The COVID-19 pandemic highlighted the challenges delivering face-to-face patient care across healthcare systems. In particular the COVID-19 pandemic challenged the imaging community to provide... Show moreThe COVID-19 pandemic highlighted the challenges delivering face-to-face patient care across healthcare systems. In particular the COVID-19 pandemic challenged the imaging community to provide timely access to essential diagnostic imaging modalities while ensuring appropriate safeguards were in place for both patients and personnel. With increasing vaccine availability and greater prevalence of vaccination in communities worldwide we are finally emerging on the other side of the COVID-19 pandemic. As we learned from our institutional and healthcare system responses to the pandemic, maintaining timely access to MR imaging is essential. Radiologists and other imaging providers partnered with their referring providers to ensure that timely access to advanced MR imaging was maintained. On behalf of the International Magnetic Resonance in Medicine (ISMRM) Safety Committee, this white paper is intended to serve as a guide for radiology departments, imaging centers, and other imaging specialists who perform MR imaging to refer to as we prepare for the next pandemic. Lessons learned including strategies to triage and prioritize MR imaging research during a pandemic are discussed. Level of Evidence 5 Technical Efficacy Stage 5 Show less
It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of... Show moreIt is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy. Show less
