Browsing by Author "Lipton, Richard"

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    Chronic neuropsychiatric sequelae of SARS-CoV-2: Protocol and methods from the Alzheimer's Association Global Consortium
    (Alzheimer’s Association, 2022-09-22) de Erausquin, Gabriel A.; Snyder, Heather; Brugha, Traolach S.; Seshadri, Sudha; Carrillo, Maria; Sagar, Rajesh; Huang, Yueqin; Newton, Charles; Tartaglia, Carmela; Teunissen, Charlotte; Håkanson, Krister; Akinyemi, Rufus; Prasad, Kameshwar; D'Avossa, Giovanni; Gonzalez-Aleman, Gabriela; Hosseini, Akram; Vavougios, George D.; Sachdev, Perminder; Bankart, John; Ole Mors, Niels Peter; Lipton, Richard; Katz, Mindy; Fox, Peter T.; Katshu, Mohammad Zia; Iyengar, M. Sriram; Weinstein, Galit; Sohrabi, Hamid R.; Jenkins, Rachel; Stein, Dan J.; Hugon, Jacques; Mavreas, Venetsanos; Blangero, John; Cruchaga, Carlos; Krishna, Murali; Wadoo, Ovais; Becerra, Rodrigo; Zwir, Igor; Longstreth, William T.; Kroenenberg, Golo; Edison, Paul; Mukaetova-Ladinska, Elizabeta; Staufenberg, Ekkehart; Figueredo-Aguiar, Mariana; Yécora, Agustín; Vaca, Fabiana; Zamponi, Hernan P.; Lo Re, Vincenzina; Majid, Abdul; Sundarakumar, Jonas; Gonzalez, Hector M.; Geerlings, Mirjam I.; Skoog, Ingmar; Salmoiraghi, Alberto; Boneschi, Filippo Martinelli; Patel, Vibuthi N.; Santos, Juan M.; Arroyo, Guillermo Rivera; Moreno, Antonio Caballero; Felix, Pascal; Gallo, Carla; Arai, Hidenori; Yamada, Masahito; Iwatsubo, Takeshi; Sharma, Malveeka; Chakraborty, Nandini; Ferreccio, Catterina; Akena, Dickens; Brayne, Carol; Maestre, Gladys; Williams Blangero, Sarah; Brusco, Luis I.; Siddarth, Prabha; Hughes, Timothy M.; Ramírez Zuñiga, Alfredo; Kambeitz, Joseph; Laza, Agustin Ruiz; Allen, Norrina; Panos, Stella; Merrill, David; Ibáñez, Agustín; Tsuang, Debby; Valishvili, Nino; Shrestha, Srishti; Wang, Sophia; Padma, Vasantha; Anstey, Kaarin J.; Ravindrdanath, Vijayalakshmi; Blennow, Kaj; Mullins, Paul; Łojek, Emilia; Pria, Anand; Mosley, Thomas H.; Gowland, Penny; Girard, Timothy D.; Bowtell, Richard; Vahidy, Farhaan S.; Psychiatry, School of Medicine
    Introduction: Coronavirus disease 2019 (COVID-19) has caused >3.5 million deaths worldwide and affected >160 million people. At least twice as many have been infected but remained asymptomatic or minimally symptomatic. COVID-19 includes central nervous system manifestations mediated by inflammation and cerebrovascular, anoxic, and/or viral neurotoxicity mechanisms. More than one third of patients with COVID-19 develop neurologic problems during the acute phase of the illness, including loss of sense of smell or taste, seizures, and stroke. Damage or functional changes to the brain may result in chronic sequelae. The risk of incident cognitive and neuropsychiatric complications appears independent from the severity of the original pulmonary illness. It behooves the scientific and medical community to attempt to understand the molecular and/or systemic factors linking COVID-19 to neurologic illness, both short and long term. Methods: This article describes what is known so far in terms of links among COVID-19, the brain, neurological symptoms, and Alzheimer's disease (AD) and related dementias. We focus on risk factors and possible molecular, inflammatory, and viral mechanisms underlying neurological injury. We also provide a comprehensive description of the Alzheimer's Association Consortium on Chronic Neuropsychiatric Sequelae of SARS-CoV-2 infection (CNS SC2) harmonized methodology to address these questions using a worldwide network of researchers and institutions. Results: Successful harmonization of designs and methods was achieved through a consensus process initially fragmented by specific interest groups (epidemiology, clinical assessments, cognitive evaluation, biomarkers, and neuroimaging). Conclusions from subcommittees were presented to the whole group and discussed extensively. Presently data collection is ongoing at 19 sites in 12 countries representing Asia, Africa, the Americas, and Europe. Discussion: The Alzheimer's Association Global Consortium harmonized methodology is proposed as a model to study long-term neurocognitive sequelae of SARS-CoV-2 infection. Key points: The following review describes what is known so far in terms of molecular and epidemiological links among COVID-19, the brain, neurological symptoms, and AD and related dementias (ADRD)The primary objective of this large-scale collaboration is to clarify the pathogenesis of ADRD and to advance our understanding of the impact of a neurotropic virus on the long-term risk of cognitive decline and other CNS sequelae. No available evidence supports the notion that cognitive impairment after SARS-CoV-2 infection is a form of dementia (ADRD or otherwise). The longitudinal methodologies espoused by the consortium are intended to provide data to answer this question as clearly as possible controlling for possible confounders. Our specific hypothesis is that SARS-CoV-2 triggers ADRD-like pathology following the extended olfactory cortical network (EOCN) in older individuals with specific genetic susceptibility. The proposed harmonization strategies and flexible study designs offer the possibility to include large samples of under-represented racial and ethnic groups, creating a rich set of harmonized cohorts for future studies of the pathophysiology, determinants, long-term consequences, and trends in cognitive aging, ADRD, and vascular disease. We provide a framework for current and future studies to be carried out within the Consortium. and offers a "green paper" to the research community with a very broad, global base of support, on tools suitable for low- and middle-income countries aimed to compare and combine future longitudinal data on the topic. The Consortium proposes a combination of design and statistical methods as a means of approaching causal inference of the COVID-19 neuropsychiatric sequelae. We expect that deep phenotyping of neuropsychiatric sequelae may provide a series of candidate syndromes with phenomenological and biological characterization that can be further explored. By generating high-quality harmonized data across sites we aim to capture both descriptive and, where possible, causal associations.
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    Predicting Dementia With Routine Care EMR Data
    (Elsevier, 2020-01) Ben Miled, Zina; Haas, Kyle; Black, Christopher M.; Khandker, Rezaul Karim; Chandrasekaran, Vasu; Lipton, Richard; Boustani, Malaz A.; Electrical and Computer Engineering, School of Engineering and Technology
    Our aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia. Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source. These data embody a rich knowledge and make related medical applications easy to deploy at scale in a cost-effective manner. Specifically, the model is trained by using structured and unstructured data from three EMR data sets: diagnosis, prescriptions, and medical notes. Each of these three data sets is used to construct an individual model along with a combined model which is derived by using all three data sets. Human-interpretable data processing and ML techniques are selected in order to facilitate adoption of the proposed model by health care providers from multiple institutions. The results show that the combined model is generalizable across multiple institutions and is able to predict dementia within one year of its onset with an accuracy of nearly 80% despite the fact that it was trained using routine care data. Moreover, the analysis of the models identified important predictors for dementia. Some of these predictors (e.g., age and hypertensive disorders) are already confirmed by the literature while others, especially the ones derived from the unstructured medical notes, require further clinical analysis.