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2. Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study
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Kloss, P, Lindholz, M, Milnik, A, Azoulay, E, Cecconi, M, Citerio, G, De Corte, T, Duska, F, Galarza, L, Greco, M, Girbes, A, Kesecioglu, J, Mellinghoff, J, Ostermann, M, Pellegrini, M, Teboul, J, De Waele, J, Wong, A, Schaller, S, Aires, B, Gira, A, Eller, P, Hamid, T, Haque, I, De Buyser, W, Cudia, A, De Backer, D, Foulon, P, Collin, V, Van Hecke, J, De Waele, E, Van Malderen, C, Mesland, J, Biston, P, Piagnerelli, M, Haentjens, L, De Schryver, N, Van Leemput, J, Vanhove, P, Bulpa, P, Ilieva, V, Katz, D, Binnie, A, Geagea, A, Tirapegui, F, Lago, G, Graf, J, Perez-Araos, R, Vargas, P, Martinez, F, Labarca, E, Franco, D, Parra-Tanoux, D, Yepes, D, Hammouda, A, Elmandouh, O, Azzam, A, Hussein, A, Galal, I, Awad, A, Azab, M, Abdalla, M, Assal, H, Alfishawy, M, Ghozy, S, Tharwat, S, Eldaly, A, Ellervee, A, Reinhard, V, Chrisment, A, Poyat, C, Badie, J, Berdaguer Ferrari, F, Weiss, B, Schellenberg, C, Grunow, J, Lorenz, M, Spieth, P, Bota, M, Fichtner, F, Fuest, K, Lahmer, T, Herrmann, J, Meybohm, P, Markou, N, Vasileiadou, G, Chrysanthopoulou, E, Papamichalis, P, Soultati, I, Jog, S, Kalvit, K, Nainan Myatra, S, Krupa, I, Tharwat, A, Nichol, A, Mccarthy, A, Mahmoodpoor, A, Tonetti, T, Isoni, P, Spadaro, S, Volta, C, Mirabella, L, Noto, A, Florio, G, Guzzardella, A, Paleari, C, Baccanelli, F, Savi, M, Antonelli, M, De Pascale, G, Vaccarini, B, Montrucchio, G, Sales, G, Donadello, K, Gottin, L, Nizzero, M, Polati, E, De Rosa, S, Sulemanji, D, Abusalama, A, Elhadi, M, Jesus, M, Gonzalez, D, Robles, V, Canedo, N, Chavez, A, Dendane, T, Grady, B, de Jong, B, van der Heiden, E, Thoral, P, van den Bogaard, B, Spronk, P, Achterberg, S, Groeneveld, M, So, R, de Wijs, C, Scholten, H, Beishuizen, A, Cornet, A, Reidinga, A, Kranen, H, Mensink, R, den Boer, S, de Groot, M, Beck, O, Bethlehem, C, van Bussel, B, Frenzel, T, de Jong, C, Wilting, R, Mehagnoul-Schipper, J, Alasia, D, Kumar, A, Qayyum, A, Rana, M, Jayyab, M, Sierra, R, Hernandez, A, Taborda, L, Anselmo, M, Ramires, T, Silva, C, Roriz, C, Morais, R, Póvoa, P, Patricio, P, Pinto, A, Santos, M, Costa, V, Cunha, P, Gonçalves, C, Nunes, S, Camões, J, Adrião, D, Oliveira, A, Omrani, A, Maslamani, M, Elbuzidi, A, Qudah, B, Akkari, A, Alkhatteb, M, Baiou, A, Husain, A, Alwraidat, M, Saif, I, Bakdach, D, Ahmed, A, Aleef, M, Bintaher, A, Petrisor, C, Popov, E, Popova, K, Dementienko, M, Teplykh, B, Pyregov, A, Davydova, L, Vladislav, B, Neporada, E, Zverev, I, Meshchaninova, S, Sokolov, D, Gavrilova, E, Shlyk, I, Poliakov, I, Vlasova, M, Aljuhani, O, Alkhalaf, A, Humaid, F, Arabi, Y, Kuhail, A, Elrabi, O, Ghannam, M, Kansal, A, Ho, V, Ng, J, García, R, Fraga, X, del Pilar García-Bonillo, M, Padilla-Serrano, A, Cuadrado, M, Ferrando, C, Catalan-Monzon, I, Frutos-Vivar, F, Jimenez, J, Rodríguez-Solis, C, Franquesa-Gonzalez, E, Acosta, G, Cabrera, L, Parra, J, Gonzalez, F, del Carmen Conesa, M, Varela, I, Pravia, O, Delgado, M, de Cabo, C, Ioan, A, Perez-Calvo, C, Santos, A, Abad-Motos, A, Ripolles-Melchor, J, Martin, B, Teruel, S, Lucas, J, Ortiz, A, de Pablo Sánchez, R, Barrueco-Francioni, J, Espina, L, Bonell-Goytisolo, J, Salaverria, I, Mir, A, Rodriguez-Ruiz, E, Valverde, V, Cubero, P, Linde, F, Leganes, N, Romeu, J, Concha, P, Berezo-Garcia, J, Fraile, V, Cuenca-Rubio, C, Pérez-Torres, D, Serrano, A, Valero, C, Suner, A, Larrañaga, L, Legaristi, N, Ferrigno, G, Khlafalla, S, Bihariesingh-Sanchit, R, Zoerner, F, Grip, J, Kilsand, K, Mårtensson, J, Österlind, J, von Seth, M, Berkius, J, Ceruti, S, Glotta, A, Izdes, S, Turan, I, Cosar, A, Halacli, B, Dereli, N, Yilmaz, M, Akbas, T, Elay, G, Eyüpoğlu, S, Bílír, Y, Saraçoğlu, K, Kaya, E, Sahin, A, Ekren, P, Mengi, T, Suner, K, Tomak, Y, Eroglu, A, Alsabbah, A, Hanlon, K, Gervin, K, Mcmahon, S, Hagan, S, Higenbottam, C, Mullhi, R, Poulton, L, Torlinski, T, Gareth, A, Truman, N, Vijayakumar, G, Hall, C, Jubb, A, Cagova, L, Jones, N, Graham, S, Robin, N, Cowton, A, Donnelly, A, Singatullina, N, Kent, M, Boulanger, C, Campbell, Z, Potter, E, Duric, N, Szakmany, T, Kviatkovske, O, Marczin, N, Ellis, C, Saha, R, Sri-Chandana, C, Allan, J, Mumelj, L, Venkatesh, H, Gotz, V, Cochrane, A, Ficial, B, Kamble, S, Lumlertgul, N, Oddy, C, Jain, S, Crapelli, G, Vlachou, A, Golden, D, Garrioch, S, Henning, J, Loveleena, G, Davey, M, Grauslyte, L, Salciute-Simene, E, Cook, M, Barling, D, Broadhurst, P, Purvis, S, Spivey, M, Shuker, B, Grecu, I, Harding, D, Dean, J, Nielsen, N, Al-Bayati, S, Al-Sadawi, M, Charron, M, Stubenrauch, P, Santanilla, J, Wentowski, C, Rosenberger, D, Eksarko, P, Jawa, R, Kloss, Philipp, Lindholz, Maximilian, Milnik, Annette, Azoulay, Elie, Cecconi, Maurizio, Citerio, Giuseppe, De Corte, Thomas, Duska, Frantisek, Galarza, Laura, Greco, Massimiliano, Girbes, Armand R. J., Kesecioglu, Jozef, Mellinghoff, Johannes, Ostermann, Marlies, Pellegrini, Mariangela, Teboul, Jean-Louis, De Waele, Jan, Wong, Adrian, Schaller, Stefan J., Aires, Buenos, Gira, Alicia, Eller, Philipp, Hamid, Tarikul, Haque, Injamam Ull, De Buyser, Wim, Cudia, Antonella, De Backer, Daniel, Foulon, Pierre, Collin, Vincent, Van Hecke, Jolien, De Waele, Elisabeth, Van Malderen, Claire, Mesland, Jean-Baptiste, Biston, Patrick, Piagnerelli, Michael, Haentjens, Lionel, De Schryver, Nicolas, Van Leemput, Jan, Vanhove, Philippe, Bulpa, Pierre, Ilieva, Viktoria, Katz, David, Binnie, Alexandra, Geagea, Anna, Tirapegui, Fernando, Lago, Gustavo, Graf, Jerónimo, Perez-Araos, Rodrigo, Vargas, Patricio, Martinez, Felipe, Labarca, Eduardo, Franco, Daniel Molano, Parra-Tanoux, Daniela, Yepes, David, Hammouda, Ahmed, Elmandouh, Omar, Azzam, Ahmed, Hussein, Aliae Mohamed, Galal, Islam, Awad, Ahmed K., Azab, Mohammed A., Abdalla, Maged, Assal, Hebatallah, Alfishawy, Mostafa, Ghozy, Sherief, Tharwat, Samar, Eldaly, Abdullah, Ellervee, Anneli, Reinhard, Veronika, Chrisment, Anne, Poyat, Chrystelle, Badie, Julio, Berdaguer Ferrari, Fernando, Weiss, Björn, Schellenberg, Clara, Grunow, Julius J, Lorenz, Marco, Schaller, Stefan J, Spieth, Peter, Bota, Marc, Fichtner, Falk, Fuest, Kristina, Lahmer, Tobias, Herrmann, Johannes, Meybohm, Patrick, Markou, Nikolaos, Vasileiadou, Georgia, Chrysanthopoulou, Evangelia, Papamichalis, Panagiotis, Soultati, Ioanna, Jog, Sameer, Kalvit, Kushal, Nainan Myatra, Sheila, Krupa, Ivan, Tharwat, Aisa, Nichol, Alistair, McCarthy, Aine, Mahmoodpoor, Ata, Tonetti, Tommaso, Isoni, Paolo, Spadaro, Savino, Volta, Carlo Alberto, Mirabella, Lucia, Noto, Alberto, Florio, Gaetano, Guzzardella, Amedeo, Paleari, Chiara, Baccanelli, Federica, Savi, Marzia, Antonelli, Massimo, De Pascale, Gennaro, Vaccarini, Barbara, Montrucchio, Giorgia, Sales, Gabriele, Donadello, Katia, Gottin, Leonardo, Nizzero, Marta, Polati, Enrico, De Rosa, Silvia, Sulemanji, Demet, Abusalama, Abdurraouf, Elhadi, Muhammed, Jesus, Montelongo Felipe De, Gonzalez, Daniel Rodriguez, Robles, Victor Hugo Madrigal, Canedo, Nancy, Chavez, Alejandro Esquivel, Dendane, Tarek, Grady, Bart, de Jong, Ben, van der Heiden, Eveline, Thoral, Patrick, van den Bogaard, Bas, Spronk, Peter E., Achterberg, Sefanja, Groeneveld, Melanie, So, Ralph K. L., de Wijs, Calvin, Scholten, Harm, Beishuizen, Albertus, Cornet, Alexander D., Reidinga, Auke C., Kranen, Hetty, Mensink, Roos, den Boer, Sylvia, de Groot, Marcel, Beck, Oliver, Bethlehem, Carina, van Bussel, Bas, Frenzel, Tim, de Jong, Celestine, Wilting, Rob, Mehagnoul-Schipper, Jannet, Alasia, Datonye, Kumar, Ashok, Qayyum, Ahad, Rana, Muhammad, Jayyab, Mustafa Abu, Sierra, Rosario Quispe, Hernandez, Aaron Mark, Taborda, Lúcia, Anselmo, Mónica, Ramires, Tiago, Silva, Catarina, Roriz, Carolina, Morais, Rui, Póvoa, Pedro, Patricio, Patricia, Pinto, André, Santos, Maria Lurdes, Costa, Vasco, Cunha, Pedro, Gonçalves, Celina, Nunes, Sandra, Camões, João, Adrião, Diana, Oliveira, Ana, Omrani, Ali, Maslamani, Muna Al, elbuzidi, Abdurrahmaan Suei, qudah, Bara Mahmoud Al, Akkari, Abdel Rauof, Alkhatteb, Mohamed, Baiou, Anas, Husain, Ahmed, Alwraidat, Mohamed, Saif, Ibrahim Abdulsalam, Bakdach, Dana, Ahmed, Amna, Aleef, Mohamed, Bintaher, Awadh, Petrisor, Cristina, Popov, Evgeniy, Popova, Ksenia, Dementienko, Mariia, Teplykh, Boris, Pyregov, Alexey, Davydova, Liubov, Vladislav, Belskii, Neporada, Elena, Zverev, Ivan, Meshchaninova, Svetlana, Sokolov, Dmitry, Gavrilova, Elena, Shlyk, Irina, Poliakov, Igor, Vlasova, Marina, Aljuhani, Ohoud, Alkhalaf, Amina, Humaid, Felwa Bin, Arabi, Yaseen, Kuhail, Ahmed, Elrabi, Omar, Ghannam, Madihah E., Kansal, Amit, Ho, Vui Kian, Ng, Jensen, García, Raquel Rodrígez, Fraga, Xiana Taboada, del Pilar García-Bonillo, Ma, Padilla-Serrano, Antonio, Cuadrado, Marta Martin, Ferrando, Carlos, Catalan-Monzon, Ignacio, Frutos-Vivar, Fernando, Jimenez, Jorge, Rodríguez-Solis, Carmen, Franquesa-Gonzalez, Enric, Acosta, Guillermo Pérez, Cabrera, Luciano Santana, Parra, Juan Pablo Aviles, Gonzalez, Francisco Muñoyerro, del Carmen Conesa, Maria Lorente, Varela, Ignacio Yago Martinez, Pravia, Orville Victoriano Baez, Delgado, Maria Cruz Martin, de Cabo, Carlos Munoz, Ioan, Ana-Maria, Perez-Calvo, Cesar, Santos, Arnoldo, Abad-Motos, Ane, Ripolles-Melchor, Javier, Martin, Belén Civantos, Teruel, Santiago Yus, Lucas, Juan Higuera, Ortiz, Aaron Blandino, de Pablo Sánchez, Raúl, Barrueco-Francioni, Jesús Emilio, Espina, Lorena Forcelledo, Bonell-Goytisolo, José M., Salaverria, Iñigo, Mir, Antonia Socias, Rodriguez-Ruiz, Emilio, Valverde, Virginia Hidalgo, Cubero, Patricia Jimeno, Linde, Francisca Arbol, Leganes, Nieves Cruza, Romeu, Juan Maria, Concha, Pablo, Berezo-Garcia, José Angel, Fraile, Virginia, Cuenca-Rubio, Cristina, Pérez-Torres, David, Serrano, Ainhoa, Valero, Clara Martínez, Suner, Andrea Ortiz, Larrañaga, Leire, Legaristi, Noemi, Ferrigno, Gerardo, Khlafalla, Safa, Bihariesingh-Sanchit, Rosita, Zoerner, Frank, Grip, Jonathan, Kilsand, Kristina, Mårtensson, Johan, Österlind, Jonas, von Seth, Magnus, Berkius, Johan, Ceruti, Samuele, Glotta, Andrea, Izdes, Seval, Turan, Işıl Özkoçak, Cosar, Ahmet, Halacli, Burcin, Dereli, Necla, Yilmaz, Mehmet, Akbas, Türkay, Elay, Gülseren, Eyüpoğlu, Selin, Bílír, Yelíz, Saraçoğlu, Kemal Tolga, Kaya, Ebru, Sahin, Ayca Sultan, Ekren, Pervin Korkmaz, Mengi, Tuğçe, Suner, Kezban Ozmen, Tomak, Yakup, Eroglu, Ahmet, Alsabbah, Asad, Hanlon, Katie, Gervin, Kevin, McMahon, Sean, Hagan, Samantha, Higenbottam, Caroline V, Mullhi, Randeep, Poulton, Lottie, Torlinski, Tomasz, Gareth, Allen, Truman, Nick, Vijayakumar, Gopal, Hall, Chris, Jubb, Alasdair, Cagova, Lenka, Jones, Nicola, Graham, Sam, Robin, Nicole, Cowton, Amanda, Donnelly, Adrian, Singatullina, Natalia, Kent, Melanie, Boulanger, Carole, Campbell, Zoë, Potter, Elizabeth, Duric, Natalie, Szakmany, Tamas, Kviatkovske, Orinta, Marczin, Nandor, Ellis, Caroline, Saha, Rajnish, Sri-Chandana, Chunda, Allan, John, Mumelj, Lana, Venkatesh, Harish, Gotz, Vera Nina, Cochrane, Anthony, Ficial, Barbara, Kamble, Shruthi, Lumlertgul, Nuttha, Oddy, Christopher, Jain, Susan, Crapelli, Giulia Beatrice, Vlachou, Aikaterini, Golden, David, Garrioch, Sweyn, Henning, Jeremy, Loveleena, Gupta, Davey, Miriam, Grauslyte, Lina, Salciute-Simene, Erika, Cook, Martin, Barling, Danny, Broadhurst, Phil, Purvis, Sarah, Spivey, Michael, Shuker, Benjamin, Grecu, Irina, Harding, Daniel, Dean, James T., Nielsen, Nathan D., Al-Bayati, Sama, Al-Sadawi, Mohammed, Charron, Mariane, Stubenrauch, Peter, Santanilla, Jairo, Wentowski, Catherine, Rosenberger, Dorothea, Eksarko, Polikseni, Jawa, Randeep, Kloss, P, Lindholz, M, Milnik, A, Azoulay, E, Cecconi, M, Citerio, G, De Corte, T, Duska, F, Galarza, L, Greco, M, Girbes, A, Kesecioglu, J, Mellinghoff, J, Ostermann, M, Pellegrini, M, Teboul, J, De Waele, J, Wong, A, Schaller, S, Aires, B, Gira, A, Eller, P, Hamid, T, Haque, I, De Buyser, W, Cudia, A, De Backer, D, Foulon, P, Collin, V, Van Hecke, J, De Waele, E, Van Malderen, C, Mesland, J, Biston, P, Piagnerelli, M, Haentjens, L, De Schryver, N, Van Leemput, J, Vanhove, P, Bulpa, P, Ilieva, V, Katz, D, Binnie, A, Geagea, A, Tirapegui, F, Lago, G, Graf, J, Perez-Araos, R, Vargas, P, Martinez, F, Labarca, E, Franco, D, Parra-Tanoux, D, Yepes, D, Hammouda, A, Elmandouh, O, Azzam, A, Hussein, A, Galal, I, Awad, A, Azab, M, Abdalla, M, Assal, H, Alfishawy, M, Ghozy, S, Tharwat, S, Eldaly, A, Ellervee, A, Reinhard, V, Chrisment, A, Poyat, C, Badie, J, Berdaguer Ferrari, F, Weiss, B, Schellenberg, C, Grunow, J, Lorenz, M, Spieth, P, Bota, M, Fichtner, F, Fuest, K, Lahmer, T, Herrmann, J, Meybohm, P, Markou, N, Vasileiadou, G, Chrysanthopoulou, E, Papamichalis, P, Soultati, I, Jog, S, Kalvit, K, Nainan Myatra, S, Krupa, I, Tharwat, A, Nichol, A, Mccarthy, A, Mahmoodpoor, A, Tonetti, T, Isoni, P, Spadaro, S, Volta, C, Mirabella, L, Noto, A, Florio, G, Guzzardella, A, Paleari, C, Baccanelli, F, Savi, M, Antonelli, M, De Pascale, G, Vaccarini, B, Montrucchio, G, Sales, G, Donadello, K, Gottin, L, Nizzero, M, Polati, E, De Rosa, S, Sulemanji, D, Abusalama, A, Elhadi, M, Jesus, M, Gonzalez, D, Robles, V, Canedo, N, Chavez, A, Dendane, T, Grady, B, de Jong, B, van der Heiden, E, Thoral, P, van den Bogaard, B, Spronk, P, Achterberg, S, Groeneveld, M, So, R, de Wijs, C, Scholten, H, Beishuizen, A, Cornet, A, Reidinga, A, Kranen, H, Mensink, R, den Boer, S, de Groot, M, Beck, O, Bethlehem, C, van Bussel, B, Frenzel, T, de Jong, C, Wilting, R, Mehagnoul-Schipper, J, Alasia, D, Kumar, A, Qayyum, A, Rana, M, Jayyab, M, Sierra, R, Hernandez, A, Taborda, L, Anselmo, M, Ramires, T, Silva, C, Roriz, C, Morais, R, Póvoa, P, Patricio, P, Pinto, A, Santos, M, Costa, V, Cunha, P, Gonçalves, C, Nunes, S, Camões, J, Adrião, D, Oliveira, A, Omrani, A, Maslamani, M, Elbuzidi, A, Qudah, B, Akkari, A, Alkhatteb, M, Baiou, A, Husain, A, Alwraidat, M, Saif, I, Bakdach, D, Ahmed, A, Aleef, M, Bintaher, A, Petrisor, C, Popov, E, Popova, K, Dementienko, M, Teplykh, B, Pyregov, A, Davydova, L, Vladislav, B, Neporada, E, Zverev, I, Meshchaninova, S, Sokolov, D, Gavrilova, E, Shlyk, I, Poliakov, I, Vlasova, M, Aljuhani, O, Alkhalaf, A, Humaid, F, Arabi, Y, Kuhail, A, Elrabi, O, Ghannam, M, Kansal, A, Ho, V, Ng, J, García, R, Fraga, X, del Pilar García-Bonillo, M, Padilla-Serrano, A, Cuadrado, M, Ferrando, C, Catalan-Monzon, I, Frutos-Vivar, F, Jimenez, J, Rodríguez-Solis, C, Franquesa-Gonzalez, E, Acosta, G, Cabrera, L, Parra, J, Gonzalez, F, del Carmen Conesa, M, Varela, I, Pravia, O, Delgado, M, de Cabo, C, Ioan, A, Perez-Calvo, C, Santos, A, Abad-Motos, A, Ripolles-Melchor, J, Martin, B, Teruel, S, Lucas, J, Ortiz, A, de Pablo Sánchez, R, Barrueco-Francioni, J, Espina, L, Bonell-Goytisolo, J, Salaverria, I, Mir, A, Rodriguez-Ruiz, E, Valverde, V, Cubero, P, Linde, F, Leganes, N, Romeu, J, Concha, P, Berezo-Garcia, J, Fraile, V, Cuenca-Rubio, C, Pérez-Torres, D, Serrano, A, Valero, C, Suner, A, Larrañaga, L, Legaristi, N, Ferrigno, G, Khlafalla, S, Bihariesingh-Sanchit, R, Zoerner, F, Grip, J, Kilsand, K, Mårtensson, J, Österlind, J, von Seth, M, Berkius, J, Ceruti, S, Glotta, A, Izdes, S, Turan, I, Cosar, A, Halacli, B, Dereli, N, Yilmaz, M, Akbas, T, Elay, G, Eyüpoğlu, S, Bílír, Y, Saraçoğlu, K, Kaya, E, Sahin, A, Ekren, P, Mengi, T, Suner, K, Tomak, Y, Eroglu, A, Alsabbah, A, Hanlon, K, Gervin, K, Mcmahon, S, Hagan, S, Higenbottam, C, Mullhi, R, Poulton, L, Torlinski, T, Gareth, A, Truman, N, Vijayakumar, G, Hall, C, Jubb, A, Cagova, L, Jones, N, Graham, S, Robin, N, Cowton, A, Donnelly, A, Singatullina, N, Kent, M, Boulanger, C, Campbell, Z, Potter, E, Duric, N, Szakmany, T, Kviatkovske, O, Marczin, N, Ellis, C, Saha, R, Sri-Chandana, C, Allan, J, Mumelj, L, Venkatesh, H, Gotz, V, Cochrane, A, Ficial, B, Kamble, S, Lumlertgul, N, Oddy, C, Jain, S, Crapelli, G, Vlachou, A, Golden, D, Garrioch, S, Henning, J, Loveleena, G, Davey, M, Grauslyte, L, Salciute-Simene, E, Cook, M, Barling, D, Broadhurst, P, Purvis, S, Spivey, M, Shuker, B, Grecu, I, Harding, D, Dean, J, Nielsen, N, Al-Bayati, S, Al-Sadawi, M, Charron, M, Stubenrauch, P, Santanilla, J, Wentowski, C, Rosenberger, D, Eksarko, P, Jawa, R, Kloss, Philipp, Lindholz, Maximilian, Milnik, Annette, Azoulay, Elie, Cecconi, Maurizio, Citerio, Giuseppe, De Corte, Thomas, Duska, Frantisek, Galarza, Laura, Greco, Massimiliano, Girbes, Armand R. J., Kesecioglu, Jozef, Mellinghoff, Johannes, Ostermann, Marlies, Pellegrini, Mariangela, Teboul, Jean-Louis, De Waele, Jan, Wong, Adrian, Schaller, Stefan J., Aires, Buenos, Gira, Alicia, Eller, Philipp, Hamid, Tarikul, Haque, Injamam Ull, De Buyser, Wim, Cudia, Antonella, De Backer, Daniel, Foulon, Pierre, Collin, Vincent, Van Hecke, Jolien, De Waele, Elisabeth, Van Malderen, Claire, Mesland, Jean-Baptiste, Biston, Patrick, Piagnerelli, Michael, Haentjens, Lionel, De Schryver, Nicolas, Van Leemput, Jan, Vanhove, Philippe, Bulpa, Pierre, Ilieva, Viktoria, Katz, David, Binnie, Alexandra, Geagea, Anna, Tirapegui, Fernando, Lago, Gustavo, Graf, Jerónimo, Perez-Araos, Rodrigo, Vargas, Patricio, Martinez, Felipe, Labarca, Eduardo, Franco, Daniel Molano, Parra-Tanoux, Daniela, Yepes, David, Hammouda, Ahmed, Elmandouh, Omar, Azzam, Ahmed, Hussein, Aliae Mohamed, Galal, Islam, Awad, Ahmed K., Azab, Mohammed A., Abdalla, Maged, Assal, Hebatallah, Alfishawy, Mostafa, Ghozy, Sherief, Tharwat, Samar, Eldaly, Abdullah, Ellervee, Anneli, Reinhard, Veronika, Chrisment, Anne, Poyat, Chrystelle, Badie, Julio, Berdaguer Ferrari, Fernando, Weiss, Björn, Schellenberg, Clara, Grunow, Julius J, Lorenz, Marco, Schaller, Stefan J, Spieth, Peter, Bota, Marc, Fichtner, Falk, Fuest, Kristina, Lahmer, Tobias, Herrmann, Johannes, Meybohm, Patrick, Markou, Nikolaos, Vasileiadou, Georgia, Chrysanthopoulou, Evangelia, Papamichalis, Panagiotis, Soultati, Ioanna, Jog, Sameer, Kalvit, Kushal, Nainan Myatra, Sheila, Krupa, Ivan, Tharwat, Aisa, Nichol, Alistair, McCarthy, Aine, Mahmoodpoor, Ata, Tonetti, Tommaso, Isoni, Paolo, Spadaro, Savino, Volta, Carlo Alberto, Mirabella, Lucia, Noto, Alberto, Florio, Gaetano, Guzzardella, Amedeo, Paleari, Chiara, Baccanelli, Federica, Savi, Marzia, Antonelli, Massimo, De Pascale, Gennaro, Vaccarini, Barbara, Montrucchio, Giorgia, Sales, Gabriele, Donadello, Katia, Gottin, Leonardo, Nizzero, Marta, Polati, Enrico, De Rosa, Silvia, Sulemanji, Demet, Abusalama, Abdurraouf, Elhadi, Muhammed, Jesus, Montelongo Felipe De, Gonzalez, Daniel Rodriguez, Robles, Victor Hugo Madrigal, Canedo, Nancy, Chavez, Alejandro Esquivel, Dendane, Tarek, Grady, Bart, de Jong, Ben, van der Heiden, Eveline, Thoral, Patrick, van den Bogaard, Bas, Spronk, Peter E., Achterberg, Sefanja, Groeneveld, Melanie, So, Ralph K. L., de Wijs, Calvin, Scholten, Harm, Beishuizen, Albertus, Cornet, Alexander D., Reidinga, Auke C., Kranen, Hetty, Mensink, Roos, den Boer, Sylvia, de Groot, Marcel, Beck, Oliver, Bethlehem, Carina, van Bussel, Bas, Frenzel, Tim, de Jong, Celestine, Wilting, Rob, Mehagnoul-Schipper, Jannet, Alasia, Datonye, Kumar, Ashok, Qayyum, Ahad, Rana, Muhammad, Jayyab, Mustafa Abu, Sierra, Rosario Quispe, Hernandez, Aaron Mark, Taborda, Lúcia, Anselmo, Mónica, Ramires, Tiago, Silva, Catarina, Roriz, Carolina, Morais, Rui, Póvoa, Pedro, Patricio, Patricia, Pinto, André, Santos, Maria Lurdes, Costa, Vasco, Cunha, Pedro, Gonçalves, Celina, Nunes, Sandra, Camões, João, Adrião, Diana, Oliveira, Ana, Omrani, Ali, Maslamani, Muna Al, elbuzidi, Abdurrahmaan Suei, qudah, Bara Mahmoud Al, Akkari, Abdel Rauof, Alkhatteb, Mohamed, Baiou, Anas, Husain, Ahmed, Alwraidat, Mohamed, Saif, Ibrahim Abdulsalam, Bakdach, Dana, Ahmed, Amna, Aleef, Mohamed, Bintaher, Awadh, Petrisor, Cristina, Popov, Evgeniy, Popova, Ksenia, Dementienko, Mariia, Teplykh, Boris, Pyregov, Alexey, Davydova, Liubov, Vladislav, Belskii, Neporada, Elena, Zverev, Ivan, Meshchaninova, Svetlana, Sokolov, Dmitry, Gavrilova, Elena, Shlyk, Irina, Poliakov, Igor, Vlasova, Marina, Aljuhani, Ohoud, Alkhalaf, Amina, Humaid, Felwa Bin, Arabi, Yaseen, Kuhail, Ahmed, Elrabi, Omar, Ghannam, Madihah E., Kansal, Amit, Ho, Vui Kian, Ng, Jensen, García, Raquel Rodrígez, Fraga, Xiana Taboada, del Pilar García-Bonillo, Ma, Padilla-Serrano, Antonio, Cuadrado, Marta Martin, Ferrando, Carlos, Catalan-Monzon, Ignacio, Frutos-Vivar, Fernando, Jimenez, Jorge, Rodríguez-Solis, Carmen, Franquesa-Gonzalez, Enric, Acosta, Guillermo Pérez, Cabrera, Luciano Santana, Parra, Juan Pablo Aviles, Gonzalez, Francisco Muñoyerro, del Carmen Conesa, Maria Lorente, Varela, Ignacio Yago Martinez, Pravia, Orville Victoriano Baez, Delgado, Maria Cruz Martin, de Cabo, Carlos Munoz, Ioan, Ana-Maria, Perez-Calvo, Cesar, Santos, Arnoldo, Abad-Motos, Ane, Ripolles-Melchor, Javier, Martin, Belén Civantos, Teruel, Santiago Yus, Lucas, Juan Higuera, Ortiz, Aaron Blandino, de Pablo Sánchez, Raúl, Barrueco-Francioni, Jesús Emilio, Espina, Lorena Forcelledo, Bonell-Goytisolo, José M., Salaverria, Iñigo, Mir, Antonia Socias, Rodriguez-Ruiz, Emilio, Valverde, Virginia Hidalgo, Cubero, Patricia Jimeno, Linde, Francisca Arbol, Leganes, Nieves Cruza, Romeu, Juan Maria, Concha, Pablo, Berezo-Garcia, José Angel, Fraile, Virginia, Cuenca-Rubio, Cristina, Pérez-Torres, David, Serrano, Ainhoa, Valero, Clara Martínez, Suner, Andrea Ortiz, Larrañaga, Leire, Legaristi, Noemi, Ferrigno, Gerardo, Khlafalla, Safa, Bihariesingh-Sanchit, Rosita, Zoerner, Frank, Grip, Jonathan, Kilsand, Kristina, Mårtensson, Johan, Österlind, Jonas, von Seth, Magnus, Berkius, Johan, Ceruti, Samuele, Glotta, Andrea, Izdes, Seval, Turan, Işıl Özkoçak, Cosar, Ahmet, Halacli, Burcin, Dereli, Necla, Yilmaz, Mehmet, Akbas, Türkay, Elay, Gülseren, Eyüpoğlu, Selin, Bílír, Yelíz, Saraçoğlu, Kemal Tolga, Kaya, Ebru, Sahin, Ayca Sultan, Ekren, Pervin Korkmaz, Mengi, Tuğçe, Suner, Kezban Ozmen, Tomak, Yakup, Eroglu, Ahmet, Alsabbah, Asad, Hanlon, Katie, Gervin, Kevin, McMahon, Sean, Hagan, Samantha, Higenbottam, Caroline V, Mullhi, Randeep, Poulton, Lottie, Torlinski, Tomasz, Gareth, Allen, Truman, Nick, Vijayakumar, Gopal, Hall, Chris, Jubb, Alasdair, Cagova, Lenka, Jones, Nicola, Graham, Sam, Robin, Nicole, Cowton, Amanda, Donnelly, Adrian, Singatullina, Natalia, Kent, Melanie, Boulanger, Carole, Campbell, Zoë, Potter, Elizabeth, Duric, Natalie, Szakmany, Tamas, Kviatkovske, Orinta, Marczin, Nandor, Ellis, Caroline, Saha, Rajnish, Sri-Chandana, Chunda, Allan, John, Mumelj, Lana, Venkatesh, Harish, Gotz, Vera Nina, Cochrane, Anthony, Ficial, Barbara, Kamble, Shruthi, Lumlertgul, Nuttha, Oddy, Christopher, Jain, Susan, Crapelli, Giulia Beatrice, Vlachou, Aikaterini, Golden, David, Garrioch, Sweyn, Henning, Jeremy, Loveleena, Gupta, Davey, Miriam, Grauslyte, Lina, Salciute-Simene, Erika, Cook, Martin, Barling, Danny, Broadhurst, Phil, Purvis, Sarah, Spivey, Michael, Shuker, Benjamin, Grecu, Irina, Harding, Daniel, Dean, James T., Nielsen, Nathan D., Al-Bayati, Sama, Al-Sadawi, Mohammed, Charron, Mariane, Stubenrauch, Peter, Santanilla, Jairo, Wentowski, Catherine, Rosenberger, Dorothea, Eksarko, Polikseni, and Jawa, Randeep
- Abstract
Background: Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods: This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results: Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions: Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04
- Published
- 2023
3. [COVID-19 in the emergency room]
- Author
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Milnik, A., Lindholz, M., Wenderoth, H., and Pietsch, C.
- Subjects
Symptome ,Klinsche Präsentation ,Pandemic ,SARS-CoV-2 ,Clinical presentation ,Pandemie ,Symptoms ,Emergency Medicine ,Kasusitiken ,Prevalence ,Häufigkeit - Abstract
Hintergrund Die Coronapandemie stellt zurzeit die größte Herausforderung weltweit für das Gesundheitswesen aller Staaten dar. Das rechtzeitige Erkennen der Erkrankung und die umgehende Separierung und Isolation von Verdachtsfällen sind ein wesentlicher Beitrag zum Durchbrechen von Infektionsketten. Methode Wir haben anhand der ersten 35 Patienten, die mit COVID-19 (Coronavirus-Krankheit-2019) stationär aufgenommen wurden, die verschiedenen Symptome ausgewertet, mit denen Patienten stationär vorstellig wurden. Ergebnisse Bei stationärer Vorstellung fanden sich neben Husten und Fieber häufig auch eine reduzierte periphere O2-Sättigung sowie pathologische Atemmuster. In mehreren Fällen standen andere Beschwerden ohne Bezug zu Atemwegserkrankungen im Vordergrund, z. B. neurologische, gastrointestinale oder unspezifische Symptome.
- Published
- 2020
4. COVID-19 in der zentralen Notaufnahme
- Author
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Milnik, A., primary, Lindholz, M., additional, Wenderoth, H., additional, and Pietsch, C., additional
- Published
- 2020
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5. Analyzing the TotalSegmentator for facial feature removal in head CT scans.
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Lindholz M, Ruppel R, Schulze-Weddige S, Baumgärtner GL, Schobert I, Panten A, Schmidt R, Auer TA, Nawabi J, Haack AM, Stepansky L, Poggi L, Hosch R, Hamm CA, and Penzkofer T
- Abstract
Background: Facial recognition technology in medical imaging, particularly with head scans, poses privacy risks due to identifiable facial features. This study evaluates the use of facial recognition software in identifying facial features from head CT scans and explores a defacing pipeline using TotalSegmentator to reduce re-identification risks while preserving data integrity for research., Methods: 1404 high-quality renderings from the UCLH EIT Stroke dataset, both with and without defacing were analysed. The performance of defacing with the face mask created by TotalSegmentator was compared to a state-of-the-art CT defacing algorithm. Face detection was performed using deep learning models. The cosine similarity between facial embeddings for intra- and inter-patient images was compared. A Support Vector Machine was trained on cosine similarity values to assess defacing performance, determining if two renderings came from the same patient. This analysis was conducted on defaced and non-defaced images using 5-fold cross-validation., Results: Faces were detected in 76.5 % of non-defaced images. Intra-patient images exhibited a median cosine similarity of 0.65 (IQR: 0.47-0.80), compared to 0.50 (IQR: 0.39-0.62) for inter-patient images. A binary classifier performed moderately on non-defaced images, achieving a ROC-AUC of 0.69 (SD = 0.01) and an accuracy of 0.65 (SD = 0.01) in distinguishing whether a scan belonged to the same or a different individual. Following defacing, performance declined markedly. Defacing with the TotalSegmentator decreased the ROC-AUC to 0.55 (SD = 0.02) and the accuracy to 0.56 (SD = 0.01), whereas the CTA-DEFACE algorithm brought the performance down to a ROC-AUC of 0.60 (SD = 0.02) and an accuracy of 0.59 (SD = 0.01). These results demonstrate the effectiveness of defacing algorithms in mitigating re-identification risks, with the TotalSegmentator providing slightly superior privacy protection., Conclusion: Facial recognition software can identify facial features from partial and complete head CT scan renderings. However, using the TotalSegmentator to deface images reduces re-identification risks to a near-chance level. We offer code to implement this privacy-preserving pipeline., Implications for Practice: Utilizing the TotalSegmentator framework, the proposed pipeline efficiently removes facial features from CT images, making it ideal for multi-site research and data sharing. It is a useful tool for radiographers and radiologists who must comply with medico-legal requirements necessitating the removal of facial features., Competing Interests: Conflict of interest statement C.A.H and T.A. receive funding from Berlin Institute of Health (Clinician Scientist Grant). T.P. receives funding from Berlin Institute of Health (Advanced Clinician Scientist Grant, Platform Grant), Ministry of Education and Research (BMBF, 01KX2021 (RACOON), 01KX2121 („NUM 2.0“, RACOON), 68GX21001A, 01ZZ2315D), German Research Foundation (DFG, SFB 1340/2), European Union (H2020, CHAIMELEON: 952172, DIGITAL, EUCAIM:101100633) and reports research agreements (no personal payments, outside of submitted work) with AGO, Aprea AB, ARCAGY-GINECO, Astellas Pharma Global Inc (APGD), Astra Zeneca, Clovis Oncology, Inc., Holaira, Incyte Corporation, Karyopharm, Lion Biotechnologies, Inc., MedImmune, Merck Sharp & Dohme Corp, Millennium Pharmaceuticals, Inc., Morphotec Inc., NovoCure Ltd., PharmaMar S.A. and PharmaMar USA, Inc., Roche, Siemens Healthineers, and TESARO Inc., and fees for a book translation (Elsevier B.V.). J.N. receives funding from Berlin Institute of Health (Digital Health Accelerator), European Union's Horizon Europe programme (COMFORT, 101079894) and reports personal fees from Eppdata GmbH outside the submitted work., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
- Published
- 2025
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6. CT-Defined Pectoralis Muscle Density Predicts 30-Day Mortality in Hospitalized Patients with COVID-19: A Nationwide Multicenter Study.
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Bucher AM, Behrend J, Ehrengut C, Müller L, Emrich T, Schramm D, Akinina A, Kloeckner R, Sieren M, Berkel L, Kuhl C, Sähn MJ, Fink MA, Móré D, Melekh B, Kardas H, Meinel FG, Schön H, Kornemann N, Renz DM, Lubina N, Wollny C, Both M, Watkinson J, Stöcklein S, Mittermeier A, Abaci G, May M, Siegler L, Penzkofer T, Lindholz M, Balzer M, Kim MS, Römer C, Wrede N, Götz S, Breckow J, Borggrefe J, Meyer HJ, and Surov A
- Abstract
Rationale and Objectives: The prognostic role of computed tomography (CT)-defined skeletal muscle features in COVID-19 is still under investigation. The aim of the present study was to evaluate the prognostic role of CT-defined skeletal muscle area and density in patients with COVID-19 in a multicenter setting., Materials and Methods: This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the COVID-19 pandemic). The acquired sample included 1379 patients, 389 (28.2%) women and 990 (71.8%) men. In each case, chest CT was analyzed and pectoralis muscle area and density were calculated. Data were analyzed by means of descriptive statistics. Group differences were calculated using the Mann-Whitney-U test and Fisher's exact test. Univariable and multivariable logistic regression analyses were performed., Results: The 30-day mortality was 17.9%. Using median values as thresholds, low pectoralis muscle density (LPMD) was a strong and independent predictor of 30-day mortality, HR=2.97, 95%-CI: 1.52-5.80, p=0.001. Also in male patients, LPMD predicted independently 30-day mortality, HR=2.96, 95%-CI: 1.42-6.18, p=0.004. In female patients, the analyzed pectoralis muscle parameters did not predict 30-day mortality. For patients under 60 years of age, LPMD was strongly associated with 30-day mortality, HR=2.72, 95%-CI: 1.17;6.30, p=0.019. For patients over 60 years of age, pectoralis muscle parameters could not predict 30-day mortality., Conclusion: In male patients with COVID-19, low pectoralis muscle density is strongly associated with 30-day mortality and can be used for risk stratification. In female patients with COVID-19, pectoralis muscle parameters cannot predict 30-day mortality., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
- Published
- 2024
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7. Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study.
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Kloss P, Lindholz M, Milnik A, Azoulay E, Cecconi M, Citerio G, De Corte T, Duska F, Galarza L, Greco M, Girbes ARJ, Kesecioglu J, Mellinghoff J, Ostermann M, Pellegrini M, Teboul JL, De Waele J, Wong A, and Schaller SJ
- Abstract
Background: Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave., Methods: This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs., Results: Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI - 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI - 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates., Conclusions: Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)., (© 2023. The Author(s).)
- Published
- 2023
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8. Mobilisation practices during the SARS-CoV-2 pandemic: A retrospective analysis (MobiCOVID).
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Schellenberg CM, Lindholz M, Grunow JJ, Boie S, Bald A, Warner LO, Ulm B, Milnik A, Zickler D, Angermair S, Reißhauer A, Witzenrath M, Menk M, Balzer F, Ocker T, Weber-Carstens S, and Schaller SJ
- Subjects
- Adult, Humans, Retrospective Studies, Pandemics, Intensive Care Units, SARS-CoV-2, COVID-19
- Abstract
Background: Corona Virus Disease 2019 (COVID-19) patients display risk factors for intensive care unit acquired weakness (ICUAW). The pandemic increased existing barriers to mobilisation. This study aimed to compare mobilisation practices in COVID-19 and non-COVID-19 patients., Methods: This retrospective cohort study was conducted at Charité-Universitätsmedizin Berlin, Germany, including adult patients admitted to one of 16 ICUs between March 2018, and November 2021. The effect of COVID-19 on mobilisation level and frequency, early mobilisation (EM) and time to active sitting position (ASP) was analysed. Subgroup analysis on COVID-19 patients and the ICU type influencing mobilisation practices was performed. Mobilisation entries were converted into the ICU mobility scale (IMS) using supervised machine learning. The groups were matched using 1:1 propensity score matching., Results: A total of 12,462 patients were included, receiving 59,415 mobilisations. After matching 611 COVID-19 and non-COVID-19 patients were analysed. They displayed no significant difference in mobilisation frequency (0.4 vs. 0.3, p = 0.7), maximum IMS (3 vs. 3; p = 0.17), EM (43.2% vs. 37.8%; p = 0.06) or time to ASP (HR 0.95; 95% CI: 0.82, 1.09; p = 0.44). Subgroup analysis showed that patients in surge ICUs, i.e., temporarily created ICUs for COVID-19 patients during the pandemic, more commonly received EM (53.9% vs. 39.8%; p = 0.03) and reached higher maximum IMS (4 vs. 3; p = 0.03) without difference in mobilisation frequency (0.5 vs. 0.3; p = 0.32) or time to ASP (HR 1.15; 95% CI: 0.85, 1.56; p = 0.36)., Conclusion: COVID-19 did not hinder mobilisation. Those treated in surge ICUs were more likely to receive EM and reached higher mobilisation levels., (Copyright © 2023 Société française d'anesthésie et de réanimation (Sfar). Published by Elsevier Masson SAS. All rights reserved.)
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- 2023
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9. Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU.
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Fuest KE, Ulm B, Daum N, Lindholz M, Lorenz M, Blobner K, Langer N, Hodgson C, Herridge M, Blobner M, and Schaller SJ
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- Humans, Middle Aged, Intensive Care Units, Critical Care, Hospitalization, Early Ambulation, Critical Illness therapy, Artificial Intelligence
- Abstract
Background: While early mobilization is commonly implemented in intensive care unit treatment guidelines to improve functional outcome, the characterization of the optimal individual dosage (frequency, level or duration) remains unclear. The aim of this study was to demonstrate that artificial intelligence-based clustering of a large ICU cohort can provide individualized mobilization recommendations that have a positive impact on the likelihood of being discharged home., Methods: This study is an analysis of a prospective observational database of two interdisciplinary intensive care units in Munich, Germany. Dosage of mobilization is determined by sessions per day, mean duration, early mobilization as well as average and maximum level achieved. A k-means cluster analysis was conducted including collected parameters at ICU admission to generate clinically definable clusters., Results: Between April 2017 and May 2019, 948 patients were included. Four different clusters were identified, comprising "Young Trauma," "Severely ill & Frail," "Old non-frail" and "Middle-aged" patients. Early mobilization (< 72 h) was the most important factor to be discharged home in "Young Trauma" patients (OR
adj 10.0 [2.8 to 44.0], p < 0.001). In the cluster of "Middle-aged" patients, the likelihood to be discharged home increased with each mobilization level, to a maximum 24-fold increased likelihood for ambulating (ORadj 24.0 [7.4 to 86.1], p < 0.001). The likelihood increased significantly when standing or ambulating was achieved in the older, non-frail cluster (ORadj 4.7 [1.2 to 23.2], p = 0.035 and ORadj 8.1 [1.8 to 45.8], p = 0.010)., Conclusions: An artificial intelligence-based learning approach was able to divide a heterogeneous critical care cohort into four clusters, which differed significantly in their clinical characteristics and in their mobilization parameters. Depending on the cluster, different mobilization strategies supported the likelihood of being discharged home enabling an individualized and resource-optimized mobilization approach., Trial Registration: Clinical Trials NCT03666286, retrospectively registered 04 September 2018., (© 2023. The Author(s).)- Published
- 2023
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10. Mobilisation of critically ill patients receiving norepinephrine: a retrospective cohort study.
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Lindholz M, Schellenberg CM, Grunow JJ, Kagerbauer S, Milnik A, Zickler D, Angermair S, Reißhauer A, Witzenrath M, Menk M, Boie S, Balzer F, and Schaller SJ
- Subjects
- Humans, Retrospective Studies, Cohort Studies, Prospective Studies, Critical Illness therapy, Norepinephrine pharmacology, Norepinephrine therapeutic use
- Abstract
Background: Mobilisation and exercise intervention in general are safe and feasible in critically ill patients. For patients requiring catecholamines, however, doses of norepinephrine safe for mobilisation in the intensive care unit (ICU) are not defined. This study aimed to describe mobilisation practice in our hospital and identify doses of norepinephrine that allowed a safe mobilisation., Methods: We conducted a retrospective single-centre cohort study of 16 ICUs at a university hospital in Germany with patients admitted between March 2018 and November 2021. Data were collected from our patient data management system. We analysed the effect of norepinephrine on level (ICU Mobility Scale) and frequency (units per day) of mobilisation, early mobilisation (within 72 h of ICU admission), mortality, and rate of adverse events. Data were extracted from free-text mobilisation entries using supervised machine learning (support vector machine). Statistical analyses were done using (generalised) linear (mixed-effect) models, as well as chi-square tests and ANOVAs., Results: A total of 12,462 patients were analysed in this study. They received a total of 59,415 mobilisation units. Of these patients, 842 (6.8%) received mobilisation under continuous norepinephrine administration. Norepinephrine administration was negatively associated with the frequency of mobilisation (adjusted difference -0.07 mobilisations per day; 95% CI - 0.09, - 0.05; p ≤ 0.001) and early mobilisation (adjusted OR 0.83; 95% CI 0.76, 0.90; p ≤ 0.001), while a higher norepinephrine dose corresponded to a lower chance to be mobilised out-of-bed (adjusted OR 0.01; 95% CI 0.00, 0.04; p ≤ 0.001). Mobilisation with norepinephrine did not significantly affect mortality (p > 0.1). Higher compared to lower doses of norepinephrine did not lead to a significant increase in adverse events in our practice (p > 0.1). We identified that mobilisation was safe with up to 0.20 µg/kg/min norepinephrine for out-of-bed (IMS ≥ 2) and 0.33 µg/kg/min for in-bed (IMS 0-1) mobilisation., Conclusions: Mobilisation with norepinephrine can be done safely when considering the status of the patient and safety guidelines. We demonstrated that safe mobilisation was possible with norepinephrine doses up to 0.20 µg/kg/min for out-of-bed (IMS ≥ 2) and 0.33 µg/kg/min for in-bed (IMS 0-1) mobilisation., (© 2022. The Author(s).)
- Published
- 2022
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