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1.
Physiological Reports ; 10(17), 2022.
Article in English | ProQuest Central | ID: covidwho-2030378

ABSTRACT

Split ventilation (using a single ventilator to ventilate multiple patients) is technically feasible. However, connecting two patients with acute respiratory distress syndrome (ARDS) and differing lung mechanics to a single ventilator is concerning. This study aimed to: (1) determine functionality of a split ventilation system in benchtop tests, (2) determine whether standard ventilation would be superior to split ventilation in a porcine model of ARDS and (3) assess usability of a split ventilation system with minimal specific training. The functionality of a split ventilation system was assessed using test lungs. The usability of the system was assessed in simulated clinical scenarios. The feasibility of the system to provide modified lung protective ventilation was assessed in a porcine model of ARDS (n = 30). In bench testing a split ventilation system independently ventilated two test lungs under conditions of varying compliance and resistance. In usability tests, a high proportion of naïve operators could assemble and use the system. In the porcine model, modified lung protective ventilation was feasible with split ventilation and produced similar respiratory mechanics, gas exchange and biomarkers of lung injury when compared to standard ventilation. Split ventilation can provide some elements of lung protective ventilation and is feasible in bench testing and an in vivo model of ARDS.

2.
Baruch, Joaquin, Rojek, Amanda, Kartsonaki, Christiana, Vijayaraghavan, Bharath K. T.; Gonçalves, Bronner P.; Pritchard, Mark G.; Merson, Laura, Dunning, Jake, Hall, Matthew, Sigfrid, Louise, Citarella, Barbara W.; Murthy, Srinivas, Yeabah, Trokon O.; Olliaro, Piero, Abbas, Ali, Abdukahil, Sheryl Ann, Abdulkadir, Nurul Najmee, Abe, Ryuzo, Abel, Laurent, Absil, Lara, Acharya, Subhash, Acker, Andrew, Adam, Elisabeth, Adrião, Diana, Al Ageel, Saleh, Ahmed, Shakeel, Ainscough, Kate, Airlangga, Eka, Aisa, Tharwat, Hssain, Ali Ait, Tamlihat, Younes Ait, Akimoto, Takako, Akmal, Ernita, Al Qasim, Eman, Alalqam, Razi, Alberti, Angela, Al‐dabbous, Tala, Alegesan, Senthilkumar, Alegre, Cynthia, Alessi, Marta, Alex, Beatrice, Alexandre, Kévin, Al‐Fares, Abdulrahman, Alfoudri, Huda, Ali, Imran, Ali, Adam, Shah, Naseem Ali, Alidjnou, Kazali Enagnon, Aliudin, Jeffrey, Alkhafajee, Qabas, Allavena, Clotilde, Allou, Nathalie, Altaf, Aneela, Alves, João, Alves, Rita, Alves, João Melo, Amaral, Maria, Amira, Nur, Ampaw, Phoebe, Andini, Roberto, Andréjak, Claire, Angheben, Andrea, Angoulvant, François, Ansart, Séverine, Anthonidass, Sivanesen, Antonelli, Massimo, de Brito, Carlos Alexandre Antunes, Apriyana, Ardiyan, Arabi, Yaseen, Aragao, Irene, Araujo, Carolline, Arcadipane, Antonio, Archambault, Patrick, Arenz, Lukas, Arlet, Jean‐Benoît, Arora, Lovkesh, Arora, Rakesh, Artaud‐Macari, Elise, Aryal, Diptesh, Asensio, Angel, Ashraf, Muhammad, Asif, Namra, Asim, Mohammad, Assie, Jean Baptiste, Asyraf, Amirul, Atique, Anika, Attanyake, A. M. Udara Lakshan, Auchabie, Johann, Aumaitre, Hugues, Auvet, Adrien, Axelsen, Eyvind W.; Azemar, Laurène, Azoulay, Cecile, Bach, Benjamin, Bachelet, Delphine, Badr, Claudine, Bævre‐Jensen, Roar, Baig, Nadia, Baillie, J. Kenneth, Baird, J. Kevin, Bak, Erica, Bakakos, Agamemnon, Bakar, Nazreen Abu, Bal, Andriy, Balakrishnan, Mohanaprasanth, Balan, Valeria, Bani‐Sadr, Firouzé, Barbalho, Renata, Barbosa, Nicholas Yuri, Barclay, Wendy S.; Barnett, Saef Umar, Barnikel, Michaela, Barrasa, Helena, Barrelet, Audrey, Barrigoto, Cleide, Bartoli, Marie, Baruch, Joaquín, Bashir, Mustehan, Basmaci, Romain, Basri, Muhammad Fadhli Hassin, Battaglini, Denise, Bauer, Jules, Rincon, Diego Fernando Bautista, Dow, Denisse Bazan, Beane, Abigail, Bedossa, Alexandra, Bee, Ker Hong, Begum, Husna, Behilill, Sylvie, Beishuizen, Albertus, Beljantsev, Aleksandr, Bellemare, David, Beltrame, Anna, Beltrão, Beatriz Amorim, Beluze, Marine, Benech, Nicolas, Benjiman, Lionel Eric, Benkerrou, Dehbia, Bennett, Suzanne, Bento, Luís, Berdal, Jan‐Erik, Bergeaud, Delphine, Bergin, Hazel, Sobrino, José Luis Bernal, Bertoli, Giulia, Bertolino, Lorenzo, Bessis, Simon, Bevilcaqua, Sybille, Bezulier, Karine, Bhatt, Amar, Bhavsar, Krishna, Bianco, Claudia, Bidin, Farah Nadiah, Singh, Moirangthem Bikram, Humaid, Felwa Bin, Kamarudin, Mohd Nazlin Bin, Bissuel, François, Bitker, Laurent, Bitton, Jonathan, Blanco‐Schweizer, Pablo, Blier, Catherine, Bloos, Frank, Blot, Mathieu, Boccia, Filomena, Bodenes, Laetitia, Bogaarts, Alice, Bogaert, Debby, Boivin, Anne‐Hélène, Bolze, Pierre‐Adrien, Bompart, François, Bonfasius, Aurelius, Borges, Diogo, Borie, Raphaël, Bosse, Hans Martin, Botelho‐Nevers, Elisabeth, Bouadma, Lila, Bouchaud, Olivier, Bouchez, Sabelline, Bouhmani, Dounia, Bouhour, Damien, Bouiller, Kévin, Bouillet, Laurence, Bouisse, Camile, Boureau, Anne‐Sophie, Bourke, John, Bouscambert, Maude, Bousquet, Aurore, Bouziotis, Jason, Boxma, Bianca, Boyer‐Besseyre, Marielle, Boylan, Maria, Bozza, Fernando Augusto, Braconnier, Axelle, Braga, Cynthia, Brandenburger, Timo, Monteiro, Filipa Brás, Brazzi, Luca, Breen, Patrick, Breen, Dorothy, Breen, Patrick, Brickell, Kathy, Browne, Shaunagh, Browne, Alex, Brozzi, Nicolas, Brunvoll, Sonja Hjellegjerde, Brusse‐Keizer, Marjolein, Buchtele, Nina, Buesaquillo, Christian, Bugaeva, Polina, Buisson, Marielle, Buonsenso, Danilo, Burhan, Erlina, Burrell, Aidan, Bustos, Ingrid G.; Butnaru, Denis, Cabie, André, Cabral, Susana, Caceres, Eder, Cadoz, Cyril, Calligy, Kate, Calvache, Jose Andres, Camões, João, Campana, Valentine, Campbell, Paul, Campisi, Josie, Canepa, Cecilia, Cantero, Mireia, Caraux‐Paz, Pauline, Cárcel, Sheila, Cardellino, Chiara Simona, Cardoso, Sofia, Cardoso, Filipe, Cardoso, Filipa, Cardoso, Nelson, Carelli, Simone, Carlier, Nicolas, Carmoi, Thierry, Carney, Gayle, Carqueja, Inês, Carret, Marie‐Christine, Carrier, François Martin, Carroll, Ida, Carson, Gail, Casanova, Maire‐Laure, Cascão, Mariana, Casey, Siobhan, Casimiro, José, Cassandra, Bailey, Castañeda, Silvia, Castanheira, Nidyanara, Castor‐Alexandre, Guylaine, Castrillón, Henry, Castro, Ivo, Catarino, Ana, Catherine, François‐Xavier, Cattaneo, Paolo, Cavalin, Roberta, Cavalli, Giulio Giovanni, Cavayas, Alexandros, Ceccato, Adrian, Cervantes‐Gonzalez, Minerva, Chair, Anissa, Chakveatze, Catherine, Chan, Adrienne, Chand, Meera, Auger, Christelle Chantalat, Chapplain, Jean‐Marc, Chas, Julie, Chatterjee, Allegra, Chaudry, Mobin, Iñiguez, Jonathan Samuel Chávez, Chen, Anjellica, Chen, Yih‐Sharng, Cheng, Matthew Pellan, Cheret, Antoine, Chiarabini, Thibault, Chica, Julian, Chidambaram, Suresh Kumar, Tho, Leong Chin, Chirouze, Catherine, Chiumello, Davide, Cho, Sung‐Min, Cholley, Bernard, Chopin, Marie‐Charlotte, Chow, Ting Soo, Chow, Yock Ping, Chua, Jonathan, Chua, Hiu Jian, Cidade, Jose Pedro, Herreros, José Miguel Cisneros, Citarella, Barbara Wanjiru, Ciullo, Anna, Clarke, Jennifer, Clarke, Emma, Granado, Rolando Claure‐Del, Clohisey, Sara, Cobb, Perren J.; Codan, Cassidy, Cody, Caitriona, Coelho, Alexandra, Coles, Megan, Colin, Gwenhaël, Collins, Michael, Colombo, Sebastiano Maria, Combs, Pamela, Connor, Marie, Conrad, Anne, Contreras, Sofía, Conway, Elaine, Cooke, Graham S.; Copland, Mary, Cordel, Hugues, Corley, Amanda, Cornelis, Sabine, Cornet, Alexander Daniel, Corpuz, Arianne Joy, Cortegiani, Andrea, Corvaisier, Grégory, Costigan, Emma, Couffignal, Camille, Couffin‐Cadiergues, Sandrine, Courtois, Roxane, Cousse, Stéphanie, Cregan, Rachel, Croonen, Sabine, Crowl, Gloria, Crump, Jonathan, Cruz, Claudina, Bermúdez, Juan Luis Cruz, Rojo, Jaime Cruz, Csete, Marc, Cullen, Ailbhe, Cummings, Matthew, Curley, Gerard, Curlier, Elodie, Curran, Colleen, Custodio, Paula, da Silva Filipe, Ana, Da Silveira, Charlene, Dabaliz, Al‐Awwab, Dagens, Andrew, Dahl, John Arne, Dahly, Darren, Dalton, Heidi, Dalton, Jo, Daly, Seamus, Daneman, Nick, Daniel, Corinne, Dankwa, Emmanuelle A.; Dantas, Jorge, D'Aragon, Frédérick, de Loughry, Gillian, de Mendoza, Diego, De Montmollin, Etienne, de Oliveira França, Rafael Freitas, de Pinho Oliveira, Ana Isabel, De Rosa, Rosanna, De Rose, Cristina, de Silva, Thushan, de Vries, Peter, Deacon, Jillian, Dean, David, Debard, Alexa, Debray, Marie‐Pierre, DeCastro, Nathalie, Dechert, William, Deconninck, Lauren, Decours, Romain, Defous, Eve, Delacroix, Isabelle, Delaveuve, Eric, Delavigne, Karen, Delfos, Nathalie M.; Deligiannis, Ionna, Dell'Amore, Andrea, Delmas, Christelle, Delobel, Pierre, Delsing, Corine, Demonchy, Elisa, Denis, Emmanuelle, Deplanque, Dominique, Depuydt, Pieter, Desai, Mehul, Descamps, Diane, Desvallées, Mathilde, Dewayanti, Santi, Dhanger, Pathik, Diallo, Alpha, Diamantis, Sylvain, Dias, André, Diaz, Juan Jose, Diaz, Priscila, Diaz, Rodrigo, Didier, Kévin, Diehl, Jean‐Luc, Dieperink, Wim, Dimet, Jérôme, Dinot, Vincent, Diop, Fara, Diouf, Alphonsine, Dishon, Yael, Djossou, Félix, Docherty, Annemarie B.; Doherty, Helen, Dondorp, Arjen M.; Donnelly, Maria, Donnelly, Christl A.; Donohue, Sean, Donohue, Yoann, Donohue, Chloe, Doran, Peter, Dorival, Céline, D'Ortenzio, Eric, Douglas, James Joshua, Douma, Renee, Dournon, Nathalie, Downer, Triona, Downey, Joanne, Downing, Mark, Drake, Tom, Driscoll, Aoife, Dryden, Murray, Fonseca, Claudio Duarte, Dubee, Vincent, Dubos, François, Ducancelle, Alexandre, Duculan, Toni, Dudman, Susanne, Duggal, Abhijit, Dunand, Paul, Dunning, Jake, Duplaix, Mathilde, Durante‐Mangoni, Emanuele, Durham, Lucian, Dussol, Bertrand, Duthoit, Juliette, Duval, Xavier, Dyrhol‐Riise, Anne Margarita, Ean, Sim Choon, Echeverria‐Villalobos, Marco, Egan, Siobhan, Eggesbø, Linn Margrete, Eira, Carla, El Sanharawi, Mohammed, Elapavaluru, Subbarao, Elharrar, Brigitte, Ellerbroek, Jacobien, Ellingjord‐Dale, Merete, Eloy, Philippine, Elshazly, Tarek, Elyazar, Iqbal, Enderle, Isabelle, Endo, Tomoyuki, Eng, Chan Chee, Engelmann, Ilka, Enouf, Vincent, Epaulard, Olivier, Escher, Martina, Esperatti, Mariano, Esperou, Hélène, Esposito‐Farese, Marina, Estevão, João, Etienne, Manuel, Ettalhaoui, Nadia, Everding, Anna Greti, Evers, Mirjam, Fabre, Marc, Fabre, Isabelle, Faheem, Amna, Fahy, Arabella, Fairfield, Cameron J.; Fakar, Zul, Fareed, Komal, Faria, Pedro, Farooq, Ahmed, Fateena, Hanan, Fatoni, Arie Zainul, Faure, Karine, Favory, Raphaël, Fayed, Mohamed, Feely, Niamh, Feeney, Laura, Fernandes, Jorge, Fernandes, Marília Andreia, Fernandes, Susana, Ferrand, François‐Xavier, Devouge, Eglantine Ferrand, Ferrão, Joana, Ferraz, Mário, Ferreira, Sílvia, Ferreira, Isabel, Ferreira, Benigno, Ferrer‐Roca, Ricard, Ferriere, Nicolas, Ficko, Céline, Figueiredo‐Mello, Claudia, Finlayson, William, Fiorda, Juan, Flament, Thomas, Flateau, Clara, Fletcher, Tom, Florio, Letizia Lucia, Flynn, Deirdre, Foley, Claire, Foley, Jean, Fomin, Victor, Fonseca, Tatiana, Fontela, Patricia, Forsyth, Simon, Foster, Denise, Foti, Giuseppe, Fourn, Erwan, Fowler, Robert A.; Fraher, Marianne, Franch‐Llasat, Diego, Fraser, John F.; Fraser, Christophe, Freire, Marcela Vieira, Ribeiro, Ana Freitas, Friedrich, Caren, Fry, Stéphanie, Fuentes, Nora, Fukuda, Masahiro, Argin, G.; Gaborieau, Valérie, Gaci, Rostane, Gagliardi, Massimo, Gagnard, Jean‐Charles, Gagneux‐Brunon, Amandine, Gaião, Sérgio, Skeie, Linda Gail, Gallagher, Phil, Gamble, Carrol, Gani, Yasmin, Garan, Arthur, Garcia, Rebekha, Barrio, Noelia García, Garcia‐Diaz, Julia, Garcia‐Gallo, Esteban, Garimella, Navya, Garot, Denis, Garrait, Valérie, Gauli, Basanta, Gault, Nathalie, Gavin, Aisling, Gavrylov, Anatoliy, Gaymard, Alexandre, Gebauer, Johannes, Geraud, Eva, Morlaes, Louis Gerbaud, Germano, Nuno, Ghisulal, Praveen Kumar, Ghosn, Jade, Giani, Marco, Gibson, Jess, Gigante, Tristan, Gilg, Morgane, Gilroy, Elaine, Giordano, Guillermo, Girvan, Michelle, Gissot, Valérie, Glikman, Daniel, Glybochko, Petr, Gnall, Eric, Goco, Geraldine, Goehringer, François, Goepel, Siri, Goffard, Jean‐Christophe, Goh, Jin Yi, Golob, Jonathan, Gomez, Kyle, Gómez‐Junyent, Joan, Gominet, Marie, Gonçalves, Bronner P.; Gonzalez, Alicia, Gordon, Patricia, Gorenne, Isabelle, Goubert, Laure, Goujard, Cécile, Goulenok, Tiphaine, Grable, Margarite, Graf, Jeronimo, Grandin, Edward Wilson, Granier, Pascal, Grasselli, Giacomo, Green, Christopher A.; Greene, Courtney, Greenhalf, William, Greffe, Segolène, Grieco, Domenico Luca, Griffee, Matthew, Griffiths, Fiona, Grigoras, Ioana, Groenendijk, Albert, Lordemann, Anja Grosse, Gruner, Heidi, Gu, Yusing, Guedj, Jérémie, Guego, Martin, Guellec, Dewi, Guerguerian, Anne‐Marie, Guerreiro, Daniela, Guery, Romain, Guillaumot, Anne, Guilleminault, Laurent, Guimarães de Castro, Maisa, Guimard, Thomas, Haalboom, Marieke, Haber, Daniel, Habraken, Hannah, Hachemi, Ali, Hackmann, Amy, Hadri, Nadir, Haidri, Fakhir, Hakak, Sheeba, Hall, Adam, Hall, Matthew, Halpin, Sophie, Hameed, Jawad, Hamer, Ansley, Hamers, Raph L.; Hamidfar, Rebecca, Hammarström, Bato, Hammond, Terese, Han, Lim Yuen, Haniffa, Rashan, Hao, Kok Wei, Hardwick, Hayley, Harrison, Ewen M.; Harrison, Janet, Harrison, Samuel Bernard Ekow, Hartman, Alan, Hasan, Mohd Shahnaz, Hashmi, Junaid, Hayat, Muhammad, Hayes, Ailbhe, Hays, Leanne, Heerman, Jan, Heggelund, Lars, Hendry, Ross, Hennessy, Martina, Henriquez‐Trujillo, Aquiles, Hentzien, Maxime, Hernandez‐Montfort, Jaime, Hershey, Andrew, Hesstvedt, Liv, Hidayah, Astarini, Higgins, Eibhilin, Higgins, Dawn, Higgins, Rupert, Hinchion, Rita, Hinton, Samuel, Hiraiwa, Hiroaki, Hirkani, Haider, Hitoto, Hikombo, Ho, Yi Bin, Ho, Antonia, Hoctin, Alexandre, Hoffmann, Isabelle, Hoh, Wei Han, Hoiting, Oscar, Holt, Rebecca, Holter, Jan Cato, Horby, Peter, Horcajada, Juan Pablo, Hoshino, Koji, Houas, Ikram, Hough, Catherine L.; Houltham, Stuart, Hsu, Jimmy Ming‐Yang, Hulot, Jean‐Sébastien, Huo, Stella, Hurd, Abby, Hussain, Iqbal, Ijaz, Samreen, Illes, Hajnal‐Gabriela, Imbert, Patrick, Imran, Mohammad, Sikander, Rana Imran, Imtiaz, Aftab, Inácio, Hugo, Dominguez, Carmen Infante, Ing, Yun Sii, Iosifidis, Elias, Ippolito, Mariachiara, Isgett, Sarah, Isidoro, Tiago, Ismail, Nadiah, Isnard, Margaux, Istre, Mette Stausland, Itai, Junji, Ivulich, Daniel, Jaafar, Danielle, Jaafoura, Salma, Jabot, Julien, Jackson, Clare, Jamieson, Nina, Jaquet, Pierre, Jaud‐Fischer, Coline, Jaureguiberry, Stéphane, Jaworsky, Denise, Jego, Florence, Jelani, Anilawati Mat, Jenum, Synne, Jimbo‐Sotomayor, Ruth, Joe, Ong Yiaw, Jorge García, Ruth N.; Jørgensen, Silje Bakken, Joseph, Cédric, Joseph, Mark, Joshi, Swosti, Jourdain, Mercé, Jouvet, Philippe, Jung, Hanna, Jung, Anna, Juzar, Dafsah, Kafif, Ouifiya, Kaguelidou, Florentia, Kaisbain, Neerusha, Kaleesvran, Thavamany, Kali, Sabina, Kalicinska, Alina, Kalleberg, Karl Trygve, Kalomoiri, Smaragdi, Kamaluddin, Muhammad Aisar Ayadi, Kamaruddin, Zul Amali Che, Kamarudin, Nadiah, Kamineni, Kavita, Kandamby, Darshana Hewa, Kandel, Chris, Kang, Kong Yeow, Kanwal, Darakhshan, Karpayah, Pratap, Kartsonaki, Christiana, Kasugai, Daisuke, Kataria, Anant, Katz, Kevin, Kaur, Aasmine, Kay, Christy, Keane, Hannah, Keating, Seán, Kedia, Pulak, Kelly, Claire, Kelly, Yvelynne, Kelly, Andrea, Kelly, Niamh, Kelly, Aoife, Kelly, Sadie, Kelsey, Maeve, Kennedy, Ryan, Kennon, Kalynn, Kernan, Maeve, Kerroumi, Younes, Keshav, Sharma, Khalid, Imrana, Khalid, Osama, Khalil, Antoine, Khan, Coralie, Khan, Irfan, Khan, Quratul Ain, Khanal, Sushil, Khatak, Abid, Khawaja, Amin, Kherajani, Krish, Kho, Michelle E.; Khoo, Ryan, Khoo, Denisa, Khoo, Saye, Khoso, Nasir, Kiat, Khor How, Kida, Yuri, Kiiza, Peter, Granerud, Beathe Kiland, Kildal, Anders Benjamin, Kim, Jae Burm, Kimmoun, Antoine, Kindgen‐Milles, Detlef, King, Alexander, Kitamura, Nobuya, Kjetland, Eyrun Floerecke Kjetland, Klenerman, Paul, Klont, Rob, Bekken, Gry Kloumann, Knight, Stephen R.; Kobbe, Robin, Kodippily, Chamira, Vasconcelos, Malte Kohns, Koirala, Sabin, Komatsu, Mamoru, Kosgei, Caroline, Kpangon, Arsène, Krawczyk, Karolina, Krishnan, Vinothini, Krishnan, Sudhir, Kruglova, Oksana, Kumar, Ganesh, Kumar, Deepali, Kumar, Mukesh, Vecham, Pavan Kumar, Kuriakose, Dinesh, Kurtzman, Ethan, Kutsogiannis, Demetrios, Kutsyna, Galyna, Kyriakoulis, Konstantinos, Lachatre, Marie, Lacoste, Marie, Laffey, John G.; Lagrange, Marie, Laine, Fabrice, Lairez, Olivier, Lakhey, Sanjay, Lalueza, Antonio, Lambert, Marc, Lamontagne, François, Langelot‐Richard, Marie, Langlois, Vincent, Lantang, Eka Yudha, Lanza, Marina, Laouénan, Cédric, Laribi, Samira, Lariviere, Delphine, Lasry, Stéphane, Lath, Sakshi, Latif, Naveed, Launay, Odile, Laureillard, Didier, Lavie‐Badie, Yoan, Law, Andy, Lawrence, Teresa, Lawrence, Cassie, Le, Minh, Le Bihan, Clément, Le Bris, Cyril, Le Falher, Georges, Le Fevre, Lucie, Le Hingrat, Quentin, Le Maréchal, Marion, Le Mestre, Soizic, Le Moal, Gwenaël, Le Moing, Vincent, Le Nagard, Hervé, Le Turnier, Paul, Leal, Ema, Santos, Marta Leal, Lee, Heng Gee, Lee, Biing Horng, Lee, Yi Lin, Lee, Todd C.; Lee, James, Lee, Jennifer, Lee, Su Hwan, Leeming, Gary, Lefebvre, Laurent, Lefebvre, Bénédicte, Lefèvre, Benjamin, LeGac, Sylvie, Lelievre, Jean‐Daniel, Lellouche, François, Lemaignen, Adrien, Lemee, Véronique, Lemeur, Anthony, Lemmink, Gretchen, Lene, Ha Sha, Lennon, Jenny, León, Rafael, Leone, Marc, Leone, Michela, Lepiller, Quentin, Lescure, François‐Xavier, Lesens, Olivier, Lesouhaitier, Mathieu, Lester‐Grant, Amy, Levy, Yves, Levy, Bruno, Levy‐Marchal, Claire, Lewandowska, Katarzyna, L'Her, Erwan, Bassi, Gianluigi Li, Liang, Janet, Liaquat, Ali, Liegeon, Geoffrey, Lim, Kah Chuan, Lim, Wei Shen, Lima, Chantre, Lina, Lim, Lina, Bruno, Lind, Andreas, Lingad, Maja Katherine, Lingas, Guillaume, Lion‐Daolio, Sylvie, Lissauer, Samantha, Liu, Keibun, Livrozet, Marine, Lizotte, Patricia, Loforte, Antonio, Lolong, Navy, Loon, Leong Chee, Lopes, Diogo, Lopez‐Colon, Dalia, Lopez‐Revilla, Jose W.; Loschner, Anthony L.; Loubet, Paul, Loufti, Bouchra, Louis, Guillame, Lourenco, Silvia, Lovelace‐Macon, Lara, Low, Lee Lee, Lowik, Marije, Loy, Jia Shyi, Lucet, Jean Christophe, Bermejo, Carlos Lumbreras, Luna, Carlos M.; 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McElroy, Aine, McElwee, Samuel, McEneany, Victoria, McGeer, Allison, McKay, Chris, McKeown, Johnny, McLean, Kenneth A.; McNally, Paul, McNicholas, Bairbre, McPartlan, Elaine, Meaney, Edel, Mear‐Passard, Cécile, Mechlin, Maggie, Meher, Maqsood, Mehkri, Omar, Mele, Ferruccio, Melo, Luis, Memon, Kashif, Mendes, Joao Joao, Menkiti, Ogechukwu, Menon, Kusum, Mentré, France, Mentzer, Alexander J.; Mercier, Noémie, Mercier, Emmanuelle, Merckx, Antoine, Mergeay‐Fabre, Mayka, Mergler, Blake, Merson, Laura, Mesquita, António, Meta, Roberta, Metwally, Osama, Meybeck, Agnès, Meyer, Dan, Meynert, Alison M.; Meysonnier, Vanina, Meziane, Amina, Mezidi, Mehdi, Michelanglei, Céline, Michelet, Isabelle, Mihelis, Efstathia, Mihnovit, Vladislav, Miranda‐Maldonado, Hugo, Misnan, Nor Arisah, Mohamed, Tahira Jamal, Mohamed, Nik Nur Eliza, Moin, Asma, Molina, David, Molinos, Elena, Molloy, Brenda, Mone, Mary, Monteiro, Agostinho, Montes, Claudia, Montrucchio, Giorgia, Moore, Shona C.; Moore, Sarah, Cely, Lina Morales, Moro, Lucia, Morton, Ben, Motherway, Catherine, Motos, Ana, Mouquet, Hugo, Perrot, Clara Mouton, Moyet, Julien, Mudara, Caroline, Mufti, Aisha Kalsoom, Muh, Ng Yong, Muhamad, Dzawani, Mullaert, Jimmy, Müller, Fredrik, Müller, Karl Erik, Munblit, Daniel, Muneeb, Syed, Munir, Nadeem, Munshi, Laveena, Murphy, Aisling, Murphy, Lorna, Murphy, Aisling, Murris, Marlène, Murthy, Srinivas, Musaab, Himed, Muvindi, Himasha, Muyandy, Gugapriyaa, Myrodia, Dimitra Melia, Mohd‐Hanafiah, Farah Nadia, Nagpal, Dave, Nagrebetsky, Alex, Narasimhan, Mangala, Narayanan, Nageswaran, Khan, Rashid Nasim, Nazerali‐Maitland, Alasdair, Neant, Nadège, Neb, Holger, Nekliudov, Nikita, Nelwan, Erni, Neto, Raul, Neumann, Emily, Ng, Pauline Yeung, Ng, Wing Yiu, Nghi, Anthony, Nguyen, Duc, Choileain, Orna Ni, Leathlobhair, Niamh Ni, Nichol, Alistair, Nitayavardhana, Prompak, Nonas, Stephanie, Noordin, Nurul Amani Mohd, Noret, Marion, Norharizam, Nurul Faten Izzati, Norman, Lisa, Notari, Alessandra, Noursadeghi, Mahdad, Nowicka, Karolina, Nowinski, Adam, Nseir, Saad, Nunez, Jose I.; Nurnaningsih, Nurnaningsih, Nusantara, Dwi Utomo, Nyamankolly, Elsa, Nygaard, Anders Benteson, Brien, Fionnuala O.; Callaghan, Annmarie O.; O'Callaghan, Annmarie, Occhipinti, Giovanna, Oconnor, Derbrenn, O'Donnell, Max, Ogston, Tawnya, Ogura, Takayuki, Oh, Tak‐Hyuk, O'Halloran, Sophie, O'Hearn, Katie, Ohshimo, Shinichiro, Oldakowska, Agnieszka, Oliveira, João, Oliveira, Larissa, Olliaro, Piero L.; Ong, Jee Yan, Ong, David S. Y.; Oosthuyzen, Wilna, Opavsky, Anne, Openshaw, Peter, Orakzai, Saijad, Orozco‐Chamorro, Claudia Milena, Ortoleva, Jamel, Osatnik, Javier, O'Shea, Linda, O'Sullivan, Miriam, Othman, Siti Zubaidah, Ouamara, Nadia, Ouissa, Rachida, Oziol, Eric, Pagadoy, Maïder, Pages, Justine, Palacios, Mario, Palacios, Amanda, Palmarini, Massimo, Panarello, Giovanna, Panda, Prasan Kumar, Paneru, Hem, Pang, Lai Hui, Panigada, Mauro, Pansu, Nathalie, Papadopoulos, Aurélie, Parke, Rachael, Parker, Melissa, Parra, Briseida, Pasha, Taha, Pasquier, Jérémie, Pastene, Bruno, Patauner, Fabian, Patel, Drashti, Pathmanathan, Mohan Dass, Patrão, Luís, Patricio, Patricia, Patrier, Juliette, Patterson, Lisa, Pattnaik, Rajyabardhan, Paul, Mical, Paul, Christelle, Paulos, Jorge, Paxton, William A.; Payen, Jean‐François, Peariasamy, Kalaiarasu, Jiménez, Miguel Pedrera, Peek, Giles J.; Peelman, Florent, Peiffer‐Smadja, Nathan, Peigne, Vincent, Pejkovska, Mare, Pelosi, Paolo, Peltan, Ithan D.; Pereira, Rui, Perez, Daniel, Periel, Luis, Perpoint, Thomas, Pesenti, Antonio, Pestre, Vincent, Petrou, Lenka, Petrovic, Michele, Petrov‐Sanchez, Ventzislava, Pettersen, Frank Olav, Peytavin, Gilles, Pharand, Scott, Picard, Walter, Picone, Olivier, de Piero, Maria, Pierobon, Carola, Piersma, Djura, Pimentel, Carlos, Pinto, Raquel, Pires, Catarina, Pironneau, Isabelle, Piroth, Lionel, Pitaloka, Ayodhia, Pius, Riinu, Plantier, Laurent, Png, Hon Shen, Poissy, Julien, Pokeerbux, Ryadh, Pokorska‐Spiewak, Maria, Poli, Sergio, Pollakis, Georgios, Ponscarme, Diane, Popielska, Jolanta, Porto, Diego Bastos, Post, Andra‐Maris, Postma, Douwe F.; Povoa, Pedro, Póvoas, Diana, Powis, Jeff, Prapa, Sofia, Preau, Sébastien, Prebensen, Christian, Preiser, Jean‐Charles, Prinssen, Anton, Pritchard, Mark G.; Priyadarshani, Gamage Dona Dilanthi, Proença, Lucia, Pudota, Sravya, Puéchal, Oriane, Semedi, Bambang Pujo, Pulicken, Mathew, Purcell, Gregory, Quesada, Luisa, Quinones‐Cardona, Vilmaris, González, Víctor Quirós, Quist‐Paulsen, Else, Quraishi, Mohammed, Rabaa, Maia, Rabaud, Christian, Rabindrarajan, Ebenezer, Rafael, Aldo, Rafiq, Marie, Rahardjani, Mutia, Rahman, Rozanah Abd, Rahman, Ahmad Kashfi Haji Ab, Rahutullah, Arsalan, Rainieri, Fernando, Rajahram, Giri Shan, Ramachandran, Pratheema, Ramakrishnan, Nagarajan, Ramli, Ahmad Afiq, Rammaert, Blandine, Ramos, Grazielle Viana, Rana, Asim, Rangappa, Rajavardhan, Ranjan, Ritika, Rapp, Christophe, Rashan, Aasiyah, Rashan, Thalha, Rasheed, Ghulam, Rasmin, Menaldi, Rätsep, Indrek, Rau, Cornelius, Ravi, Tharmini, Raza, Ali, Real, Andre, Rebaudet, Stanislas, Redl, Sarah, Reeve, Brenda, Rehman, Attaur, Reid, Liadain, Reikvam, Dag Henrik, Reis, Renato, Rello, Jordi, Remppis, Jonathan, Remy, Martine, Ren, Hongru, Renk, Hanna, Resseguier, Anne‐Sophie, Revest, Matthieu, Rewa, Oleksa, Reyes, Luis Felipe, Reyes, Tiago, Ribeiro, Maria Ines, Ricchiuto, Antonia, Richardson, David, Richardson, Denise, Richier, Laurent, Ridzuan, Siti Nurul Atikah Ahmad, Riera, Jordi, Rios, Ana L.; Rishu, Asgar, Rispal, Patrick, Risso, Karine, Nuñez, Maria Angelica Rivera, Rizer, Nicholas, Robba, Chiara, Roberto, André, Roberts, Stephanie, Robertson, David L.; Robineau, Olivier, Roche‐Campo, Ferran, Rodari, Paola, Rodeia, Simão, Abreu, Julia Rodriguez, Roessler, Bernhard, Roger, Pierre‐Marie, Roger, Claire, Roilides, Emmanuel, Rojek, Amanda, Romaru, Juliette, Roncon‐Albuquerque, Roberto, Roriz, Mélanie, Rosa‐Calatrava, Manuel, Rose, Michael, Rosenberger, Dorothea, Roslan, Nurul Hidayah Mohammad, Rossanese, Andrea, Rossetti, Matteo, Rossignol, Bénédicte, Rossignol, Patrick, Rousset, Stella, Roy, Carine, Roze, Benoît, Rusmawatiningtyas, Desy, Russell, Clark D.; Ryan, Maria, Ryan, Maeve, Ryckaert, Steffi, Holten, Aleksander Rygh, Saba, Isabela, Sadaf, Sairah, Sadat, Musharaf, Sahraei, Valla, Saint‐Gilles, Maximilien, Sakiyalak, Pranya, Salahuddin, Nawal, Salazar, Leonardo, Saleem, Jodat, Sales, Gabriele, Sallaberry, Stéphane, Salmon Gandonniere, Charlotte, Salvator, Hélène, Sanchez, Olivier, Sanchez‐Miralles, Angel, Sancho‐Shimizu, Vanessa, Sandhu, Gyan, Sandhu, Zulfiqar, Sandrine, Pierre‐François, Sandulescu, Oana, Santos, Marlene, Sarfo‐Mensah, Shirley, Banheiro, Bruno Sarmento, Sarmiento, Iam Claire E.; Sarton, Benjamine, Satya, Ankana, Satyapriya, Sree, Satyawati, Rumaisah, Saviciute, Egle, Savvidou, Parthena, Saw, Yen Tsen, Schaffer, Justin, Schermer, Tjard, Scherpereel, Arnaud, Schneider, Marion, Schroll, Stephan, Schwameis, Michael, Schwartz, Gary, Scott, Janet T.; Scott‐Brown, James, Sedillot, Nicholas, Seitz, Tamara, Selvanayagam, Jaganathan, Selvarajoo, Mageswari, Semaille, Caroline, Semple, Malcolm G.; Senian, Rasidah Bt, Senneville, Eric, Sequeira, Filipa, Sequeira, Tânia, Neto, Ary Serpa, Balazote, Pablo Serrano, Shadowitz, Ellen, Shahidan, Syamin Asyraf, Shamsah, Mohammad, Shankar, Anuraj, Sharjeel, Shaikh, Sharma, Pratima, Shaw, Catherine A.; Shaw, Victoria, Sheharyar, Ashraf, Shetty, Rohan, Shetty, Rajesh Mohan, Shi, Haixia, Shiekh, Mohiuddin, Shime, Nobuaki, Shimizu, Keiki, Shrapnel, Sally, Shrestha, Pramesh Sundar, Shrestha, Shubha Kalyan, Shum, Hoi Ping, Mohammed, Nassima Si, Siang, Ng Yong, Sibiude, Jeanne, Siddiqui, Atif, Sigfrid, Louise, Sillaots, Piret, Silva, Catarina, Silva, Rogério, Silva, Maria Joao, Heng, Benedict Sim Lim, Sin, Wai Ching, Sinatti, Dario, Singh, Punam, Singh, Budha Charan, Sitompul, Pompini Agustina, Sivam, Karisha, Skogen, Vegard, Smith, Sue, Smood, Benjamin, Smyth, Coilin, Smyth, Michelle, Snacken, Morgane, So, Dominic, Soh, Tze Vee, Solberg, Lene Bergendal, Solomon, Joshua, Solomon, Tom, Somers, Emily, Sommet, Agnès, Song, Rima, Song, Myung Jin, Song, Tae, Chia, Jack Song, Sonntagbauer, Michael, Soom, Azlan Mat, Søraas, Arne, Søraas, Camilla Lund, Sotto, Alberto, Soum, Edouard, Sousa, Marta, Sousa, Ana Chora, Uva, Maria Sousa, Souza‐Dantas, Vicente, Sperry, Alexandra, Spinuzza, Elisabetta, Darshana, B. P. Sanka Ruwan Sri, Sriskandan, Shiranee, Stabler, Sarah, Staudinger, Thomas, Stecher, Stephanie‐Susanne, Steinsvik, Trude, Stienstra, Ymkje, Stiksrud, Birgitte, Stolz, Eva, Stone, Amy, Streinu‐Cercel, Adrian, Streinu‐Cercel, Anca, Stuart, David, Stuart, Ami, Subekti, Decy, Suen, Gabriel, Suen, Jacky Y.; Sultana, Asfia, Summers, Charlotte, Supic, Dubravka, Suppiah, Deepashankari, Surovcová, Magdalena, Suwarti, Suwarti, Svistunov, Andrey, Syahrin, Sarah, Syrigos, Konstantinos, Sztajnbok, Jaques, Szuldrzynski, Konstanty, Tabrizi, Shirin, Taccone, Fabio S.; Tagherset, Lysa, Taib, Shahdattul Mawarni, Talarek, Ewa, Taleb, Sara, Talsma, Jelmer, Tamisier, Renaud, Tampubolon, Maria Lawrensia, Tan, Kim Keat, Tan, Yan Chyi, Tanaka, Taku, Tanaka, Hiroyuki, Taniguchi, Hayato, Taqdees, Huda, Taqi, Arshad, Tardivon, Coralie, Tattevin, Pierre, Taufik, M. Azhari, Tawfik, Hassan, Tedder, Richard S.; Tee, Tze Yuan, Teixeira, João, Tejada, Sofia, Tellier, Marie‐Capucine, Teoh, Sze Kye, Teotonio, Vanessa, Téoulé, François, Terpstra, Pleun, Terrier, Olivier, Terzi, Nicolas, Tessier‐Grenier, Hubert, Tey, Adrian, Thabit, Alif Adlan Mohd, Thakur, Anand, Tham, Zhang Duan, Thangavelu, Suvintheran, Thibault, Vincent, Thiberville, Simon‐Djamel, Thill, Benoît, Thirumanickam, Jananee, Thompson, Shaun, Thomson, Emma C.; Thurai, Surain Raaj Thanga, Thwaites, Ryan S.; Tierney, Paul, Tieroshyn, Vadim, Timashev, Peter S.; Timsit, Jean‐François, Vijayaraghavan, Bharath Kumar Tirupakuzhi, Tissot, Noémie, Toh, Jordan Zhien Yang, Toki, Maria, Tonby, Kristian, Tonnii, Sia Loong, Torres, Margarida, Torres, Antoni, Santos‐Olmo, Rosario Maria Torres, Torres‐Zevallos, Hernando, Towers, Michael, Trapani, Tony, Treoux, Théo, Tromeur, Cécile, Trontzas, Ioannis, Trouillon, Tiffany, Truong, Jeanne, Tual, Christelle, Tubiana, Sarah, Tuite, Helen, Turmel, Jean‐Marie, Turtle, Lance C. W.; Tveita, Anders, Twardowski, Pawel, Uchiyama, Makoto, Udayanga, P. G. Ishara, Udy, Andrew, Ullrich, Roman, Uribe, Alberto, Usman, Asad.
Influenza and Other Respiratory Viruses ; 2022.
Article in English | Web of Science | ID: covidwho-2019369

ABSTRACT

Introduction: Case definitions are used to guide clinical practice, surveillance and research protocols. However, how they identify COVID-19-hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory-confirmed COVID-19, in the ISARIC prospective cohort study database, meeting widely used case definitions. Methods: Patients were assessed using the Centers for Disease Control (CDC), European Centre for Disease Prevention and Control (ECDC), World Health Organization (WHO) and UK Health Security Agency (UKHSA) case definitions by age, region and time. Case fatality ratios (CFRs) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non-laboratory-confirmed test result were excluded. Results: A total of 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe arid Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC) and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years;geographical and time variations were also observed. Estimated CFRs were similar for the patients who met the case definitions. However, when more patients did riot meet the case definition, the CFR increased. Conclusions: The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions, risking delayed medical care. While epidemiologists must balance their analytics with field applicability, ongoing revision of case definitions is necessary to improve patient care through early diagnosis and limit potential nosocomial spread.

3.
Semin Respir Crit Care Med ; 43(3): 335-345, 2022 06.
Article in English | MEDLINE | ID: covidwho-2004821

ABSTRACT

Computer simulation offers a fresh approach to traditional medical research that is particularly well suited to investigating issues related to mechanical ventilation. Patients receiving mechanical ventilation are routinely monitored in great detail, providing extensive high-quality data-streams for model design and configuration. Models based on such data can incorporate very complex system dynamics that can be validated against patient responses for use as investigational surrogates. Crucially, simulation offers the potential to "look inside" the patient, allowing unimpeded access to all variables of interest. In contrast to trials on both animal models and human patients, in silico models are completely configurable and reproducible; for example, different ventilator settings can be applied to an identical virtual patient, or the same settings applied to different patients, to understand their mode of action and quantitatively compare their effectiveness. Here, we review progress on the mathematical modeling and computer simulation of human anatomy, physiology, and pathophysiology in the context of mechanical ventilation, with an emphasis on the clinical applications of this approach in various disease states. We present new results highlighting the link between model complexity and predictive capability, using data on the responses of individual patients with acute respiratory distress syndrome to changes in multiple ventilator settings. The current limitations and potential of in silico modeling are discussed from a clinical perspective, and future challenges and research directions highlighted.


Subject(s)
Respiration, Artificial , Respiratory Distress Syndrome , Computer Simulation , Humans , Respiration, Artificial/methods , Respiratory Distress Syndrome/therapy , Ventilators, Mechanical
4.
Semin Respir Crit Care Med ; 43(3): 319-320, 2022 06.
Article in English | MEDLINE | ID: covidwho-1984496
5.
Semin Respir Crit Care Med ; 43(3): 346-368, 2022 06.
Article in English | MEDLINE | ID: covidwho-1958550

ABSTRACT

Acute respiratory distress syndrome (ARDS) is a severe form of respiratory failure burden by high hospital mortality. No specific pharmacologic treatment is currently available and its ventilatory management is a key strategy to allow reparative and regenerative lung tissue processes. Unfortunately, a poor management of mechanical ventilation can induce ventilation induced lung injury (VILI) caused by physical and biological forces which are at play. Different parameters have been described over the years to assess lung injury severity and facilitate optimization of mechanical ventilation. Indices of lung injury severity include variables related to gas exchange abnormalities, ventilatory setting and respiratory mechanics, ventilation intensity, and the presence of lung hyperinflation versus derecruitment. Recently, specific indexes have been proposed to quantify the stress and the strain released over time using more comprehensive algorithms of calculation such as the mechanical power, and the interaction between driving pressure (DP) and respiratory rate (RR) in the novel DP multiplied by four plus RR [(4 × DP) + RR] index. These new parameters introduce the concept of ventilation intensity as contributing factor of VILI. Ventilation intensity should be taken into account to optimize protective mechanical ventilation strategies, with the aim to reduce intensity to the lowest level required to maintain gas exchange to reduce the potential for VILI. This is further gaining relevance in the current era of phenotyping and enrichment strategies in ARDS.


Subject(s)
Lung Injury , Respiratory Distress Syndrome , Humans , Lung , Respiration, Artificial/adverse effects , Respiratory Distress Syndrome/therapy , Respiratory Mechanics
6.
JMIR mHealth and uHealth ; 2021:1-23, 2021.
Article in English | APA PsycInfo | ID: covidwho-1918840

ABSTRACT

Background: Digital contact tracing apps have the potential to augment contact tracing systems and disrupt COVID-19 transmission by rapidly identifying secondary cases prior to the onset of infectiousness and linking them into a system of quarantine, testing, and health care worker case management. The international experience of digital contact tracing apps during the COVID-19 pandemic demonstrates how challenging their design and deployment are. Objective: This study aims to derive and summarize best practice guidance for the design of the ideal digital contact tracing app. Methods: A collaborative cross-disciplinary approach was used to derive best practice guidance for designing the ideal digital contact tracing app. A search of the indexed and gray literature was conducted to identify articles describing or evaluating digital contact tracing apps. MEDLINE was searched using a combination of free-text terms and Medical Subject Headings search terms. Gray literature sources searched were the World Health Organization Institutional Repository for Information Sharing, the European Centre for Disease Prevention and Control publications library, and Google, including the websites of many health protection authorities. Articles that were acceptable for inclusion in this evidence synthesis were peer-reviewed publications, cohort studies, randomized trials, modeling studies, technical reports, white papers, and media reports related to digital contact tracing. Results: Ethical, user experience, privacy and data protection, technical, clinical and societal, and evaluation considerations were identified from the literature. The ideal digital contact tracing app should be voluntary and should be equitably available and accessible. User engagement could be enhanced by small financial incentives, enabling users to tailor aspects of the app to their particular needs and integrating digital contact tracing apps into the wider public health information campaign. Adherence to the principles of good data protection and privacy by design is important to convince target populations to download and use digital contact tracing apps. Bluetooth Low Energy is recommended for a digital contact tracing app's contact event detection, but combining it with ultrasound technology may improve a digital contact tracing app's accuracy. A decentralized privacy-preserving protocol should be followed to enable digital contact tracing app users to exchange and record temporary contact numbers during contact events. The ideal digital contact tracing app should define and risk-stratify contact events according to proximity, duration of contact, and the infectiousness of the case at the time of contact. Evaluating digital contact tracing apps requires data to quantify app downloads, use among COVID-19 cases, successful contact alert generation, contact alert receivers, contact alert receivers that adhere to quarantine and testing recommendations, and the number of contact alert receivers who subsequently are tested positive for COVID-19. The outcomes of digital contact tracing apps' evaluations should be openly reported to allow for the wider public to review the evaluation of the app. Conclusions: In conclusion, key considerations and best practice guidance for the design of the ideal digital contact tracing app were derived from the literature. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

7.
Med (N Y) ; 3(4): 233-248.e6, 2022 04 08.
Article in English | MEDLINE | ID: covidwho-1882364

ABSTRACT

Background: Patients with severe coronavirus disease 2019 (COVID-19) develop a febrile pro-inflammatory cytokinemia with accelerated progression to acute respiratory distress syndrome (ARDS). Here we report the results of a phase 2, multicenter, randomized, double-blind, placebo-controlled trial of intravenous (IV) plasma-purified alpha-1 antitrypsin (AAT) for moderate to severe ARDS secondary to COVID-19 (EudraCT 2020-001391-15). Methods: Patients (n = 36) were randomized to receive weekly placebo, weekly AAT (Prolastin, Grifols, S.A.; 120 mg/kg), or AAT once followed by weekly placebo. The primary endpoint was the change in plasma interleukin (IL)-6 concentration at 1 week. In addition to assessing safety and tolerability, changes in plasma levels of IL-1ß, IL-8, IL-10, and soluble tumor necrosis factor receptor 1 (sTNFR1) and clinical outcomes were assessed as secondary endpoints. Findings: Treatment with IV AAT resulted in decreased inflammation and was safe and well tolerated. The study met its primary endpoint, with decreased circulating IL-6 concentrations at 1 week in the treatment group. This was in contrast to the placebo group, where IL-6 was increased. Similarly, plasma sTNFR1 was substantially decreased in the treatment group while remaining unchanged in patients receiving placebo. IV AAT did not definitively reduce levels of IL-1ß, IL-8, and IL-10. No difference in mortality or ventilator-free days was observed between groups, although a trend toward decreased time on ventilator was observed in AAT-treated patients. Conclusions: In patients with COVID-19 and moderate to severe ARDS, treatment with IV AAT was safe, feasible, and biochemically efficacious. The data support progression to a phase 3 trial and prompt further investigation of AAT as an anti-inflammatory therapeutic. Funding: ECSA-2020-009; Elaine Galwey Research Bursary.


Subject(s)
COVID-19 , Respiratory Distress Syndrome , alpha 1-Antitrypsin Deficiency , COVID-19/complications , Humans , Interleukin-10/therapeutic use , Interleukin-6/therapeutic use , Interleukin-8/therapeutic use , Respiratory Distress Syndrome/drug therapy , alpha 1-Antitrypsin/therapeutic use , alpha 1-Antitrypsin Deficiency/drug therapy
9.
Trials ; 23(1): 401, 2022 May 13.
Article in English | MEDLINE | ID: covidwho-1846859

ABSTRACT

BACKGROUND: Mesenchymal stromal cells (MSCs) may be of benefit in ARDS due to immunomodulatory and reparative properties. This trial investigates a novel CD362 enriched umbilical cord derived MSC product (REALIST ORBCEL-C), produced to Good Manufacturing Practice standards, in patients with moderate to severe ARDS due to COVID-19 and ARDS due to other causes. METHODS: Phase 1 is a multicentre open-label dose-escalation pilot trial. Patients will receive a single infusion of REALIST ORBCEL-C (100 × 106 cells, 200 × 106 cells or 400 × 106 cells) in a 3 + 3 design. Phase 2 is a multicentre randomised, triple blind, allocation concealed placebo-controlled trial. Two cohorts of patients, with ARDS due to COVID-19 or ARDS due to other causes, will be recruited and randomised 1:1 to receive either a single infusion of REALIST ORBCEL-C (400 × 106 cells or maximal tolerated dose in phase 1) or placebo. Planned recruitment to each cohort is 60 patients. The primary safety outcome is the incidence of serious adverse events. The primary efficacy outcome is oxygenation index at day 7. The trial will be reported according to the Consolidated Standards for Reporting Trials (CONSORT 2010) statement. DISCUSSION: The development and manufacture of an advanced therapy medicinal product to Good Manufacturing Practice standards within NHS infrastructure are discussed, including challenges encountered during the early stages of trial set up. The rationale to include a separate cohort of patients with ARDS due to COVID-19 in phase 2 of the trial is outlined. TRIAL REGISTRATION: ClinicalTrials.gov NCT03042143. Registered on 3 February 2017. EudraCT Number 2017-000584-33.


Subject(s)
COVID-19 , Mesenchymal Stem Cells , Respiratory Distress Syndrome , Clinical Trials, Phase I as Topic , Clinical Trials, Phase II as Topic , Double-Blind Method , Humans , Multicenter Studies as Topic , Randomized Controlled Trials as Topic , Respiratory Distress Syndrome/drug therapy , SARS-CoV-2 , Treatment Outcome
10.
Crit Care ; 26(1): 141, 2022 05 17.
Article in English | MEDLINE | ID: covidwho-1846858

ABSTRACT

BACKGROUND: The role of neuromuscular blocking agents (NMBAs) in coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) is not fully elucidated. Therefore, we aimed to investigate in COVID-19 patients with moderate-to-severe ARDS the impact of early use of NMBAs on 90-day mortality, through propensity score (PS) matching analysis. METHODS: We analyzed a convenience sample of patients with COVID-19 and moderate-to-severe ARDS, admitted to 244 intensive care units within the COVID-19 Critical Care Consortium, from February 1, 2020, through October 31, 2021. Patients undergoing at least 2 days and up to 3 consecutive days of NMBAs (NMBA treatment), within 48 h from commencement of IMV were compared with subjects who did not receive NMBAs or only upon commencement of IMV (control). The primary objective in the PS-matched cohort was comparison between groups in 90-day in-hospital mortality, assessed through Cox proportional hazard modeling. Secondary objectives were comparisons in the numbers of ventilator-free days (VFD) between day 1 and day 28 and between day 1 and 90 through competing risk regression. RESULTS: Data from 1953 patients were included. After propensity score matching, 210 cases from each group were well matched. In the PS-matched cohort, mean (± SD) age was 60.3 ± 13.2 years and 296 (70.5%) were male and the most common comorbidities were hypertension (56.9%), obesity (41.1%), and diabetes (30.0%). The unadjusted hazard ratio (HR) for death at 90 days in the NMBA treatment vs control group was 1.12 (95% CI 0.79, 1.59, p = 0.534). After adjustment for smoking habit and critical therapeutic covariates, the HR was 1.07 (95% CI 0.72, 1.61, p = 0.729). At 28 days, VFD were 16 (IQR 0-25) and 25 (IQR 7-26) in the NMBA treatment and control groups, respectively (sub-hazard ratio 0.82, 95% CI 0.67, 1.00, p = 0.055). At 90 days, VFD were 77 (IQR 0-87) and 87 (IQR 0-88) (sub-hazard ratio 0.86 (95% CI 0.69, 1.07; p = 0.177). CONCLUSIONS: In patients with COVID-19 and moderate-to-severe ARDS, short course of NMBA treatment, applied early, did not significantly improve 90-day mortality and VFD. In the absence of definitive data from clinical trials, NMBAs should be indicated cautiously in this setting.


Subject(s)
COVID-19 , Neuromuscular Blocking Agents , Respiratory Distress Syndrome , Aged , COVID-19/drug therapy , Female , Humans , Intensive Care Units , Male , Middle Aged , Neuromuscular Blocking Agents/therapeutic use , Propensity Score , Respiration, Artificial , Respiratory Distress Syndrome/drug therapy
11.
Encyclopedia ; 1(3):831, 2021.
Article in English | ProQuest Central | ID: covidwho-1834752

ABSTRACT

Definitionβ-glucans are complex polysaccharides that are found in several plants and foods, including mushrooms. β-glucans display an array of potentially therapeutic properties.

12.
Respir Res ; 23(1): 101, 2022 Apr 26.
Article in English | MEDLINE | ID: covidwho-1813343

ABSTRACT

BACKGROUND: Airway pressure release ventilation (APRV) is widely available on mechanical ventilators and has been proposed as an early intervention to prevent lung injury or as a rescue therapy in the management of refractory hypoxemia. Driving pressure ([Formula: see text]) has been identified in numerous studies as a key indicator of ventilator-induced-lung-injury that needs to be carefully controlled. [Formula: see text] delivered by the ventilator in APRV is not directly measurable in dynamic conditions, and there is no "gold standard" method for its estimation. METHODS: We used a computational simulator matched to data from 90 patients with acute respiratory distress syndrome (ARDS) to evaluate the accuracy of three "at-the-bedside" methods for estimating ventilator [Formula: see text] during APRV. RESULTS: Levels of [Formula: see text] delivered by the ventilator in APRV were generally within safe limits, but in some cases exceeded levels specified by protective ventilation strategies. A formula based on estimating the intrinsic positive end expiratory pressure present at the end of the APRV release provided the most accurate estimates of [Formula: see text]. A second formula based on assuming that expiratory flow, volume and pressure decay mono-exponentially, and a third method that requires temporarily switching to volume-controlled ventilation, also provided accurate estimates of true [Formula: see text]. CONCLUSIONS: Levels of [Formula: see text] delivered by the ventilator during APRV can potentially exceed levels specified by standard protective ventilation strategies, highlighting the need for careful monitoring. Our results show that [Formula: see text] delivered by the ventilator during APRV can be accurately estimated at the bedside using simple formulae that are based on readily available measurements.


Subject(s)
Respiratory Distress Syndrome , Ventilator-Induced Lung Injury , Computer Simulation , Continuous Positive Airway Pressure/methods , Humans , Respiratory Distress Syndrome/diagnosis , Respiratory Distress Syndrome/therapy , Ventilator-Induced Lung Injury/prevention & control , Ventilators, Mechanical
13.
Eur J Anaesthesiol ; 39(5): 463-472, 2022 05 01.
Article in English | MEDLINE | ID: covidwho-1806662

ABSTRACT

Tracheal intubation is among the most commonly performed and high-risk procedures in critical care. Indeed, 45% of patients undergoing intubation experience at least one major peri-intubation adverse event, with cardiovascular instability being the most common event reported in 43%, followed by severe hypoxemia in 9% and cardiac arrest in 3% of cases. These peri-intubation adverse events may expose patients to a higher risk of 28-day mortality, and they are more frequently observed with an increasing number of attempts to secure the airway. The higher risk of peri-intubation complications in critically ill patients, compared with the anaesthesia setting, is the consequence of their deranged physiology (e.g. underlying respiratory failure, shock and/or acidosis) and, in this regard, airway management in critical care has been defined as "physiologically difficult". In recent years, several randomised studies have investigated the most effective preoxy-genation strategies, and evidence for the use of positive pressure ventilation in moderate-to-severe hypoxemic patients is established. On the other hand, evidence on interventions to mitigate haemodynamic collapse after intubation has been elusive. Airway management in COVID-19 patients is even more challenging because of the additional risk of infection for healthcare workers, which has influenced clinical choices in this patient group. The aim of this review is to provide an update of the evidence for intubation in critically ill patients with a focus on understanding peri-intubation risks and evaluating interventions to prevent or mitigate adverse events.


Subject(s)
COVID-19 , Respiratory Insufficiency , Airway Management/adverse effects , Critical Illness/therapy , Humans , Intubation, Intratracheal/adverse effects , Intubation, Intratracheal/methods
14.
Crit Care Explor ; 2(9): e0202, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-1795075

ABSTRACT

OBJECTIVES: Patients with coronavirus disease 2019 acute respiratory distress syndrome appear to present with at least two distinct phenotypes: severe hypoxemia with relatively well-preserved lung compliance and lung gas volumes (type 1) and a more conventional acute respiratory distress syndrome phenotype, displaying the typical characteristics of the "baby lung" (type 2). We aimed to test plausible hypotheses regarding the pathophysiologic mechanisms underlying coronavirus disease 2019 acute respiratory distress syndrome and to evaluate the resulting implications for ventilatory management. DESIGN: We adapted a high-fidelity computational simulator, previously validated in several studies of acute respiratory distress syndrome, to: 1) develop quantitative insights into the key pathophysiologic differences between the coronavirus disease 2019 acute respiratory distress syndrome and the conventional acute respiratory distress syndrome and 2) assess the impact of different positive end-expiratory pressure, Fio2, and tidal volume settings. SETTING: Interdisciplinary Collaboration in Systems Medicine Research Network. SUBJECTS: The simulator was calibrated to represent coronavirus disease 2019 acute respiratory distress syndrome patients with both normal and elevated body mass indices undergoing invasive mechanical ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: An acute respiratory distress syndrome model implementing disruption of hypoxic pulmonary vasoconstriction and vasodilation leading to hyperperfusion of collapsed lung regions failed to replicate clinical data on type 1 coronavirus disease 2019 acute respiratory distress syndrome patients. Adding mechanisms to reflect disruption of alveolar gas-exchange due to the effects of pneumonitis and heightened vascular resistance due to the emergence of microthrombi produced levels of ventilation perfusion mismatch and hypoxemia consistent with data from type 1 coronavirus disease 2019 acute respiratory distress syndrome patients, while preserving close-to-normal lung compliance and gas volumes. Atypical responses to positive end-expiratory pressure increments between 5 and 15 cm H2O were observed for this type 1 coronavirus disease 2019 acute respiratory distress syndrome model across a range of measures: increasing positive end-expiratory pressure resulted in reduced lung compliance and no improvement in oxygenation, whereas mechanical power, driving pressure, and plateau pressure all increased. Fio2 settings based on acute respiratory distress syndrome network protocols at different positive end-expiratory pressure levels were insufficient to achieve adequate oxygenation. Incrementing tidal volumes from 5 to 10 mL/kg produced similar increases in multiple indicators of ventilator-induced lung injury in the type 1 coronavirus disease 2019 acute respiratory distress syndrome model to those seen in a conventional acute respiratory distress syndrome model. CONCLUSIONS: Our model suggests that use of standard positive end-expiratory pressure/Fio2 tables, higher positive end-expiratory pressure strategies, and higher tidal volumes may all be potentially deleterious in type 1 coronavirus disease 2019 acute respiratory distress syndrome patients, and that a highly personalized approach to treatment is advisable.

15.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-331451

ABSTRACT

The INHALE-HEP meta-trial is a prospective collaborative individual participant data meta-analysis of randomised controlled trials and early phase studies, to evaluate whether inhaled nebulised UFH in hospitalised patients with COVID-19 who do not require immediate invasive mechanical ventilation, significantly reduces intubation (or death, for patients who died before intubation) at day 28 compared to standard care alone. Objective: In keeping with best practice and with the published protocol, a pre-specified statistical analysis plan has been described and made public before completion of patient recruitment and data collection into the INHALE-HEP meta-trial. Methods: Our statistical analysis plan was designed by the INHALE-HEP executive committee and statisticians and approved by the INHALE-HEP steering committee. We reviewed the data collected as specified in the meta-trial protocol and collected in individual contributing studies. We present information pertaining to data collection, pre-specified subgroups, and study outcomes. Primary and secondary outcomes are defined, and additional subgroup analyses of pre-defined variables are described. Results: We have described our methods for presenting the trial profile and baseline characteristics, as well as our Bayesian approach to monitoring and meta-analysing individual patient data, outcomes and adverse events. All analyses will follow the intention-to-treat principle, considering all participants in the treatment group to which they were assigned, except for cases lost to follow-up or withdrawn. Conclusion: To minimise analytical bias, we have developed a statistical analysis plan and made this available to the public domain before completion of patient recruitment and data collection into the INHALE-HEP meta-trial.

16.
Crit Care ; 26(1): 84, 2022 03 28.
Article in English | MEDLINE | ID: covidwho-1765461

ABSTRACT

BACKGROUND: Awake prone positioning (APP) improves oxygenation in coronavirus disease (COVID-19) patients and, when successful, may decrease the risk of intubation. However, factors associated with APP success remain unknown. In this secondary analysis, we aimed to assess whether APP can reduce intubation rate in patients with COVID-19 and to focus on the factors associated with success. METHODS: In this multicenter randomized controlled trial, conducted in three high-acuity units, we randomly assigned patients with COVID-19-induced acute hypoxemic respiratory failure (AHRF) requiring high-flow nasal cannula (HFNC) oxygen to APP or standard care. Primary outcome was intubation rate at 28 days. Multivariate analyses were performed to identify the predictors associated to treatment success (survival without intubation). RESULTS: Among 430 patients randomized, 216 were assigned to APP and 214 to standard care. The APP group had a lower intubation rate (30% vs 43%, relative risk [RR] 0.70; CI95 0.54-0.90, P = 0.006) and shorter hospital length of stay (11 interquartile range [IQR, 9-14] vs 13 [IQR, 10-17] days, P = 0.001). A respiratory rate ≤ 25 bpm at enrollment, an increase in ROX index > 1.25 after first APP session, APP duration > 8 h/day, and a decrease in lung ultrasound score ≥ 2 within the first 3 days were significantly associated with treatment success for APP. CONCLUSION: In patients with COVID-19-induced AHRF treated by HFNC, APP reduced intubation rate and improved treatment success. A longer APP duration is associated with APP success, while the increase in ROX index and decrease in lung ultrasound score after APP can also help identify patients most likely to benefit. TRIAL REGISTRATION: This study was retrospectively registered in ClinicalTrials.gov at July 20, 2021. Identification number NCT04477655. https://clinicaltrials.gov/ct2/show/NCT04477655?term=PRO-CARF&draw=2&rank=1.


Subject(s)
COVID-19 , Respiratory Insufficiency , COVID-19/complications , COVID-19/therapy , Cannula , Humans , Prone Position , Respiratory Insufficiency/complications , Respiratory Insufficiency/therapy , Wakefulness
17.
Digital health ; 8, 2022.
Article in English | EuropePMC | ID: covidwho-1749642

ABSTRACT

Objective This study aims to gather public opinion on the Irish “COVID Tracker” digital contact tracing (DCT) App, with particular focus on App usage, usability, usefulness, technological issues encountered, and potential changes to the App. Methods A 35-item online questionnaire was deployed for 10 days in October 2020, 3 months after the launch of the Irish DCT App. Results A total of 2889 completed responses were recorded, with 2553 (88%) respondents currently using the App. Although four in five users felt the App is easy to download, is easy to use and looks professional, 615 users (22%) felt it had slowed down their phone, and 757 (28%) felt it had a negative effect on battery life. Seventy-nine percent of respondents reported the App's main function is to aid contact tracing. Inclusion of national COVID-19 trends is a useful ancillary function according to 87% of respondents, and there was an appetite for more granular local data. Overall, 1265 (44%) respondents believed the App is helping the national effort, while 1089 (38%) were unsure. Conclusions DCT Apps may potentially augment traditional contact tracing methods. Despite some reports of negative effects on phone performance, just 7% of users who have tried the App have deleted it. Ancillary functionality, such as up-to-date regional COVID-19, may encourage DCT App use. This study describes general positivity toward the Irish COVID Tracker App among users but also highlights the need for transparency on effectiveness of App-enabled contact tracing and for study of non-users to better establish barriers to use.

18.
Br J Anaesth ; 128(6): 1052-1058, 2022 06.
Article in English | MEDLINE | ID: covidwho-1748195

ABSTRACT

BACKGROUND: Optimal respiratory support in early COVID-19 pneumonia is controversial and remains unclear. Using computational modelling, we examined whether lung injury might be exacerbated in early COVID-19 by assessing the impact of conventional oxygen therapy (COT), high-flow nasal oxygen therapy (HFNOT), continuous positive airway pressure (CPAP), and noninvasive ventilation (NIV). METHODS: Using an established multi-compartmental cardiopulmonary simulator, we first modelled COT at a fixed FiO2 (0.6) with elevated respiratory effort for 30 min in 120 spontaneously breathing patients, before initiating HFNOT, CPAP, or NIV. Respiratory effort was then reduced progressively over 30-min intervals. Oxygenation, respiratory effort, and lung stress/strain were quantified. Lung-protective mechanical ventilation was also simulated in the same cohort. RESULTS: HFNOT, CPAP, and NIV improved oxygenation compared with conventional therapy, but also initially increased total lung stress and strain. Improved oxygenation with CPAP reduced respiratory effort but lung stress/strain remained elevated for CPAP >5 cm H2O. With reduced respiratory effort, HFNOT maintained better oxygenation and reduced total lung stress, with no increase in total lung strain. Compared with 10 cm H2O PEEP, 4 cm H2O PEEP in NIV reduced total lung stress, but high total lung strain persisted even with less respiratory effort. Lung-protective mechanical ventilation improved oxygenation while minimising lung injury. CONCLUSIONS: The failure of noninvasive ventilatory support to reduce respiratory effort may exacerbate pulmonary injury in patients with early COVID-19 pneumonia. HFNOT reduces lung strain and achieves similar oxygenation to CPAP/NIV. Invasive mechanical ventilation may be less injurious than noninvasive support in patients with high respiratory effort.


Subject(s)
COVID-19 , Lung Injury , Noninvasive Ventilation , Respiratory Insufficiency , COVID-19/therapy , Computer Simulation , Humans , Oxygen , Respiratory Insufficiency/therapy
19.
Lancet Respir Med ; 10(6): 573-583, 2022 06.
Article in English | MEDLINE | ID: covidwho-1740330

ABSTRACT

BACKGROUND: Awake prone positioning has been broadly utilised for non-intubated patients with COVID-19-related acute hypoxaemic respiratory failure, but the results from published randomised controlled trials (RCTs) in the past year are contradictory. We aimed to systematically synthesise the outcomes associated with awake prone positioning, and evaluate these outcomes in relevant subpopulations. METHODS: In this systematic review and meta-analysis, two independent groups of researchers searched MEDLINE, Embase, PubMed, Web of Science, Scopus, MedRxiv, BioRxiv, and ClinicalTrials.gov for RCTs and observational studies (with a control group) of awake prone positioning in patients with COVID-19-related acute hypoxaemic respiratory failure published in English from Jan 1, 2020, to Nov 8, 2021. We excluded trials that included patients intubated before or at enrolment, paediatric patients (ie, younger than 18 years), or trials that did not include the supine position in the control group. The same two independent groups screened studies, extracted the summary data from published reports, and assessed the risk of bias. We used a random-effects meta-analysis to pool individual studies. We used the Grading of Recommendations Assessment, Development, and Evaluation approach to assess the certainty and quality of the evidence. The primary outcome was the reported cumulative intubation risk across RCTs, and effect estimates were calculated as risk ratios (RR;95% CI). The analysis was primarily conducted on RCTs, and observational studies were used for sensitivity analyses. No serious adverse events associated with awake prone positioning were reported. The study protocol was prospectively registered with PROSPERO, CRD42021271285. FINDINGS: A total of 1243 studies were identified, we assessed 138 full-text articles and received the aggregated results of three unpublished RCTs; therefore, after exclusions, 29 studies were included in the study. Ten were RCTs (1985 patients) and 19 were observational studies (2669 patients). In ten RCTs, awake prone positioning compared with the supine position significantly reduced the need for intubation in the overall population (RR 0·84 [95% CI 0·72-0·97]). A reduced need for intubation was shown among patients who received advanced respiratory support (ie, high-flow nasal cannula or non-invasive ventilation) at enrolment (RR 0·83 [0·71-0·97]) and in intensive care unit (ICU) settings (RR 0·83 [0·71-0·97]) but not in patients receiving conventional oxygen therapy (RR 0·87 [0·45-1·69]) or in non-ICU settings (RR 0·88 [0·44-1·76]). No obvious risk of bias and publication bias was found among the included RCTs for the primary outcome. INTERPRETATION: In patients with COVID-19-related acute hypoxaemic respiratory failure, awake prone positioning reduced the need for intubation, particularly among those requiring advanced respiratory support and those in ICU settings. Awake prone positioning should be used in patients who have acute hypoxaemic respiratory failure due to COVID-19 and require advanced respiratory support or are treated in the ICU. FUNDING: OpenAI, Rice Foundation, National Institute for Health Research, and Oxford Biomedical Research Centre.


Subject(s)
COVID-19 , Respiratory Insufficiency , COVID-19/complications , Child , Humans , Patient Positioning/methods , Prone Position , Respiratory Insufficiency/etiology , Respiratory Insufficiency/therapy , Wakefulness
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