Dagliati A, Strasser ZH, Hossein Abad ZS, Klann JG, Wagholikar KB, Mesa R, Visweswaran S, Morris M, Luo Y, Henderson DW, Samayamuthu MJ, Tan BW, Verdy G, Omenn GS, Xia Z, Bellazzi R, Aaron JR, Agapito G, Albayrak A, Albi G, Alessiani M, Alloni A, Amendola DF, François Angoulvant , Anthony LL, Aronow BJ, Ashraf F, Atz A, Avillach P, Azevedo PS, Balshi J, Beaulieu-Jones BK, Bell DS, Bellasi A, Benoit V, Beraghi M, Bernal-Sobrino JL, Bernaux M, Bey R, Bhatnagar S, Blanco-Martínez A, Bonzel CL, Booth J, Bosari S, Bourgeois FT, Bradford RL, Brat GA, Bréant S, Brown NW, Bruno R, Bryant WA, Bucalo M, Bucholz E, Burgun A, Cai T, Cannataro M, Carmona A, Caucheteux C, Champ J, Chen J, Chen KY, Chiovato L, Chiudinelli L, Cho K, Cimino JJ, Colicchio TK, Cormont S, Cossin S, Craig JB, Cruz-Bermúdez JL, Cruz-Rojo J, Daniar M, Daniel C, Das P, Devkota B, Dionne A, Duan R, Dubiel J, DuVall SL, Esteve L, Estiri H, Fan S, Follett RW, Ganslandt T, Barrio NG, Garmire LX, Gehlenborg N, Getzen EJ, Geva A, Gradinger T, Gramfort A, Griffier R, Griffon N, Grisel O, Gutiérrez-Sacristán A, Han L, Hanauer DA, Haverkamp C, Hazard DY, He B, Hilka M, Ho YL, Holmes JH, Hong C, Huling KM, Hutch MR, Issitt RW, Jannot AS, Jouhet V, Kavuluru R, Keller MS, Kennedy CJ, Key DA, Kirchoff K, Kohane IS, Krantz ID, Kraska D, Krishnamurthy AK, L'Yi S, Le TT, Leblanc J, Lemaitre G, Lenert L, Leprovost D, Liu M, Will Loh NH, Long Q, Lozano-Zahonero S, Lynch KE, Mahmood S, Maidlow SE, Makoudjou A, Malovini A, Mandl KD, Mao C, Maram A, Martel P, Martins MR, Marwaha JS, Masino AJ, Mazzitelli M, Mensch A, Milano M, Minicucci MF, Moal B, Ahooyi TM, Moore JH, Moraleda C, Morris JS, Moshal KL, Mousavi S, Mowery DL, Murad DA, Murphy SN, Naughton TP, Breda Neto CT, Neuraz A, Newburger J, Ngiam KY, Njoroge WF, Norman JB, Obeid J, Okoshi MP, Olson KL, Orlova N, Ostasiewski BD, Palmer NP, Paris N, Patel LP, Pedrera-Jiménez M, Pfaff ER, Pfaff AC, Pillion D, Pizzimenti S, Prokosch HU, Prudente RA, Prunotto A, Quirós-González V, Ramoni RB, Raskin M, Rieg S, Roig-Domínguez G, Rojo P, Rubio-Mayo P, Sacchi P, Sáez C, Salamanca E, Sanchez-Pinto LN, Sandrin A, Santhanam N, Santos JC, Sanz Vidorreta FJ, Savino M, Schriver ER, Schubert P, Schuettler J, Scudeller L, Sebire NJ, Serrano-Balazote P, Serre P, Serret-Larmande A, Shah M, Silvio D, Sliz P, Son J, Sonday C, South AM, Spiridou A, Tan AL, Tan BW, Tanni SE, Taylor DM, Terriza-Torres AI, Tibollo V, Tippmann P, Toh EM, Torti C, Trecarichi EM, Tseng YJ, Vallejos AK, Varoquaux G, Vella ME, Verdy G, Vie JJ, Vitacca M, Waitman LR, Wang X, Wassermann D, Weber GM, Wolkewitz M, Wong S, Xiong X, Ye Y, Yehya N, Yuan W, Zambelli A, Zhang HG, Zo¨ller D, Zuccaro V, Zucco C (2023)
Publication Type: Journal article
Publication year: 2023
Book Volume: 64
DOI: 10.1016/j.eclinm.2023.102210
BACKGROUND: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning. METHODS: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. FINDINGS: Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively. INTERPRETATION: We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. FUNDING: Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.
APA:
Dagliati, A., Strasser, Z.H., Hossein Abad, Z.S., Klann, J.G., Wagholikar, K.B., Mesa, R.,... Zucco, C. (2023). Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study. EClinicalMedicine, 64. https://doi.org/10.1016/j.eclinm.2023.102210
MLA:
Dagliati, Arianna, et al. "Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study." EClinicalMedicine 64 (2023).
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