Person:

Rudolph, James

Background: Delirium (acute confusion), is a common, morbid, and costly complication of acute illness in older adults. Yet, researchers and clinicians lack short, efficient, and sensitive case identification tools for delirium. Though the Confusion Assessment Method (CAM) is the most widely used algorithm for delirium, the existing assessments that operationalize the CAM algorithm may be too long or complicated for routine clinical use. Item response theory (IRT) models help facilitate the development of short screening tools for use in clinical applications or research studies. This study utilizes IRT to identify a reduced set of optimally performing screening indicators for the four CAM features of delirium. Methods: Older adults were screened for enrollment in a large scale delirium study conducted in Boston-area post-acute facilities (n = 4,598). Trained interviewers conducted a structured delirium assessment that culminated in rating the presence or absence of four features of delirium based on the CAM. A pool of 135 indicators from established cognitive testing and delirium assessment tools were assigned by an expert panel into two indicator sets per CAM feature representing (a) direct interview questions, including cognitive testing, and (b) interviewer observations. We used IRT models to identify the best items to screen for each feature of delirium. Results: We identified 10 dimensions and chose up to five indicators per dimension. Preference was given to items with peak psychometric information in the latent trait region relevant for screening for delirium. The final set of 48 indicators, derived from 39 items, maintains fidelity to clinical constructs of delirium and maximizes psychometric information relevant for screening. Conclusions: We identified optimal indicators from a large item pool to screen for delirium. The selected indicators maintain fidelity to clinical constructs of delirium while maximizing psychometric information important for screening. This reduced item set facilitates development of short screening tools suitable for use in clinical applications or research studies. This study represents the first step in the establishment of an item bank for delirium screening with potential questions for clinical researchers to select from and tailor according to their research objectives.

Background: The Diagnostic and Statistical Manual fifth edition (DSM-5) provides new criteria for delirium diagnosis. We examined delirium diagnosis using these new criteria compared with the Diagnostic and Statistical Manual fourth edition (DSM-IV) in a large dataset of patients assessed for delirium and related presentations. Methods: Patient data (n = 768) from six prospectively collected cohorts, clinically assessed using DSM-IV and the Delirium Rating Scale-Revised-98 (DRS-R98), were pooled. Post hoc application of DRS-R98 item scores were used to rate DSM-5 criteria. ‘Strict’ and ‘relaxed’ DSM-5 criteria to ascertain delirium were compared to rates determined by DSM-IV. Results: Using DSM-IV by clinical assessment, delirium was found in 510/768 patients (66%). Strict DSM-5 criteria categorized 158 as delirious including 155 (30%) with DSM-IV delirium, whereas relaxed DSM-5 criteria identified 466 as delirious, including 455 (89%) diagnosed by DSM-IV (P <0.001). The concordance between the different diagnostic methods was: 53% (ĸ = 0.22) between DSM-IV and the strict DSM-5, 91% (ĸ = 0.82) between the DSM-IV and relaxed DSM-5 criteria and 60% (ĸ = 0.29) between the strict versus relaxed DSM-5 criteria. Only 155 cases were identified as delirium by all three approaches. The 55 (11%) patients with DSM-IV delirium who were not rated as delirious by relaxed criteria had lower mean DRS-R98 total scores than those rated as delirious (13.7 ± 3.9 versus 23.7 ± 6.0; P <0.001). Conversely, mean DRS-R98 score (21.1 ± 6.4) for the 70% not rated as delirious by strict DSM-5 criteria was consistent with suggested cutoff scores for full syndromal delirium. Only 11 cases met DSM-5 criteria that were not deemed to have DSM-IV delirium. Conclusions: The concordance between DSM-IV and the new DSM-5 delirium criteria varies considerably depending on the interpretation of criteria. Overly-strict adherence for some new text details in DSM-5 criteria would reduce the number of delirium cases diagnosed; however, a more ‘relaxed’ approach renders DSM-5 criteria comparable to DSM-IV with minimal impact on their actual application and is thus recommended.

The purpose of this analysis was to determine if postoperative delirium was associated with early postoperative cognitive dysfunction (at 7 days) and long-term postoperative cognitive dysfunction (at 3 months). The International Study of Postoperative Cognitive Dysfunction recruited 1218 subjects ≥ 60 years old undergoing elective, non-cardiac surgery. Postoperatively, subjects were evaluated for delirium using the criteria of the Diagnostic and Statistical Manual. Subjects underwent neuropsychological testing pre-operatively and postoperatively at 7 days (n = 1018) and 3 months (n = 946). Postoperative cognitive dysfunction was defined as a composite Z-score > 2 across tests or at least two individual test Z-scores > 2. Subjects with delirium were significantly less likely to participate in postoperative testing. Delirium was associated with an increased incidence of early postoperative cognitive dysfunction (adjusted risk ratio 1.6, 95% CI 1.1–2.1), but not long-term postoperative cognitive dysfunction (adjusted risk ratio 1.3, 95% CI 0.6–2.4). Delirium was associated with early postoperative cognitive dysfunction, but the relationship of delirium to long-term postoperative cognitive dysfunction remains unclear.

Background— Delirium is a common outcome after cardiac surgery. Delirium prediction rules identify patients at risk for delirium who may benefit from targeted prevention strategies, early identification, and treatment of underlying causes. The purpose of the present prospective study was to develop a prediction rule for delirium in a cardiac surgery cohort and to validate it in an independent cohort.

Methods and Results— Prospectively, cardiac surgery patients ≥60 years of age were enrolled in a derivation sample (n=122) and then a validation sample (n=109). Beginning on the second postoperative day, patients underwent a standardized daily delirium assessment, and delirium was diagnosed according to the confusion assessment method. Delirium occurred in 63 (52%) of the derivation cohort patients. Multivariable analysis identified 4 variables independently associated with delirium: prior stroke or transient ischemic attack, Mini Mental State Examination score, abnormal serum albumin, and the Geriatric Depression Scale. Points were assigned to each variable: Mini Mental State Examination ≤23 received 2 points, and Mini Mental State Examination score of 24 to 27 received 1 point; Geriatric Depression Scale >4, prior stroke/transient ischemic attack, and abnormal albumin received 1 point each. In the derivation sample, the cumulative incidence of delirium for point levels of 0, 1, 2, and ≥3 was 19%, 47%, 63%, and 86%, respectively (C statistic, 0.74). The corresponding incidence of delirium in the validation sample was 18%, 43%, 60%, and 87%, respectively (C statistic, 0.75).

Conclusions— Delirium occurs frequently after cardiac surgery. Using 4 preoperative characteristics, clinicians can determine cardiac surgery patients’ risk for delirium. Patients at higher delirium risk could be candidates for close postoperative monitoring and interventions to prevent delirium.

Background Brief cognitive screening measures are valuable tools for both research and clinical applications. The most widely used instrument, the Mini-Mental State Examination (MMSE), is limited in that it must be administered face-to-face, cannot be used in participants with visual or motor impairments, and is protected by copyright. Screening instruments such as the Telephone Interview for Cognitive Status (TICS) were developed to provide a valid alternative, with comparable cut-point scores to rate global cognitive function.

Methods The MMSE, TICS-30, and TICS-40 scores from 746 community-dwelling elders who participated in the Aging, Demographics, and Memory Study (ADAMS) were analyzed with equipercentile equating, a statistical process of determining comparable scores based on percentile equivalents for different forms of an examination.

Results Scores from the MMSE and TICS-30 and TICS-40 corresponded well, and clinically relevant cut-point scores were determined. For example, an MMSE score of 23 is equivalent to 17 and 20 on the TICS-30 and TICS-40, respectively.

Conclusions These findings indicate that TICS and MMSE scores can be linked directly. Clinically relevant and important MMSE cut points and the respective ADAMS TICS-30 and TICS-40 cut-point scores are included, to identify the degree of cognitive impairment among respondents with any type of cognitive disorder. These results will help in the widespread application of TICS in both research and clinical practice.

The objective of this analysis was to develop a measure of neuropsychological performance for cardiac surgery and to assess its psychometric properties. Older patients (n = 210) underwent a neuropsychological battery using nine assessments. The number of factors was identified with variable reduction methods. Factor analysis methods based on item response theory were used to evaluate the measure. Modified parallel analysis supported a single factor, and the battery formed an internally consistent set (coefficient alpha = .82). The developed measure provided a reliable, continuous measure (reliability > .90) across a broad range of performance (–1.5 SDs to +1.0 SDs) with minimal ceiling and floor effects.

OBJECTIVES: To determine whether patients who developed delirium after cardiac surgery were at risk of functional decline.

DESIGN: Prospective cohort study.

SETTING: Two academic hospitals and a Veterans Affairs Medical Center.

PARTICIPANTS: One hundred ninety patients aged 60 and older undergoing elective or urgent cardiac surgery.

MEASUREMENTS: Delirium was assessed daily and was diagnosed according to the Confusion Assessment Method. Before surgery and 1 and 12 months postoperatively, patients were assessed for function using the instrumental activities of daily living (IADL) scale. Functional decline was defined as a decrease in ability to perform one IADL at follow-up.

RESULTS: Delirium occurred in 43.1% (n=82) of the patients (mean age 73.7±6.7). Functional decline occurred in 36.3% (n=65/179) at 1 month and in 14.6% (n=26/178) at 12 months. Delirium was associated with greater risk of functional decline at 1 month (relative risk (RR)=1.9, 95% confidence interval (CI)=1.3–2.8) and tended toward greater risk at 12 months (RR=1.9, 95% CI=0.9–3.8). After adjustment for age, cognition, comorbidity, and baseline function, delirium remained significantly associated with functional decline at 1 month (adjusted RR=1.8, 95% CI=1.2–2.6) but not at 12 months (adjusted RR=1.5, 95% CI=0.6–3.3).

CONCLUSION: Delirium was independently associated with functional decline at 1 month and had a trend toward association at 12 months. These findings provide justification for intervention trials to evaluate whether delirium prevention or treatment strategies might improve postoperative functional recovery.

OBJECTIVES: To use an expert consensus process to identify indicators of delirium features to help enhance bedside recognition of delirium. DESIGN: Modified Delphi consensus process to assign existing cognitive and delirium assessment items to delirium features in the Confusion Assessment Method (CAM) diagnostic algorithm. SETTING: Meetings of expert panel. PARTICIPANTS: Panel of seven interdisciplinary clinical experts. MEASUREMENTS: Panelists' assignments of each assessment item to indicate CAM features. RESULTS: From an initial pool of 119 assessment items, the panel assigned 66 items to at least one CAM feature, and many items were assigned to more than one feature. Experts achieved a high level of consensus, with a postmeeting kappa for agreement of 0.98. The study staff compiled the assignment results to create a comprehensive list of CAM feature indicators, consisting of 107 patient interview questions, cognitive tasks, and interviewer observations, with some items assigned to multiple features. A subpanel shortened this list to 28 indicators of important delirium features. CONCLUSION: A systematic, well-described qualitative methodology was used to create a list of indicators for delirium based on the features of the CAM diagnostic algorithm. This indicator list may be useful as a clinical tool for enhancing delirium recognition at the bedside and for aiding in the development of a brief delirium screening instrument.