Comparative Diagnostic Accuracy of Nursing Delirium Screening Scale Versus Confusion Assessment Method for Postoperative Delirium: A Systematic Review and Meta-Analysis.

AIMS: To synthesise the evidence on and to compare the diagnostic accuracy of the Nu-DESC and CAM in detecting postoperative delirium among hospitalised patients. DESIGN: Systematic review and diagnostic meta-analysis. DATA SOURCES: The PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, ProQuest Dissertations and Theses A&I, and PsycINFO databases were systematically searched from their inception to February 10, 2023. RESULTS: In total, 10 (n = 1950) and seven (n = 830) reports were included for the Nu-DESC and CAM, respectively. For Nu-DESC and CAM, the pooled sensitivities were 0.69 and 0.65, respectively, while the summary specificities were 0.99 for Nu-DESC and 0.92 for CAM. The pooled specificity differed significantly between the two tools (p < 0.001), despite comparable pooled sensitivities. The duration of stay in the intensive care unit significantly moderated the summary specificity of Nu-DESC (B = -0.0003, p = 0.009). Regarding CAM, the percentage of female participants showed a positive correlation with its pooled sensitivity (B = 0.005, p = 0.02). Furthermore, studies where clinical specialists served as assessors demonstrated a higher summary sensitivity than those assessed by nurses (0.87 vs. 0.25, p = 0.01). CONCLUSION: The sensitivities of the Nu-DESC and CAM for detecting postoperative delirium did not achieve optimal levels. Therefore, developing more accurate tools to detect postoperative delirium by integrating features from related risk factors or incorporating technology-based algorithms to enhance the screening capability is warranted. REPORTING METHOD: The study has adhered to PRISMA-DTA guideline. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: The study protocol has been registered on PROSPERO (CRD42023398961).

Addressing the Health Risks of Climate Change in Older Adults.

Our climate is changing. These changes have an impact on health, especially in vulnerable populations such as older adults. Many older adults lack the physical, cognitive, social, and economic resources to avoid and/or mitigate the effects of exposure to extreme weather events. The purpose of the current article is to help nurses understand climate change and how that relates to the need for specific interventions to support climate adaptation for the older adult population. A model of exposure, contact to stressors, and adaptive capacity are used to address the health needs of older adults in the face of climate change. Gaps in nursing knowledge, resources for nurses, and a proposed agenda for research and practice in climate change are offered. Gerontological nurses are in an important position to lessen the harm of climate change in older adults through practice, research, and policy. [Journal of Gerontological Nursing, 45(11), 21-29.].

Cognitive and functional change in skilled nursing facilities: Differences by delirium and Alzheimer's disease and related dementias.

BACKGROUND: Whether cognitive and functional recovery in skilled nursing facilities (SNF) following hospitalization differs by delirium and Alzheimer's disease related dementias (ADRD) has not been examined. OBJECTIVE: To compare change in cognition and function among short-stay SNF patients with delirium, ADRD, or both. DESIGN: Retrospective cohort study using claims data from 2011 to 2013. SETTING: Centers for Medicare and Medicaid certified SNFs. PARTICIPANTS: A total of 740,838 older adults newly admitted to a short-stay SNF without prevalent ADRD who had at least two assessments of cognition and function. MEASUREMENTS: Incident delirium was measured by the Minimum Data Set (MDS) Confusion Assessment Method and ICD-9 codes, and incident ADRD by ICD-9 codes and MDS diagnoses. Cognitive improvement was a better or maximum score on the MDS Brief Interview for Mental Status, and functional recovery was a better or maximum score on the MDS Activities of Daily Living Scale. RESULTS: Within 30 days of SNF admission, the rate of cognitive improvement in patients with both delirium/ADRD was half that of patients with neither delirium/ADRD (HR = 0.45, 95% CI:0.43, 0.46). The ADRD-only and delirium-only groups also were 43% less likely to have improved cognition or function compared to those with neither delirium/ADRD (HR = 0.57, 95% CI:0.56, 0.58 and HR = 0.57, 95% CI:0.55, 0.60, respectively). Functional improvement was less likely in patients with both delirium/ADRD, as well (HR = 0.85, 95% CI:0.83, 0.87). The ADRD only and delirium only groups were also less likely to improve in function (HR = 0.93, 95% CI:0.92, 0.94 and HR = 0.92, 95% CI:0.90, 0.93, respectively) compared to those with neither delirium/ADRD. CONCLUSIONS: Among older adults without dementia admitted to SNF for post-acute care following hospitalization, a positive screen for delirium and a new diagnosis of ADRD, within 7 days of SNF admission, were both significantly associated with worse cognitive and functional recovery. Patients with both delirium and new ADRD had the worst cognitive and functional recovery.

Optimizing ChatGPT's Interpretation and Reporting of Delirium Assessment Outcomes: Exploratory Study.

BACKGROUND: Generative artificial intelligence (AI) and large language models, such as OpenAI's ChatGPT, have shown promising potential in supporting medical education and clinical decision-making, given their vast knowledge base and natural language processing capabilities. As a general purpose AI system, ChatGPT can complete a wide range of tasks, including differential diagnosis without additional training. However, the specific application of ChatGPT in learning and applying a series of specialized, context-specific tasks mimicking the workflow of a human assessor, such as administering a standardized assessment questionnaire, followed by inputting assessment results in a standardized form, and interpretating assessment results strictly following credible, published scoring criteria, have not been thoroughly studied. OBJECTIVE: This exploratory study aims to evaluate and optimize ChatGPT's capabilities in administering and interpreting the Sour Seven Questionnaire, an informant-based delirium assessment tool. Specifically, the objectives were to train ChatGPT-3.5 and ChatGPT-4 to understand and correctly apply the Sour Seven Questionnaire to clinical vignettes using prompt engineering, assess the performance of these AI models in identifying and scoring delirium symptoms against scores from human experts, and refine and enhance the models' interpretation and reporting accuracy through iterative prompt optimization. METHODS: We used prompt engineering to train ChatGPT-3.5 and ChatGPT-4 models on the Sour Seven Questionnaire, a tool for assessing delirium through caregiver input. Prompt engineering is a methodology used to enhance the AI's processing of inputs by meticulously structuring the prompts to improve accuracy and consistency in outputs. In this study, prompt engineering involved creating specific, structured commands that guided the AI models in understanding and applying the assessment tool's criteria accurately to clinical vignettes. This approach also included designing prompts to explicitly instruct the AI on how to format its responses, ensuring they were consistent with clinical documentation standards. RESULTS: Both ChatGPT models demonstrated promising proficiency in applying the Sour Seven Questionnaire to the vignettes, despite initial inconsistencies and errors. Performance notably improved through iterative prompt engineering, enhancing the models' capacity to detect delirium symptoms and assign scores. Prompt optimizations included adjusting the scoring methodology to accept only definitive "Yes" or "No" responses, revising the evaluation prompt to mandate responses in a tabular format, and guiding the models to adhere to the 2 recommended actions specified in the Sour Seven Questionnaire. CONCLUSIONS: Our findings provide preliminary evidence supporting the potential utility of AI models such as ChatGPT in administering standardized clinical assessment tools. The results highlight the significance of context-specific training and prompt engineering in harnessing the full potential of these AI models for health care applications. Despite the encouraging results, broader generalizability and further validation in real-world settings warrant additional research.

Psychoactive medication therapy and delirium screening in skilled nursing facilities.

BACKGROUND: A positive delirium screen at skilled-nursing facility (SNF) admission can trigger a simultaneous diagnosis of Alzheimer's Disease or related dementia (AD/ADRD) and lead to psychoactive medication treatment despite a lack of evidence supporting use. METHODS: This was a nationwide historical cohort study of 849,086 Medicare enrollees from 2011-2013 who were admitted to the SNF from a hospital without a history of dementia. Delirium was determined through positive Confusion Assessment Method screen and incident AD/ADRD through active diagnosis or claims. Cox proportional hazard models predicted the risk of receiving one of three psychoactive medications (i.e., antipsychotics, benzodiazepines, antiepileptics) within 7 days of SNF admission and within the entire SNF stay. RESULTS: Of 849,086 newly-admitted SNF patients (62.6% female, mean age 78), 6.1% had delirium (of which 35.4% received an incident diagnosis of AD/ADRD); 12.6% received antipsychotics, 30.4% benzodiazepines, and 5.8% antiepileptics. Within 7 days of admission, patients with delirium and incident dementia were more likely to receive an antipsychotic (relative risk [RR] 3.09; 95% confidence interval [CI] 2.99 to 3.20), or a benzodiazepine (RR 1.23; 95% CI 1.19 to 1.27) than patients without either condition. By the end of the SNF stay, patients with both delirium and incident dementia were more likely to receive an antipsychotic (RR 3.04; 95% CI 2.95 to 3.14) and benzodiazepine (RR 1.32; 95% CI 1.29 to 1.36) than patients without either condition. CONCLUSION: In this historical cohort, a positive delirium screen was associated with a higher risk of receiving psychoactive medication within 7 days of SNF admission, particularly in patients with an incident AD/ADRD diagnosis. Future research should examine strategies to reduce inappropriate psychoactive medication prescribing in older adults admitted with delirium to SNFs.

Actualizing Better Health And Health Care For Older Adults.

By 2030 more people in the United States will be older than age sixty-five than younger than age five. Our health care system is unprepared for the complexity of caring for a heterogenous population of older adults-a problem that has been magnified by the coronavirus disease 2019 (COVID-19) pandemic. Here, as part of the National Academy of Medicine's Vital Directions for Health and Health Care: Priorities for 2021 initiative, we identify six vital directions to improve the care and quality of life for all older Americans. The next administration must create an adequately prepared workforce; strengthen the role of public health; remediate disparities and inequities; develop, evaluate, and implement new approaches to care delivery; allocate resources to achieve patient-centered care and outcomes, including palliative and end-of-life care; and redesign the structure and financing of long-term services and supports. If these priorities are addressed proactively, an infrastructure can be created that promotes better health and equitable, goal-directed care that recognizes the preferences and needs of older adults.

Association of Positive Delirium Screening with Incident Dementia in Skilled Nursing Facilities.

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.

How to try this: Delirium superimposed on dementia.

Delirium is common in older adults who have dementia, but too often nurses confuse the symptoms of delirium with those of dementia and it goes unrecognized and untreated. Delirium can signal a serious underlying condition such as infection or dehydration and can increase the risk of falling and the length of hospitalization. This article presents an algorithm meant to guide nurses in the assessment and treatment of delirium superimposed on dementia. For a free online video demonstrating the use of this algorithm, go to http://links.lww.com/A211 [corrected].

The Diagnosis of Delirium Superimposed on Dementia: An Emerging Challenge.

Delirium occurring in patients with dementia is referred to as delirium superimposed on dementia (DSD). People who are older with dementia and who are institutionalized are at increased risk of developing delirium when hospitalized. In addition, their prior cognitive impairment makes detecting their delirium a challenge. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and the International Statistical Classification of Diseases and Related Health Problems, 10th Revision are considered the standard reference for the diagnosis of delirium and include criteria of impairments in cognitive processes such as attention, additional cognitive disturbances, or altered level of arousal. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and the International Statistical Classification of Diseases and Related Health Problems, 10th Revision does not provide guidance regarding specific tests for assessment of the cognitive process impaired in delirium. Importantly, the assessment or inclusion of preexisting cognitive impairment is also not addressed by these standards. The challenge of DSD gets more complex as types of dementia, particularly dementia with Lewy bodies, which has features of both delirium and dementia, are considered. The objective of this article is to critically review key elements for the diagnosis of DSD, including the challenge of neuropsychological assessment in patients with dementia and the influence of particular tests used to diagnose DSD. To address the challenges of DSD diagnosis, we present a framework for guiding the focus of future research efforts to develop a reliable reference standard to diagnose DSD. A key feature of a reliable reference standard will improve the ability to clinically diagnose DSD in facility-based patients and research studies.