohort, 102 were categorized as delirious. Among the 206 subjects from the second cohort, 42 were categorized as delirious. The areas under the curve (AUCs) based on BSEEG score were 0.72 (1st cohort, Fp1-A1), 0.76 (1st cohort, Fp2-A2), and 0.67 (2nd cohort). AUCs from TDA were much higher at 0.82 (1st cohort, Fp1-A1), 0.84 (1st cohort, Fp2-A2), and 0.78 (2nd cohort). When sensitivity was set to be 0.80, the TDA drastically improved specificity to 0.66 (1st cohort, Fp1-A1), 0.72 (1st cohort, Fp2-A2), and 0.62 (2nd cohort), compared to 0.48 (1st cohort, Fp1-A1), 0.54 (1st cohort, Fp2-A2), and 0.46 (2nd cohort) with BSEEG. BSEEG has the potential to detect delirium, and TDA is helpful to improve the performance.

Bispectral EEG (BSEEG) quantifying neuro-inflammation in mice induced by systemic inflammation: A potential mouse model of delirium.

Most of the animal studies using inflammation-induced cognitive change have relied on behavioral testing without objective and biologically solid methods to quantify the severity of cognitive disturbances. We have developed a bispectral EEG (BSEEG) method using a novel algorithm in clinical study. This method effectively differentiates between patients with and without delirium, and predict long-term mortality. In the present study, we aimed to apply our bispectral EEG (BSEEG) method, which can detect patients with delirium, to a mouse model of delirium with systemic inflammation induced by lipopolysaccharides (LPS) injection. We recorded EEG after LPS injection using wildtype early adulthood mice (2~3-month-old) and aged mice (18-19-month-old). Animal EEG recordings were converted for power spectral density to calculate BSEEG score using the similar BSEEG algorithm previously developed for our human study. The BSEEG score was relatively stable and slightly high during the day. Alternatively, the BSEEG score was erratic and low in average during the night. LPS injection increased the BSEEG score dose-dependently and diminished the diurnal changes. The mean BSEEG score increased much more in the aged mice group as dosage increased. Our results suggest that BSEEG method can objectively "quantify" level of neuro-Inflammation induced by systemic inflammation (LPS), and that this BSEEG method can be useful as a model of delirium in mice.

New Cutoff Scores for Delirium Screening Tools to Predict Patient Mortality.

BACKGROUND/OBJECTIVES: Detecting delirium is important to identify patients with a high risk of poor outcomes. Although many different kinds of screening instruments for delirium exist, there is no solid consensus about which methods are the most effective. In addition, it is important to find the most useful tools in predicting outcomes such as mortality. DESIGN: Retrospective cohort study. SETTING: University of Iowa Hospitals and Clinics. PARTICIPANTS: A total of 1,125 adult inpatients (mean age = 67.7; median age = 69). MEASUREMENTS: Post hoc analyses were performed based on existing data from the Confusion Assessment Method for Intensive Care Unit (CAM-ICU), Delirium Rating Scale-Revised-98 (DRS), and the Delirium Observation Screening Scale (DOSS). Correlation among these scales and relationships between 365-day mortality and each scale were evaluated. RESULTS: A positive result on the CAM-ICU ("CAM-ICU positive") was associated with higher DRS and DOSS scores. A DRS score = 9/10 was the best cutoff to detect CAM-ICU positive, and DOSS = 2/3 was the best cutoff to detect CAM-ICU positive. CAM-ICU positive was associated with high 365-day mortality. DRS score = 9/10 and DOSS score = 0/1 were found to differentiate mortality risk the most significantly. Higher DRS and DOSS scores significantly coincided with a decrease in a patient's survival rate at 365 days. CONCLUSION: The best DRS and DOSS cutoff scores to differentiate 365-day mortality risk were lower than those commonly used to detect delirium in the literature. New cutoff scores for the DRS and DOSS might be useful in differentiating risk of mortality among hospital patients.