ACR Appropriateness Criteria® Dementia

A Narrative Review on Financial Challenges and Health Care Costs Associated with Traumatic Brain Injury in the United States

BACKGROUND

Traumatic brain injury (TBI) is a highly prevalent and potentially severe medical condition. Challenges regarding TBI management are related to accurate diagnostics, defining its severity, and establishing prompt interventions to affect outcomes. Among the health care components in the TBI handling strategy is intracranial pressure (ICP) monitoring, which is fundamental to therapy decisions. However, ICP monitoring is an Achilles tendon, imposing a significant financial burden on health care systems, particularly in middle and low-income communities. This article arises from the understanding from the authors that there is insufficient scientific evidence about the potential economic impacts from the use of noninvasive technologies in the monitoring of TBI. Based on personal experience, as well as from reading other, clinically focused studies, the thesis is that the use of such technologies could greatly affect the health care system and this article seeks to address this lack of literature, show ways in which such systems could be evaluated, and show estimations of possible results from these investigations.

OBJECTIVE

This review primarily investigates the economic burden of TBI and whether new technologies are suitable to reduce its health care costs without compromising the quality of care, according to the levels of evidence available. The objective is to stimulate more research and attention in the area.

METHODS

For this narrative review, a PubMed search was conducted for articles discussing TBI health care costs, as well as monitoring technologies (tomography, magnetic resonance imaging, optic nerve sheath diameter, transcranial Doppler, pupillometry, and noninvasive ICP waveform) and their application in managing TBI. Strategies were first evaluated from a medical noninferiority perspective before calculating the average savings of each selected strategy. All applicable studies were analyzed for quality using the Consolidated Health Economic Evaluation Reporting Standards 2022 Statement117 and this article was written to conform as much as possible with it.

RESULTS

The review included 109 references and showed a consistent potential in noninvasive technologies to reduce costs and maintain or improve the quality of care.

CONCLUSIONS

TBI prevalence has increased with a disproportionate health care burden in the last decades. Noninvasive monitoring techniques seem to be effective in reducing TBI health care costs, with few limitations, especially the need for more supporting scientific evidence. The undeniable clinical and financial potential of these techniques is compelling to further investigate their role in TBI management, as well as the creation of more comprehensive monitoring models to the understanding of complex phenomena occurring in the injured brain.

The use of an anterior-posterior atrophy index to distinguish Alzheimer's disease from frontotemporal disorders: an automated volumetric MRI Study

BACKGROUND

Alzheimer's disease (AD) and frontotemporal dementia (FTD) require different treatments. Since clinical presentation can be nuanced, imaging biomarkers aid in diagnosis. Automated software such as Neuroreader (NR) provides volumetric imaging data, and indices between anterior and posterior brain areas have proven useful in distinguishing dementia subtypes in research cohorts. Existing indices are complex and require further validation in clinical settings.

PURPOSE

To provide initial validation for a simplified anterior-posterior index (API) from NR in distinguishing FTD and AD in a clinical cohort.

MATERIAL AND METHODS

A retrospective chart review was completed. We derived a simplified API: API = (logVA/VP-μ)/σ where V A is weighted volume of frontal and temporal lobes and V P of parietal and occipital lobes. μ and σ are the mean and standard deviation of logVA/VP computed for AD participants. Receiver operating characteristic (ROC) curves and regression analyses assessed the efficacy of the API versus brain areas in predicting diagnosis of AD versus FTD.

RESULTS

A total of 39 participants with FTD and 78 participants with AD were included. The API had an excellent performance in distinguishing AD from FTD with an area under the ROC curve of 0.82 and a positive association with diagnostic classification on logistic regression analysis (B = 1.491, P < 0.001).

CONCLUSION

The API successfully distinguished AD and FTD with excellent performance. The results provide preliminary validation of the API in a clinical setting.

Detection rate of contrast-enhanced brain magnetic resonance imaging in patients with cognitive impairment

INTRODUCTION

The number of brain MRI with contrast media performed in patients with cognitive impairment has increased without universal agreement. We aimed to evaluate the detection rate of contrast-enhanced brain MRI in patients with cognitive impairment.

MATERIALS AND METHODS

This single-institution, retrospective study included 4,838 patients who attended outpatient clinics for cognitive impairment evaluation and underwent brain MRI with or without contrast enhancement from December 2015 to February 2020. Patients who tested positive for cognitive impairment were followed-up to confirm whether the result was true-positive and provide follow-up management. Detection rate was defined as the proportion of patients with true-positive results and was compared between groups with and without contrast enhancement. Individual matching in a 1:2 ratio according to age, sex, and year of test was performed.

RESULTS

The overall detection rates of brain MRI with and without contrast media were 4.7% (57/1,203; 95% CI: 3.6%-6.1%) and 1.8% (65/3,635; 95% CI: 1.4%-2.3%), respectively (P<0.001); individual matching demonstrated similar results (4.7% and 1.9%). Among 1,203 patients with contrast media, 3.6% was only detectable with the aid of contrast media. The proportion of patients who underwent follow-up imaging or treatment for the detected lesions were significantly higher in the group with contrast media (2.0% and 0.6%, P < .001).

CONCLUSIONS

Detection rate of brain MRI for lesions only detectable with contrast media in patients with cognitive impairment was not high enough and further study is needed to identify whom would truly benefit with contrast media.

Brain PET Imaging: Frontotemporal Dementia

Brain PET adds value in diagnosing neurodegenerative disorders, especially frontotemporal dementia (FTD) due to its syndromic presentation that overlaps with a variety of other neurodegenerative and psychiatric disorders. 18F-FDG-PET has improved sensitivity and specificity compared with structural MR imaging, with optimal diagnostic results achieved when both techniques are utilized. PET demonstrates superior sensitivity compared with SPECT for FTD diagnosis that is primarily a supplement to other imaging and clinical evaluations. Tau-PET and amyloid-PET primary use in FTD diagnosis is differentiation from Alzheimer disease, although these methods are limited mainly to research settings.

Development of a deep learning network for Alzheimer’s disease classification with evaluation of imaging modality and longitudinal data

Objective. Neuroimaging uncovers important information about disease in the brain. Yet in Alzheimer’s disease (AD), there remains a clear clinical need for reliable tools to extract diagnoses from neuroimages. Significant work has been done to develop deep learning (DL) networks using neuroimaging for AD diagnosis. However, no particular model has emerged as optimal. Due to a lack of direct comparisons and evaluations on independent data, there is no consensus on which modality is best for diagnostic models or whether longitudinal information enhances performance. The purpose of this work was (1) to develop a generalizable DL model to distinguish neuroimaging scans of AD patients from controls and (2) to evaluate the influence of imaging modality and longitudinal data on performance. Approach. We trained a 2-class convolutional neural network (CNN) with and without a cascaded recurrent neural network (RNN). We used datasets of 772 (N AD = 364, N control = 408) 3D 18F-FDG PET scans and 780 (N AD = 280, N control = 500) T1-weighted volumetric-3D MR images (containing 131 and 144 patients with multiple timepoints) from the Alzheimer’s Disease Neuroimaging Initiative, plus an independent set of 104 (N AD = 63, N NC = 41) 18F-FDG PET scans (one per patient) for validation. Main Results. ROC analysis showed that PET-trained models outperformed MRI-trained, achieving maximum AUC with the CNN + RNN model of 0.93 ± 0.08, with accuracy 82.5 ± 8.9%. Adding longitudinal information offered significant improvement to performance on 18F-FDG PET, but not on T1-MRI. CNN model validation with an independent 18F-FDG PET dataset achieved AUC of 0.99. Layer-wise relevance propagation heatmaps added CNN interpretability. Significance. The development of a high-performing tool for AD diagnosis, with the direct evaluation of key influences, reveals the advantage of using 18F-FDG PET and longitudinal data over MRI and single timepoint analysis. This has significant implications for the potential of neuroimaging for future research on AD diagnosis and clinical management of suspected AD patients.

Therapy for Alzheimer's disease: Missing targets and functional markers?

The development of the next generation therapy for Alzheimer's disease (AD) presents a huge challenge given the number of promising treatment candidates that failed in trials, despite recent advancements in understanding of genetic, pathophysiologic and clinical characteristics of the disease. This review reflects some of the most current concepts and controversies in developing disease-modifying and new symptomatic treatments. It elaborates on recent changes in the AD research strategy for broadening drug targets, and potentials of emerging non-pharmacological treatment interventions. Established and novel biomarkers are discussed, including emerging cerebrospinal fluid and plasma biomarkers reflecting tau pathology, neuroinflammation and neurodegeneration. These fluid biomarkers together with neuroimaging findings can provide innovative objective assessments of subtle changes in brain reflecting disease progression. A particular emphasis is given to neurophysiological biomarkers which are well-suited for evaluating the brain overall neural network integrity and function. Combination of multiple biomarkers, including target engagement and outcome biomarkers will empower translational studies and facilitate successful development of effective therapies.

Brain SPECT as a Biomarker of Neurodegeneration in Dementia in the Era of Molecular Imaging: Still a Valid Option?

Biomarkers are playing a progressively leading role in both clinical practice and scientific research in dementia. Although amyloid and tau biomarkers have gained ground in the clinical community in recent years, neurodegeneration biomarkers continue to play a key role due to their ability to identify different patterns of brain involvement that sign the transition between asymptomatic and symptomatic stages of the disease with high sensitivity and specificity. Both ¹⁸F-FDG positron emission tomography (PET) and perfusion single photon emission computed tomography (SPECT) have proved useful to reveal the functional alterations underlying various neurodegenerative diseases. Although the focus of nuclear neuroimaging has shifted to PET, the lower cost and wider availability of SPECT make it a still valid alternative for the study of patients with dementia. This review discusses the principles of both techniques, compares their diagnostic performance for the diagnosis of neurodegenerative diseases and highlights the role of SPECT to characterize patients from low- and middle-income countries, where special care of additional costs is particularly needed to meet the new recommendations for the diagnosis and characterization of patients with dementia.

Usefulness of Brain Positron Emission Tomography with Different Tracers in the Evaluation of Patients with Idiopathic Normal Pressure Hydrocephalous

Idiopathic normal pressure hydrocephalus (iNPH) is the only form of dementia that can be cured by surgery. Its diagnosis relies on clinical and radiological criteria. Identifying patients who can benefit from surgery is challenging, as other neurological diseases can be concomitant or mimic iNPH. We performed a systematic review on the role of positron emission tomography (PET) in iNPH. We retrieved 35 papers evaluating four main functional aspects with different PET radiotracers: (1) PET with amyloid tracers, revealing Alzheimer's disease (AD) pathology in 20-57% of suspected iNPH patients, could be useful in predictions of surgical outcome. (2) PET with radiolabeled water as perfusion tracer showed a global decreased cerebral blood flow (CBF) and regional reduction of CBF in basal ganglia in iNPH; preoperative perfusion parameters could predict surgical outcome. (3) PET with 2-Deoxy-2-[18F]fluoroglucose ([18F]FDG ) showed a global reduction of glucose metabolism without a specific cortical pattern and a hypometabolism in basal ganglia; [18F]FDG PET may identify a coexisting neurodegenerative disease, helping in patient selection for surgery; postsurgery increase in glucose metabolism was associated with clinical improvement. (4) Dopaminergic PET imaging showed a postsynaptic D2 receptor reduction and striatal upregulation of D2 receptor after treatment, associated with clinical improvement. Overall, PET imaging could be a useful tool in iNPH diagnoses and treatment response.