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1. Informing pandemic management in Germany with trustworthy living evidence syntheses and guideline development: lessons learned from the COVID-19 evidence ecosystem

Author

Kunzler, Angela M., Iannizzi, Claire, Burns, Jacob, Metzendorf, Maria-Inti, Voigt-Radloff, Sebastian, Piechotta, Vanessa, Schmaderer, Christoph, Holzmann-Littig, Christopher, Balzer, Felix, Benstoem, Carina, Binder, Harald, Boeker, Martin, Dirnagl, Ulrich, Fichtner, Falk, Golinski, Martin, Grundmann, Hajo, Hengel, Hartmut, Jabs, Jonas, Kern, Winfried V., Kopp, Ina, Kranke, Peter, Kreuzberger, Nina, Laudi, Sven, Lichtner, Gregor, Lieb, Klaus, Maun, Andy, Moerer, Onnen, Müller, Anika, Mutters, Nico T., Nothacker, Monika, Pfadenhauer, Lisa M., Popp, Maria, Rüschemeyer, Georg, Schmucker, Christine, Schwingshackl, Lukas, Spies, Claudia, Steckelberg, Anke, Stegemann, Miriam, Strech, Daniel, von Dincklage, Falk, Weibel, Stephanie, Wunderlich, Maximilian Markus, Zöller, Daniela, Rehfuess, Eva, Skoetz, Nicole, and Meerpohl, Joerg J.

Published
2024
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7. Weaning from mechanical ventilation in intensive care units across 50 countries (WEAN SAFE): a multicentre, prospective, observational cohort study

Author

Abrough, Fekri, Acharya, Subhash P, Amin, Pravin, Arabi, Yaseen, Aragao, Irene, Bauer, Philippe, Beduneau, Gaëtan, Beitler, Jeremy, Berkius, Johan, Bugedo, Guillermo, Camporota, Luigi, Cerny, Vladimir, Cho, Young-Jae, Clarkson, Kevin, Estenssoro, Elisa, Goligher, Ewan, Grasselli, Giacomo, Gritsan, Alexey, Hashemian, Seyed Mohammadreza, Hermans, Greet, Heunks, Leo M, Jovanovic, Bojan, Kurahashi, Kiyoyasu, Laake, Jon Henrik, Matamis, Dimitrios, Moerer, Onnen, Molnar, Zsolt, Ozyilmaz, Ezgi, Panka, Bernardo, Papali, Alfred, Peñuelas, Óscar, Perbet, Sébastien, Piquilloud, Lise, Qiu, Haibo, Razek, Assem Abdel, Rittayamai, Nuttapol, Roldan, Rollin, Serpa Neto, Ary, Szuldrzynski, Konstanty, Talmor, Daniel, Tomescu, Dana, Van Haren, Frank, Villagomez, Asisclo, Zeggwagh, Amine Ali, Abe, Toshikazu, Aboshady, Abdelrhman, Acampo-de Jong, Melanie, Acharya, Subhash, Adderley, Jane, Adiguzel, Nalan, Agrawal, Vijay Kumar, Aguilar, Gerardo, Aguirre, Gaston, Aguirre-Bermeo, Hernan, Ahlström, Björn, Akbas, Türkay, Akker, Mustafa, Al Sadeh, Ghamdan, Alamri, Sultan, Algaba, Angela, Ali, Muneeb, Aliberti, Anna, Allegue, Jose Manuel, Alvarez, Diana, Amador, Joaquin, Andersen, Finn H, Ansari, Sharique, Apichatbutr, Yutthana, Apostolopoulou, Olympia, Arellano, Daniel, Arica, Mestanza, Arikan, Huseyin, Arinaga, Koichi, Arnal, Jean-Michel, Asano, Kengo, Asín-Corrochano, Marta, Avalos Cabrera, Jesus Milagrito, Avila Fuentes, Silvia, Aydemir, Semih, Aygencel, Gulbin, Azevedo, Luciano, Bacakoglu, Feza, Badie, Julio, Baedorf Kassis, Elias, Bai, Gabriela, Balaraj, Govindan, Ballico, Bruno, Banner-Goodspeed, Valerie, Banwarie, Preveen, Barbieri, Rosella, Baronia, Arvind, Barrett, Jonathan, Barrot, Loïc, Barrueco-Francioni, Jesus Emilio, Barry, Jeffrey, Bawangade, Harshal, Beavis, Sarah, Beck, Eduardo, Beehre, Nina, Belenguer Muncharaz, Alberto, Bellani, Giacomo, Belliato, Mirko, Bellissima, Agrippino, Beltramelli, Rodrigo, Ben Souissi, Asma, Benitez-Cano, Adela, Benlamin, Mohamed, Benslama, Abdellatif, Bento, Luis, Benvenuti, Daniela, Bernabe, Laura, Bersten, Andrew, Berta, Giacomo, Bertini, Pietro, Bertram-Ralph, Elliot, Besbes, Mohamed, Bettini, Lisandro Roberto, Beuret, Pascal, Bewley, Jeremy, Bezzi, Marco, Bhakhtiani, Lakshay, Bhandary, Rakesh, Bhowmick, Kaushik, Bihari, Shailesh, Bissett, Bernie, Blythe, David, Bocher, Simon, Boedjawan, Narain, Bojanowski, Christine M, Boni, Elisa, Boraso, Sabrina, Borelli, Massimo, Borello, Silvina, Borislavova, Margarita, Bosma, Karen J, Bottiroli, Maurizio, Boyd, Owen, Bozbay, Suha, Briva, Arturo, Brochard, Laurent, Bruel, Cédric, Bruni, Andrea, Buehner, Ulrike, Bulpa, Pierre, Burt, Karen, Buscot, Mathieu, Buttera, Stefania, Cabrera, Jorge, Caccese, Roberta, Caironi, Pietro, Canchos Gutierrez, Ivan, Canedo, Nancy, Cani, Alma, Cappellini, Iacopo, Carazo, Jesus, Cardonnet, Luis Pablo, Carpio, David, Carriedo, Demetrio, Carrillo, Ramón, Carvalho, João, Caser, Eliana, Castelli, Antonio, Castillo Quintero, Manuel, Castro, Heloisa, Catorze, Nuno, Cengiz, Melike, Cereijo, Enrique, Ceunen, Helga, Chaintoutis, Christos, Chang, Youjin, Chaparro, Gustavogcha, Chapman, Carmel, Chau, Simon, Chavez, Cecilia Eugenia, Chelazzi, Cosimo, Chelly, Jonathan, Chemouni, Frank, Chen, Kai, Chena, Ariel, Chiarandini, Paolo, Chilton, Phil, Chiumello, Davide, Chou-Lie, Yvette, Chudeau, Nicolas, Cinel, Ismail, Cinnella, Gilda, Clark, Michele, Clark, Thomas, Clementi, Stefano, Coaguila, Luis, Codecido, Alexis Jaspe, Collins, Amy, Colombo, Riccardo, Conde, Juan, Consales, Guglielmo, Cook, Tim, Coppadoro, Andrea, Cornejo, Rodrigo, Cortegiani, Andrea, Coxo, Cristina, Cracchiolo, Andrea Neville, Crespo Ramirez, Mónica, Crova, Philippe, Cruz, José, Cubattoli, Lucia, Çukurova, Zafer, Curto, Francesco, Czempik, Piotr, D'Andrea, Rocco, da Silva Ramos, Fernando, Dangers, Laurence, Danguy des Déserts, Marc, Danin, Pierre-Eric, Dantas, Fabianne, Daubin, Cédric, Dawei, Wu, de Haro, Candelaria, de Jesus Montelongo, Felipe, De Mendoza, Diego, de Pablo, Raúl, De Pascale, Gennaro, De Rosa, Silvia, Decavèle, Maxens, Declercq, Pierre-Louis, Deicas, Alberto, del Carmen Campos Moreno, María, Dellamonica, Jean, Delmas, Benjamin, Demirkiran, Oktay, Demirkiran, Hilmi, Dendane, Tarek, di Mussi, Rossella, Diakaki, Chrysi, Diaz, Anatilde, Diaz, Willy, Dikmen, Yalim, Dimoula, Aikaterini, Doble, Patricia, Doha, Nagwa, Domingos, Guilherme, Dres, Martin, Dries, David, Duggal, Abhijit, Duke, Graeme, Dunts, Pavel, Dybwik, Knut, Dykyy, Maksym, Eckert, Philippe, Efe, Serdar, Elatrous, Souheil, Elay, Gülseren, Elmaryul, Abubaker S, Elsaadany, Mohamed, Elsayed, Hany, Elsayed, Samar, Emery, Malo, Ena, Sébastien, Eng, Kevin, Englert, Joshua A, Erdogan, Elif, Ergin Ozcan, Perihan, Eroglu, Ege, Escobar, Miguel, Esen, Figen, Esen Tekeli, Arzu, Esquivel, Alejandro, Esquivel Gallegos, Helbert, Ezzouine, Hanane, Facchini, Alberto, Faheem, Mohammad, Fanelli, Vito, Farina, Maria Fernanda, Fartoukh, Muriel, Fehrle, Lutz, Feng, Feng, Feng, Yufeng, Fernandez, Irene, Fernandez, Borja, Fernandez-Rodriguez, Maria Lorena, Ferrando, Carlos, Ferreira da Silva, Maria João, Ferreruela, Mireia, Ferrier, Janet, Flamm Zamorano, Matias Jesús, Flood, Laura, Floris, Leda, Fluckiger, Martin, Forteza, Catalina, Fortunato, Antonella, Frans, Eric, Frattari, Antonella, Fredes, Sebastian, Frenzel, Tim, Fumagalli, Roberto, Furche, Mariano Andres, Fusari, Maurizio, Fysh, Edward, Galeas-Lopez, Juan Luis, Galerneau, Louis-Marie, Garcia, Analía, Garcia, María Fernanda, Garcia, Elisabet, Garcia Olivares, Pablo, Garlicki, Jaroslaw, Garnero, Aude, Garofalo, Eugenio, Gautam, Prabha, Gazenkampf, Andrey, Gelinotte, Stéphanie, Gelormini, Domenico, Ghrenassia, Etienne, Giacomucci, Angelo, Giannoni, Robert, Gigante, Andrea, Glober, Nancy, Gnesin, Paolo, Gollo, Yari, Gomaa, Dina, Gomero Paredes, Rosita, Gomes, Rui, Gomez, Raúl Alejandro, Gomez, Oscar, Gomez, Aroa, Gondim, Louise, Gonzalez, Manuel, Gonzalez, Isabel, Gonzalez-Castro, Alejandro, Gordillo Romero, Orlando, Gordo, Federico, Gouin, Philippe, Graf Santos, Jerónimo, Grainne, Rooney, Grando, Matilde, Granov Grabovica, Sanja, Grasso, Salvatore, Grasso, Rinaldo, Grimmer, Lisa, Grissom, Colin, Gu, Qing, Guan, Xiang-Dong, Guarracino, Fabio, Guasch, Neus, Guatteri, Luca, Gueret, Renaud, Guérin, Claude, Guerot, Emmanuel, Guitard, Pierre-Gildas, Gül, Fethi, Gumus, Ayca, Gurjar, Mohan, Gutierrez, Patricia, Hachimi, Abdelhamid, Hadzibegovic, Adi, Hagan, Samantha, Hammel, Clare, Han Song, Joo, Hanlon, Gabrielle, Heines, Serge, Henriksson, Johanna, Herbrecht, Jean-Etienne, Heredia Orbegoso, Gabriel Omar, Hermon, Andrew, Hernandez, Rosana, Hernandez, Carmen, Herrera, Luis, Herrera-Gutierrez, Manuel, Heunks, Leo, Hidalgo, Juan, Hill, Dianne, Holmquist, Dagmar, Homez, Marcela, Hongtao, Xia, Hormis, Anil, Horner, Daniel, Hornos, M Carmen, Hou, Meihong, House, Stacy, Housni, Brahim, Hugill, Keith, Humphreys, Sally, Humbert, Louis, Hunter, Stephanie, Hwa Young, Lee, Iezzi, Nicolas, Ilutovich, Santiago, Inal, Volkan, Innes, Richard, Ioannides, Panagiotis, Iotti, Giorgio Antonio, Ippolito, Mariachiara, Irie, Hiromasa, Iriyama, Hiroki, Itagaki, Taiga, Izura, Javier, Izza, Santiago, Jabeen, Rakhshanda, Jamaati, Hamidreza, Jamadarkhana, Sunil, Jamoussi, Amira, Jankowski, Milosz, Jaramillo, Luis Alberto, Jeon, Kyeongman, Jeong Lee, Seok, Jeswani, Deepak, Jha, Simant, Jiang, Liangyan, Jing, Chen, Jochmans, Sébastien, Johnstad, Bror Anders, Jongmin, Lee, Joret, Aurélie, Junhasavasdikul, Detajin, Jurado, Maria Teresa, Kam, Elisa, Kamohara, Hidenobu, Kane, Caroline, Kara, Iskender, Karakurt, Sait, Karnjanarachata, Cherdkiat, Kataoka, Jun, Katayama, Shinshu, Kaushik, Shuchi, Kelebek Girgin, Nermin, Kerr, Kathryn, Kerslake, Ian, Khairnar, Prakash, Khalid, Abidi, Khan, Akram, Khanna, Ashish K, Khorasanee, Reza, Kienhorst, Dieneke, Kirakli, Cenk, Knafelj, Rihard, Kol, Mark Kol, Kongpolprom, Napplika, Kopitko, Csaba, Korkmaz Ekren, Pervin, Kubisz-Pudelko, Agnieszka, Kulcsar, Zoltan, Kumasawa, Junji, Kuriyama, Akira, Kutchak, Fernanda, Labarca, Eduardo, Labat, Françoise, Laborda, César, Laca Barrera, Manuel Alberto, Lagache, Laurie, Landaverde Lopez, Antonio, Lanspa, Michael, Lascari, Valeria, Le Meur, Matthieu, Lee, Su Hwan, Lee, Young Ju, Lee, Jinwoo, Lee, Won-Yeon, Lee, Jarone, Legernaes, Terje, Leiner, Tamaas, Lemiale, Virginie, Leonor, Tiago, Lepper, Philipp M, Li, Dahuan, Li, Hongbin, Li, Oleg, Lima, Ana Raquel, Lind, Dan, Litton, Edward, Liu, Ning, Liu, Ling, Liu, Jialin, Llitjos, Jean-François, Llorente, Beatriz, Lopez, Rodolfo, Lopez, Claudia Elizabeth, Lopez Nava, Claudia, Lovazzano, Pablo, Lu, Min, Lucchese, Francesca, Lugano, Manuela, Lugo Goytia, Gustavo, Luo, Hua, Lynch, Ceri, Macheda, Sebastiano, Madrigal Robles, Victor Hugo, Maggiore, Salvatore Maurizio, Magret Iglesias, Mònica, Malaga, Peter, Mallapura Maheswarappa, Harish, Malpartida, Guillermo, Malyarchikov, Andrey, Mansson, Helena, Manzano, Anaid, Marey, Ismael, Marin, Nathalie, Marin, Maria del Carmen, Markman, Eliana, Martin, Felix, Martin, Alex, Martin Dal Gesso, Cristina, Martinez, Felipe, Martínez-Fidalgo, Conchita, Martin-Loeches, Ignacio, Mas, Arantxa, Masaaki, Sakuraya, Maseda, Emilio, Massa, Eleni, Mattsson, Anna, Maugeri, Jessica, McCredie, Victoria, McCullough, James, McGuinness, Shay, McKown, Andrew, Medve, László, Mei, Chengqing, Mellado Artigas, Ricard, Mendes, Vitor, Mervat, Mohamed Khalaf Ebraheim, Michaux, Isabelle, Mikhaeil, Michael, Milagros, Olga, Milet, Igor, Millan, Maria Teresa, Minwei, Zhang, Mirabella, Lucia, Mishra, Sanghamitra, Mistraletti, Giovanni, Mochizuki, Katsunori, Moghal, Arif, Mojoli, Francesco, Molin, Alexandre, Montiel, Raquel, Montini, Luca, Monza, Gianmario, Mora Aznar, Maria, Morakul, Sunthiti, Morales, Maria, Moreno Torres, Daniel, Morocho Tutillo, Diego Rolando, Motherway, Catherine, Mouhssine, Doumiri, Mouloudi, Eleni, Muñoz, Tapia, Munoz de Cabo, Carlos, Mustafa, Mohamed, Muthuchellappan, Radhakrishnan, Muthukrishnan, Muraleekrishnan, Muttini, Stefano, Nagata, Isao, Nahar, Dick, Nakanishi, Misuzu, Nakayama, Izumi, Namendys-Silva, Silvio Antonio, Nanchal, Rahul, Nandakumar, Sivakumar, Nasi, Alessandra, Nasir, Kamal, Navalesi, Paolo, Naz Aslam, Tayyba, Nga Phan, Thuy, Nichol, Alistair, Niiyama, Shuhei, Nikolakopoulou, Sofia, Nikolic, Elena, Nitta, Kenichi, Noc, Marko, Nonas, Stephanie, Nseir, Saad, Nur Soyturk, Ayse, Obata, Yukako, Oeckler, Richard, Oguchi, Moe, Ohshimo, Shinichiro, Oikonomou, Marina, Ojados, Agueda, Oliveira, Maria Teresa, Oliveira Filho, Wilson, Oliveri, Carlo, Olmos, Aitor, Omura, Kazuya, Orlandi, Maria Cristina, Orsenigo, Francesca, Ortiz-Ruiz De Gordoa, Laura, Ota, Kei, Ovalle Olmos, Rainier, Öveges, Nándo, Oziemski, Peter, Ozkan Kuscu, Ozlem, Pachas Alvarado, Fernando, Pagella, Gonzalo, Palaniswamy, Vijayanand, Palazon Sanchez, Eugenio Luis, Palmese, Salvatore, Pan, Guojun, Pan, Wensen, Papanikolaou, Metaxia, Papavasilopoulou, Theonymfi, Parekh, Ameet, Parke, Rachael, Parrilla, Francisco J, Parrilla, Dácil, Pasha, Taha, Pasin, Laura, Patão, Luis, Patel, Mayur, Patel, Grisma, Pati, Basanta Kumar, Patil, Jayaprakash, Pattnaik, Saroj, Paul, Daniel, Pavesi, Maurizio, Pavlotsky, Vanesa Alejandra, Paz, Graciela, Paz, Enrique, Pecci, Elisabetta, Pellegrini, Carlos, Peña Padilla, Andrea Gabriela, Perchiazzi, Gaetano, Pereira, Tiago, Pereira, Vera, Perez, Manuel, Perez Calvo, Cesar, Perez Cheng, Meisy, Perez Maita, Ronald, Pérez-Araos, Rodrigo, Perez-Teran, Purificación, Perez-Torres, David, Perkins, Gavin, Persona, Paolo, Petnak, Tananchai, Petrova, Marina, Pham, Tai, Philippart, François, Picetti, Edoardo, Pierucci, Elisabetta, Piervincenzi, Edoardo, Pinciroli, Riccardo, Pintado, Maria-Consuelo, Piraino, Thomas, Piras, Stephanie, Piras, Claudio, Pirompanich, Pattarin, Pisani, Luigi, Platas, Enrique, Plotnikow, Gustavo, Porras, Willy, Porta, Virginia, Portilla, Mariana, Portugal, José, Povoa, Pedro, Prat, Gwenael, Pratto, Romina, Preda, Gabriel, Prieto, Isidro, Prol-Silva, Estefania, Pugh, Richard, Qi, Yupeng, Qian, Chuanyun, Qin, Tiehe, Qu, Hongping, Quintana, Teobaldo, Quispe Sierra, Rosari, Quispe Soto, Rocio, Rabbani, Raihan, Rabee, Mohamed, Rabie, Ahmed, Rahe Pereira, Maria Augusta, Rai, Ashish, Raj Ashok, Sundar, Rajab, Mostafa, Ramdhani, Navin, Ramey, Elizabeth, Ranieri, Marco, Rathod, Darshana, Ray, Banambar, Redwanul Huq, Shihan Mahmud, Regli, Adrian, Reina, Rosa, Resano Sarmiento, Natalia, Reynaud, Faustine, Rialp, Gemma, Ricart, Pilar, Rice, Todd, Richardson, Angus, Rieder, Marcelo, Rinket, Martin, Rios, Fernando, Risso Vazquez, Alejandro, Riva, Ivano, Rivette, Monaly, Roca, Oriol, Roche-Campo, Ferran, Rodriguez, Covadonga, Rodriguez, Gabriel, Rodriguez Gonzalez, Daniel, Rodriguez Tucto, Xandra Yanina, Rogers, Angela, Romano, María Elena, Rørtveit, Linda, Rose, Alastair, Roux, Damien, Rouze, Anahita, Rubatto Birri, Paolo Nahuel, Ruilan, Wang, Ruiz Robledo, Aldana, Ruiz-Aguilar, Antonio Luis, Sadahiro, Tomohito, Saez, Ignacio, Sagardia, Judith, Saha, Rajnish, Saha, Rohit, Saiphoklang, Narongkorn, Saito, Shigeki, Salem, Maie, Sales, Gabriele, Salgado, Patricia, Samavedam, Srinivas, Sami Mebazaa, Mhamed, Samuelsson, Line, San Juan Roman, Nandyelly, Sanchez, Patricia, Sanchez-Ballesteros, Jesus, Sandoval, Yazcitk, Sani, Emanuele, Santos, Martin, Santos, Carla, Sanui, Masamitsu, Saravanabavan, Lakshmikanthcharan, Sari, Sema, Sarkany, Agnes, Sauneuf, Bertrand, Savioli, Monica, Sazak, Hilal, Scano, Riccardo, Schneider, Francis, Schortgen, Frédérique, Schultz, Marcus J, Schwarz, Gabriele Leonie, Seçkin Yücesoy, Faruk, Seely, Andrew, Seiler, Frederik, Seker Tekdos, Yasemin, Seok Chan, Kim, Serano, Luca, Serednicki, Wojciech, Setten, Mariano, Shah, Asim, Shah, Bhagyesh, Shang, You, Shanmugasundaram, Pradeep, Shapovalov, Konstantin, Shebl, Eman, Shiga, Takuya, Shime, Nobuaki, Shin, Phil, Short, Jack, Shuhua, Chen, Siddiqui, Sughrat, Silesky Jimenez, Juan Ignacio, Silva, Daniel, Silva Sales, Betania, Simons, Koen, Sjøbø, Brit Ågot, Slessor, David, Smiechowicz, Jakub, Smischney, Nathan, Smith, Paul, Smith, Tim, Smith, Mark, Snape, Sarah, Snyman, Lindi, Soetens, Filiep, Sook Hong, Kyung, Sosa Medellin, Miguel Ángel, Soto, Giovanna, Souloy, Xavier, Sousa, Elsa, Sovatzis, Stefania, Sozutek, Didem, Spadaro, Savino, Spagnoli, Marco, Spångfors, Martin, Spittle, Nick, Spivey, Mike, Stapleton, Andrew, Stefanovic, Branislava, Stephenson, Lorraine, Stevenson, Elizabeth, Strand, Kristian, Strano, Maria Teresa, Straus, Slavenka, Sun, Chenliang, Sun, Rongqing, Sundaram, Venkat, SunPark, Tai, Surlemont, Elisabeth, Sutherasan, Yuda, Szabo, Zsuzsanna, Tainter, Christopher, Takaba, Akihiro, Tallott, Mandy, Tamasato, Tamasato, Tang, Zhanhong, Tangsujaritvijit, Viratch, Taniguchi, Leandro, Taniguchi, Daisuke, Tarantino, Fabio, Teerapuncharoen, Krittika, Temprano, Susana, Terragni, Pierpaolo, Terzi, Nicolas, Thakur, Anand, Theerawit, Pongdhep, Thille, Arnaud W, Thomas, Matt, Thungtitigul, Poungrat, Thyrault, Martial, Tilouch, Nejla, Timenetsky, Karina, Tirapu, Juna, Todeschini, Manuel, Tomas, Roser, Tomaszewski, Christian, Tonetti, Tommaso, Tonnelier, Alexandre, Trinder, John, Trongtrakul, Konlawij, Truwit, Jonathon, Tsuei, Betty, Tulaimat, Aiman, Turan, Sema, Turkoglu, Melda, Tyagi, Sanjeev, Ubeda, Alejandro, Vagginelli, Federica, Valenti, María Florencia, Vallverdu, Imma, Van Axel, Alisha, van den Hul, Ingrid, van der Hoeven, Hans, Van Der Meer, Nardo, Vanhoof, Marc, Vargas-Ordoñez, Mónica, Vaschetto, Rosanna, Vascotto, Ettore, Vatsik, Maria, Vaz, Ana, Vazquez-Sanchez, Antonia, Ventura, Sara, Vermeijden, Jan Wytze, Vidal, Anxela, Vieira, Jocyelle, Vilela Costa Pinto, Bruno, Villagra, Ana, Villegas Succar, Cristina, Vinorum, Ole Georg, Vitale, Giovanni, Vj, Ramesh, Vochin, Ana, Voiriot, Guillaume, Volta, Carlo Alberto, von Seth, Magnus, Wajdi, Maazouzi, Walsh, Don, Wang, Shouhong, Wardi, Gabriel, Ween-Velken, Nils Christian, Wei, Bi-Lin, Weller, Dolf, Welsh, Deborah, Welters, Ingeborg, Wert, Michael, Whiteley, Simon, Wilby, Elizabeth, Williams, Erin, Williams, Karen, Wilson, Antoinette, Wojtas, Jadwiga, Won Huh, Jin, Wrathall, David, Wright, Christopher, Wu, Jian-Feng, Xi, Guo, Xing, Zheng-Jiang, Xu, Hongyang, Yamamoto, Kotaro, Yan, Jie, Yáñez, Julio, Yang, Xiaobo, Yates, Elliot, Yazicioglu Mocin, Ozlem, Ye, Zhenglong, Yildirim, Fatma, Yoshida, Norifumi, Yoshido, Hector Higo Leon, Young Lee, Bo, Yu, Rongguo, Yu, Gong, Yu, Tao, Yuan, Boyun, Yuangtrakul, Nadwipa, Yumoto, Tetsuya, Yun, Xie, Zakalik, Graciela, Zaki, Ahmad, Zalba-Etayo, Begoña, Zambon, Massimo, Zang, Bin, Zani, Gianluca, Zarka, Jonathan, Zerbi, Simone Maria, Zerman, Avsar, Zetterquist, Harald, Zhang, Jiuzhi, Zhang, Hongwen, Zhang, Wei, Zhang, Guoxiu, Zhang, Weixin, Zhao, Hongsheng, Zheng, Jia, Zhu, Bin, Zumaran, Ronald, Pham, Tài, Madotto, Fabiana, Goligher, Ewan C, Mancebo, Jordi, Peñuelas, Oscar, Pesenti, Antonio, Wunsch, Hannah, van Haren, Frank, and Laffey, John G

Published
2023
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10. Serum cytokine dysregulation signatures associated with COVID-19 outcomes in high mortality intensive care unit cohorts across pandemic waves and variants.

Author

Maaß, Henrike, Ynga-Durand, Mario, Milošević, Marko, Krstanović, Fran, Matešić, Marina Pribanić, Žuža, Iva, Jonjić, Stipan, Brizić, Ilija, Šustić, Alan, Bloos, Frank, SepNet Critical Care Trials Group, Marx, Gernot, Jaschinski, Ulrich, Reinhart, Konrad, Spies, Claudia, Reil, Lorenz, Putensen, Christian, Ragaller, Maximilian, Utzlino, Stefan, and Mörer, Onnen

Abstract

The aim of this study was to characterize the systemic cytokine signature of critically ill COVID-19 patients in a high mortality setting aiming to identify biomarkers of severity, and to explore their associations with viral loads and clinical characteristics. We studied two COVID-19 critically ill patient cohorts from a referral centre located in Central Europe. The cohorts were recruited during the pre-alpha/alpha (November 2020 to April 2021) and delta (end of 2021) period respectively. We determined both the serum and bronchoalveolar SARS-CoV-2 viral load and identified the variant of concern (VoC) involved. Using a cytokine multiplex assay, we quantified systemic cytokine concentrations and analyzed their relationship with clinical findings, routine laboratory workup and pulmonary function data obtained during the ICU stay. Patients who did not survive had a significantly higher systemic and pulmonary viral load. Patients infected with the pre-alpha VoC showed a significantly lower viral load in comparison to those infected with the alpha- and delta-variants. Levels of systemic CTACK, M-CSF and IL-18 were significantly higher in non-survivors in comparison to survivors. CTACK correlated directly with APACHE II scores. We observed differences in lung compliance and the association between cytokine levels and pulmonary function, dependent on the VoC identified. An intra-cytokine analysis revealed a loss of correlation in the non-survival group in comparison to survivors in both cohorts. Critically ill COVID-19 patients exhibited a distinct systemic cytokine profile based on their survival outcomes. CTACK, M-CSF and IL-18 were identified as mortality-associated analytes independently of the VoC involved. The Intra-cytokine correlation analysis suggested the potential role of a dysregulated systemic network of inflammatory mediators in severe COVID-19 mortality. [ABSTRACT FROM AUTHOR]

Published
2024
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15. (1 → 3)-β-d-Glucan-guided antifungal therapy in adults with sepsis: the CandiSep randomized clinical trial

Author

Bloos, Frank, Held, Jürgen, Kluge, Stefan, Simon, Philipp, Kogelmann, Klaus, de Heer, Geraldine, Kuhn, Sven-Olaf, Jarczak, Dominik, Motsch, Johann, Hempel, Gunther, Weiler, Norbert, Weyland, Andreas, Drüner, Matthias, Gründling, Matthias, Meybohm, Patrick, Richter, Daniel, Jaschinski, Ulrich, Moerer, Onnen, Günther, Ulf, Schädler, Dirk, Weiss, Raphael, Putensen, Christian, Castellanos, Ixchel, Kurzai, Oliver, Schlattmann, Peter, Cornely, Oliver A., Bauer, Michael, Thomas-Rüddel, Daniel, Schmidt, Stefanie, Wehrfritz, Andreas, Kränzlein, Diana, Zacharowski, Kai, Mutlak, Haitham, Lindau, Simone, Wiedenbeck, Carolin, Mörer, Onnen, Kluge, Stephan, Weigand, Markus A., Lehmann, Thomas, Zarbock, Alexander, Rohe, Georg, Walter, Grit, and Helmer, Philipp

Subjects
Adult, Antifungal Agents, beta-Glucans, Sepsis, Humans, Candidiasis, Invasive, ddc:610, Critical Care and Intensive Care Medicine, Glucans, Sensitivity and Specificity
Abstract

To investigate whether (1 → 3)-β-d-Glucan (BDG)-guidance shortens time to antifungal therapy and thereby reduces mortality of sepsis patients with high risk of invasive Candida infection (ICI).Multicenter, randomized, controlled trial carried out between September 2016 and September 2019 in 18 intensive care units enrolling adult sepsis patients at high risk for ICI. Patients in the control group received targeted antifungal therapy driven by culture results. In addition to targeted therapy, patients in the BDG group received antifungals if at least one of two consecutive BDG samples taken during the first two study days was ≥ 80 pg/mL. Empirical antifungal therapy was discouraged in both groups. The primary endpoint was 28-day-mortality.339 patients were enrolled. ICI was diagnosed in 48 patients (14.2%) within the first 96 h after enrollment. In the BDG-group, 48.8% (84/172) patients received antifungals during the first 96 h after enrollment and 6% (10/167) patients in the control group. Death until day 28 occurred in 58 of 172 patients (33.7%) in the BDG group and 51 of 167 patients (30.5%) in the control group (relative risk 1.10; 95% confidence interval, 0.80-1.51; p = 0.53). Median time to antifungal therapy was 1.1 [interquartile range (IQR) 1.0-2.2] days in the BDG group and 4.4 (IQR 2.0-9.1, p 0.01) days in the control group.Serum BDG guided antifungal treatment did not improve 28-day mortality among sepsis patients with risk factors for but unexpected low rate of IC. This study cannot comment on the potential benefit of BDG-guidance in a more selected at-risk population.

Published
2022

17. Ökonomie und Qualitätsmanagement

Author

Martin, Jörg, Waydhas, Christian, Mörer, Onnen, Burchardi, Hilmar, editor, Larsen, Reinhard, editor, Marx, Gernot, editor, Muhl, Elke, editor, and Schölmerich, Jürgen, editor

Published
2011
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18. Immunity after COVID-19 vaccination: protocol for a scoping review

Author

Kreuzberger, Nina, Hirsch, Caroline, Andreas, Marike, Böhm, Lena, Bröckelmann, Paul, Di Cristanziano, Veronica, Golinski, Martin, Kappler, Verena, Klein, Florian, Lange, Berit, Meerpohl, Joerg, Mikolajewska, Agata, Mörer, Onnen, Monsef, Ina, Park, Yun, Piechotta, Vanessa, Prinz, Carolin, Rodiah, Isti, Salomon, Susanne, Sander, Leif, Schmaderer, Christoph, Stegemann, Miriam, Stephani, Caspar, Vanshylla, Kanika, Weibel, Stephanie, and Skoetz, Nicole

Subjects
Virus Diseases, Medicine and Health Sciences, Medical Specialties, COVID-19, Infectious Disease, Diseases, scoping review, vaccines, immunity
Abstract

The objective of the planned scoping review is to provide an overview on the existing literature on immune response and long-term efficacy outcomes after COVID-19 vaccination, and to map this evidence according to the examined populations, specific vaccines, immunity parameters and their way of determining relevant long-term outcomes and the availability of mapping between immune reactivity and relevant outcomes. We will limit inclusion to literature on the four vaccines currently authorized by the EMA and vaccines authorized in at least 10 countries worldwide: • Comirnaty [Pfizer / Biontech], • Spikevax [Moderna], and its equivalent TAK-919 [Moderna formulation, Takeda] • Vaxzevria [Oxford / AstraZeneca] and its equivalent Covishield [Oxford / AstraZeneca formulation, Serum Institute of India] • COVID-19 Vaccine Janssen [Ad26.COV2.S] • Sputnik V [Gamaleya] • Sputnik Light [Gamaleya] • BBIBP-CorV (Vero Cells) [Sinopharm (Beijing)] • CoronaVac [Sinovac] (COVID-19 Vaccine (Vero Cell) Inactivated)

Published
2022
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19. Immunity after COVID-19 vaccination: a scoping review

Author

Kreuzberger, Nina, Hirsch, Caroline, Andreas, Marike, Böhm, Lena, Bröckelmann, Paul, Di Cristanziano, Veronica, Golinski, Martin, Kappler, Verena, Klein, Florian, Lange, Berit, Meerpohl, Joerg, Mikolajewska, Agata, Mörer, Onnen, Monsef, Ina, Park, Yun, Piechotta, Vanessa, Prinz, Carolin, Rodiah, Isti, Salomon, Susanne, Sander, Leif, Schmaderer, Christoph, Stegemann, Miriam, Stephani, Caspar, Vanshylla, Kanika, Weibel, Stephanie, and Skoetz, Nicole

Subjects
COVID-19, scoping review, vaccines, comorbidities
Abstract

The objective of the planned scoping review is to provide an overview on the existing literature on immune response and long-term efficacy outcomes after COVID-19 vaccination, and to map this evidence according to the examined populations, specific vaccines, immunity parameters and their way of determining relevant long-term outcomes and the availability of mapping between immune reactivity and relevant outcomes. We will limit inclusion to literature on the four vaccines currently authorized by the EMA and vaccines authorized in at least 10 countries worldwide: • Comirnaty [Pfizer / Biontech], • Spikevax [Moderna], and its equivalent TAK-919 [Moderna formulation, Takeda] • Vaxzevria [Oxford / AstraZeneca] and its equivalent Covishield [Oxford / AstraZeneca formulation, Serum Institute of India] • COVID-19 Vaccine Janssen [Ad26.COV2.S] • Sputnik V [Gamaleya] • Sputnik Light [Gamaleya] • BBIBP-CorV (Vero Cells) [Sinopharm (Beijing)] • CoronaVac [Sinovac] (COVID-19 Vaccine (Vero Cell) Inactivated)

Published
2022
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20. Einfluss der Krankheitslast aufgrund von Komorbiditäten auf die Prognose von Patienten mit aneurysmatischer Subarachnoidalblutung

Author

Malinova, Vesna, Tuzi, Sheri, Kranawetter, Beate, Mörer, Onnen, Rohde, Veit, and Mielke, Dorothee

Subjects
ddc: 610, Medicine and health, cardiovascular diseases
Abstract

Objective: Several factors such as age and delayed cerebral ischemia (DCI) have been already identified as outcome determinants after aneurysmal subarachnoid hemorrhage (aSAH). In the last years, a growing evidence has evolved pointing out that aSAH should be rather regarded as a systemic disease affecting [for full text, please go to the a.m. URL]

Published
2022

21. Einfluss der Dysfunktion des gastrointestinalen Trakts in Patienten mit schwerer Subarachnoidalblutung auf Medikamentenaufnahme, eine prospektive vergleichende Studie

Author

Sindern, Juliane, Bröckmöller, Jürgen, Bruns, Ellen, Mörer, Onnen, Mielke, Dorothee, Rohde, Veit, and Abboud, Tammam

Subjects
ddc: 610, Medicine and health, cardiovascular diseases, nervous system diseases
Abstract

Objective: Patients with high grade subarachnoid hemorrhage (SAH) are considered critically ill. Complications in various organ systems often occur here, in particular dysfunction of the gastrointestinal tract can lead to an insufficient absorption of enterally administered drugs such as nimodipine. [for full text, please go to the a.m. URL]

Published
2022
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22. Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort

Author

Magunia, Harry, Lederer, Simone, Verbuecheln, Raphael, Gilot, Bryant, Köppen, Michael, Häberle, Helene, Mirakaj, Valbona, Hofmann, Pascal, Marx, Gernot, Bickenbach, Johannes, Nohe, Boris, Lay, Michael, Spies, Claudia D., Edel, Andreas, Schiefenhövel, Fridtjof, Rahmel, Tim, Putensen, Christian, Sellmann, Timur, Koch, Thea, Brandenburger, Timo, Kindgen-Milles, Detlef, Brenner, Thorsten, Berger, Marc, Zacharowski, Kai, Adam, Elisabeth, Posch, Matthias, Mörer, Onnen, Scheer, Christian S., Sedding, Daniel, Weigand, Markus A., Fichtner, Falk, Nau, Carla, Prätsch, Florian, Wiesmann, Thomas, Koch, Christian, Schneider, Gerhard, Lahmer, Tobias, Straub, Andreas, Meiser, Andreas, Weiß, Manfred, Jungwirth, Bettina, Wappler, Frank, Meybohm, Patrick, Herrmann, Johannes, Malek, Nisar Peter, Kohlbacher, Oliver, Biergans, Stephanie, Rosenberger, Peter, Magunia, Harry, Lederer, Simone, Verbuecheln, Raphael, Gilot, Bryant, Köppen, Michael, Häberle, Helene, Mirakaj, Valbona, Hofmann, Pascal, Marx, Gernot, Bickenbach, Johannes, Nohe, Boris, Lay, Michael, Spies, Claudia D., Edel, Andreas, Schiefenhövel, Fridtjof, Rahmel, Tim, Putensen, Christian, Sellmann, Timur, Koch, Thea, Brandenburger, Timo, Kindgen-Milles, Detlef, Brenner, Thorsten, Berger, Marc, Zacharowski, Kai, Adam, Elisabeth, Posch, Matthias, Mörer, Onnen, Scheer, Christian S., Sedding, Daniel, Weigand, Markus A., Fichtner, Falk, Nau, Carla, Prätsch, Florian, Wiesmann, Thomas, Koch, Christian, Schneider, Gerhard, Lahmer, Tobias, Straub, Andreas, Meiser, Andreas, Weiß, Manfred, Jungwirth, Bettina, Wappler, Frank, Meybohm, Patrick, Herrmann, Johannes, Malek, Nisar Peter, Kohlbacher, Oliver, Biergans, Stephanie, and Rosenberger, Peter

Abstract

Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.

Published
2021

27. The use of mid-regional proadrenomedullin to identify disease severity and treatment response to sepsis - a secondary analysis of a large randomised controlled trial

Author

SepNet Critical Care Trials Group, Elke, Gunnar, Bloos, Frank, Wilson, Darius Cameron, Brunkhorst, Frank Martin, Briegel, Josef, Reinhart, Konrad, Löffler, Markus, Kluge, Stefan, Nierhaus, Axel, Jaschinski, Ulrich, Mörer, Onnen, Weyland, Andreas, Meybohm, Patrick, SepNet Critical Care Trials Group, Elke, Gunnar, Bloos, Frank, Wilson, Darius Cameron, Brunkhorst, Frank Martin, Briegel, Josef, Reinhart, Konrad, Löffler, Markus, Kluge, Stefan, Nierhaus, Axel, Jaschinski, Ulrich, Mörer, Onnen, Weyland, Andreas, and Meybohm, Patrick

Abstract

Background: This study assessed the ability of mid-regional proadrenomedullin (MR-proADM) in comparison to conventional biomarkers (procalcitonin (PCT), lactate, C-reactive protein) and clinical scores to identify disease severity in patients with sepsis. Methods: This is a secondary analysis of a randomised controlled trial in patients with severe sepsis or septic shock across 33 German intensive care units. The association between biomarkers and clinical scores with mortality was assessed by Cox regression analysis, area under the receiver operating characteristic and Kaplan-Meier curves. Patients were stratified into three severity groups (low, intermediate, high) for all biomarkers and scores based on cutoffs with either a 90% sensitivity or specificity. Results: 1089 patients with a 28-day mortality rate of 26.9% were analysed. According to the Sepsis-3 definition, 41.2% and 58.8% fulfilled the criteria for sepsis and septic shock, with respective mortality rates of 20.0% and 32.1%. MR-proADM had the strongest association with mortality across all Sepsis-1 and Sepsis-3 subgroups and could facilitate a more accurate classification of low (e.g. MR-proADM vs. SOFA: N = 265 vs. 232; 9.8% vs. 13.8% mortality) and high (e.g. MR-proADM vs. SOFA: N = 161 vs. 155; 55.9% vs. 41.3% mortality) disease severity. Patients with decreasing PCT concentrations of either ≥ 20% (baseline to day 1) or ≥ 50% (baseline to day 4) but continuously high MR-proADM concentrations had a significantly increased mortality risk (HR (95% CI): 19.1 (8.0–45.9) and 43.1 (10.1–184.0)). Conclusions: MR-proADM identifies disease severity and treatment response more accurately than established biomarkers and scores, adding additional information to facilitate rapid clinical decision-making and improve personalised sepsis treatment.

Published
2018

28. Effects of regional perfusion block in healthy and injured lungs

Author

Cambiaghi, Barbara, primary, Vasques, Francesco, additional, Mörer, Onnen, additional, Ritter, Christian, additional, Mauri, Tommaso, additional, Kunze-Szikszay, Nils, additional, Holke, Karin, additional, Collino, Francesca, additional, Maiolo, Giorgia, additional, Rapetti, Francesca, additional, Schulze-Kalthoff, Elias, additional, Tonetti, Tommaso, additional, Hahn, Günter, additional, Quintel, Michael, additional, and Gattinoni, Luciano, additional

Published
2017
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30. A German national prevalence study on the cost of intensive care : an evaluation from 51 intensive care units

Author

Mörer, Onnen, Plock, Enno, Mgbor, Uchenna, Schmid, Alexandra, Schneider, Heinz, Wischnewsky, Manfred Bernd, Burchardi, Hilmar, Mörer, Onnen, Plock, Enno, Mgbor, Uchenna, Schmid, Alexandra, Schneider, Heinz, Wischnewsky, Manfred Bernd, and Burchardi, Hilmar

Abstract

INTRODUCTION: Intensive care unit (ICU) costs account for up to 20% of a hospital's costs. We aimed to analyse the individual patient-related cost of intensive care at various hospital levels and for different groups of disease. METHODS: Data from 51 ICUs all over Germany (15 primary care hospitals and 14 general care hospitals, 10 maximal care hospitals and 12 focused care hospitals) were collected in an observational, cross-sectional, one-day point prevalence study by two external study physicians (January-October 2003). All ICU patients (length of stay > 24 hours) treated on the study day were included. The reason for admission, severity of illness, surgical/diagnostic procedures, resource consumption, ICU/hospital length of stay, outcome and ICU staffing structure were documented. RESULTS: Altogether 453 patients were included. ICU (hospital) mortality was 12.1% (15.7%). The reason for admission and the severity of illness differed between the hospital levels of care, with a higher amount of unscheduled surgical procedures and patients needing mechanical ventilation in maximal care hospital and focused care hospital facilities. The mean total costs per day were euro 791 +/- 305 (primary care hospitals, euro 685 +/- 234; general care hospitals, euro 672 +/- 199; focused care hospitals, euro 816 +/- 363; maximal care hospitals, euro 923 +/- 306), with the highest cost in septic patients (euro 1,090 +/- 422). Differences were associated with staffing, the amount of prescribed drugs/blood products and diagnostic procedures. CONCLUSION: The reason for admission, the severity of illness and the occurrence of severe sepsis are directly related to the level of ICU cost. A high fraction of costs result from staffing (up to 62%). Specialized and maximum care hospitals treat a higher proportion of the more severely ill and most expensive patients.

Published
2007
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31. Zerebrale Mikrodialyse

Author

Malinova, Vesna, von der Brelie, Christian, Harnisch, Lars-Olav, editor, Mörer, Onnen, editor, and Stephani, Caspar, editor

Published
2023
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48. Effects of regional perfusion block in healthy and injured lungs

Author

Cambiaghi, B., Vasques, F., Morer, O., Ritter, C., Mauri, T., Kunze-Szikszay, N., Holke, K., Collino, F., Maiolo, G., Rapetti, F., Schulze-Kalthoff, E., Tonetti, T., Hahn, G., Quintel, M., Gattinoni, L., Cambiaghi, Barbara, Vasques, Francesco, Mörer, Onnen, Ritter, Christian, Mauri, Tommaso, Kunze-Szikszay, Nil, Holke, Karin, Collino, Francesca, Maiolo, Giorgia, Rapetti, Francesca, Schulze-Kalthoff, Elia, Tonetti, Tommaso, Hahn, Günter, Quintel, Michael, and Gattinoni, Luciano

Subjects
Ventilator-induced lung injury, Computed tomography (CT), Experimental animal model, Lung injury, Pulmonary circulation, Pulmonary embolism, Research, lcsh:Medical emergencies. Critical care. Intensive care. First aid, lcsh:RC86-88.9, respiratory system, Pulmonary embolism, Lung injury, Ventilator-induced lung injury, Experimental animal model, Pulmonary circulation, Computed tomography (CT), respiratory tract diseases
Abstract

Background Severe hypoperfusion can cause lung damage. We studied the effects of regional perfusion block in normal lungs and in the lungs that had been conditioned by lavage with 500 ml saline and high V T (20 ml kg−1) ventilation. Methods Nineteen pigs (61.2 ± 2.5 kg) were randomized to five groups: controls (n = 3), the right lower lobe block alone (n = 3), lavage and high V T (n = 4), lung lavage, and high V T plus perfusion block of the right (n = 5) or left (n = 4) lower lobe. Gas exchange, respiratory mechanics, and hemodynamics were measured hourly. After an 8-h observation period, CT scans were obtained at 0 and 15 cmH2O airway pressure. Results Perfusion block did not damage healthy lungs. In conditioned lungs, the left perfusion block caused more edema in the contralateral lung (777 ± 62 g right lung vs 484 ± 204 g left; p

Published
2017

49. Incidence of difficult intubation in intensive care patients: analysis of contributing factors.

Author

Heuer JF, Barwing TA, Barwing J, Russo SG, Bleckmann E, Quintel M, and Moerer O

Subjects
Aged, Aged, 80 and over, Algorithms, Female, Fiber Optic Technology, Humans, Intubation, Intratracheal instrumentation, Male, Middle Aged, Risk Factors, Intensive Care Units, Intubation, Intratracheal methods, Laryngeal Masks, Laryngoscopy methods
Abstract

Difficulties in endotracheal intubation increase morbidity and mortality in intensive care patients. We studied the problem in surgical intensive care patients with the aim of risk reduction. Patients intubated in the intensive care unit were evaluated. The intubations were performed or supervised by anaesthetists following the algorithm valid at the time of the study. Fifty percent of the 198 intubations were performed by specialist anaesthetists, 41.5% by anaesthesia trainees and 8.5% by surgical trainees. The initial attempt was by direct laryngoscopy (n=173), flexible fibrescope (n=8) or blind nasal technique (n=17). When direct laryngoscopy failed (n=7), intubation was accomplished with an intubating laryngeal mask airway (n=5), Frova stylet (n=1) or fibrescope (n=1). Thirty percent were rated as easy, 47% as moderately easy and 23% as difficult. Difficult intubations were associated with a higher incidence of anatomic anomalies, difficult bag-mask ventilation and severe oxygen desaturation. Every intubation in the ICU setting should be considered potentially difficult. The existing algorithm should be modified to incorporate the American Society of Anesthesiologists difficult airway algorithm adapted to the needs of the intensive care unit. A training program for alternative methods of airway management for difficult intubations should be established.

Published
2012
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