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

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Pham, T, Heunks, L, Bellani, G, Madotto, F, Aragao, I, Beduneau, G, Goligher, E, Grasselli, G, Laake, J, Mancebo, J, Penuelas, O, Piquilloud, L, Pesenti, A, Wunsch, H, van Haren, F, Brochard, L, Laffey, J, Abrough, F, Acharya, S, Amin, P, Arabi, Y, Bauer, P, Beitler, J, Berkius, J, Bugedo, G, Camporota, L, Cerny, V, Cho, Y, Clarkson, K, Estenssoro, E, Gritsan, A, Hashemian, S, Hermans, G, Jovanovic, B, Kurahashi, K, Matamis, D, Moerer, O, Molnar, Z, Ozyilmaz, E, Panka, B, Papali, A, Perbet, S, Qiu, H, Razek, A, Rittayamai, N, Roldan, R, Serpa Neto, A, Szuldrzynski, K, Talmor, D, Tomescu, D, Villagomez, A, Zeggwagh, A, Abe, T, Aboshady, A, Acampo-de Jong, M, Adderley, J, Adiguzel, N, Agrawal, V, Aguilar, G, Aguirre, G, Aguirre-Bermeo, H, Ahlstrom, B, Akbas, T, Akker, M, Al Sadeh, G, Alamri, S, Algaba, A, Ali, M, Aliberti, A, Allegue, J, Alvarez, D, Amador, J, Andersen, F, Ansari, S, Apichatbutr, Y, Apostolopoulou, O, Arellano, D, Arica, M, Arikan, H, Arinaga, K, Arnal, J, Asano, K, Asin-Corrochano, M, Avalos Cabrera, J, Avila Fuentes, S, Aydemir, S, Aygencel, G, Azevedo, L, Bacakoglu, F, Badie, J, Baedorf Kassis, E, Bai, G, Balaraj, G, Ballico, B, Banner-Goodspeed, V, Banwarie, P, Barbieri, R, Baronia, A, Barrett, J, Barrot, L, Barrueco-Francioni, J, Barry, J, Bawangade, H, Beavis, S, Beck, E, Beehre, N, Belenguer Muncharaz, A, Belliato, M, Bellissima, A, Beltramelli, R, Ben Souissi, A, Benitez-Cano, A, Benlamin, M, Benslama, A, Bento, L, Benvenuti, D, Bernabe, L, Bersten, A, Berta, G, Bertini, P, Bertram-Ralph, E, Besbes, M, Bettini, L, Beuret, P, Bewley, J, Bezzi, M, Bhakhtiani, L, Bhandary, R, Bhowmick, K, Bihari, S, Bissett, B, Blythe, D, Bocher, S, Boedjawan, N, Bojanowski, C, Boni, E, Boraso, S, Borelli, M, Borello, S, Borislavova, M, Bosma, K, Bottiroli, M, Boyd, O, Bozbay, S, Briva, A, Bruel, C, Bruni, A, Buehner, U, Bulpa, P, Burt, K, Buscot, M, Buttera, S, Cabrera, J, Caccese, R, Caironi, P, Canchos Gutierrez, I, Canedo, N, Cani, A, Cappellini, I, Carazo, J, Cardonnet, L, Carpio, D, Carriedo, D, Carrillo, R, Carvalho, J, Caser, E, Castelli, A, Castillo Quintero, M, Castro, H, Catorze, N, Cengiz, M, Cereijo, E, Ceunen, H, Chaintoutis, C, Chang, Y, Chaparro, G, Chapman, C, Chau, S, Chavez, C, Chelazzi, C, Chelly, J, Chemouni, F, Chen, K, Chena, A, Chiarandini, P, Chilton, P, Chiumello, D, Chou-Lie, Y, Chudeau, N, Cinel, I, Cinnella, G, Clark, M, Clark, T, Clementi, S, Coaguila, L, Codecido, A, Collins, A, Colombo, R, Conde, J, Consales, G, Cook, T, Coppadoro, A, Cornejo, R, Cortegiani, A, Coxo, C, Cracchiolo, A, Crespo Ramirez, M, Crova, P, Cruz, J, Cubattoli, L, Cukurova, Z, Curto, F, Czempik, P, D'Andrea, R, da Silva Ramos, F, Dangers, L, Danguy des Deserts, M, Danin, P, Dantas, F, Daubin, C, Dawei, W, de Haro, C, de Jesus Montelongo, F, De Mendoza, D, de Pablo, R, De Pascale, G, De Rosa, S, Decavele, M, Declercq, P, Deicas, A, del Carmen Campos Moreno, M, Dellamonica, J, Delmas, B, Demirkiran, O, Demirkiran, H, Dendane, T, di Mussi, R, Diakaki, C, Diaz, A, Diaz, W, Dikmen, Y, Dimoula, A, Doble, P, Doha, N, Domingos, G, Dres, M, Dries, D, Duggal, A, Duke, G, Dunts, P, Dybwik, K, Dykyy, M, Eckert, P, Efe, S, Elatrous, S, Elay, G, Elmaryul, A, Elsaadany, M, Elsayed, H, Elsayed, S, Emery, M, Ena, S, Eng, K, Englert, J, Erdogan, E, Ergin Ozcan, P, Eroglu, E, Escobar, M, Esen, F, Esen Tekeli, A, Esquivel, A, Esquivel Gallegos, H, Ezzouine, H, Facchini, A, Faheem, M, Fanelli, V, Farina, M, Fartoukh, M, Fehrle, L, Feng, F, Feng, Y, Fernandez, I, Fernandez, B, Fernandez-Rodriguez, M, Ferrando, C, Ferreira da Silva, M, Ferreruela, M, Ferrier, J, Flamm Zamorano, M, Flood, L, Floris, L, Fluckiger, M, Forteza, C, Fortunato, A, Frans, E, Frattari, A, Fredes, S, Frenzel, T, Fumagalli, R, Furche, M, Fusari, M, Fysh, E, Galeas-Lopez, J, Galerneau, L, Garcia, A, Garcia, M, Garcia, E, Garcia Olivares, P, Garlicki, J, Garnero, A, Garofalo, E, Gautam, P, Gazenkampf, A, Gelinotte, S, Gelormini, D, Ghrenassia, E, Giacomucci, A, Giannoni, R, Gigante, A, Glober, N, Gnesin, P, Gollo, Y, Gomaa, D, Gomero Paredes, R, Gomes, R, Gomez, R, Gomez, O, Gomez, A, Gondim, L, Gonzalez, M, Gonzalez, I, Gonzalez-Castro, A, Gordillo Romero, O, Gordo, F, Gouin, P, Graf Santos, J, Grainne, R, Grando, M, Granov Grabovica, S, Grasso, S, Grasso, R, Grimmer, L, Grissom, C, Gu, Q, Guan, X, Guarracino, F, Guasch, N, Guatteri, L, Gueret, R, Guerin, C, Guerot, E, Guitard, P, Gul, F, Gumus, A, Gurjar, M, Gutierrez, P, Hachimi, A, Hadzibegovic, A, Hagan, S, Hammel, C, Han Song, J, Hanlon, G, Heines, S, Henriksson, J, Herbrecht, J, Heredia Orbegoso, G, Hermon, A, Hernandez, R, Hernandez, C, Herrera, L, Herrera-Gutierrez, M, Hidalgo, J, Hill, D, Holmquist, D, Homez, M, Hongtao, X, Hormis, A, Horner, D, Hornos, M, Hou, M, House, S, Housni, B, Hugill, K, Humphreys, S, Humbert, L, Hunter, S, Hwa Young, L, Iezzi, N, Ilutovich, S, Inal, V, Innes, R, Ioannides, P, Iotti, G, Ippolito, M, Irie, H, Iriyama, H, Itagaki, T, Izura, J, Izza, S, Jabeen, R, Jamaati, H, Jamadarkhana, S, Jamoussi, A, Jankowski, M, Jaramillo, L, Jeon, K, Jeong Lee, S, Jeswani, D, Jha, S, Jiang, L, Jing, C, Jochmans, S, Johnstad, B, Jongmin, L, Joret, A, Junhasavasdikul, D, Jurado, M, Kam, E, Kamohara, H, Kane, C, Kara, I, Karakurt, S, Karnjanarachata, C, Kataoka, J, Katayama, S, Kaushik, S, Kelebek Girgin, N, Kerr, K, Kerslake, I, Khairnar, P, Khalid, A, Khan, A, Khanna, A, Khorasanee, R, Kienhorst, D, Kirakli, C, Knafelj, R, Kol, M, Kongpolprom, N, Kopitko, C, Korkmaz Ekren, P, Kubisz-Pudelko, A, Kulcsar, Z, Kumasawa, J, Kuriyama, A, Kutchak, F, Labarca, E, Labat, F, Laborda, C, Laca Barrera, M, Lagache, L, Landaverde Lopez, A, Lanspa, M, Lascari, V, Le Meur, M, Lee, S, Lee, Y, Lee, J, Lee, W, Legernaes, T, Leiner, T, Lemiale, V, Leonor, T, Lepper, P, Li, D, Li, H, Li, O, Lima, A, Lind, D, Litton, E, Liu, N, Liu, L, Liu, J, Llitjos, J, Llorente, B, Lopez, R, Lopez, C, Lopez Nava, C, Lovazzano, P, Lu, M, Lucchese, F, Lugano, M, Lugo Goytia, G, Luo, H, Lynch, C, Macheda, S, Madrigal Robles, V, Maggiore, S, Magret Iglesias, M, Malaga, P, Mallapura Maheswarappa, H, Malpartida, G, Malyarchikov, A, Mansson, H, Manzano, A, Marey, I, Marin, N, Marin, M, Markman, E, Martin, F, Martin, A, Martin Dal Gesso, C, Martinez, F, Martinez-Fidalgo, C, Martin-Loeches, I, Mas, A, Masaaki, S, Maseda, E, Massa, E, Mattsson, A, Maugeri, J, Mccredie, V, Mccullough, J, Mcguinness, S, Mckown, A, Medve, L, Mei, C, Mellado Artigas, R, Mendes, V, Mervat, M, Michaux, I, Mikhaeil, M, Milagros, O, Milet, I, Millan, M, Minwei, Z, Mirabella, L, Mishra, S, Mistraletti, G, Mochizuki, K, Moghal, A, Mojoli, F, Molin, A, Montiel, R, Montini, L, Monza, G, Mora Aznar, M, Morakul, S, Morales, M, Moreno Torres, D, Morocho Tutillo, D, Motherway, C, Mouhssine, D, Mouloudi, E, Munoz, T, Munoz de Cabo, C, Mustafa, M, Muthuchellappan, R, Muthukrishnan, M, Muttini, S, Nagata, I, Nahar, D, Nakanishi, M, Nakayama, I, Namendys-Silva, S, Nanchal, R, Nandakumar, S, Nasi, A, Nasir, K, Navalesi, P, Naz Aslam, T, Nga Phan, T, Nichol, A, Niiyama, S, Nikolakopoulou, S, Nikolic, E, Nitta, K, Noc, M, Nonas, S, Nseir, S, Nur Soyturk, A, Obata, Y, Oeckler, R, Oguchi, M, Ohshimo, S, Oikonomou, M, Ojados, A, Oliveira, M, Oliveira Filho, W, Oliveri, C, Olmos, A, Omura, K, Orlandi, M, Orsenigo, F, Ortiz-Ruiz De Gordoa, L, Ota, K, Ovalle Olmos, R, Oveges, N, Oziemski, P, Ozkan Kuscu, O, Pachas Alvarado, F, Pagella, G, Palaniswamy, V, Palazon Sanchez, E, Palmese, S, Pan, G, Pan, W, Papanikolaou, M, Papavasilopoulou, T, Parekh, A, Parke, R, Parrilla, F, Parrilla, D, Pasha, T, Pasin, L, Patao, L, Patel, M, Patel, G, Pati, B, Patil, J, Pattnaik, S, Paul, D, Pavesi, M, Pavlotsky, V, Paz, G, Paz, E, Pecci, E, Pellegrini, C, Pena Padilla, A, Perchiazzi, G, Pereira, T, Pereira, V, Perez, M, Perez Calvo, C, Perez Cheng, M, Perez Maita, R, Perez-Araos, R, Perez-Teran, P, Perez-Torres, D, Perkins, G, Persona, P, Petnak, T, Petrova, M, Philippart, F, Picetti, E, Pierucci, E, Piervincenzi, E, Pinciroli, R, Pintado, M, Piraino, T, Piras, S, Piras, C, Pirompanich, P, Pisani, L, Platas, E, Plotnikow, G, Porras, W, Porta, V, Portilla, M, Portugal, J, Povoa, P, Prat, G, Pratto, R, Preda, G, Prieto, I, Prol-Silva, E, Pugh, R, Qi, Y, Qian, C, Qin, T, Qu, H, Quintana, T, Quispe Sierra, R, Quispe Soto, R, Rabbani, R, Rabee, M, Rabie, A, Rahe Pereira, M, Rai, A, Raj Ashok, S, Rajab, M, Ramdhani, N, Ramey, E, Ranieri, M, Rathod, D, Ray, B, Redwanul Huq, S, Regli, A, Reina, R, Resano Sarmiento, N, Reynaud, F, Rialp, G, Ricart, P, Rice, T, Richardson, A, Rieder, M, Rinket, M, Rios, F, Risso Vazquez, A, Riva, I, Rivette, M, Roca, O, Roche-Campo, F, Rodriguez, C, Rodriguez, G, Rodriguez Gonzalez, D, Rodriguez Tucto, X, Rogers, A, Romano, M, Rortveit, L, Rose, A, Roux, D, Rouze, A, Rubatto Birri, P, Ruilan, W, Ruiz Robledo, A, Ruiz-Aguilar, A, Sadahiro, T, Saez, I, Sagardia, J, Saha, R, Saiphoklang, N, Saito, S, Salem, M, Sales, G, Salgado, P, Samavedam, S, Sami Mebazaa, M, Samuelsson, L, San Juan Roman, N, Sanchez, P, Sanchez-Ballesteros, J, Sandoval, Y, Sani, E, Santos, M, Santos, C, Sanui, M, Saravanabavan, L, Sari, S, Sarkany, A, Sauneuf, B, Savioli, M, Sazak, H, Scano, R, Schneider, F, Schortgen, F, Schultz, M, Schwarz, G, Seckin Yucesoy, F, Seely, A, Seiler, F, Seker Tekdos, Y, Seok Chan, K, Serano, L, Serednicki, W, Setten, M, Shah, A, Shah, B, Shang, Y, Shanmugasundaram, P, Shapovalov, K, Shebl, E, Shiga, T, Shime, N, Shin, P, Short, J, Shuhua, C, Siddiqui, S, Silesky Jimenez, J, Silva, D, Silva Sales, B, Simons, K, Sjobo, B, Slessor, D, Smiechowicz, J, Smischney, N, Smith, P, Smith, T, Smith, M, Snape, S, Snyman, L, Soetens, F, Sook Hong, K, Sosa Medellin, M, Soto, G, Souloy, X, Sousa, E, Sovatzis, S, Sozutek, D, Spadaro, S, Spagnoli, M, Spangfors, M, Spittle, N, Spivey, M, Stapleton, A, Stefanovic, B, Stephenson, L, Stevenson, E, Strand, K, Strano, M, Straus, S, Sun, C, Sun, R, Sundaram, V, Sunpark, T, Surlemont, E, Sutherasan, Y, Szabo, Z, Tainter, C, Takaba, A, Tallott, M, Tamasato, T, Tang, Z, Tangsujaritvijit, V, Taniguchi, L, Taniguchi, D, Tarantino, F, Teerapuncharoen, K, Temprano, S, Terragni, P, Terzi, N, Thakur, A, Theerawit, P, Thille, A, Thomas, M, Thungtitigul, P, Thyrault, M, Tilouch, N, Timenetsky, K, Tirapu, J, Todeschini, M, Tomas, R, Tomaszewski, C, Tonetti, T, Tonnelier, A, Trinder, J, Trongtrakul, K, Truwit, J, Tsuei, B, Tulaimat, A, Turan, S, Turkoglu, M, Tyagi, S, Ubeda, A, Vagginelli, F, Valenti, M, Vallverdu, I, Van Axel, A, van den Hul, I, van der Hoeven, H, Van Der Meer, N, Vanhoof, M, Vargas-Ordonez, M, Vaschetto, R, Vascotto, E, Vatsik, M, Vaz, A, Vazquez-Sanchez, A, Ventura, S, Vermeijden, J, Vidal, A, Vieira, J, Vilela Costa Pinto, B, Villagra, A, Villegas Succar, C, Vinorum, O, Vitale, G, Vj, R, Vochin, A, Voiriot, G, Volta, C, von Seth, M, Wajdi, M, Walsh, D, Wang, S, Wardi, G, Ween-Velken, N, Wei, B, Weller, D, Welsh, D, Welters, I, Wert, M, Whiteley, S, Wilby, E, Williams, E, Williams, K, Wilson, A, Wojtas, J, Won Huh, J, Wrathall, D, Wright, C, Wu, J, Xi, G, Xing, Z, Xu, H, Yamamoto, K, Yan, J, Yanez, J, Yang, X, Yates, E, Yazicioglu Mocin, O, Ye, Z, Yildirim, F, Yoshida, N, Yoshido, H, Young Lee, B, Yu, R, Yu, G, Yu, T, Yuan, B, Yuangtrakul, N, Yumoto, T, Yun, X, Zakalik, G, Zaki, A, Zalba-Etayo, B, Zambon, M, Zang, B, Zani, G, Zarka, J, Zerbi, S, Zerman, A, Zetterquist, H, Zhang, J, Zhang, H, Zhang, W, Zhang, G, Zhao, H, Zheng, J, Zhu, B, Zumaran, R, Pham T., Heunks L., Bellani G., Madotto F., Aragao I., Beduneau G., Goligher E. C., Grasselli G., Laake J. H., Mancebo J., Penuelas O., Piquilloud L., Pesenti A., Wunsch H., van Haren F., Brochard L., Laffey J. G., Abrough F., Acharya S. P., Amin P., Arabi Y., Bauer P., Beitler J., Berkius J., Bugedo G., Camporota L., Cerny V., Cho Y. -J., Clarkson K., Estenssoro E., Goligher E., Gritsan A., Hashemian S. M., Hermans G., Heunks L. M., Jovanovic B., Kurahashi K., Matamis D., Moerer O., Molnar Z., Ozyilmaz E., Panka B., Papali A., Perbet S., Qiu H., Razek A. A., Rittayamai N., Roldan R., Serpa Neto A., Szuldrzynski K., Talmor D., Tomescu D., Villagomez A., Zeggwagh A. A., Abe T., Aboshady A., Acampo-de Jong M., Acharya S., Adderley J., Adiguzel N., Agrawal V. K., Aguilar G., Aguirre G., Aguirre-Bermeo H., Ahlstrom B., Akbas T., Akker M., Al Sadeh G., Alamri S., Algaba A., Ali M., Aliberti A., Allegue J. M., Alvarez D., Amador J., Andersen F. H., Ansari S., Apichatbutr Y., Apostolopoulou O., Arellano D., Arica M., Arikan H., Arinaga K., Arnal J. -M., Asano K., Asin-Corrochano M., Avalos Cabrera J. M., Avila Fuentes S., Aydemir S., Aygencel G., Azevedo L., Bacakoglu F., Badie J., Baedorf Kassis E., Bai G., Balaraj G., Ballico B., Banner-Goodspeed V., Banwarie P., Barbieri R., Baronia A., Barrett J., Barrot L., Barrueco-Francioni J. E., Barry J., Bawangade H., Beavis S., Beck E., Beehre N., Belenguer Muncharaz A., Belliato M., Bellissima A., Beltramelli R., Ben Souissi A., Benitez-Cano A., Benlamin M., Benslama A., Bento L., Benvenuti D., Bernabe L., Bersten A., Berta G., Bertini P., Bertram-Ralph E., Besbes M., Bettini L. R., Beuret P., Bewley J., Bezzi M., Bhakhtiani L., Bhandary R., Bhowmick K., Bihari S., Bissett B., Blythe D., Bocher S., Boedjawan N., Bojanowski C. M., Boni E., Boraso S., Borelli M., Borello S., Borislavova M., Bosma K. J., Bottiroli M., Boyd O., Bozbay S., Briva A., Bruel C., Bruni A., Buehner U., Bulpa P., Burt K., Buscot M., Buttera S., Cabrera J., Caccese R., Caironi P., Canchos Gutierrez I., Canedo N., Cani A., Cappellini I., Carazo J., Cardonnet L. P., Carpio D., Carriedo D., Carrillo R., Carvalho J., Caser E., Castelli A., Castillo Quintero M., Castro H., Catorze N., Cengiz M., Cereijo E., Ceunen H., Chaintoutis C., Chang Y., Chaparro G., Chapman C., Chau S., Chavez C. E., Chelazzi C., Chelly J., Chemouni F., Chen K., Chena A., Chiarandini P., Chilton P., Chiumello D., Chou-Lie Y., Chudeau N., Cinel I., Cinnella G., Clark M., Clark T., Clementi S., Coaguila L., Codecido A. J., Collins A., Colombo R., Conde J., Consales G., Cook T., Coppadoro A., Cornejo R., Cortegiani A., Coxo C., Cracchiolo A. N., Crespo Ramirez M., Crova P., Cruz J., Cubattoli L., Cukurova Z., Curto F., Czempik P., D'Andrea R., da Silva Ramos F., Dangers L., Danguy des Deserts M., Danin P. -E., Dantas F., Daubin C., Dawei W., de Haro C., de Jesus Montelongo F., De Mendoza D., de Pablo R., De Pascale G., De Rosa S., Decavele M., Declercq P. -L., Deicas A., del Carmen Campos Moreno M., Dellamonica J., Delmas B., Demirkiran O., Demirkiran H., Dendane T., di Mussi R., Diakaki C., Diaz A., Diaz W., Dikmen Y., Dimoula A., Doble P., Doha N., Domingos G., Dres M., Dries D., Duggal A., Duke G., Dunts P., Dybwik K., Dykyy M., Eckert P., Efe S., Elatrous S., Elay G., Elmaryul A. S., Elsaadany M., Elsayed H., Elsayed S., Emery M., Ena S., Eng K., Englert J. A., Erdogan E., Ergin Ozcan P., Eroglu E., Escobar M., Esen F., Esen Tekeli A., Esquivel A., Esquivel Gallegos H., Ezzouine H., Facchini A., Faheem M., Fanelli V., Farina M. F., Fartoukh M., Fehrle L., Feng F., Feng Y., Fernandez I., Fernandez B., Fernandez-Rodriguez M. L., Ferrando C., Ferreira da Silva M. J., Ferreruela M., Ferrier J., Flamm Zamorano M. J., Flood L., Floris L., Fluckiger M., Forteza C., Fortunato A., Frans E., Frattari A., Fredes S., Frenzel T., Fumagalli R., Furche M. A., Fusari M., Fysh E., Galeas-Lopez J. L., Galerneau L. -M., Garcia A., Garcia M. F., Garcia E., Garcia Olivares P., Garlicki J., Garnero A., Garofalo E., Gautam P., Gazenkampf A., Gelinotte S., Gelormini D., Ghrenassia E., Giacomucci A., Giannoni R., Gigante A., Glober N., Gnesin P., Gollo Y., Gomaa D., Gomero Paredes R., Gomes R., Gomez R. A., Gomez O., Gomez A., Gondim L., Gonzalez M., Gonzalez I., Gonzalez-Castro A., Gordillo Romero O., Gordo F., Gouin P., Graf Santos J., Grainne R., Grando M., Granov Grabovica S., Grasso S., Grasso R., Grimmer L., Grissom C., Gu Q., Guan X. -D., Guarracino F., Guasch N., Guatteri L., Gueret R., Guerin C., Guerot E., Guitard P. -G., Gul F., Gumus A., Gurjar M., Gutierrez P., Hachimi A., Hadzibegovic A., Hagan S., Hammel C., Han Song J., Hanlon G., Heines S., Henriksson J., Herbrecht J. -E., Heredia Orbegoso G. O., Hermon A., Hernandez R., Hernandez C., Herrera L., Herrera-Gutierrez M., Hidalgo J., Hill D., Holmquist D., Homez M., Hongtao X., Hormis A., Horner D., Hornos M. C., Hou M., House S., Housni B., Hugill K., Humphreys S., Humbert L., Hunter S., Hwa Young L., Iezzi N., Ilutovich S., Inal V., Innes R., Ioannides P., Iotti G. A., Ippolito M., Irie H., Iriyama H., Itagaki T., Izura J., Izza S., Jabeen R., Jamaati H., Jamadarkhana S., Jamoussi A., Jankowski M., Jaramillo L. A., Jeon K., Jeong Lee S., Jeswani D., Jha S., Jiang L., Jing C., Jochmans S., Johnstad B. A., Jongmin L., Joret A., Junhasavasdikul D., Jurado M. T., Kam E., Kamohara H., Kane C., Kara I., Karakurt S., Karnjanarachata C., Kataoka J., Katayama S., Kaushik S., Kelebek Girgin N., Kerr K., Kerslake I., Khairnar P., Khalid A., Khan A., Khanna A. K., Khorasanee R., Kienhorst D., Kirakli C., Knafelj R., Kol M. K., Kongpolprom N., Kopitko C., Korkmaz Ekren P., Kubisz-Pudelko A., Kulcsar Z., Kumasawa J., Kuriyama A., Kutchak F., Labarca E., Labat F., Laborda C., Laca Barrera M. A., Lagache L., Landaverde Lopez A., Lanspa M., Lascari V., Le Meur M., Lee S. H., Lee Y. J., Lee J., Lee W. -Y., Legernaes T., Leiner T., Lemiale V., Leonor T., Lepper P. M., Li D., Li H., Li O., Lima A. R., Lind D., Litton E., Liu N., Liu L., Liu J., Llitjos J. -F., Llorente B., Lopez R., Lopez C. E., Lopez Nava C., Lovazzano P., Lu M., Lucchese F., Lugano M., Lugo Goytia G., Luo H., Lynch C., Macheda S., Madrigal Robles V. H., Maggiore S. M., Magret Iglesias M., Malaga P., Mallapura Maheswarappa H., Malpartida G., Malyarchikov A., Mansson H., Manzano A., Marey I., Marin N., Marin M. D. C., Markman E., Martin F., Martin A., Martin Dal Gesso C., Martinez F., Martinez-Fidalgo C., Martin-Loeches I., Mas A., Masaaki S., Maseda E., Massa E., Mattsson A., Maugeri J., McCredie V., McCullough J., McGuinness S., McKown A., Medve L., Mei C., Mellado Artigas R., Mendes V., Mervat M. K. E., Michaux I., Mikhaeil M., Milagros O., Milet I., Millan M. T., Minwei Z., Mirabella L., Mishra S., Mistraletti G., Mochizuki K., Moghal A., Mojoli F., Molin A., Montiel R., Montini L., Monza G., Mora Aznar M., Morakul S., Morales M., Moreno Torres D., Morocho Tutillo D. R., Motherway C., Mouhssine D., Mouloudi E., Munoz T., Munoz de Cabo C., Mustafa M., Muthuchellappan R., Muthukrishnan M., Muttini S., Nagata I., Nahar D., Nakanishi M., Nakayama I., Namendys-Silva S. A., Nanchal R., Nandakumar S., Nasi A., Nasir K., Navalesi P., Naz Aslam T., Nga Phan T., Nichol A., Niiyama S., Nikolakopoulou S., Nikolic E., Nitta K., Noc M., Nonas S., Nseir S., Nur Soyturk A., Obata Y., Oeckler R., Oguchi M., Ohshimo S., Oikonomou M., Ojados A., Oliveira M. T., Oliveira Filho W., Oliveri C., Olmos A., Omura K., Orlandi M. C., Orsenigo F., Ortiz-Ruiz De Gordoa L., Ota K., Ovalle Olmos R., Oveges N., Oziemski P., Ozkan Kuscu O., Pachas Alvarado F., Pagella G., Palaniswamy V., Palazon Sanchez E. L., Palmese S., Pan G., Pan W., Papanikolaou M., Papavasilopoulou T., Parekh A., Parke R., Parrilla F. J., Parrilla D., Pasha T., Pasin L., Patao L., Patel M., Patel G., Pati B. K., Patil J., Pattnaik S., Paul D., Pavesi M., Pavlotsky V. 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M., Regli A., Reina R., Resano Sarmiento N., Reynaud F., Rialp G., Ricart P., Rice T., Richardson A., Rieder M., Rinket M., Rios F., Risso Vazquez A., Riva I., Rivette M., Roca O., Roche-Campo F., Rodriguez C., Rodriguez G., Rodriguez Gonzalez D., Rodriguez Tucto X. Y., Rogers A., Romano M. E., Rortveit L., Rose A., Roux D., Rouze A., Rubatto Birri P. N., Ruilan W., Ruiz Robledo A., Ruiz-Aguilar A. L., Sadahiro T., Saez I., Sagardia J., Saha R., Saiphoklang N., Saito S., Salem M., Sales G., Salgado P., Samavedam S., Sami Mebazaa M., Samuelsson L., San Juan Roman N., Sanchez P., Sanchez-Ballesteros J., Sandoval Y., Sani E., Santos M., Santos C., Sanui M., Saravanabavan L., Sari S., Sarkany A., Sauneuf B., Savioli M., Sazak H., Scano R., Schneider F., Schortgen F., Schultz M. J., Schwarz G. 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W., Thomas M., Thungtitigul P., Thyrault M., Tilouch N., Timenetsky K., Tirapu J., Todeschini M., Tomas R., Tomaszewski C., Tonetti T., Tonnelier A., Trinder J., Trongtrakul K., Truwit J., Tsuei B., Tulaimat A., Turan S., Turkoglu M., Tyagi S., Ubeda A., Vagginelli F., Valenti M. F., Vallverdu I., Van Axel A., van den Hul I., van der Hoeven H., Van Der Meer N., Vanhoof M., Vargas-Ordonez M., Vaschetto R., Vascotto E., Vatsik M., Vaz A., Vazquez-Sanchez A., Ventura S., Vermeijden J. W., Vidal A., Vieira J., Vilela Costa Pinto B., Villagra A., Villegas Succar C., Vinorum O. G., Vitale G., Vj R., Vochin A., Voiriot G., Volta C. A., von Seth M., Wajdi M., Walsh D., Wang S., Wardi G., Ween-Velken N. 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F., Fartoukh M., Fehrle L., Feng F., Feng Y., Fernandez I., Fernandez B., Fernandez-Rodriguez M. L., Ferrando C., Ferreira da Silva M. J., Ferreruela M., Ferrier J., Flamm Zamorano M. J., Flood L., Floris L., Fluckiger M., Forteza C., Fortunato A., Frans E., Frattari A., Fredes S., Frenzel T., Fumagalli R., Furche M. A., Fusari M., Fysh E., Galeas-Lopez J. L., Galerneau L. -M., Garcia A., Garcia M. F., Garcia E., Garcia Olivares P., Garlicki J., Garnero A., Garofalo E., Gautam P., Gazenkampf A., Gelinotte S., Gelormini D., Ghrenassia E., Giacomucci A., Giannoni R., Gigante A., Glober N., Gnesin P., Gollo Y., Gomaa D., Gomero Paredes R., Gomes R., Gomez R. A., Gomez O., Gomez A., Gondim L., Gonzalez M., Gonzalez I., Gonzalez-Castro A., Gordillo Romero O., Gordo F., Gouin P., Graf Santos J., Grainne R., Grando M., Granov Grabovica S., Grasso S., Grasso R., Grimmer L., Grissom C., Gu Q., Guan X. -D., Guarracino F., Guasch N., Guatteri L., Gueret R., Guerin C., Guerot E., Guitard P. -G., Gul F., Gumus A., Gurjar M., Gutierrez P., Hachimi A., Hadzibegovic A., Hagan S., Hammel C., Han Song J., Hanlon G., Heines S., Henriksson J., Herbrecht J. -E., Heredia Orbegoso G. O., Hermon A., Hernandez R., Hernandez C., Herrera L., Herrera-Gutierrez M., Hidalgo J., Hill D., Holmquist D., Homez M., Hongtao X., Hormis A., Horner D., Hornos M. C., Hou M., House S., Housni B., Hugill K., Humphreys S., Humbert L., Hunter S., Hwa Young L., Iezzi N., Ilutovich S., Inal V., Innes R., Ioannides P., Iotti G. A., Ippolito M., Irie H., Iriyama H., Itagaki T., Izura J., Izza S., Jabeen R., Jamaati H., Jamadarkhana S., Jamoussi A., Jankowski M., Jaramillo L. A., Jeon K., Jeong Lee S., Jeswani D., Jha S., Jiang L., Jing C., Jochmans S., Johnstad B. A., Jongmin L., Joret A., Junhasavasdikul D., Jurado M. T., Kam E., Kamohara H., Kane C., Kara I., Karakurt S., Karnjanarachata C., Kataoka J., Katayama S., Kaushik S., Kelebek Girgin N., Kerr K., Kerslake I., Khairnar P., Khalid A., Khan A., Khanna A. K., Khorasanee R., Kienhorst D., Kirakli C., Knafelj R., Kol M. K., Kongpolprom N., Kopitko C., Korkmaz Ekren P., Kubisz-Pudelko A., Kulcsar Z., Kumasawa J., Kuriyama A., Kutchak F., Labarca E., Labat F., Laborda C., Laca Barrera M. A., Lagache L., Landaverde Lopez A., Lanspa M., Lascari V., Le Meur M., Lee S. H., Lee Y. J., Lee J., Lee W. -Y., Legernaes T., Leiner T., Lemiale V., Leonor T., Lepper P. M., Li D., Li H., Li O., Lima A. R., Lind D., Litton E., Liu N., Liu L., Liu J., Llitjos J. -F., Llorente B., Lopez R., Lopez C. E., Lopez Nava C., Lovazzano P., Lu M., Lucchese F., Lugano M., Lugo Goytia G., Luo H., Lynch C., Macheda S., Madrigal Robles V. H., Maggiore S. M., Magret Iglesias M., Malaga P., Mallapura Maheswarappa H., Malpartida G., Malyarchikov A., Mansson H., Manzano A., Marey I., Marin N., Marin M. D. C., Markman E., Martin F., Martin A., Martin Dal Gesso C., Martinez F., Martinez-Fidalgo C., Martin-Loeches I., Mas A., Masaaki S., Maseda E., Massa E., Mattsson A., Maugeri J., McCredie V., McCullough J., McGuinness S., McKown A., Medve L., Mei C., Mellado Artigas R., Mendes V., Mervat M. K. E., Michaux I., Mikhaeil M., Milagros O., Milet I., Millan M. T., Minwei Z., Mirabella L., Mishra S., Mistraletti G., Mochizuki K., Moghal A., Mojoli F., Molin A., Montiel R., Montini L., Monza G., Mora Aznar M., Morakul S., Morales M., Moreno Torres D., Morocho Tutillo D. R., Motherway C., Mouhssine D., Mouloudi E., Munoz T., Munoz de Cabo C., Mustafa M., Muthuchellappan R., Muthukrishnan M., Muttini S., Nagata I., Nahar D., Nakanishi M., Nakayama I., Namendys-Silva S. A., Nanchal R., Nandakumar S., Nasi A., Nasir K., Navalesi P., Naz Aslam T., Nga Phan T., Nichol A., Niiyama S., Nikolakopoulou S., Nikolic E., Nitta K., Noc M., Nonas S., Nseir S., Nur Soyturk A., Obata Y., Oeckler R., Oguchi M., Ohshimo S., Oikonomou M., Ojados A., Oliveira M. T., Oliveira Filho W., Oliveri C., Olmos A., Omura K., Orlandi M. C., Orsenigo F., Ortiz-Ruiz De Gordoa L., Ota K., Ovalle Olmos R., Oveges N., Oziemski P., Ozkan Kuscu O., Pachas Alvarado F., Pagella G., Palaniswamy V., Palazon Sanchez E. L., Palmese S., Pan G., Pan W., Papanikolaou M., Papavasilopoulou T., Parekh A., Parke R., Parrilla F. J., Parrilla D., Pasha T., Pasin L., Patao L., Patel M., Patel G., Pati B. K., Patil J., Pattnaik S., Paul D., Pavesi M., Pavlotsky V. A., Paz G., Paz E., Pecci E., Pellegrini C., Pena Padilla A. G., Perchiazzi G., Pereira T., Pereira V., Perez M., Perez Calvo C., Perez Cheng M., Perez Maita R., Perez-Araos R., Perez-Teran P., Perez-Torres D., Perkins G., Persona P., Petnak T., Petrova M., Philippart F., Picetti E., Pierucci E., Piervincenzi E., Pinciroli R., Pintado M. -C., Piraino T., Piras S., Piras C., Pirompanich P., Pisani L., Platas E., Plotnikow G., Porras W., Porta V., Portilla M., Portugal J., Povoa P., Prat G., Pratto R., Preda G., Prieto I., Prol-Silva E., Pugh R., Qi Y., Qian C., Qin T., Qu H., Quintana T., Quispe Sierra R., Quispe Soto R., Rabbani R., Rabee M., Rabie A., Rahe Pereira M. A., Rai A., Raj Ashok S., Rajab M., Ramdhani N., Ramey E., Ranieri M., Rathod D., Ray B., Redwanul Huq S. M., Regli A., Reina R., Resano Sarmiento N., Reynaud F., Rialp G., Ricart P., Rice T., Richardson A., Rieder M., Rinket M., Rios F., Risso Vazquez A., Riva I., Rivette M., Roca O., Roche-Campo F., Rodriguez C., Rodriguez G., Rodriguez Gonzalez D., Rodriguez Tucto X. Y., Rogers A., Romano M. E., Rortveit L., Rose A., Roux D., Rouze A., Rubatto Birri P. N., Ruilan W., Ruiz Robledo A., Ruiz-Aguilar A. L., Sadahiro T., Saez I., Sagardia J., Saha R., Saiphoklang N., Saito S., Salem M., Sales G., Salgado P., Samavedam S., Sami Mebazaa M., Samuelsson L., San Juan Roman N., Sanchez P., Sanchez-Ballesteros J., Sandoval Y., Sani E., Santos M., Santos C., Sanui M., Saravanabavan L., Sari S., Sarkany A., Sauneuf B., Savioli M., Sazak H., Scano R., Schneider F., Schortgen F., Schultz M. J., Schwarz G. L., Seckin Yucesoy F., Seely A., Seiler F., Seker Tekdos Y., Seok Chan K., Serano L., Serednicki W., Setten M., Shah A., Shah B., Shang Y., Shanmugasundaram P., Shapovalov K., Shebl E., Shiga T., Shime N., Shin P., Short J., Shuhua C., Siddiqui S., Silesky Jimenez J. I., Silva D., Silva Sales B., Simons K., Sjobo B. A., Slessor D., Smiechowicz J., Smischney N., Smith P., Smith T., Smith M., Snape S., Snyman L., Soetens F., Sook Hong K., Sosa Medellin M. A., Soto G., Souloy X., Sousa E., Sovatzis S., Sozutek D., Spadaro S., Spagnoli M., Spangfors M., Spittle N., Spivey M., Stapleton A., Stefanovic B., Stephenson L., Stevenson E., Strand K., Strano M. T., Straus S., Sun C., Sun R., Sundaram V., SunPark T., Surlemont E., Sutherasan Y., Szabo Z., Tainter C., Takaba A., Tallott M., Tamasato T., Tang Z., Tangsujaritvijit V., Taniguchi L., Taniguchi D., Tarantino F., Teerapuncharoen K., Temprano S., Terragni P., Terzi N., Thakur A., Theerawit P., Thille A. W., Thomas M., Thungtitigul P., Thyrault M., Tilouch N., Timenetsky K., Tirapu J., Todeschini M., Tomas R., Tomaszewski C., Tonetti T., Tonnelier A., Trinder J., Trongtrakul K., Truwit J., Tsuei B., Tulaimat A., Turan S., Turkoglu M., Tyagi S., Ubeda A., Vagginelli F., Valenti M. F., Vallverdu I., Van Axel A., van den Hul I., van der Hoeven H., Van Der Meer N., Vanhoof M., Vargas-Ordonez M., Vaschetto R., Vascotto E., Vatsik M., Vaz A., Vazquez-Sanchez A., Ventura S., Vermeijden J. W., Vidal A., Vieira J., Vilela Costa Pinto B., Villagra A., Villegas Succar C., Vinorum O. G., Vitale G., Vj R., Vochin A., Voiriot G., Volta C. A., von Seth M., Wajdi M., Walsh D., Wang S., Wardi G., Ween-Velken N. C., Wei B. -L., Weller D., Welsh D., Welters I., Wert M., Whiteley S., Wilby E., Williams E., Williams K., Wilson A., Wojtas J., Won Huh J., Wrathall D., Wright C., Wu J. -F., Xi G., Xing Z. -J., Xu H., Yamamoto K., Yan J., Yanez J., Yang X., Yates E., Yazicioglu Mocin O., Ye Z., Yildirim F., Yoshida N., Yoshido H. H. L., Young Lee B., Yu R., Yu G., Yu T., Yuan B., Yuangtrakul N., Yumoto T., Yun X., Zakalik G., Zaki A., Zalba-Etayo B., Zambon M., Zang B., Zani G., Zarka J., Zerbi S. M., Zerman A., Zetterquist H., Zhang J., Zhang H., Zhang W., Zhang G., Zhao H., Zheng J., Zhu B., and Zumaran R.

Abstract

Background: Current management practices and outcomes in weaning from invasive mechanical ventilation are poorly understood. We aimed to describe the epidemiology, management, timings, risk for failure, and outcomes of weaning in patients requiring at least 2 days of invasive mechanical ventilation. Methods: WEAN SAFE was an international, multicentre, prospective, observational cohort study done in 481 intensive care units in 50 countries. Eligible participants were older than 16 years, admitted to a participating intensive care unit, and receiving mechanical ventilation for 2 calendar days or longer. We defined weaning initiation as the first attempt to separate a patient from the ventilator, successful weaning as no reintubation or death within 7 days of extubation, and weaning eligibility criteria based on positive end-expiratory pressure, fractional concentration of oxygen in inspired air, and vasopressors. The primary outcome was the proportion of patients successfully weaned at 90 days. Key secondary outcomes included weaning duration, timing of weaning events, factors associated with weaning delay and weaning failure, and hospital outcomes. This study is registered with ClinicalTrials.gov, NCT03255109. Findings: Between Oct 4, 2017, and June 25, 2018, 10 232 patients were screened for eligibility, of whom 5869 were enrolled. 4523 (77·1%) patients underwent at least one separation attempt and 3817 (65·0%) patients were successfully weaned from ventilation at day 90. 237 (4·0%) patients were transferred before any separation attempt, 153 (2·6%) were transferred after at least one separation attempt and not successfully weaned, and 1662 (28·3%) died while invasively ventilated. The median time from fulfilling weaning eligibility criteria to first separation attempt was 1 day (IQR 0–4), and 1013 (22·4%) patients had a delay in initiating first separation of 5 or more days. Of the 4523 (77·1%) patients with separation attempts, 2927 (64·7%) had a short wean (≤1 d

Published
2023

35. Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction.

Author

Nagarajan V, Shashikumar SP, Malhotra A, Nemati S, and Wardi G

Subjects
Humans, Retrospective Studies, Female, Male, Middle Aged, Entropy, Aged, Adult, ROC Curve, Patient Readmission statistics & numerical data, Wearable Electronic Devices, Deep Learning, Neural Networks, Computer
Abstract

Objective: Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission., Materials and Methods: We conducted a multi-center retrospective cohort study using data from the All of Us data repository. We included subjects with wearable data and developed a baseline Feedforward Neural Network (FNN) model and a Long Short-Term Memory (LSTM) time-series deep learning model to predict daily, unplanned rehospitalizations up to 90 days from discharge. In addition to demographic and laboratory data from subjects, post-discharge data input features include wearable data and multiscale entropy features based on intraday wearable time series. The most significant features in the LSTM model were determined by permutation feature importance testing., Results: In sum, 612 patients met inclusion criteria. The complete LSTM model had a higher area under the receiver operating characteristic curve than the FNN model (0.83 vs 0.795). The 5 most important input features included variables from multiscale entropy (steps) and number of active steps per day., Discussion: Data available from wearable devices can improve ability to predict readmissions. Prior work has focused on predictors available up to discharge or on additional data abstracted from wearable devices. Our results from 35 institutions highlight how multiscale entropy can improve readmission prediction and may impact future work in this domain., Conclusion: Wearable data and multiscale entropy can improve prediction of a deep-learning model to predict unplanned 90-day readmissions. Prospective studies are needed to validate these findings., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Published
2024
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36. Large Language Models for More Efficient Reporting of Hospital Quality Measures.

Author

Boussina A, Krishnamoorthy R, Quintero K, Joshi S, Wardi G, Pour H, Hilbert N, Malhotra A, Hogarth M, Sitapati AM, VanDenBerg C, Singh K, Longhurst CA, and Nemati S

Abstract

Hospital quality measures are a vital component of a learning health system, yet they can be costly to report, statistically underpowered, and inconsistent due to poor interrater reliability. Large language models (LLMs) have recently demonstrated impressive performance on health care-related tasks and offer a promising way to provide accurate abstraction of complete charts at scale. To evaluate this approach, we deployed an LLM-based system that ingests Fast Healthcare Interoperability Resources data and outputs a completed Severe Sepsis and Septic Shock Management Bundle (SEP-1) abstraction. We tested the system on a sample of 100 manual SEP-1 abstractions that University of California San Diego Health reported to the Centers for Medicare & Medicaid Services in 2022. The LLM system achieved agreement with manual abstractors on the measure category assignment in 90 of the abstractions (90%; κ=0.82; 95% confidence interval, 0.71 to 0.92). Expert review of the 10 discordant cases identified four that were mistakes introduced by manual abstraction. This pilot study suggests that LLMs using interoperable electronic health record data may perform accurate abstractions for complex quality measures. (Funded by the National Institute of Allergy and Infectious Diseases [1R42AI177108-1] and others.).

Published
2024
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37. The Discover In-Hospital Cardiac Arrest (Discover IHCA) Study: An Investigation of Hospital Practices After In-Hospital Cardiac Arrest.

Author

Andrea L, Herman NS, Vine J, Berg KM, Choudhury S, Vaena M, Nogle JE, Halablab SM, Kaviyarasu A, Elmer J, Wardi G, Pearce AK, Crowley C, Long MT, Herbert JT, Shipley K, Bissell Turpin BD, Lanspa MJ, Green A, Ghamande SA, Khan A, Dugar S, Joffe AM, Baram M, March C, Johnson NJ, Reyes A, Denchev K, Loewe M, and Moskowitz A

Subjects
Humans, Prospective Studies, Male, Female, United States epidemiology, Aged, Middle Aged, Cohort Studies, Hospitals, Hospitalization statistics & numerical data, Return of Spontaneous Circulation, Heart Arrest therapy, Heart Arrest mortality, Cardiopulmonary Resuscitation
Abstract

Importance: In-hospital cardiac arrest (IHCA) is a significant public health burden. Rates of return of spontaneous circulation (ROSC) have been improving, but the best way to care for patients after the initial resuscitation remains poorly understood, and improvements in survival to discharge are stagnant. Existing North American cardiac arrest databases lack comprehensive data on the post-resuscitation period, and we do not know current post-IHCA practice patterns. To address this gap, we developed the Discover In-Hospital Cardiac Arrest (Discover IHCA) study, which will thoroughly evaluate current post-IHCA care practices across a diverse cohort., Objectives: Our study collects granular data on post-IHCA treatment practices, focusing on temperature control and prognostication, with the objective of describing variation in current post-IHCA practice., Design, Setting, and Participants: This is a multicenter, prospectively collected, observational cohort study of patients who have suffered IHCA and have been successfully resuscitated (achieved ROSC). There are 24 enrolling hospital systems (23 in the United States) with 69 individual enrolling hospitals (39 in the United States). We developed a standardized data dictionary, and data collection began in October 2023, with a projected 1000 total enrollments. Discover IHCA is endorsed by the Society of Critical Care Medicine., Interventions, Outcomes, and Analysis: The study collects data on patient characteristics including pre-arrest frailty, arrest characteristics, and detailed information on post-arrest practices and outcomes. Data collection on post-IHCA practice was structured around current American Heart Association and European Resuscitation Council guidelines. Among other data elements, the study captures post-arrest temperature control interventions and post-arrest prognostication methods. Analysis will evaluate variations in practice and their association with mortality and neurologic function., Conclusions: We expect this study, Discover IHCA, to identify variability in practice and outcomes following IHCA, and be a vital resource for future investigations into best-practice for managing patients after IHCA., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Published
2024
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38. Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration.

Author

Shashikumar SP, Le JP, Yung N, Ford J, Singh K, Malhotra A, Nemati S, and Wardi G

Subjects
Humans, Retrospective Studies, Male, Female, Middle Aged, Aged, Clinical Deterioration, Triage methods, Adult, Cohort Studies, Inpatients, Deep Learning, Emergency Service, Hospital
Abstract

Background: Prediction-based strategies for physiologic deterioration offer the potential for earlier clinical interventions that improve patient outcomes. Current strategies are limited because they operate on inconsistent definitions of deterioration, attempt to dichotomize a dynamic and progressive phenomenon, and offer poor performance., Objective: Can a deep learning deterioration prediction model (Deep Learning Enhanced Triage and Emergency Response for Inpatient Optimization [DETERIO]) based on a consensus definition of deterioration (the Adult Inpatient Decompensation Event [AIDE] criteria) and that approaches deterioration as a state "value-estimation" problem outperform a commercially available deterioration score?, Derivation Cohort: The derivation cohort contained retrospective patient data collected from both inpatient services (inpatient) and emergency departments (EDs) of two hospitals within the University of California San Diego Health System. There were 330,729 total patients; 71,735 were inpatient and 258,994 were ED. Of these data, 20% were randomly sampled as a retrospective "testing set.", Validation Cohort: The validation cohort contained temporal patient data. There were 65,898 total patients; 13,750 were inpatient and 52,148 were ED., Prediction Model: DETERIO was developed and validated on these data, using the AIDE criteria to generate a composite score. DETERIO's architecture builds upon previous work. DETERIO's prediction performance up to 12 hours before T0 was compared against Epic Deterioration Index (EDI)., Results: In the retrospective testing set, DETERIO's area under the receiver operating characteristic curve (AUC) was 0.797 and 0.874 for inpatient and ED subsets, respectively. In the temporal validation cohort, the corresponding AUC were 0.775 and 0.856, respectively. DETERIO outperformed EDI in the inpatient validation cohort (AUC, 0.775 vs. 0.721; p < 0.01) while maintaining superior sensitivity and a comparable rate of false alarms (sensitivity, 45.50% vs. 30.00%; positive predictive value, 20.50% vs. 16.11%)., Conclusions: DETERIO demonstrates promise in the viability of a state value-estimation approach for predicting adult physiologic deterioration. It may outperform EDI while offering additional clinical utility in triage and clinician interaction with prediction confidence and explanations. Additional studies are needed to assess generalizability and real-world clinical impact., Competing Interests: Dr. Wardi has been supported by the National Foundation of Emergency Medicine and the National Institutes of Health (NIH; No. K23GM146092). Dr. Nemati is funded by the NIH (Nos. R01LM013998, R01HL157985, and R35GM143121). Dr. Malhotra is funded by the NIH. Drs. Nemati, Malhotra, and Shashikumar are co-founders of a University of California San Diego (UCSD) start-up, Healcisio, which is focused on commercialization of advanced analytical decision support tools. Healcisio is formed in compliance with the UCSD conflict of interest policies. Dr. Malhotra reports income from Livanova, Eli Lilly, Zoll, and Powell Mansfield outside of this topic area. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Published
2024
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40. Video-recorded Endotracheal Intubations: An Educational Tool in Airway Management Training for Pulmonary and Critical Care Fellows.

Author

Jiang AA, Wardi G, and Sweeney DA

Abstract

Background: Expert airway management is an essential skill for pulmonary and critical care fellows. Providing high-quality real-time feedback to trainees performing emergent intubations is often limited because of the acuity of the situation and the lack of full airway visualization by the supervising provider., Objective: We sought to improve the quality of airway management education in a pulmonary and critical care fellowship training program by recording all emergent intubations and systematically reviewing select videos at a regularly scheduled airway management conference., Methods: We introduced several modifications to our airway training curriculum, including the recording of all fellow-performed emergent tracheal intubations along with a regularly scheduled conference in which selected videos recordings were systematically reviewed. Surveys completed by trainees before and after the redesign of the curriculum were used to determine the efficacy of the individual curriculum modifications. Paired Student's t tests, χ 2 tests, and Kruskal-Wallis tests were used for statistical analysis. A P value lower than 0.05 was considered significant in all analyses., Results: After completion of the redesigned curriculum, trainees (100% response rate) demonstrated improved technical knowledge ( P  < 0.04) and procedural confidence ( P  < 0.04) with regard to airway management. Of the modifications incorporated into the curriculum redesign, fellows ranked the video-recorded intubation review conference as the most beneficial ( P  = 0.001) of the educational interventions., Conclusion: Recording of trainee-performed intubations and subsequent review of these videos using a standardized rubric was a highly valued modification to our fellowship airway training curriculum., (Copyright © 2024 by the American Thoracic Society.)

Published
2024
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41. Prediction of Readmission Following Sepsis Using Social Determinants of Health.

Author

Amrollahi F, Kennis BD, Shashikumar SP, Malhotra A, Taylor SP, Ford J, Rodriguez A, Weston J, Maheshwary R, Nemati S, Wardi G, and Meier A

Subjects
Humans, Female, Male, Retrospective Studies, Middle Aged, Aged, Adult, United States epidemiology, Logistic Models, Risk Factors, Cohort Studies, Patient Readmission statistics & numerical data, Social Determinants of Health, Sepsis mortality, Sepsis therapy
Abstract

Objectives: To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables., Design: Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data., Settings: Thirty-five hospitals across the United States from 2017 to 2021., Patients: Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization., Interventions: None., Measurements and Main Results: Unplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission., Conclusions: In this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables., Competing Interests: Drs. Shashikumar, Malhotra, and Nemati disclosed that they are cofounders of Healcisio. Dr. Malhotra received funding from Zoll, LivaNova, Jazz, and Eli Lilly; he disclosed that ResMed provided a philanthropic donation from University of California at San Diego (UCSD). Drs. Malhotra, Taylor, Rodriguez, Nemati, Wardi, and Meier received support for article research from the National Institutes of Health (NIH). Drs. Malhotra and Nemati are cofounders of Healciscio, a medical start-up forming in approval with the UCSD and in accordance with the UCSD’s conflicts of interest management policies. Dr. Taylor’s institution received funding from the National Institute of Nursing Research. Dr. Wardi’s institution received funding from the NIH; he received funding from Northwest Anesthesia. Dr. Meier received funding through her institution from the NIH. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Published
2024
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42. Temporal Trends in Methamphetamine Use in Patients Admitted to the Hospital: A Retrospective Cohort Study.

Author

Suto DJ, Xiao J, Bellinghausen AL, Odish M, Sweeney DA, Wardi G, and Owens RL

Subjects
Humans, Retrospective Studies, Adult, Male, Female, Middle Aged, Prevalence, Length of Stay statistics & numerical data, Intensive Care Units statistics & numerical data, Methamphetamine, Amphetamine-Related Disorders epidemiology, Hospitalization statistics & numerical data, Patient Readmission statistics & numerical data
Abstract

Objectives: Although methamphetamine use is common, the scope of methamphetamine use and outcomes for patients admitted to the hospital is unclear. This study aims to identify the prevalence of methamphetamine use from January 2012 to January 2022, coingestions, hospital course, and readmission rate of admitted patients., Methods: This was a retrospective cohort study conducted on patients admitted to our center with the following inclusions: age older than 18 years, positive/"pending confirm" value for methamphetamine on urine drug screen, and/or an International Classification of Diseases , Tenth Revision , code related to stimulant use disorder as an active issue. Urine drug screen data are reported as methamphetamine +/- and polysubstance (PS) +/-. Patient demographics, admission diagnosis, and hospital course were extracted. Statistical tests used included t tests and Mann-Whitney U tests., Results: A total of 19,159 encounters were included, representing 12,057 unique patients. The median (interquartile range) age was 43 (33-54) years. Of all encounters, 35.3% were methamphetamine + and PS -, and 46.3% were methamphetamine + and PS +. Hospitalizations increased from 883 in 2012 to 2532 in 2021. The median (IQR) hospital stay was 48 (48-120) hours. Of all encounters, 16.8% included an intensive care unit (ICU) admission, and the median ICU stay was 42 (21-87) hours. A total of 2988 patients (24.7%) were readmitted within the study period, and 4988 (71.5%) returned within 1 year of the previous encounter. In context of all emergency department admissions from 2013 to 2022, 13.1% had a urine drug screen + for methamphetamine., Conclusions: Hospitalizations with recent methamphetamine use doubled at our institution from 2012 to 2022. In addition, 1 in 4 is readmitted (typically within 1 year), and a minority requires ICU care., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 American Society of Addiction Medicine.)

Published
2024
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43. Ransomware Cyberattack Associated With Cardiac Arrest Incidence and Outcomes at Untargeted, Adjacent Hospitals.

Author

Pham TT, Loo TM, Malhotra A, Longhurst CA, Hylton D, Dameff C, Tully J, Wardi G, Sell RE, and Pearce AK

Abstract

Objectives: Healthcare ransomware cyberattacks have been associated with major regional hospital disruptions, but data reporting patient-oriented outcomes in critical conditions such as cardiac arrest (CA) are limited. This study examined the CA incidence and outcomes of untargeted hospitals adjacent to a ransomware-infected healthcare delivery organization (HDO)., Design Setting and Patients: This cohort study compared the CA incidence and outcomes of two untargeted academic hospitals adjacent to an HDO under a ransomware cyberattack during the pre-attack (April 3-30, 2021), attack (May 1-28, 2021), and post-attack (May 29, 2021-June 25, 2021) phases., Interventions: None., Measurements and Main Results: Emergency department and hospital mean daily census, number of CAs, mean daily CA incidence per 1,000 admissions, return of spontaneous circulation, survival to discharge, and survival with favorable neurologic outcome were measured. The study evaluated 78 total CAs: 44 out-of-hospital CAs (OHCAs) and 34 in-hospital CAs. The number of total CAs increased from the pre-attack to attack phase (21 vs. 38; p = 0.03), followed by a decrease in the post-attack phase (38 vs. 19; p = 0.01). The number of total CAs exceeded the cyberattack month forecast (May 2021: 41 observed vs. 27 forecasted cases; 95% CI, 17.0-37.4). OHCA cases also exceeded the forecast (May 2021: 24 observed vs. 12 forecasted cases; 95% CI, 6.0-18.8). Survival with favorable neurologic outcome rates for all CAs decreased, driven by increases in OHCA mortality: survival with favorable neurologic rates for OHCAs decreased from the pre-attack phase to attack phase (40.0% vs. 4.5%; p = 0.02) followed by an increase in the post-attack phase (4.5% vs. 41.2%; p = 0.01)., Conclusions: Untargeted hospitals adjacent to ransomware-infected HDOs may see worse outcomes for patients suffering from OHCA. These findings highlight the critical need for cybersecurity disaster planning and resiliency., Competing Interests: Drs. Malhotra, Dameff, Wardi, and Pearce received support for article research from the National Institutes of Health (NIH). Dr. Malhotra received funding from Zoll, Jazz, Eli Lilly, and Livanova. ResMed gave a philanthropic donation to the University of California San Diego. Dr. Longhurst receives support from the Joan and Irwin Jacobs Center for Health Innovation. Dr. Dameff is supported by Career Development Award (1-K08-EB032477) from the National Institute of Biomedical Imaging and Bioengineering. Dr. Wardi’s institution received funding from the NIH (K23 GM146092) and National Foundation of Emergency Medicine; he received funding from Northwest Anesthesia. Dr. Sell received funding from Zoll. Dr. Pearce received funding from the National Heart, Lung, and Blood Institute (T32). The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Published
2024
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44. Emergency Department Take-Home Naloxone Improves Access Compared with Pharmacy-Dispensed Naloxone.

Author

Hardin J, Seltzer J, Galust H, Deguzman A, Campbell I, Friedman N, Wardi G, Clark RF, and Lasoff D

Subjects
Humans, United States, Naloxone therapeutic use, Narcotic Antagonists therapeutic use, Emergency Service, Hospital, Analgesics, Opioid therapeutic use, Opioid-Related Disorders drug therapy, Drug Overdose drug therapy, Pharmacy
Abstract

Background: Opioid overdose is a major cause of mortality in the United States. In spite of efforts to increase naloxone availability, distribution to high-risk populations remains a challenge., Objective: To assess the effects of multiple different naloxone distribution methods on patient obtainment of naloxone in the emergency department (ED) setting., Methods: Naloxone was provided to patients in three 12-month phases between February 2020 and February 2023. In Phase 1, physicians could offer patients electronic prescriptions, which were filled in a nearby in-hospital discharge pharmacy. In Phase 2, physicians directly provided patients with take-home naloxone at discharge. In Phase 3, distribution was expanded to allow ED staff to hand patients take-home naloxone at time of discharge. The total number of prescriptions, rate of prescription filling, and amount of take-home naloxone kits provided to patients were then statistically analyzed using 95% confidence intervals (CI) and chi-squared testing., Results: In Phase 1, 348 naloxone prescriptions were written, with 133 (95% CI 112.5-153.5) filled. In Phase 2, 327 (95% CI 245.5-408.5) take-home naloxone kits were given to patients by physicians. In Phase 3, 677 (95% CI 509.5-844.5) take-home naloxone kits were provided to patients by ED staff. There were statistically significant increases in naloxone distribution from Phase 1 to Phase 2, and Phase 2 to Phase 3., Conclusions: Take-home naloxone increases access when compared with naloxone prescriptions in the ED setting. A multidisciplinary approach combined with the removal of regulatory and administrative barriers allowed for further increased distribution of no-cost naloxone to patients., 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., (Published by Elsevier Inc.)

Published
2024
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45. Impact of a deep learning sepsis prediction model on quality of care and survival.

Author

Boussina A, Shashikumar SP, Malhotra A, Owens RL, El-Kareh R, Longhurst CA, Quintero K, Donahue A, Chan TC, Nemati S, and Wardi G

Abstract

Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist with the early recognition of sepsis may improve outcomes, but relatively few studies have examined their impact on real-world patient outcomes. Our objective was to assess the impact of a deep-learning model (COMPOSER) for the early prediction of sepsis on patient outcomes. We completed a before-and-after quasi-experimental study at two distinct Emergency Departments (EDs) within the UC San Diego Health System. We included 6217 adult septic patients from 1/1/2021 through 4/30/2023. The exposure tested was a nurse-facing Best Practice Advisory (BPA) triggered by COMPOSER. In-hospital mortality, sepsis bundle compliance, 72-h change in sequential organ failure assessment (SOFA) score following sepsis onset, ICU-free days, and the number of ICU encounters were evaluated in the pre-intervention period (705 days) and the post-intervention period (145 days). The causal impact analysis was performed using a Bayesian structural time-series approach with confounder adjustments to assess the significance of the exposure at the 95% confidence level. The deployment of COMPOSER was significantly associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality (95% CI, 0.3%-3.5%), a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance (95% CI, 2.4%-8.0%), and a 4% (95% CI, 1.1%-7.1%) reduction in 72-h SOFA change after sepsis onset in causal inference analysis. This study suggests that the deployment of COMPOSER for early prediction of sepsis was associated with a significant reduction in mortality and a significant increase in sepsis bundle compliance., (© 2024. The Author(s).)

Published
2024
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47. Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.

Author

Le JP, Shashikumar SP, Malhotra A, Nemati S, and Wardi G

Subjects
Humans, Intensive Care Units, Machine Learning, Algorithms, Sepsis diagnosis, Sepsis therapy
Abstract

Syndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. Individual hospital practice patterns may limit external generalizability. Data missingness is another barrier to optimal algorithm performance and various strategies exist to mitigate this. Recent advances in data science, such as transfer learning, conformal prediction, and continual learning, may improve generalizability of machine-learning algorithms in critically ill patients. Randomized trials with these approaches are indicated to demonstrate improvements in patient-centered outcomes at this point., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Published
2023
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48. A Review of Bicarbonate Use in Common Clinical Scenarios.

Author

Wardi G, Holgren S, Gupta A, Sobel J, Birch A, Pearce A, Malhotra A, and Tainter C

Subjects
Humans, Child, Bicarbonates therapeutic use, Sodium Bicarbonate pharmacology, Sodium Bicarbonate therapeutic use, Acidosis, Lactic etiology, Acidosis drug therapy, Heart Arrest drug therapy
Abstract

Background: The use of sodium bicarbonate to treat metabolic acidosis is intuitive, yet data suggest that not all patients benefit from this therapy., Objective: In this narrative review, we describe the physiology behind commonly encountered nontoxicologic causes of metabolic acidosis, highlight potential harm from the indiscriminate administration of sodium bicarbonate in certain scenarios, and provide evidence-based recommendations to assist emergency physicians in the rational use of sodium bicarbonate., Discussion: Sodium bicarbonate can be administered as a hypertonic push, as a resuscitation fluid, or as an infusion. Lactic acidosis and cardiac arrest are two common scenarios where there is limited benefit to routine use of sodium bicarbonate, although certain circumstances, such as patients with concomitant acute kidney injury and lactic acidosis may benefit from sodium bicarbonate. Patients with cardiac arrest secondary to sodium channel blockade or hyperkalemia also benefit from sodium bicarbonate therapy. Recent data suggest that the use of sodium bicarbonate in diabetic ketoacidosis does not confer improved patient outcomes and may cause harm in pediatric patients. Available evidence suggests that alkalinization of urine in rhabdomyolysis does not improve patient-centered outcomes. Finally, patients with a nongap acidosis benefit from sodium bicarbonate supplementation., Conclusions: Empiric use of sodium bicarbonate in patients with nontoxicologic causes of metabolic acidosis is not warranted and likely does not improve patient-centered outcomes, except in select scenarios. Emergency physicians should reserve use of this medication to conditions with clear benefit to patients., 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 © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2023
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49. Bringing the Promise of Artificial Intelligence to Critical Care: What the Experience With Sepsis Analytics Can Teach Us.

Author

Wardi G, Owens R, Josef C, Malhotra A, Longhurst C, and Nemati S

Subjects
Humans, Critical Care, Artificial Intelligence, Sepsis diagnosis, Sepsis therapy
Abstract

Competing Interests: Dr. Wardi received funding from Northwest Anesthesia and Medicolegal consulting. Drs. Wardi, Malhotra, and Nemati received support for article research from the National Institutes of Health (NIH). Dr. Malhotra’s institution received funding from ResMed; he received funding from the NIH, Livanova, Eli Lilly, Zoll, and Jazz. Drs. Malhotra and Nemati disclosed that they are cofounders and equity shareholders in Healcisio, Inc. Dr. Josef received funding from Healcisio, Inc. Dr. Longhurst disclosed that he is an equity shareholder in Doximity. Dr. Owens has disclosed that he does not have any potential conflicts of interest.

Published
2023
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50. Predicting Hospital Readmission among Patients with Sepsis Using Clinical and Wearable Data.

Author

Amrollahi F, Shashikumar SP, Boussina A, Yhdego H, Nayebnazar A, Yung N, Wardi G, and Nemati S

Subjects
Humans, Patient Readmission, Aftercare, Patient Discharge, Sepsis diagnosis, Wearable Electronic Devices
Abstract

Sepsis is a life-threatening condition that occurs due to a dysregulated host response to infection. Recent data demonstrate that patients with sepsis have a significantly higher readmission risk than other common conditions, such as heart failure, pneumonia and myocardial infarction and associated economic burden. Prior studies have demonstrated an association between a patient's physical activity levels and readmission risk. In this study, we show that distribution of activity level prior and post-discharge among patients with sepsis are predictive of unplanned rehospitalization in 90 days (P-value<1e-3). Our preliminary results indicate that integrating Fitbit data with clinical measurements may improve model performance on predicting 90 days readmission.Clinical relevance Sepsis, Activity level, Hospital readmission, Wearable data.

Published
2023
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