Publications

The authors' sought to develop an ultrabrief screen for postoperative delirium in cognitively intact patients older than 70 years undergoing major elective surgery. All possible combinations of one-, two- and three-item screens and their sensitivities, specificities, and 95% confidence intervals were calculated and compared with the delirium reference standard Confusion Assessment Method (CAM). Among the 560 participants (mean age, 77 years; 58% women), delirium occurred in 134 (24%). We considered 1,100 delirium assessments from postoperative days 1 and 2. The screen with the best overall performance consisted of three items: (1) Patient reports feeling confused, (2) Months of the year backward, and (3) "Does the patient appear sleepy?" with sensitivity of 92% and specificity of 72%. This brief, three-item screen rules out delirium quickly, identifies a subset of patients who require further testing, and may be an important tool to improve recognition of postoperative delirium.

BACKGROUND/OBJECTIVES: Delirium is a common postoperative complication associated with prolonged length of stay, hospital readmission, and premature mortality. We explored the association between neighborhood-level characteristics and delirium incidence and severity, and compared neighborhood- with individual-level indicators of socioeconomic status in predicting delirium incidence.

DESIGN: A prospective observational cohort of patients enrolled between June 18, 2010, and August 8, 2013. Baseline interviews were conducted before surgery, and delirium/delirium severity was evaluated daily during hospitalization. Research staff evaluating delirium were blinded to baseline cognitive status.

SETTING: Two academic medical centers in Boston, MA.

PARTICIPANTS: A total of 560 older adults, aged 70 years or older, undergoing major noncardiac surgery.

INTERVENTION: The Area Deprivation Index (ADI) was used to characterize each neighborhood's socioeconomic disadvantage.

MEASUREMENTS: Delirium was assessed using the Confusion Assessment Method (CAM) long form. Delirium severity was calculated using the highest value of CAM Severity score (CAM-S) occurring during daily hospital assessments (CAM-S Peak).

RESULTS: Residing in the most disadvantaged neighborhoods (ADI > 44) was associated with a higher risk of incident delirium (12/26; 46%), compared with the least disadvantaged neighborhoods (122/534; 23%) (risk ratio (RR) (95% confidence interval (CI)) = 2.0 (1.3-3.1). The CAM-S Peak score was significantly associated with ADI (Spearman rank correlation, ρ = 0.11; P = .009). Mean CAM-S Peak scores generally rose from 3.7 to 5.3 across levels of increasing neighborhood disadvantage. The RR (95% CI) values associated with individual-level markers of socioeconomic status and cultural background were: 1.2 (0.9-1.7) for education of 12 years or less; 1.3 (0.8-2.1) for non-White race; and 1.7 (1.1-2.6) for annual household income of less than $20,000. None of these individual-level markers exceeded the ADI in terms of effect size or significance for prediction of delirium risk.

CONCLUSIONS: Neighborhood-level makers of social disadvantage are associated with delirium incidence and severity, and demonstrated an exposure-response relationship. Future studies should consider contextual-level metrics, such as the ADI, as risk markers of social disadvantage that can help to guide delirium treatment and prevention.

OBJECTIVE: To examine the association of the plasma neuroaxonal injury markers neurofilament light (NfL), total tau, glial fibrillary acid protein, and ubiquitin carboxyl-terminal hydrolase L1 with delirium, delirium severity, and cognitive performance.

METHODS: Delirium case-no delirium control (n = 108) pairs were matched by age, sex, surgery type, cognition, and vascular comorbidities. Biomarkers were measured in plasma collected preoperatively (PREOP), and 2 days (POD2) and 30 days postoperatively (PO1MO) using Simoa technology (Quanterix, Lexington, MA). The Confusion Assessment Method (CAM) and CAM-S (Severity) were used to measure delirium and delirium severity, respectively. Cognitive function was measured with General Cognitive Performance (GCP) scores.

RESULTS: Delirium cases had higher NfL on POD2 and PO1MO (median matched pair difference = 16.2pg/ml and 13.6pg/ml, respectively; p < 0.05). Patients with PREOP and POD2 NfL in the highest quartile (Q4) had increased risk for incident delirium (adjusted odds ratio [OR] = 3.7 [95% confidence interval (CI) = 1.1-12.6] and 4.6 [95% CI = 1.2-18.2], respectively) and experienced more severe delirium, with sum CAM-S scores 7.8 points (95% CI = 1.6-14.0) and 9.3 points higher (95% CI = 3.2-15.5). At PO1MO, delirium cases had continued high NfL (adjusted OR = 9.7, 95% CI = 2.3-41.4), and those with Q4 NfL values showed a -2.3 point decline in GCP score (-2.3 points, 95% CI = -4.7 to -0.9).

INTERPRETATION: Patients with the highest PREOP or POD2 NfL levels were more likely to develop delirium. Elevated NfL at PO1MO was associated with delirium and greater cognitive decline. These findings suggest NfL may be useful as a predictive biomarker for delirium risk and long-term cognitive decline, and once confirmed would provide pathophysiological evidence for neuroaxonal injury following delirium. ANN NEUROL 2020;88:984-994.

BACKGROUND: Delirium is a major risk factor for poor recovery after surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR). It is unclear whether preoperative physical performance tests improve delirium prediction.

OBJECTIVE: To examine whether physical performance tests can predict delirium after SAVR and TAVR, and adapt an existing delirium prediction rule for cardiac surgery, which includes Mini-Mental State Examination (MMSE), depression, prior stroke, and albumin level.

DESIGN: Prospective cohort, 2014-2017.

SETTING: Single academic center.

SUBJECTS: A total of 187 patients undergoing SAVR (n=77) or TAVR (n=110).

METHODS: The Short Physical Performance Battery (SPPB) score was calculated based on gait speed, balance, and chair stands (range: 0-12 points, lower scores indicate poor performance). Delirium was assessed using the Confusion Assessment Method. We fitted logistic regression to predict delirium using SPPB components and risk factors of delirium.

RESULTS: Delirium occurred in 35.8% (50.7% in SAVR and 25.5% in TAVR). The risk of delirium increased for lower SPPB scores: 10-12 (28.2%), 7-9 (34.5%), 4-6 (37.5%) and 0-3 (44.1%) (p-for-trend=0.001). A model that included gait speed <0.46 meter/second (OR, 2.7; 95% CI, 1.2-6.4), chair stands time ≥11.2 seconds (OR, 3.5; 95% CI, 1.0-12.4), MMSE <24 points (OR, 2.9; 95% CI, 1.3-6.4), isolated SAVR (OR, 5.4; 95% CI, 2.1-13.8), and SAVR and coronary artery bypass grafting (OR, 15.8; 95% CI, 5.5-45.7) predicted delirium better than the existing prediction rule (C statistics: 0.71 vs 0.61; p=0.035).

CONCLUSION: Assessing physical performance, in addition to cognitive function, can help identify high-risk patients for delirium after SAVR and TAVR.

BACKGROUND AND OBJECTIVE: Early detection of delirium in skilled nursing facilities (SNFs) is a priority. The extent to which delirium screening leads to a potentially inappropriate diagnosis of Alzheimer's disease and related dementia (ADRD) is unknown.

DESIGN: Nationwide retrospective cohort study from 2011 to 2013.

SETTING: An SNF.

PARTICIPANTS: A total of 1,175,550 Medicare enrollees who entered the SNF from a hospital and had no prior diagnosis of dementia.

EXPOSURE: A positive screen for delirium using the validated Confusion Assessment Method (CAM), performed as part of the federally mandated Minimum Data Set (MDS) assessment.

MEASUREMENTS: Incident all-cause dementia, ascertained through International Classification of Diseases, Ninth Revision (ICD-9), diagnosis in Medicare claims or active diagnoses in MDS.

RESULTS: Positive screening for delirium was identified in 7.7% of cases (n = 90,449), and most occurred within the first 7 days of SNF admission (62.5%). The overall incidence of ADRD was 6.3% (n = 73,542). Nearly all new diagnoses of ADRD (93.5%) occurred within the first 30 days of SNF admission. Patients who screened CAM positive for delirium had a nearly threefold increased risk of receiving an incident ADRD diagnosis on the same day (hazard ratio (HR) = 2.63; 95% confidence interval (CI) = 1.50-4.63). Among patients who screened CAM positive for delirium, those who were cognitively intact or had mild cognitive impairments were, on average, six times more likely to receive an incident ADRD diagnosis (HR = 6.64; 95% CI = 1.76-25.0) relative to those testing CAM negative.

CONCLUSION AND RELEVANCE: Among older adults not previously diagnosed with dementia, a positive screen for delirium was significantly associated with higher risk of ADRD diagnosis after admission to a SNF. This risk was highest for patients in the first days of their stay and with the least cognitive impairment, suggesting that the ADRD diagnosis was potentially inappropriate.

BACKGROUND/OBJECTIVES: Systematic screening can improve detection of delirium, but lack of time is often cited as why such screening is not performed. We investigated the time required to implement four screening protocols that use the Ultra-Brief two-item screener for delirium (UB-2) and the 3-Minute Diagnostic Interview for Confusion Assessment Method (CAM)-defined Delirium (3D-CAM), with and without a skip pattern that can further shorten the assessment. Our objective was to compare the sensitivity, specificity, and time required to complete four protocols: (1) full 3D-CAM on all patients, (2) 3D-CAM with skip on all patients, (3) UB-2, followed by the full 3D-CAM in "positives," and (4) UB-2, followed by the 3D-CAM with skip in "positives."

DESIGN: Comparative efficiency simulation study using secondary data.

SETTING: Two studies (3D-CAM and Researching Efficient Approaches to Delirium Identification (READI)) conducted at a large academic medical center (3D-CAM and READI) and a small community hospital (READI only).

PARTICIPANTS: General medicine inpatients, aged 70 years and older (3D-CAM, n = 201; READI, n = 330).

MEASUREMENTS: We used 3D-CAM data to simulate the items administered under each protocol and READI data to calculate median administration time per item. We calculated sensitivity, specificity, and total administration time for each of the four protocols.

RESULTS: The 3D-CAM and READI samples had similar characteristics, and all four protocols had similar simulated sensitivity and specificity. Mean administration times were 3 minutes 13 seconds for 3D-CAM, 2 minutes 19 seconds for 3D-CAM with skip, 1 minute 52 seconds for UB-2 + 3D-CAM in positives, and 1 minute 14 seconds for UB-2 + 3D-CAM with skip in positives, which was 1 minute 59 seconds faster than the 3D-CAM (P < .001).

CONCLUSION: The UB-CAM, consisting of the UB-2, followed in positives by the 3D-CAM with skip pattern, is a time-efficient delirium screening protocol that holds promise for increasing systematic screening for delirium in hospitalized older adults.

OBJECTIVES: To determine delirium occurrence rate, duration, and severity in patients admitted to the ICU with coronavirus disease 2019.

DESIGN: Retrospective data extraction study from March 1, 2020, to June 7, 2020. Delirium outcomes were assessed for up to the first 14 days in ICU.

SETTING: Two large, academic centers serving the state of Indiana.

PATIENTS: Consecutive patients admitted to the ICU with positive severe acute respiratory syndrome coronavirus 2 nasopharyngeal swab polymerase chain reaction test from March 1, 2020, to June 7, 2020, were included. Individuals younger than 18 years of age, without any delirium assessments, or without discharge disposition were excluded.

MEASUREMENTS AND MAIN RESULTS: Primary outcomes were delirium rates and duration, and the secondary outcome was delirium severity. Two-hundred sixty-eight consecutive patients were included in the analysis with a mean age of 58.4 years (sd, 15.6 yr), 40.3% were female, 44.4% African American, 20.7% Hispanic, and a median Acute Physiology and Chronic Health Evaluation II score of 18 (interquartile range, 13-25). Delirium without coma occurred in 29.1% of patients, delirium prior to coma in 27.9%, and delirium after coma in 23.1%. The first Confusion Assessment Method for the ICU assessment was positive for delirium in 61.9%. Hypoactive delirium was the most common subtype (87.4%). By day 14, the median number of delirium/coma-free were 5 days (interquartile range, 4-11 d), and median Confusion Assessment Method for the ICU-7 score was 6.5 (interquartile range, 5-7) indicating severe delirium. Benzodiazepines were ordered for 78.4% of patients in the cohort. Mechanical ventilation was associated with greater odds of developing delirium (odds ratio, 5.0; 95% CI, 1.1-22.2; p = 0.033) even after adjusting for sedative medications. There were no between-group differences in mortality.

CONCLUSIONS: Delirium without coma occurred in 29.1% of patients admitted to the ICU. Delirium persisted for a median of 5 days and was severe. Mechanical ventilation was significantly associated with odds of delirium even after adjustment for sedatives. Clinical attention to manage delirium duration and severity, and deeper understanding of the virus' neurologic effects is needed for patients with coronavirus disease 2019.

BACKGROUND: Delirium is common, morbid, and costly, yet its biology is poorly understood. We aimed to develop a multi-protein signature of delirium by identifying proteins associated with delirium from unbiased proteomics and combining them with delirium biomarkers identified in our prior work (interleukin [IL]-6 and IL-2).

METHODS: We used the Successful Aging after Elective Surgery (SAGES) Study of adults age ≥70 undergoing major noncardiac surgery (N = 560; 24% delirium). Plasma was collected preoperatively (PREOP) and on postoperative day 2 (POD2). In a nested matched case-control study involving 12 pairs of delirium cases and no-delirium controls, isobaric tags for relative and absolute quantitation-based (iTRAQ) mass spectrometry proteomics was applied to identify the top set of delirium-related proteins. With these proteins, we then conducted enzyme-linked immunosorbent assay (ELISA) confirmation, and if confirmed, ELISA validation in 75 matched pairs. Multi-marker conditional logistic regression was used to select the "best" PREOP and POD2 models for delirium.

RESULTS: We identified three proteins from iTRAQ: C-reactive protein (CRP), zinc alpha-2 glycoprotein (AZGP1), and alpha-1 antichymotrypsin (SERPINA3). The "best" multi-protein models of delirium included: PREOP: CRP and AZGP1 (Bayesian information criteria [BIC]: 93.82, c-statistic: 0.77); and POD2: IL-6, IL-2, and CRP (BIC: 87.11, c-statistic: 0.84).

CONCLUSION: The signature of postoperative delirium is dynamic, with some proteins important before surgery (risk markers) and others at the time of delirium (disease markers). Our dynamic, multi-protein signature for delirium improves our understanding of delirium pathophysiology and may identify patients at-risk of this devastating disorder that threatens independence of older adults.

BACKGROUND AND OBJECTIVES: Delirium creates distinct emotional distress in patients and family caregivers, yet there are limited tools to assess the experience. Our objective was to develop separate patient and family caregiver delirium burden instruments and to test their content and construct validity.

RESEARCH DESIGN AND METHODS: Two hundred forty-seven patients and 213 family caregivers were selected from an ongoing prospective cohort of medical-surgical admissions aged ≥70 years old. New patient and family caregiver delirium burden instruments were developed and used to measure the subjective experiences of in-hospital delirium. Delirium and delirium severity were measured by the Confusion Assessment Method (CAM) and CAM-Severity (long form).

RESULTS: Both Delirium Burden (DEL-B) instruments consist of eight questions and are measured on a 0 - 40 point scale. Final questions had good clarity and relevancy, as rated by the expert panel, and good internal consistency (Cronbach's α = .82-.86). In the cohort validation, Patient DEL-B (DEL-B-P) was 5.1 points higher and Family Caregiver DEL-B (DEL-B-C) was 5.8 points higher, on average, for patients who developed delirium compared to those who did not (p < .001). Test-retest reliability of DEL-B-C at baseline and 1 month was strong (correlation = .73). Delirium severity was mildly-moderately correlated with DEL-B-P (correlation = .34) and DEL-B-C (correlation = .26), suggesting contribution of other factors.

DISCUSSION AND IMPLICATIONS: We created instruments to reliably measure and evaluate the burden of delirium for patients and their family caregivers. Although additional validation is indicated, these instruments provide a key first step toward measuring and improving the subjective experience of delirium for patients and their families.

BACKGROUND/OBJECTIVES: To describe the design, procedures, and cohort for the Better ASsessment of ILlness -(BASIL) study, which is conducted to develop and test new delirium severity measures, compare them with existing measures, and examine related clinical outcomes.

METHODS: Prospective cohort study with 1 year follow-up of study participants at a large teaching hospital in Boston, Massachusetts. After brief cognitive testing and the Delirium Symptom Interview, delirium and delirium severity were rated daily in the hospital using the Confusion Assessment Method (CAM) and CAM-Severity score, the Delirium Rating Scale-Revised-98 (DRS-R-98), and the Memorial Delirium Assessment Scale (MDAS). Other key study variables included comorbidity, physical function (basic and instrumental activities of daily living [ADL]), ratings of subjective health and well-being, and clinical outcomes (length of stay, 30 day rehospitalization, nursing home admission, healthcare utilization). Follow-up interviews occurred at 1- and 12-month with patients and families. In 42 patient interviews, inter-rater reliability for key variables was assessed.

RESULTS: Of 768 eligible patients approached, 469 were screened and 352 enrolled, yielding an overall study response rate of 67% for potentially eligible participants. The mean participant was 80.3 years old (SD 6.8) and 203 (58%) were female. The majority of patients were medically complex with Charlson Comorbidity Scores ≥2 (192 patients, 55%), and 102 (29%) met criteria for dementia. Inter-rater reliability assessments (n = 42 pairs) were high for overall ratings of presence or absence of delirium by CAM (κ = 1.0), delirium severity by DRS-R-98 and MDAS (weighted kappa, κ = 1.0 for each) and for ADL impairment (κ = 1.0). For eligible participants at each time point, 278 out of 308 (90%) completed the 1-month follow-up and 132 out of 256 (53%) have completed the 12-month follow-up to date, which is still in progress. Among those who completed interviews, there was only 1-3% missing data on most major outcomes (delirium, basic ADL, and readmission).

CONCLUSION: The BASIL study presents an innovative effort to advance the conceptualization and measurement of delirium severity. Unique strengths include the diverse cohort with complete high quality data and longitudinal follow-up, along with detailed collection of multiple delirium measures daily during hospitalization.