Amyloid PET ordering practices in a memory disorders clinic

Incremental value of amyloid PET in a tertiary memory clinic setting in China

INTRODUCTION

The objective of this study is to investigate the incremental value of amyloid positron emission tomography (Aβ-PET) in a tertiary memory clinic setting in China.

METHODS

A total of 1073 patients were offered Aβ-PET using 18F-florbetapir. The neurologists determined a suspected etiology (Alzheimer's disease [AD] or non-AD) with a percentage estimate of their confidence and medication prescription both before and after receiving the Aβ-PET results.

RESULTS

After disclosure of the Aβ-PET results, etiological diagnoses changed in 19.3% of patients, and diagnostic confidence increased from 69.3% to 85.6%. Amyloid PET results led to a change of treatment plan in 36.5% of patients. Compared to the late-onset group, the early-onset group had a more frequent change in diagnoses and a higher increase in diagnostic confidence.

DISCUSSION

Aβ-PET has significant impacts on the changes of diagnoses and management in Chinese population. Early-onset cases are more likely to benefit from Aβ-PET than late-onset cases.

HIGHLIGHTS

Amyloid PET contributes to diagnostic changes and its confidence in Chinese patients. Amyloid PET leads to a change of treatment plans in Chinese patients. Early-onset cases are more likely to benefit from amyloid PET than late-onset cases.

"And Does That Necessarily Mean Absolutely Alzheimer's?" An Analysis of Questions Raised Following Amyloid PET Results Disclosure

INTRODUCTION

Amyloid PET scans provide individuals with mild cognitive impairment (MCI) information about their risk of progressing to Alzheimer's dementia (AD). Given the wide-ranging implications of this information, best practice guidelines are needed to support researchers and clinicians disclosing these high-stakes test results. To inform the development of such guidelines, this analysis aims to describe questions and concerns raised during the disclosure of amyloid PET results in the context of MCI.

METHODS

Qualitative description was performed to analyze (n = 34) transcripts of audio-recorded amyloid PET results disclosure sessions involving MCI care dyads. The analysis focused on characterizing the frequency and nature of questions raised during an open question-and-answer (Q&A) period following the return of scan results using a standardized protocol.

RESULTS

Nearly all (n = 32/34) dyads posed questions during Q&A. Questions fell within six main categories with the most common being requests for clarification regarding AD/MCI, and next steps given the result. Questions were interspersed with comments reflecting the need for emotional support. Independently administered assessments of comprehension of results showed that, following the disclosure and Q&A, 31/32 participants with MCI and 31/31 care partners scored ≥4 on a 5-point scale. The number of questions asked by care partners during Q&A positively correlated with their level of comprehension (n = 31, Spearman's r = 0.370, p = 0.040).

DISCUSSION

This analysis highlights the value of providing opportunities for patients and their family members to ask questions upon learning patients' brain amyloid status. Disclosing clinicians should be prepared to provide clarification, resources, and support to patients and families during the return of amyloid PET results.

The Use of Event Related Potentials to Predict Amyloid PET Status Among Patients from a Memory Disorders Clinic

Background

Amyloid positron emission tomography (PET) scans provide in vivo evidence of Alzheimer's disease (AD); however, their high cost limits their use in standard clinical care. Event related potentials (ERPs) may represent an inexpensive and non-invasive additional method for detecting AD pathology.

Objective

We investigated whether ERPs, along with neuropsychological data, serve as predictors of amyloid PET status in patients with memory complaints.

Methods

Veterans aged 50-100 were recruited from a memory disorders clinic. Participants underwent a neuropsychological battery and an ERP auditory oddball protocol. Twenty-eight patients had a positive amyloid PET scan, and thirty-nine patients had a negative scan.

Results

ERP-P200 target amplitude and P200 standard latency were predictors of amyloid PET status. When submitting to ROC analysis, P200 standard latency exhibited the highest specificity and sensitivity in predicting amyloid PET positivity, correctly classifying the amyloid PET status for 86% of patients.

Conclusions

ERP-P200 measures are strong indicators of amyloid-β presence in patients from a memory disorder clinic. Increased P200 amplitude and decreased P200 latency in patients with a positive amyloid PET scan may be attributed to hyperactivation of perceptual bottom-up processes compensating for AD-related synaptic loss in the fronto-parietal networks.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.