Dataset Information

Identifying and Responding to Delirium in Acute Stroke: Clinical Team Members' Understandings.

ABSTRACT: Delirium is associated with increased mortality, morbidity, and length of hospital stay. In the acute stroke setting, delirium identification is challenging due to the complexity of cognitive screening in this patient group. The aim of this study was to explore how members of interprofessional stroke-unit teams identified and responded to a potential delirium in a patient. Online focus groups and interviews utilizing case vignettes were conducted with 15 participants: nurses, occupational therapists, speech and language therapists, and physiotherapists working in acute stroke services. Participants' understandings of delirium varied, most participants did not identify the symptoms of a possible hypoactive delirium, and nearly all participants discussed delirium symptoms in tentative terms. Aspects of interprofessional working were discussed through the expression of distinct roles around delirium identification. Although participants demonstrated an ethos of person-focused care, there are ongoing challenges involved in early identification and management of delirium in stroke survivors.

SUBMITTER: Carin-Levy G

PROVIDER: S-EPMC7750676 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Identifying and Responding to Delirium in Acute Stroke: Clinical Team Members' Understandings.

Carin-Levy Gail G Nicol Kath K van Wijck Frederike F Mead Gillian G McVittie Chris C

Qualitative health research 20200924 1

Delirium is associated with increased mortality, morbidity, and length of hospital stay. In the acute stroke setting, delirium identification is challenging due to the complexity of cognitive screening in this patient group. The aim of this study was to explore how members of interprofessional stroke-unit teams identified and responded to a potential delirium in a patient. Online focus groups and interviews utilizing case vignettes were conducted with 15 participants: nurses, occupational therap ...[more]

PMID: 32969764

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Project description:BackgroundEach year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes.MethodsUsing a linked electronic health record (EHR)-Medicare claims dataset, 2 neurologists and 2 psychiatrists performed a standardized review of EHR records between 2016 and 2018 for a stratified random sample of 1002 patients among 40,690 eligible subjects. Reviewers adjudicated delirium status (reference standard) during this 3-year window using a structured protocol. We calculated the probability that each patient had delirium as a function of classification algorithms based on longitudinal Medicare claims data. We compared the performance of various algorithms against the reference standard, computing calibration-in-the-large, calibration slope, and the area-under-receiver-operating-curve using 10-fold cross-validation (CV).ResultsBeneficiaries had a mean age of 75 years, were predominately female (59%), and non-Hispanic Whites (93%); a review of the EHR indicated that 6% of patients had delirium during the 3 years. Although several classification algorithms performed well, a relatively simple model containing counts of delirium-related diagnoses combined with patient age, dementia status, and receipt of antipsychotic medications had the best overall performance [CV- calibration-in-the-large <0.001, CV-slope 0.94, and CV-area under the receiver operating characteristic curve (0.88 95% confidence interval: 0.84-0.91)].ConclusionsA delirium classification model using Medicare administrative data and International Classification of Diseases, 10th Edition diagnosis codes can identify beneficiaries with delirium in large datasets.

Dataset Information

Identifying and Responding to Delirium in Acute Stroke: Clinical Team Members' Understandings.

Publications

Identifying and Responding to Delirium in Acute Stroke: Clinical Team Members' Understandings.

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