Dataset Information

Clinicians' communication with patients receiving a MCI diagnosis: The ABIDE project.

ABSTRACT: BACKGROUND:We aimed to explore clinicians' communication, including the discussion of diagnosis, cause, prognosis and care planning, in routine post-diagnostic testing consultations with patients with Mild Cognitive Impairment (MCI). METHODS:Thematic content analysis was used to analyze audiotaped consultations in which 10 clinicians (eight neurologists and two geriatricians) from 7 memory clinics, disclosed diagnostic information to 13 MCI patients and their care partners. We assessed clinician-patient communication regarding diagnostic label, cause, prognosis and care planning to identify core findings. RESULTS:Core findings were: clinicians 1) differed in how they informed about the MCI label; 2) tentatively addressed cause of symptoms; 3) (implicitly) steered against further biomarker testing; 4) rarely informed about the patient's risk of developing dementia; 5) often informed about the expected course of symptoms emphasizing potential symptom stabilization and/or improvement, and; 6) did not engage in a conversation on long-term (care) planning. DISCUSSION:Clinicians' information provision about the underlying cause, prognosis and implications for long-term (care) planning in MCI could be more specific. Since most patients and care partners have a strong need to understand the patient's symptoms, and for information on the prognosis and implications for the future, clinicians' current approach may not match with those needs.

SUBMITTER: Visser LNC

PROVIDER: S-EPMC6974141 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Clinicians' communication with patients receiving a MCI diagnosis: The ABIDE project.

Visser Leonie N C LNC van Maurik Ingrid S IS Bouwman Femke H FH Staekenborg Salka S Vreeswijk Ralph R Hempenius Liesbeth L de Beer Marlijn H MH Roks Gerwin G Boelaarts Leo L Kleijer Mariska M van der Flier Wiesje M WM Smets Ellen M A EMA

PloS one 20200121 1

<h4>Background</h4>We aimed to explore clinicians' communication, including the discussion of diagnosis, cause, prognosis and care planning, in routine post-diagnostic testing consultations with patients with Mild Cognitive Impairment (MCI).<h4>Methods</h4>Thematic content analysis was used to analyze audiotaped consultations in which 10 clinicians (eight neurologists and two geriatricians) from 7 memory clinics, disclosed diagnostic information to 13 MCI patients and their care partners. We ass ...[more]

PMID: 31961882

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Project description:ImportanceBiomarkers do not determine conversion to Alzheimer disease (AD) perfectly, and criteria do not specify how to take patient characteristics into account. Consequently, biomarker use may be challenging for clinicians, especially in patients with mild cognitive impairment (MCI).ObjectiveTo construct biomarker-based prognostic models that enable determination of future AD dementia in patients with MCI.Design, setting, and participantsThis study is part of the Alzheimer's Biomarkers in Daily Practice (ABIDE) project. A total of 525 patients with MCI from the Amsterdam Dementia Cohort (longitudinal cohort, tertiary referral center) were studied. All patients had their baseline visit to a memory clinic from September 1, 1997, through August 31, 2014. Prognostic models were constructed by Cox proportional hazards regression with patient characteristics (age, sex, and Mini-Mental State Examination [MMSE] score), magnetic resonance imaging (MRI) biomarkers (hippocampal volume, normalized whole-brain volume), cerebrospinal fluid (CSF) biomarkers (amyloid-β1-42, tau), and combined biomarkers. Data were analyzed from November 1, 2015, to October 1, 2016.Main outcomes and measuresClinical end points were AD dementia and any type of dementia after 1 and 3 years.ResultsOf the 525 patients, 210 (40.0%) were female, and the mean (SD) age was 67.3 (8.4) years. On the basis of age, sex, and MMSE score only, the 3-year progression risk to AD dementia ranged from 26% (95% CI, 19%-34%) in younger men with MMSE scores of 29 to 76% (95% CI, 65%-84%) in older women with MMSE scores of 24 (1-year risk: 6% [95% CI, 4%-9%] to 24% [95% CI, 18%-32%]). Three- and 1-year progression risks were 86% (95% CI, 71%-95%) and 27% (95% CI, 17%-41%) when MRI results were abnormal, 82% (95% CI, 73%-89%) and 26% (95% CI, 20%-33%) when CSF test results were abnormal, and 89% (95% CI, 79%-95%) and 26% (95% CI, 18%-36%) when the results of both tests were abnormal. Conversely, 3- and 1-year progression risks were 18% (95% CI, 13%-27%) and 3% (95% CI, 2%-5%) after normal MRI results, 6% (95% CI, 3%-9%) and 1% (95% CI, 0.5%-2%) after normal CSF test results, and 4% (95% CI, 2%-7%) and 0.5% (95% CI, 0.2%-1%) after combined normal MRI and CSF test results. The prognostic value of models determining any type of dementia were in the same order of magnitude although somewhat lower. External validation in Alzheimer's Disease Neuroimaging Initiative 2 showed that our models were highly robust.Conclusions and relevanceThis study provides biomarker-based prognostic models that may help determine AD dementia and any type of dementia in patients with MCI at the individual level. This finding supports clinical decision making and application of biomarkers in daily practice.

Project description:BackgroundThe National Academy of Medicine recently identified improving clinicians' serious illness communication skills as a necessary step in improving patient and family outcomes near the end of life, but there is not an accepted set of core communication skills for engaging with surrogate decision makers.ObjectiveTo determine the core serious illness communication skills clinicians should acquire to care for incapacitated, hospitalized patients with acute, life-threatening illness, including patients with Alzheimer's disease and related dementias.MethodsFrom January 2019 to July 2020, we conducted a modified Delphi study with a panel of 79 experts in the field of serious illness communication. We developed a preliminary list of candidate communication skills through a structured literature review. We presented the candidate skills to the panelists in the context of three prototypical serious illness conversations. Over three rounds, panelists first augmented the list of candidate skills, then voted on the skills. The final set included skills deemed "very important" or "essential" by 70% of panelists. For external validation, we engaged 11 practicing clinicians and 7 community stakeholders for their perspectives on the expert-endorsed list of skills.ResultsThe panelists' ratings indicate the importance of a diverse set of communication skills related to providing clear information exchange as well as emotional and psychological support to surrogates. The final set included 33 skills, 12 of which were endorsed for all three prototypical serious illness conversations. Practicing clinicians and community stakeholders supported the expert-endorsed framework with only minor additions.ConclusionWe generated a stakeholder-endorsed list of skills that can inform the content of communication skills training programs for clinicians who care for incapacitated patients in the inpatient setting. The skills go beyond those required to provide traditional cognitive decision support and suggest the need for a paradigm shift in curricular content for communication training.

Project description:Background: Natural language processing (NLP), a form of computer-assisted data abstraction, rapidly identifies serious illness communication domains such as code-status confirmation and goals of care (GOC) discussions within free-text notes, using a codebook of phrases. Differences in the phrases associated with palliative care for patients with different types of illness are unknown. Objective: To compare communication of code-status clarification and GOC discussions between patients with advanced pancreatic cancer undergoing palliative procedures and patients admitted with life-threatening trauma. Design: Retrospective cohort study. Setting/Subjects: Patients with in-hospital admissions within two academic medical centers. Measurements: Sensitivity and specificity of NLP-identified communication domains compared with manual review. Results: Among patients with advanced pancreatic cancer (n = 523), NLP identified code-status clarification in 54% of admissions and GOC discussions in 49% of admissions. The sensitivity and specificity for code-status clarification were 94% and 99% respectively, while the sensitivity and specificity for a GOC discussion were 93% and 100%, respectively. Using the same codebook in patients with life-threatening trauma (n = 2093), NLP identified code-status clarification in 25.9% of admissions and GOC discussions in 6.3% of admissions. While NLP identification had 100% specificity, the sensitivity for code-status clarification and GOC discussion was reduced to 86% and 50%, respectively. Adding dynamic phrases such as "ongoing discussions" and phrases related to "family meetings" increased the sensitivity of the NLP codebook for code status to 98% and for GOC discussions to 100%. Conclusions: Communication of code status and GOC differ between patients with advanced cancer and those with life-threatening trauma. Recognition of these differences can aid in identification in patterns of palliative care delivery.

Dataset Information

Clinicians' communication with patients receiving a MCI diagnosis: The ABIDE project.

Publications

Clinicians' communication with patients receiving a MCI diagnosis: The ABIDE project.

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