Person:

Marcantonio, Edward

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.

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.