33 results on '"Glober, Nancy"'
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2. Utility of the Healthy Aging Brain Care Monitor as a Patient-Reported Symptom Monitoring Tool in Older Injury Survivors
- Author
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Fuchita, Mikita, Perkins, Anthony, Holler, Emma, Glober, Nancy, Lasiter, Sue, Mohanty, Sanjay, Ortiz, Damaris, Gao, Sujuan, French, Dustin D., Boustani, Malaz, and Zarzaur, Ben L.
- Published
- 2023
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3. Weaning from mechanical ventilation in intensive care units across 50 countries (WEAN SAFE): a multicentre, prospective, observational cohort study
- Author
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Abrough, Fekri, Acharya, Subhash P, Amin, Pravin, Arabi, Yaseen, Aragao, Irene, Bauer, Philippe, Beduneau, Gaëtan, Beitler, Jeremy, Berkius, Johan, Bugedo, Guillermo, Camporota, Luigi, Cerny, Vladimir, Cho, Young-Jae, Clarkson, Kevin, Estenssoro, Elisa, Goligher, Ewan, Grasselli, Giacomo, Gritsan, Alexey, Hashemian, Seyed Mohammadreza, Hermans, Greet, Heunks, Leo M, Jovanovic, Bojan, Kurahashi, Kiyoyasu, Laake, Jon Henrik, Matamis, Dimitrios, Moerer, Onnen, Molnar, Zsolt, Ozyilmaz, Ezgi, Panka, Bernardo, Papali, Alfred, Peñuelas, Óscar, Perbet, Sébastien, Piquilloud, Lise, Qiu, Haibo, Razek, Assem Abdel, Rittayamai, Nuttapol, Roldan, Rollin, Serpa Neto, Ary, Szuldrzynski, Konstanty, Talmor, Daniel, Tomescu, Dana, Van Haren, Frank, Villagomez, Asisclo, Zeggwagh, Amine Ali, Abe, Toshikazu, Aboshady, Abdelrhman, Acampo-de Jong, Melanie, Acharya, Subhash, Adderley, Jane, Adiguzel, Nalan, Agrawal, Vijay Kumar, Aguilar, Gerardo, Aguirre, Gaston, Aguirre-Bermeo, Hernan, Ahlström, Björn, Akbas, Türkay, Akker, Mustafa, Al Sadeh, Ghamdan, Alamri, Sultan, Algaba, Angela, Ali, Muneeb, Aliberti, Anna, Allegue, Jose Manuel, Alvarez, Diana, Amador, Joaquin, Andersen, Finn H, Ansari, Sharique, Apichatbutr, Yutthana, Apostolopoulou, Olympia, Arellano, Daniel, Arica, Mestanza, Arikan, Huseyin, Arinaga, Koichi, Arnal, Jean-Michel, Asano, Kengo, Asín-Corrochano, Marta, Avalos Cabrera, Jesus Milagrito, Avila Fuentes, Silvia, Aydemir, Semih, Aygencel, Gulbin, Azevedo, Luciano, Bacakoglu, Feza, Badie, Julio, Baedorf Kassis, Elias, Bai, Gabriela, Balaraj, Govindan, Ballico, Bruno, Banner-Goodspeed, Valerie, Banwarie, Preveen, Barbieri, Rosella, Baronia, Arvind, Barrett, Jonathan, Barrot, Loïc, Barrueco-Francioni, Jesus Emilio, Barry, Jeffrey, Bawangade, Harshal, Beavis, Sarah, Beck, Eduardo, Beehre, Nina, Belenguer Muncharaz, Alberto, Bellani, Giacomo, Belliato, Mirko, Bellissima, Agrippino, Beltramelli, Rodrigo, Ben Souissi, Asma, Benitez-Cano, Adela, Benlamin, Mohamed, Benslama, Abdellatif, Bento, Luis, Benvenuti, Daniela, Bernabe, Laura, Bersten, Andrew, Berta, Giacomo, Bertini, Pietro, Bertram-Ralph, Elliot, Besbes, Mohamed, Bettini, Lisandro Roberto, Beuret, Pascal, Bewley, Jeremy, Bezzi, Marco, Bhakhtiani, Lakshay, Bhandary, Rakesh, Bhowmick, Kaushik, Bihari, Shailesh, Bissett, Bernie, Blythe, David, Bocher, Simon, Boedjawan, Narain, Bojanowski, Christine M, Boni, Elisa, Boraso, Sabrina, Borelli, Massimo, Borello, Silvina, Borislavova, Margarita, Bosma, Karen J, Bottiroli, Maurizio, Boyd, Owen, Bozbay, Suha, Briva, Arturo, Brochard, Laurent, Bruel, Cédric, Bruni, Andrea, Buehner, Ulrike, Bulpa, Pierre, Burt, Karen, Buscot, Mathieu, Buttera, Stefania, Cabrera, Jorge, Caccese, Roberta, Caironi, Pietro, Canchos Gutierrez, Ivan, Canedo, Nancy, Cani, Alma, Cappellini, Iacopo, Carazo, Jesus, Cardonnet, Luis Pablo, Carpio, David, Carriedo, Demetrio, Carrillo, Ramón, Carvalho, João, Caser, Eliana, Castelli, Antonio, Castillo Quintero, Manuel, Castro, Heloisa, Catorze, Nuno, Cengiz, Melike, Cereijo, Enrique, Ceunen, Helga, Chaintoutis, Christos, Chang, Youjin, Chaparro, Gustavogcha, Chapman, Carmel, Chau, Simon, Chavez, Cecilia Eugenia, Chelazzi, Cosimo, Chelly, Jonathan, Chemouni, Frank, Chen, Kai, Chena, Ariel, Chiarandini, Paolo, Chilton, Phil, Chiumello, Davide, Chou-Lie, Yvette, Chudeau, Nicolas, Cinel, Ismail, Cinnella, Gilda, Clark, Michele, Clark, Thomas, Clementi, Stefano, Coaguila, Luis, Codecido, Alexis Jaspe, Collins, Amy, Colombo, Riccardo, Conde, Juan, Consales, Guglielmo, Cook, Tim, Coppadoro, Andrea, Cornejo, Rodrigo, Cortegiani, Andrea, Coxo, Cristina, Cracchiolo, Andrea Neville, Crespo Ramirez, Mónica, Crova, Philippe, Cruz, José, Cubattoli, Lucia, Çukurova, Zafer, Curto, Francesco, Czempik, Piotr, D'Andrea, Rocco, da Silva Ramos, Fernando, Dangers, Laurence, Danguy des Déserts, Marc, Danin, Pierre-Eric, Dantas, Fabianne, Daubin, Cédric, Dawei, Wu, de Haro, Candelaria, de Jesus Montelongo, Felipe, De Mendoza, Diego, de Pablo, Raúl, De Pascale, Gennaro, De Rosa, Silvia, Decavèle, Maxens, Declercq, Pierre-Louis, Deicas, Alberto, del Carmen Campos Moreno, María, Dellamonica, Jean, Delmas, Benjamin, Demirkiran, Oktay, Demirkiran, Hilmi, Dendane, Tarek, di Mussi, Rossella, Diakaki, Chrysi, Diaz, Anatilde, Diaz, Willy, Dikmen, Yalim, Dimoula, Aikaterini, Doble, Patricia, Doha, Nagwa, Domingos, Guilherme, Dres, Martin, Dries, David, Duggal, Abhijit, Duke, Graeme, Dunts, Pavel, Dybwik, Knut, Dykyy, Maksym, Eckert, Philippe, Efe, Serdar, Elatrous, Souheil, Elay, Gülseren, Elmaryul, Abubaker S, Elsaadany, Mohamed, Elsayed, Hany, Elsayed, Samar, Emery, Malo, Ena, Sébastien, Eng, Kevin, Englert, Joshua A, Erdogan, Elif, Ergin Ozcan, Perihan, Eroglu, Ege, Escobar, Miguel, Esen, Figen, Esen Tekeli, Arzu, Esquivel, Alejandro, Esquivel Gallegos, Helbert, Ezzouine, Hanane, Facchini, Alberto, Faheem, Mohammad, Fanelli, Vito, Farina, Maria Fernanda, Fartoukh, Muriel, Fehrle, Lutz, Feng, Feng, Feng, Yufeng, Fernandez, Irene, Fernandez, Borja, Fernandez-Rodriguez, Maria Lorena, Ferrando, Carlos, Ferreira da Silva, Maria João, Ferreruela, Mireia, Ferrier, Janet, Flamm Zamorano, Matias Jesús, Flood, Laura, Floris, Leda, Fluckiger, Martin, Forteza, Catalina, Fortunato, Antonella, Frans, Eric, Frattari, Antonella, Fredes, Sebastian, Frenzel, Tim, Fumagalli, Roberto, Furche, Mariano Andres, Fusari, Maurizio, Fysh, Edward, Galeas-Lopez, Juan Luis, Galerneau, Louis-Marie, Garcia, Analía, Garcia, María Fernanda, Garcia, Elisabet, Garcia Olivares, Pablo, Garlicki, Jaroslaw, Garnero, Aude, Garofalo, Eugenio, Gautam, Prabha, Gazenkampf, Andrey, Gelinotte, Stéphanie, Gelormini, Domenico, Ghrenassia, Etienne, Giacomucci, Angelo, Giannoni, Robert, Gigante, Andrea, Glober, Nancy, Gnesin, Paolo, Gollo, Yari, Gomaa, Dina, Gomero Paredes, Rosita, Gomes, Rui, Gomez, Raúl Alejandro, Gomez, Oscar, Gomez, Aroa, Gondim, Louise, Gonzalez, Manuel, Gonzalez, Isabel, Gonzalez-Castro, Alejandro, Gordillo Romero, Orlando, Gordo, 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Innes, Richard, Ioannides, Panagiotis, Iotti, Giorgio Antonio, Ippolito, Mariachiara, Irie, Hiromasa, Iriyama, Hiroki, Itagaki, Taiga, Izura, Javier, Izza, Santiago, Jabeen, Rakhshanda, Jamaati, Hamidreza, Jamadarkhana, Sunil, Jamoussi, Amira, Jankowski, Milosz, Jaramillo, Luis Alberto, Jeon, Kyeongman, Jeong Lee, Seok, Jeswani, Deepak, Jha, Simant, Jiang, Liangyan, Jing, Chen, Jochmans, Sébastien, Johnstad, Bror Anders, Jongmin, Lee, Joret, Aurélie, Junhasavasdikul, Detajin, Jurado, Maria Teresa, Kam, Elisa, Kamohara, Hidenobu, Kane, Caroline, Kara, Iskender, Karakurt, Sait, Karnjanarachata, Cherdkiat, Kataoka, Jun, Katayama, Shinshu, Kaushik, Shuchi, Kelebek Girgin, Nermin, Kerr, Kathryn, Kerslake, Ian, Khairnar, Prakash, Khalid, Abidi, Khan, Akram, Khanna, Ashish K, Khorasanee, Reza, Kienhorst, Dieneke, Kirakli, Cenk, Knafelj, Rihard, Kol, Mark Kol, Kongpolprom, Napplika, Kopitko, Csaba, Korkmaz Ekren, Pervin, Kubisz-Pudelko, Agnieszka, Kulcsar, Zoltan, Kumasawa, Junji, Kuriyama, 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Smiechowicz, Jakub, Smischney, Nathan, Smith, Paul, Smith, Tim, Smith, Mark, Snape, Sarah, Snyman, Lindi, Soetens, Filiep, Sook Hong, Kyung, Sosa Medellin, Miguel Ángel, Soto, Giovanna, Souloy, Xavier, Sousa, Elsa, Sovatzis, Stefania, Sozutek, Didem, Spadaro, Savino, Spagnoli, Marco, Spångfors, Martin, Spittle, Nick, Spivey, Mike, Stapleton, Andrew, Stefanovic, Branislava, Stephenson, Lorraine, Stevenson, Elizabeth, Strand, Kristian, Strano, Maria Teresa, Straus, Slavenka, Sun, Chenliang, Sun, Rongqing, Sundaram, Venkat, SunPark, Tai, Surlemont, Elisabeth, Sutherasan, Yuda, Szabo, Zsuzsanna, Tainter, Christopher, Takaba, Akihiro, Tallott, Mandy, Tamasato, Tamasato, Tang, Zhanhong, Tangsujaritvijit, Viratch, Taniguchi, Leandro, Taniguchi, Daisuke, Tarantino, Fabio, Teerapuncharoen, Krittika, Temprano, Susana, Terragni, Pierpaolo, Terzi, Nicolas, Thakur, Anand, Theerawit, Pongdhep, Thille, Arnaud W, Thomas, Matt, Thungtitigul, Poungrat, Thyrault, Martial, Tilouch, Nejla, Timenetsky, Karina, Tirapu, Juna, Todeschini, Manuel, Tomas, Roser, Tomaszewski, Christian, Tonetti, Tommaso, Tonnelier, Alexandre, Trinder, John, Trongtrakul, Konlawij, Truwit, Jonathon, Tsuei, Betty, Tulaimat, Aiman, Turan, Sema, Turkoglu, Melda, Tyagi, Sanjeev, Ubeda, Alejandro, Vagginelli, Federica, Valenti, María Florencia, Vallverdu, Imma, Van Axel, Alisha, van den Hul, Ingrid, van der Hoeven, Hans, Van Der Meer, Nardo, Vanhoof, Marc, Vargas-Ordoñez, Mónica, Vaschetto, Rosanna, Vascotto, Ettore, Vatsik, Maria, Vaz, Ana, Vazquez-Sanchez, Antonia, Ventura, Sara, Vermeijden, Jan Wytze, Vidal, Anxela, Vieira, Jocyelle, Vilela Costa Pinto, Bruno, Villagra, Ana, Villegas Succar, Cristina, Vinorum, Ole Georg, Vitale, Giovanni, Vj, Ramesh, Vochin, Ana, Voiriot, Guillaume, Volta, Carlo Alberto, von Seth, Magnus, Wajdi, Maazouzi, Walsh, Don, Wang, Shouhong, Wardi, Gabriel, Ween-Velken, Nils Christian, Wei, Bi-Lin, Weller, Dolf, Welsh, Deborah, Welters, Ingeborg, Wert, Michael, Whiteley, Simon, Wilby, Elizabeth, Williams, Erin, Williams, Karen, Wilson, Antoinette, Wojtas, Jadwiga, Won Huh, Jin, Wrathall, David, Wright, Christopher, Wu, Jian-Feng, Xi, Guo, Xing, Zheng-Jiang, Xu, Hongyang, Yamamoto, Kotaro, Yan, Jie, Yáñez, Julio, Yang, Xiaobo, Yates, Elliot, Yazicioglu Mocin, Ozlem, Ye, Zhenglong, Yildirim, Fatma, Yoshida, Norifumi, Yoshido, Hector Higo Leon, Young Lee, Bo, Yu, Rongguo, Yu, Gong, Yu, Tao, Yuan, Boyun, Yuangtrakul, Nadwipa, Yumoto, Tetsuya, Yun, Xie, Zakalik, Graciela, Zaki, Ahmad, Zalba-Etayo, Begoña, Zambon, Massimo, Zang, Bin, Zani, Gianluca, Zarka, Jonathan, Zerbi, Simone Maria, Zerman, Avsar, Zetterquist, Harald, Zhang, Jiuzhi, Zhang, Hongwen, Zhang, Wei, Zhang, Guoxiu, Zhang, Weixin, Zhao, Hongsheng, Zheng, Jia, Zhu, Bin, Zumaran, Ronald, Pham, Tài, Madotto, Fabiana, Goligher, Ewan C, Mancebo, Jordi, Peñuelas, Oscar, Pesenti, Antonio, Wunsch, Hannah, van Haren, Frank, and Laffey, John G
- Published
- 2023
- Full Text
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4. OpenMRS as an emergency EMR—How we used a global good to create an emergency EMR in a week
- Author
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Mamlin, Burke W., Shivers, Jennifer E., Glober, Nancy K., and Dick, Jonathan J.
- Published
- 2021
- Full Text
- View/download PDF
5. Weaning from mechanical ventilation in intensive care units across 50 countries (WEAN SAFE)
- Author
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Pham, Tài, Heunks, Leo, Bellani, Giacomo, Madotto, Fabiana, Aragao, Irene, Beduneau, Gaëtan, Goligher, Ewan C, Grasselli, Giacomo, Laake, Jon Henrik, Mancebo, Jordi, Peñuelas, Oscar, Piquilloud, Lise, Pesenti, Antonio, Wunsch, Hannah, van Haren, Frank, Brochard, Laurent, Laffey, John G, Abrough, Fekri, Acharya, Subhash P, Amin, Pravin, Arabi, Yaseen, Bauer, Philippe, Beitler, Jeremy, Berkius, Johan, Bugedo, Guillermo, Camporota, Luigi, Cerny, Vladimir, Cho, Young-Jae, Clarkson, Kevin, Estenssoro, Elisa, Goligher, Ewan, Gritsan, Alexey, Hashemian, Seyed Mohammadreza, Hermans, Greet, Heunks, Leo M, Jovanovic, Bojan, Kurahashi, Kiyoyasu, Matamis, Dimitrios, Moerer, Onnen, Molnar, Zsolt, Ozyilmaz, Ezgi, Panka, Bernardo, Papali, Alfred, Peñuelas, Óscar, Perbet, Sébastien, Qiu, Haibo, Razek, Assem Abdel, Rittayamai, Nuttapol, Roldan, Rollin, Serpa Neto, Ary, Szuldrzynski, Konstanty, Talmor, Daniel, Tomescu, Dana, Van Haren, Frank, Villagomez, Asisclo, Zeggwagh, Amine Ali, Abe, Toshikazu, Aboshady, Abdelrhman, Acampo-de Jong, Melanie, Acharya, Subhash, Adderley, Jane, Adiguzel, Nalan, Agrawal, Vijay Kumar, Aguilar, Gerardo, Aguirre, Gaston, Aguirre-Bermeo, Hernan, Ahlström, Björn, Akbas, Türkay, Akker, Mustafa, Al Sadeh, Ghamdan, Alamri, Sultan, Algaba, Angela, Ali, Muneeb, Aliberti, Anna, Allegue, Jose Manuel, Alvarez, Diana, Amador, Joaquin, Andersen, Finn H, Ansari, Sharique, Apichatbutr, Yutthana, Apostolopoulou, Olympia, Arellano, Daniel, Arica, Mestanza, Arikan, Huseyin, Arinaga, Koichi, Arnal, Jean-Michel, Asano, Kengo, Asín-Corrochano, Marta, Avalos Cabrera, Jesus Milagrito, Avila Fuentes, Silvia, Aydemir, Semih, Aygencel, Gulbin, Azevedo, Luciano, Bacakoglu, Feza, Badie, Julio, Baedorf Kassis, Elias, Bai, Gabriela, Balaraj, Govindan, Ballico, Bruno, Banner-Goodspeed, Valerie, Banwarie, Preveen, Barbieri, Rosella, Baronia, Arvind, Barrett, Jonathan, Barrot, Loïc, Barrueco-Francioni, Jesus Emilio, Barry, Jeffrey, Bawangade, Harshal, Beavis, Sarah, Beck, Eduardo, Beehre, Nina, Belenguer Muncharaz, Alberto, Belliato, Mirko, Bellissima, Agrippino, Beltramelli, Rodrigo, Ben Souissi, Asma, Benitez-Cano, Adela, Benlamin, Mohamed, Benslama, Abdellatif, Bento, Luis, Benvenuti, Daniela, Bernabe, Laura, Bersten, Andrew, Berta, Giacomo, Bertini, Pietro, Bertram-Ralph, Elliot, Besbes, Mohamed, Bettini, Lisandro Roberto, Beuret, Pascal, Bewley, Jeremy, Bezzi, Marco, Bhakhtiani, Lakshay, Bhandary, Rakesh, Bhowmick, Kaushik, Bihari, Shailesh, Bissett, Bernie, Blythe, David, Bocher, Simon, Boedjawan, Narain, Bojanowski, Christine M, Boni, Elisa, Boraso, Sabrina, Borelli, Massimo, Borello, Silvina, Borislavova, Margarita, Bosma, Karen J, Bottiroli, Maurizio, Boyd, Owen, Bozbay, Suha, Briva, Arturo, Bruel, Cédric, Bruni, Andrea, Buehner, Ulrike, Bulpa, Pierre, Burt, Karen, Buscot, Mathieu, Buttera, Stefania, Cabrera, Jorge, Caccese, Roberta, Caironi, Pietro, Canchos Gutierrez, Ivan, Canedo, Nancy, Cani, Alma, Cappellini, Iacopo, Carazo, Jesus, Cardonnet, Luis 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Iacopo, Carazo, Jesu, Cardonnet, Luis Pablo, Carpio, David, Carriedo, Demetrio, Carrillo, Ramón, Carvalho, João, Caser, Eliana, Castelli, Antonio, Castillo Quintero, Manuel, Castro, Heloisa, Catorze, Nuno, Cengiz, Melike, Cereijo, Enrique, Ceunen, Helga, Chaintoutis, Christo, Chang, Youjin, Chaparro, Gustavogcha, Chapman, Carmel, Chau, Simon, Chavez, Cecilia Eugenia, Chelazzi, Cosimo, Chelly, Jonathan, Chemouni, Frank, Chen, Kai, Chena, Ariel, Chiarandini, Paolo, Chilton, Phil, Chiumello, Davide, Chou-Lie, Yvette, Chudeau, Nicola, Cinel, Ismail, Cinnella, Gilda, Clark, Michele, Clark, Thoma, Clementi, Stefano, Coaguila, Lui, Codecido, Alexis Jaspe, Collins, Amy, Colombo, Riccardo, Conde, Juan, Consales, Guglielmo, Cook, Tim, Coppadoro, Andrea, Cornejo, Rodrigo, Cortegiani, Andrea, Coxo, Cristina, Cracchiolo, Andrea Neville, Crespo Ramirez, Mónica, Crova, Philippe, Cruz, José, Cubattoli, Lucia, Çukurova, Zafer, Curto, Francesco, Czempik, Piotr, D'Andrea, Rocco, da Silva Ramos, Fernando, Dangers, Laurence, Danguy des Déserts, Marc, Danin, Pierre-Eric, Dantas, Fabianne, Daubin, Cédric, Dawei, Wu, de Haro, Candelaria, de Jesus Montelongo, Felipe, De Mendoza, Diego, de Pablo, Raúl, De Pascale, Gennaro, De Rosa, Silvia, Decavèle, Maxen, Declercq, Pierre-Loui, Deicas, Alberto, del Carmen Campos Moreno, María, Dellamonica, Jean, Delmas, Benjamin, Demirkiran, Oktay, Demirkiran, Hilmi, Dendane, Tarek, di Mussi, Rossella, Diakaki, Chrysi, Diaz, Anatilde, Diaz, Willy, Dikmen, Yalim, Dimoula, Aikaterini, Doble, Patricia, Doha, Nagwa, Domingos, Guilherme, Dres, Martin, Dries, David, Duggal, Abhijit, Duke, Graeme, Dunts, Pavel, Dybwik, Knut, Dykyy, Maksym, Eckert, Philippe, Efe, Serdar, Elatrous, Souheil, Elay, Gülseren, Elmaryul, Abubaker S, Elsaadany, Mohamed, Elsayed, Hany, Elsayed, Samar, Emery, Malo, Ena, Sébastien, Eng, Kevin, Englert, Joshua A, Erdogan, Elif, Ergin Ozcan, Perihan, Eroglu, Ege, Escobar, Miguel, Esen, Figen, Esen Tekeli, Arzu, Esquivel, Alejandro, Esquivel Gallegos, Helbert, Ezzouine, Hanane, Facchini, Alberto, Faheem, Mohammad, Fanelli, Vito, Farina, Maria Fernanda, Fartoukh, Muriel, Fehrle, Lutz, Feng, Feng, Feng, Yufeng, Fernandez, Irene, Fernandez, Borja, Fernandez-Rodriguez, Maria Lorena, Ferrando, Carlo, Ferreira da Silva, Maria João, Ferreruela, Mireia, Ferrier, Janet, Flamm Zamorano, Matias Jesú, Flood, Laura, Floris, Leda, Fluckiger, Martin, Forteza, Catalina, Fortunato, Antonella, Frans, Eric, Frattari, Antonella, Fredes, Sebastian, Frenzel, Tim, Fumagalli, Roberto, Furche, Mariano Andre, Fusari, Maurizio, Fysh, Edward, Galeas-Lopez, Juan Lui, Galerneau, Louis-Marie, Garcia, Analía, Garcia, María Fernanda, Garcia, Elisabet, Garcia Olivares, Pablo, Garlicki, Jaroslaw, Garnero, Aude, Garofalo, Eugenio, Gautam, Prabha, Gazenkampf, Andrey, Gelinotte, Stéphanie, Gelormini, Domenico, Ghrenassia, Etienne, Giacomucci, Angelo, Giannoni, Robert, Gigante, Andrea, Glober, Nancy, Gnesin, Paolo, Gollo, Yari, Gomaa, Dina, Gomero Paredes, Rosita, Gomes, Rui, Gomez, Raúl Alejandro, Gomez, Oscar, Gomez, Aroa, Gondim, Louise, Gonzalez, Manuel, Gonzalez, Isabel, Gonzalez-Castro, Alejandro, Gordillo Romero, Orlando, Gordo, Federico, Gouin, Philippe, Graf Santos, Jerónimo, Grainne, Rooney, Grando, Matilde, Granov Grabovica, Sanja, Grasso, Salvatore, Grasso, Rinaldo, Grimmer, Lisa, Grissom, Colin, Gu, Qing, Guan, Xiang-Dong, Guarracino, Fabio, Guasch, Neu, Guatteri, Luca, Gueret, Renaud, Guérin, Claude, Guerot, Emmanuel, Guitard, Pierre-Gilda, Gül, Fethi, Gumus, Ayca, Gurjar, Mohan, Gutierrez, Patricia, Hachimi, Abdelhamid, Hadzibegovic, Adi, Hagan, Samantha, Hammel, Clare, Han Song, Joo, Hanlon, Gabrielle, Heines, Serge, Henriksson, Johanna, Herbrecht, Jean-Etienne, Heredia Orbegoso, Gabriel Omar, Hermon, Andrew, Hernandez, Rosana, Hernandez, Carmen, Herrera, Lui, Herrera-Gutierrez, Manuel, Hidalgo, Juan, Hill, Dianne, Holmquist, Dagmar, Homez, Marcela, Hongtao, Xia, Hormis, Anil, Horner, Daniel, Hornos, M Carmen, Hou, Meihong, House, Stacy, Housni, Brahim, Hugill, Keith, Humphreys, Sally, Humbert, Loui, Hunter, Stephanie, Hwa Young, Lee, Iezzi, Nicola, Ilutovich, Santiago, Inal, Volkan, Innes, Richard, Ioannides, Panagioti, Iotti, Giorgio Antonio, Ippolito, Mariachiara, Irie, Hiromasa, Iriyama, Hiroki, Itagaki, Taiga, Izura, Javier, Izza, Santiago, Jabeen, Rakhshanda, Jamaati, Hamidreza, Jamadarkhana, Sunil, Jamoussi, Amira, Jankowski, Milosz, Jaramillo, Luis Alberto, Jeon, Kyeongman, Jeong Lee, Seok, Jeswani, Deepak, Jha, Simant, Jiang, Liangyan, Jing, Chen, Jochmans, Sébastien, Johnstad, Bror Ander, Jongmin, Lee, Joret, Aurélie, Junhasavasdikul, Detajin, Jurado, Maria Teresa, Kam, Elisa, Kamohara, Hidenobu, Kane, Caroline, Kara, Iskender, Karakurt, Sait, Karnjanarachata, Cherdkiat, Kataoka, Jun, Katayama, Shinshu, Kaushik, Shuchi, Kelebek Girgin, Nermin, Kerr, Kathryn, Kerslake, Ian, Khairnar, Prakash, Khalid, Abidi, Khan, Akram, Khanna, Ashish K, Khorasanee, Reza, Kienhorst, Dieneke, Kirakli, Cenk, Knafelj, Rihard, Kol, Mark Kol, Kongpolprom, Napplika, Kopitko, Csaba, Korkmaz Ekren, Pervin, Kubisz-Pudelko, Agnieszka, Kulcsar, Zoltan, Kumasawa, Junji, Kuriyama, Akira, Kutchak, Fernanda, Labarca, Eduardo, Labat, Françoise, Laborda, César, Laca Barrera, Manuel Alberto, Lagache, Laurie, Landaverde Lopez, Antonio, Lanspa, Michael, Lascari, Valeria, Le Meur, Matthieu, Lee, Su Hwan, Lee, Young Ju, Lee, Jinwoo, Lee, Won-Yeon, Lee, Jarone, Legernaes, Terje, Leiner, Tamaa, Lemiale, Virginie, Leonor, Tiago, Lepper, Philipp M, Li, Dahuan, Li, Hongbin, Li, Oleg, Lima, Ana Raquel, Lind, Dan, Litton, Edward, Liu, Ning, Liu, Ling, Liu, Jialin, Llitjos, Jean-Françoi, Llorente, Beatriz, Lopez, Rodolfo, Lopez, Claudia Elizabeth, Lopez Nava, Claudia, Lovazzano, Pablo, Lu, Min, Lucchese, Francesca, Lugano, Manuela, Lugo Goytia, Gustavo, Luo, Hua, Lynch, Ceri, Macheda, Sebastiano, Madrigal Robles, Victor Hugo, Maggiore, Salvatore Maurizio, Magret Iglesias, Mònica, Malaga, Peter, Mallapura Maheswarappa, Harish, Malpartida, Guillermo, Malyarchikov, Andrey, Mansson, Helena, Manzano, Anaid, Marey, Ismael, Marin, Nathalie, Marin, Maria del Carmen, Markman, Eliana, Martin, Felix, Martin, Alex, Martin Dal Gesso, Cristina, Martinez, Felipe, Martínez-Fidalgo, Conchita, Martin-Loeches, Ignacio, Mas, Arantxa, Masaaki, Sakuraya, Maseda, Emilio, Massa, Eleni, Mattsson, Anna, Maugeri, Jessica, McCredie, Victoria, McCullough, Jame, McGuinness, Shay, McKown, Andrew, Medve, László, Mei, Chengqing, Mellado Artigas, Ricard, Mendes, Vitor, Mervat, Mohamed Khalaf Ebraheim, Michaux, Isabelle, Mikhaeil, Michael, Milagros, Olga, Milet, Igor, Millan, Maria Teresa, Minwei, Zhang, Mirabella, Lucia, Mishra, Sanghamitra, Mistraletti, Giovanni, Mochizuki, Katsunori, Moghal, Arif, Mojoli, Francesco, Molin, Alexandre, Montiel, Raquel, Montini, Luca, Monza, Gianmario, Mora Aznar, Maria, Morakul, Sunthiti, Morales, Maria, Moreno Torres, Daniel, Morocho Tutillo, Diego Rolando, Motherway, Catherine, Mouhssine, Doumiri, Mouloudi, Eleni, Muñoz, Tapia, Munoz de Cabo, Carlo, Mustafa, Mohamed, Muthuchellappan, Radhakrishnan, Muthukrishnan, Muraleekrishnan, Muttini, Stefano, Nagata, Isao, Nahar, Dick, Nakanishi, Misuzu, Nakayama, Izumi, Namendys-Silva, Silvio Antonio, Nanchal, Rahul, Nandakumar, Sivakumar, Nasi, Alessandra, Nasir, Kamal, Navalesi, Paolo, Naz Aslam, Tayyba, Nga Phan, Thuy, Nichol, Alistair, Niiyama, Shuhei, Nikolakopoulou, Sofia, Nikolic, Elena, Nitta, Kenichi, Noc, Marko, Nonas, Stephanie, Nseir, Saad, Nur Soyturk, Ayse, Obata, Yukako, Oeckler, Richard, Oguchi, Moe, Ohshimo, Shinichiro, Oikonomou, Marina, Ojados, Agueda, Oliveira, Maria Teresa, Oliveira Filho, Wilson, Oliveri, Carlo, Olmos, Aitor, Omura, Kazuya, Orlandi, Maria Cristina, Orsenigo, Francesca, Ortiz-Ruiz De Gordoa, Laura, Ota, Kei, Ovalle Olmos, Rainier, Öveges, Nándo, Oziemski, Peter, Ozkan Kuscu, Ozlem, Özyilmaz, Ezgi, Pachas Alvarado, Fernando, Pagella, Gonzalo, Palaniswamy, Vijayanand, Palazon Sanchez, Eugenio Lui, Palmese, Salvatore, Pan, Guojun, Pan, Wensen, Papanikolaou, Metaxia, Papavasilopoulou, Theonymfi, Parekh, Ameet, Parke, Rachael, Parrilla, Francisco J, Parrilla, Dácil, Pasha, Taha, Pasin, Laura, Patão, Lui, Patel, Mayur, Patel, Grisma, Pati, Basanta Kumar, Patil, Jayaprakash, Pattnaik, Saroj, Paul, Daniel, Pavesi, Maurizio, Pavlotsky, Vanesa Alejandra, Paz, Graciela, Paz, Enrique, Pecci, Elisabetta, Pellegrini, Carlo, Peña Padilla, Andrea Gabriela, Perchiazzi, Gaetano, Pereira, Tiago, Pereira, Vera, Perez, Manuel, Perez Calvo, Cesar, Perez Cheng, Meisy, Perez Maita, Ronald, Pérez-Araos, Rodrigo, Perez-Teran, Purificación, Perez-Torres, David, Perkins, Gavin, Persona, Paolo, Petnak, Tananchai, Petrova, Marina, Pham, Tai, Philippart, Françoi, Picetti, Edoardo, Pierucci, Elisabetta, Piervincenzi, Edoardo, Pinciroli, Riccardo, Pintado, Maria-Consuelo, Piraino, Thoma, Piras, Stephanie, Piras, Claudio, Pirompanich, Pattarin, Pisani, Luigi, Platas, Enrique, Plotnikow, Gustavo, Porras, Willy, Porta, Virginia, Portilla, Mariana, Portugal, José, Povoa, Pedro, Prat, Gwenael, Pratto, Romina, Preda, Gabriel, Prieto, Isidro, Prol-Silva, Estefania, Pugh, Richard, Qi, Yupeng, Qian, Chuanyun, Qin, Tiehe, Qu, Hongping, Quintana, Teobaldo, Quispe Sierra, Rosari, Quispe Soto, Rocio, Rabbani, Raihan, Rabee, Mohamed, Rabie, Ahmed, Rahe Pereira, Maria Augusta, Rai, Ashish, Raj Ashok, Sundar, Rajab, Mostafa, Ramdhani, Navin, Ramey, Elizabeth, Ranieri, Marco, Rathod, Darshana, Ray, Banambar, Redwanul Huq, Shihan Mahmud, Regli, Adrian, Reina, Rosa, Resano Sarmiento, Natalia, Reynaud, Faustine, Rialp, Gemma, Ricart, Pilar, Rice, Todd, Richardson, Angu, Rieder, Marcelo, Rinket, Martin, Rios, Fernando, Risso Vazquez, Alejandro, Riva, Ivano, Rivette, Monaly, Roca, Oriol, Roche-Campo, Ferran, Rodriguez, Covadonga, Rodriguez, Gabriel, Rodriguez Gonzalez, Daniel, Rodriguez Tucto, Xandra Yanina, Rogers, Angela, Romano, María Elena, Rørtveit, Linda, Rose, Alastair, Roux, Damien, Rouze, Anahita, Rubatto Birri, Paolo Nahuel, Ruilan, Wang, Ruiz Robledo, Aldana, Ruiz-Aguilar, Antonio Lui, Sadahiro, Tomohito, Saez, Ignacio, Sagardia, Judith, Saha, Rajnish, Saha, Rohit, Saiphoklang, Narongkorn, Saito, Shigeki, Salem, Maie, Sales, Gabriele, Salgado, Patricia, Samavedam, Sriniva, Sami Mebazaa, Mhamed, Samuelsson, Line, San Juan Roman, Nandyelly, Sanchez, Patricia, Sanchez-Ballesteros, Jesu, Sandoval, Yazcitk, Sani, Emanuele, Santos, Martin, Santos, Carla, Sanui, Masamitsu, Saravanabavan, Lakshmikanthcharan, Sari, Sema, Sarkany, Agne, Sauneuf, Bertrand, Savioli, Monica, Sazak, Hilal, Scano, Riccardo, Schneider, Franci, Schortgen, Frédérique, Schultz, Marcus J, Schwarz, Gabriele Leonie, Seçkin Yücesoy, Faruk, Seely, Andrew, Seiler, Frederik, Seker Tekdos, Yasemin, Seok Chan, Kim, Serano, Luca, Serednicki, Wojciech, Setten, Mariano, Shah, Asim, Shah, Bhagyesh, Shang, You, Shanmugasundaram, Pradeep, Shapovalov, Konstantin, Shebl, Eman, Shiga, Takuya, Shime, Nobuaki, Shin, Phil, Short, Jack, Shuhua, Chen, Siddiqui, Sughrat, Silesky Jimenez, Juan Ignacio, Silva, Daniel, Silva Sales, Betania, Simons, Koen, Sjøbø, Brit Ågot, Slessor, David, Smiechowicz, Jakub, Smischney, Nathan, Smith, Paul, Smith, Tim, Smith, Mark, Snape, Sarah, Snyman, Lindi, Soetens, Filiep, Sook Hong, Kyung, Sosa Medellin, Miguel Ángel, Soto, Giovanna, Souloy, Xavier, Sousa, Elsa, Sovatzis, Stefania, Sozutek, Didem, Spadaro, Savino, Spagnoli, Marco, Spångfors, Martin, Spittle, Nick, Spivey, Mike, Stapleton, Andrew, Stefanovic, Branislava, Stephenson, Lorraine, Stevenson, Elizabeth, Strand, Kristian, Strano, Maria Teresa, Straus, Slavenka, Sun, Chenliang, Sun, Rongqing, Sundaram, Venkat, SunPark, Tai, Surlemont, Elisabeth, Sutherasan, Yuda, Szabo, Zsuzsanna, Tainter, Christopher, Takaba, Akihiro, Tallott, Mandy, Tamasato, Tamasato, Tang, Zhanhong, Tangsujaritvijit, Viratch, Taniguchi, Leandro, Taniguchi, Daisuke, Tarantino, Fabio, Teerapuncharoen, Krittika, Temprano, Susana, Terragni, Pierpaolo, Terzi, Nicola, Thakur, Anand, Theerawit, Pongdhep, Thille, Arnaud W, Thomas, Matt, Thungtitigul, Poungrat, Thyrault, Martial, Tilouch, Nejla, Timenetsky, Karina, Tirapu, Juna, Todeschini, Manuel, Tomas, Roser, Tomaszewski, Christian, Tonetti, Tommaso, Tonnelier, Alexandre, Trinder, John, Trongtrakul, Konlawij, Truwit, Jonathon, Tsuei, Betty, Tulaimat, Aiman, Turan, Sema, Turkoglu, Melda, Tyagi, Sanjeev, Ubeda, Alejandro, Vagginelli, Federica, Valenti, María Florencia, Vallverdu, Imma, Van Axel, Alisha, van den Hul, Ingrid, van der Hoeven, Han, Van Der Meer, Nardo, Vanhoof, Marc, Vargas-Ordoñez, Mónica, Vaschetto, Rosanna, Vascotto, Ettore, Vatsik, Maria, Vaz, Ana, Vazquez-Sanchez, Antonia, Ventura, Sara, Vermeijden, Jan Wytze, Vidal, Anxela, Vieira, Jocyelle, Vilela Costa Pinto, Bruno, Villagra, Ana, Villegas Succar, Cristina, Vinorum, Ole Georg, Vitale, Giovanni, Vj, Ramesh, Vochin, Ana, Voiriot, Guillaume, Volta, Carlo Alberto, von Seth, Magnu, Wajdi, Maazouzi, Walsh, Don, Wang, Shouhong, Wardi, Gabriel, Ween-Velken, Nils Christian, Wei, Bi-Lin, Weller, Dolf, Welsh, Deborah, Welters, Ingeborg, Wert, Michael, Whiteley, Simon, Wilby, Elizabeth, Williams, Erin, Williams, Karen, Wilson, Antoinette, Wojtas, Jadwiga, Won Huh, Jin, Wrathall, David, Wright, Christopher, Wu, Jian-Feng, Xi, Guo, Xing, Zheng-Jiang, Xu, Hongyang, Yamamoto, Kotaro, Yan, Jie, Yáñez, Julio, Yang, Xiaobo, Yates, Elliot, Yazicioglu Mocin, Ozlem, Ye, Zhenglong, Yildirim, Fatma, Yoshida, Norifumi, Yoshido, Hector Higo Leon, Young Lee, Bo, Yu, Rongguo, Yu, Gong, Yu, Tao, Yuan, Boyun, Yuangtrakul, Nadwipa, Yumoto, Tetsuya, Yun, Xie, Zakalik, Graciela, Zaki, Ahmad, Zalba-Etayo, Begoña, Zambon, Massimo, Zang, Bin, Zani, Gianluca, Zarka, Jonathan, Zerbi, Simone Maria, Zerman, Avsar, Zetterquist, Harald, Zhang, Jiuzhi, Zhang, Hongwen, Zhang, Wei, Zhang, Guoxiu, Zhang, Weixin, Zhao, Hongsheng, Zheng, Jia, Zhu, Bin, Zumaran, Ronald, Pham T., Heunks L., Bellani G., Madotto F., Aragao I., Beduneau G., Goligher E. C., Grasselli G., Laake J. H., Mancebo J., et al., Intensive Care Medicine, ACS - Diabetes & metabolism, ACS - Microcirculation, ACS - Pulmonary hypertension & thrombosis, AII - Inflammatory diseases, Intensive Care, Econometrics, Neurosurgery, Cardiothoracic Surgery, and Internal Medicine
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Pulmonary and Respiratory Medicine ,Internal Medicine Sciences ,Klinik Tıp ,RESPIRATORY SYSTEM ,Dahili Tıp Bilimleri ,Göğüs Hastalıkları ve Allerji ,CLINICAL MEDICINE ,Sağlık Bilimleri ,Clinical Medicine (MED) ,Tıp ,SOLUNUM SİSTEMİ ,Mechanical ventilation ,N/A ,Health Sciences ,Settore MED/41 - ANESTESIOLOGIA ,Akciğer ve Solunum Tıbbı ,Medicine ,Klinik Tıp (MED) ,Chest Diseases and Allergy - 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 day), 457 (10·1%) had intermediate weaning (2–6 days), 433 (9·6%) required prolonged weaning (≥7 days), and 706 (15·6%) had weaning failure. Higher sedation scores were independently associated with delayed initiation of weaning. Delayed initiation of weaning and higher sedation scores were independently associated with weaning failure. 1742 (31·8%) of 5479 patients died in the intensive care unit and 2095 (38·3%) of 5465 patients died in hospital. Interpretation: In critically ill patients receiving at least 2 days of invasive mechanical ventilation, only 65% were weaned at 90 days. A better understanding of factors that delay the weaning process, such as delays in weaning initiation or excessive sedation levels, might improve weaning success rates. Funding: European Society of Intensive Care Medicine, European Respiratory Society.
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- 2023
6. Impact of COVID-19 Pandemic on Drug Overdoses in Indianapolis
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Glober, Nancy, Mohler, George, Huynh, Philip, Arkins, Tom, O’Donnell, Dan, Carter, Jeremy, and Ray, Brad
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- 2020
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7. Incidence and characteristics of arterial thromboemboli in patients with COVID-19
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Glober, Nancy, Stewart, Lauren, Seo, JangDong, Kabrhel, Christopher, Nordenholz, Kristen, Camargo, Carlos, and Kline, Jeffrey
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- 2021
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8. A simple decision rule predicts futile resuscitation of out-of-hospital cardiac arrest
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Glober, Nancy K, Tainter, Christopher R, Abramson, Tiffany M, Staats, Katherine, Gilbert, Gregory, and Kim, David
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- 2019
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9. Description of the Public Safety Medical Response and Patient Encounters Within and During the Indianapolis (USA) Spring 2020 Civil Unrest.
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Arkins, Thomas P., Liao, Mark, O'Donnell, Daniel, Glober, Nancy, Faris, Gregory, Weinstein, Elizabeth, Supples, Michael W., Vaizer, Julia, Hunter, Benton R., and Lardaro, Thomas
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SOCIAL unrest ,PUBLIC safety ,EMERGENCY medical services ,HOSPITAL patients ,PENETRATING wounds - Abstract
Objective: This study describes the local Emergency Medical Services (EMS) response and patient encounters corresponding to the civil unrest occurring over a four-day period in Spring 2020 in Indianapolis, Indiana (USA). Methods: This study describes the non-conventional EMS response to civil unrest. The study included patients encountered by EMS in the area of the civil unrest occurring in Indianapolis, Indiana from May 29 through June 1, 2020. The area of civil unrest defined by Indianapolis Metropolitan Police Department covered 15 blocks by 12 blocks (roughly 4.0 square miles) and included central Indianapolis. The study analyzed records and collected demographics, scene times, interventions, dispositions, EMS clinician narratives, transport destinations, and hospital course with outcomes from receiving hospitals for patients extracted from the area of civil unrest by EMS. Results: Twenty-nine patients were included with ages ranging from two to sixty-eight years. In total, EMS transported 72.4% (21 of 29) of the patients, with the remainder declining transport. Ballistic injuries from gun violence accounted for 10.3% (3 of 29) of injuries. Two additional fatalities from penetrating trauma occurred among patients without EMS contact within and during the civil unrest. Conditions not involving trauma occurred in 37.9% (11 of 29). Among transported patients, 33.3% (7 of 21) were admitted to the hospital and there was one fatality. Conclusions: While most EMS transports did not result in hospitalization, it is important to note that the majority of EMS calls did result in a transport. There was a substantial amount of non-traumatic patient encounters. Trauma in many of the encounters was relatively severe, and the findings imply the need for rapid extraction methods from dangerous areas to facilitate timely in-hospital stabilization. [ABSTRACT FROM AUTHOR]
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- 2024
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10. Patient Demographics Are Associated with Differences in Prehospital Pain Management among Trauma Patients.
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Supples, Michael W., Vaizer, Julia, Liao, Mark, Arkins, Thomas, Lardaro, Thomas A., Faris, Gregory, O'Donnell, Daniel P., and Glober, Nancy K.
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KRUSKAL-Wallis Test ,PAIN measurement ,CONFIDENCE intervals ,TRAFFIC accidents ,ANALGESICS ,PATIENTS ,RETROSPECTIVE studies ,FISHER exact test ,EMERGENCY medical services ,CHI-squared test ,GLASGOW Coma Scale ,DESCRIPTIVE statistics ,ACCIDENTAL falls ,DEMOGRAPHY ,HEALTH equity ,METROPOLITAN areas ,WHITE people ,ODDS ratio ,LOGISTIC regression analysis ,EMERGENCY medicine ,PAIN management ,AFRICAN Americans - Abstract
Disparities have been observed in the treatment of pain in emergency department patients. However, few studies have evaluated such disparities in emergency medical services (EMS). We describe pain medication administration for trauma indications in an urban EMS system and how it varies with patient demographics. We performed a retrospective review of the electronic medical records of adult patients transported for isolated trauma (without accompanying medical complaint) from 1/1/18 to 6/30/2020 by a third service EMS agency in a major United States metropolitan area. We performed descriptive statistics on epidemiology, type of pain medications administered, and pain scores. Kruskall-Wallis and chi-square or Fisher's exact tests were used to compare continuous and categorical variables, respectively. We constructed a logistic regression model to estimate the odds of nontreatment of pain by age, race, sex, transport interval, pain score, and Glasgow Coma Scale (GCS) score for patients with pain scores of at least four on a one to ten scale, the threshold for pain treatment per the EMS protocol. Of 32,463 EMS patients with traumatic injuries included in the analysis, 40% (12,881/32,463) were African American, 50% (16,284/32,463) were female, the median age was 27 years (IQR 45-64), and the median initial pain score was 5 (IQR 2-8). Fifteen percent (4,989/32,463) received any analgesic. Initial pain scores were significantly higher for African American and female patients. African American patients were less likely to receive analgesia compared to White and Hispanic patients (19% versus 25% and 23%, respectively, p < 0.0001). Adjusting for age, pain score, transport interval, and GCS, African American compared to White, and female compared to male patients were less likely to be treated for pain, OR 1.59 (95% CI 1.47-1.72) and OR 1.20 (95% CI 1.11-1.28), respectively. Among patients with isolated traumatic injuries treated in a single, urban EMS system, African American and female patients were less likely to receive analgesia than White or male patients. Analgesics were given to a small percentage of patients who were eligible for treatment by protocol, and intravenous opioids were used in the vast majority patients who received treatment. [ABSTRACT FROM AUTHOR]
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- 2023
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11. Factors Affecting Interfacility Transport Intervals in Stroke Patients Transferred for Endovascular Therapy.
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Glober, Nancy, Faris, Greg, Montelauro, Nicholas, Tainter, Christopher, Myers, Scott M., Arkins, Thomas, Vaizer, Julia, Latta, Cassie, and Lardaro, Thomas
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MEDICAL quality control ,LENGTH of stay in hospitals ,STROKE ,TRANSPORTATION of patients ,RETROSPECTIVE studies ,HOSPITAL admission & discharge ,STROKE patients ,DESCRIPTIVE statistics ,CHI-squared test ,RESEARCH funding ,ENDOVASCULAR surgery ,COMPUTED tomography ,DATA analysis software - Abstract
To describe interfacility transfer (IFT) intervals, transfer vehicle type, and levels of care in patients with large vessel occlusion (LVO) strokes transferred for emergent endovascular therapy (EVT). We included all patients transferred by a single IFT agency in the state of Indiana from July 1, 2018 to December 1, 2020 to a comprehensive stroke center in Indianapolis for emergent EVT. Data were collected from the transfer center electronic medical records and matched to IFT and receiving hospital data. Two hundred eighty-eight patients were included, of which 150 (52.0%) received EVT. The median call-to-needle interval (from call to the transfer center to EVT needle puncture) was 155.5 minutes (IQR 135.8-195.3). The median resource activation interval (call to the transfer center to IFT deployment) was 16 minutes (IQR 10-27 minutes); the median IFT response interval (call to IFT to arrival of the transferring unit) was 34 minutes (IQR 25-43 minutes); the median pre-transfer interval (call to the transfer center until departure from the sending hospital) was 60.4 minutes (IQR 47.1-72.6); and the median sending hospital interval at bedside was 25 minutes (IQR 20-30 minutes). Most patients (197, 68.4%) were sent via critical care rotor. Only 61 (21.2%) required interventions other than tissue plasminogen administration, such as titration of actively transfusing medications (e.g., nicardipine, propofol) (37 of 61, 59.7%), or intubation or ventilator management (25 of 61, 40.3%). Patients sent via critical care rotor had longer sending hospital intervals (26 minutes, IQR 22-32, vs 19 minutes, IQR 16-25; p < 0.001) but shorter transfer intervals than those sent via critical care ground. At longer distances, rotor transport saved significant time specifically in the total IFT interval of patients with LVO strokes. Emphasizing processes to reduce the resource activation interval and the sending hospital interval may help reduce the overall time-to-EVT. [ABSTRACT FROM AUTHOR]
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- 2023
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12. Emergency Medical Services Clinicians Have a High Prevalence of Metabolic Syndrome.
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Supples, Michael W., Glober, Nancy K., Lardaro, Thomas A., Mahler, Simon A., and Stopyra, Jason P.
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OBESITY risk factors ,CAUSES of death ,GLYCOSYLATED hemoglobin ,LIFESTYLES ,PHYSICAL diagnosis ,CROSS-sectional method ,ATTITUDES of medical personnel ,AGE distribution ,MULTIPLE regression analysis ,MEDICAL personnel ,CARDIOVASCULAR diseases ,FIRE fighters ,RACE ,MANN Whitney U Test ,FISHER exact test ,RISK assessment ,SEX distribution ,LABOR supply ,MEDICAL protocols ,COMPARATIVE studies ,EMERGENCY medical services ,PSYCHOSOCIAL factors ,DISEASE prevalence ,METABOLIC syndrome ,WAIST circumference ,QUESTIONNAIRES ,DESCRIPTIVE statistics ,CHI-squared test ,RESEARCH funding ,LOGISTIC regression analysis ,BODY mass index ,INDUSTRIAL hygiene ,ODDS ratio ,LONGITUDINAL method ,CHOLESTEROL ,DISEASE complications - Abstract
Metabolic syndrome is a constellation of risk factors associated with the development of cardiovascular disease and increased all-cause mortality. Data examining the prevalence of metabolic syndrome among emergency medical services (EMS) clinicians are limited. We conducted a cross-sectional study of EMS clinicians and firefighters from three fire departments with transport-capable EMS divisions. Data were collected from compulsory annual physical exams for 2021 that included age, sex, race, body mass index (BMI), waist circumference, blood pressure, cholesterol levels, and hemoglobin A1c level. These data were used to determine the prevalence of meeting metabolic syndrome criteria. We calculated descriptive statistics of demographics, anthropometrics, and metabolic syndrome criteria for EMS clinicians and firefighters. We used chi-square tests to compare the proportion of EMS clinicians and firefighters meeting criteria for the whole group and among age groups of <40 years old, 40 to 59 years old, and ≥60 years old. We used logistic regression to estimate the odds of meeting criteria in EMS clinicians compared to firefighters, adjusted for age, sex, race, and BMI. We reviewed data for 65 EMS clinicians and 239 firefighters. For the combined cohort, 13.2% (40/304) were female and 95.1% (289/304) were White. The median age for EMS clinicians was 34 years versus 45 years in firefighters (p < 0.0001). Metabolic syndrome criteria were met in 27.3% (83/304) of the entire group. The prevalence of meeting criteria among EMS clinicians and firefighters was 33.9% (22/65) and 25.5% (61/239), respectively (p = 0.18). Of the participants who were younger than age 40, 36.6% (15/41) of EMS clinicians versus 9.1% (7/74) of firefighters met criteria for metabolic syndrome (p < 0.001). EMS clinicians had significantly higher odds of meeting criteria [OR 4.62 (p = 0.001)] compared to firefighters when adjusted for age, sex, race, and BMI. EMS clinicians had a high prevalence of metabolic syndrome at an early age, and had a higher adjusted odds of having metabolic syndrome compared to firefighters. [ABSTRACT FROM AUTHOR]
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- 2023
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13. Impact of interhospital transfer on patients with Alzheimer's disease and other related dementias.
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Glober, Nancy, LaShell, Alexandra, Montelauro, Nicholas, Troyer, Lindsay, Supples, Michael, Unroe, Kathleen, Tainter, Christopher, Faris, Greg, Fuchita, Mikita, and Boustani, Malaz
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ALZHEIMER'S patients ,DEMENTIA ,INTENSIVE care units ,OLDER people ,ALZHEIMER'S disease - Abstract
Older adults are often transferred from one emergency department (ED) to another hospital for speciality care, but little is known about whether those transfers positively impact patients, particularly those with Alzheimer's disease and other related dementias (ADRD). In this study we aimed to describe the impact of interhospital transfer on older adults with and without ADRD. In a retrospective review of electronic medical records, we collected data on demographics, insurance type, initial code status, intensive care, length of stay, specialist consult, procedure within 48 hours, and discharge disposition for older adults (≥65$ \ge 65{\mathrm{\;}}$years). We included older adults with at least one ED visit, who were transferred to a tertiary care hospital. With logistic regression, we estimated odds of death, intensive care stay, or procedure within 48 hours by ADRD diagnosis. Patients with ADRD more often received a geriatrics (p < 0.001) or palliative care consult (p = 0.038). They were less likely to be full code at admission (p < 0.001) or to be discharged home (p < 0.001). Patients living with ADRD less often received intensive care or a procedure within 48 hours of transfer (odds ratio [OR] 1.87, 95% confidence interval [CI] 1.22–2.88). Patients with ADRD were less likely to receive intensive care unit admission or specialist procedures after transfer. Further study is indicated to comprehensively understand patient‐centered outcomes. [ABSTRACT FROM AUTHOR]
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- 2023
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14. Mass Casualty Incident Involving Rapid Acting Insulin.
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Liao, Mark Y., Fulks, Tyler, Garner, Jason, Supples, Michael, and Glober, Nancy King
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TUBERCULOSIS diagnosis ,BLOOD sugar monitoring ,DISASTERS ,MEDICATION errors ,BACTERIAL antigens ,INSULIN ,EMERGENCY medical services ,HYPOGLYCEMIA ,CARBOHYDRATES ,MASS casualties - Abstract
We report on an unusual prehospital incident involving the inadvertent administration of short-acting insulin among a group of high school students. Sixteen students iatrogenically received 10 units of insulin lispro intradermally instead of tuberculin purified protein derivative (PPD), resulting in several students experiencing symptomatic hypoglycemia. A mass casualty incident was declared and the local poison center consulted. An incident command system, with the support of on-scene EMS physicians, was established to track, treat, and transport the involved patients. [ABSTRACT FROM AUTHOR]
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- 2023
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15. Safety of an Alternative Care Protocol for EMS Non-Transport in the COVID-19 Pandemic.
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Glober, Nancy, Hamilton, Jacob, Montelauro, Nicholas, Ulintz, Alex, Arkins, Thomas, Supples, Michael, Liao, Mark, O'Donnell, Daniel, and Faris, Greg
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COVID-19 ,PROFESSIONS ,ASTHMA ,HOME care services ,PATIENT selection ,VITAL signs ,RETROSPECTIVE studies ,EMERGENCY medical technicians ,PATIENTS ,PEDIATRICS ,RACE ,SCHIZOAFFECTIVE disorders ,MEDICAL protocols ,COMPARATIVE studies ,SEPSIS ,MEDICAL errors ,EMERGENCY medical services ,LEGAL compliance ,DESCRIPTIVE statistics ,HOSPITAL care ,CHEST pain ,QUALITY assurance ,CHI-squared test ,INFLUENZA ,MEDICAL history taking ,ESSENTIAL hypertension ,DEATH ,DATA analysis software ,METROPOLITAN areas ,HYPERKALEMIA ,COVID-19 pandemic ,PATIENT safety ,LONGITUDINAL method - Abstract
Our primary goal was to evaluate safety of a new emergency medical services (EMS) protocol directing non-transport of low-acuity patients during the COVID-19 pandemic. We performed a retrospective cohort analysis of all patients in Marion County, Indiana, from March 23, 2020 to May 25, 2020 for whom a novel non-transport protocol was used by EMS for patients with low-acuity COVID-19 symptoms. We assessed paramedic compliance with the protocol to determine numbers and types of deviations. We further reviewed a statewide health information exchange database to identify any patients with emergency department (ED) visits, hospital admissions, or death within 30 days of the EMS non-transport. For ED and hospital visits, we collected ED or admission diagnoses to determine if the etiologies were COVID-related. Between March 24, 2020 and May 25, 2020, 222 patients were documented as "Treated, Released (per protocol)." The protocol was correctly applied 144 times (64.8%). The other 78 times, although the EMS clinicians documented use of the protocol, it was not actually used (e.g., another protocol such as "no medical emergency" was used). Of the 144 patients for whom the protocol was used, in 55 cases (38.2%), the clinicians documented patient factors that should have contraindicated use of the protocol (e.g., chest pain, past medical history of asthma). The protocol was applied 5 times (3.5%) in pediatric patients. Two patients were admitted to the hospital within 72 hours of incorrect application of the protocol; both were for COVID-related complaints. Two patients were admitted to the hospital within 72 hours of correct protocol use; one was for a COVID-related complaint. In this case series, paramedics demonstrated large deviations from the novel non-transport protocol. Several patients were admitted to the hospital within 72 hours of non-transport both when the protocol was used correctly, and when it was used incorrectly. [ABSTRACT FROM AUTHOR]
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- 2023
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16. Descriptive analysis of emergency medical services 72-hour repeat patient encounters in a single, Urban Agency.
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Supples, Michael W., Liao, Mark, O'Donnell, Daniel P., Duszynski, Thomas J., and Glober, Nancy K.
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Emergency department unscheduled return visits within 72-h of discharge, called a "bounceback", have been used as a metric of quality of care. We hypothesize that specific demographics and dispositions may be associated with Emergency Medical Services (EMS) 72-h bouncebacks. For all patient encounters within one calendar year from a large, urban EMS agency, we recorded demographics (name, date of birth, race, gender), primary impression, disposition, and vital signs for EMS encounters. A bounceback was defined as a patient, identified by matching first name, last name and date of birth, with more than one EMS encounter within 72 h. We performed descriptive statistics for patients that did and did not have a subsequent bounceback using median (interquartile range) and Wilcoxon Rank Sum test for age and frequency (percent) and chi square test for gender, race and run disposition. For patients with a bounceback, we describe the frequency and percentage of EMS professional primary impressions on initial encounter. 98,043 encounters from January 1, 2021 to December 31, 2021, were analyzed. The median age was 50 years (IQR 32–65); 49.4% (46,147) were female and 50.7% (47,376) were White patients. 3951 encounters had a subsequent bounceback, and compared to those without bouncebacks, they were more often male patients (58.7% versus 50.2%, p < 0.001) and more commonly not transported (22.3% versus 15.5%, p < 0.001). A multivariable logistic regression model estimated the odds of bounceback were lower for females [OR 0.64 (95% CI 0.61–0.68)], Asian and Latino patients compared to White patients [OR 0.33 (95% CI 0.21–0.53) and 0.42 (95% CI 0.34–0.51)], respectively, no significant difference for Black patients compared to White patients, and higher for non-transported patients [OR 1.25 (95% CI 1.16–1.34)]. The The most common EMS primary impression for initial and subsequent encounters was mental health [576 (14.7%) and 944 (17.0%), respectively]. For subsequent encounters, the primary impression was cardiac arrest or death in 67 (1.2%) of cases. Bouncebacks were common in this single year study of a high-volume urban EMS agency. Male and non-transported patients most often experienced bouncebacks. The most common primary impression for encounters with bounceback was mental health related. Out-of-hospital cardiac arrest occurred in 1 % of bounceback cases. Further study is necessary to understand the effect on patient-centered outcomes. [ABSTRACT FROM AUTHOR]
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- 2023
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17. Increased weight in patients with time-sensitive diagnosis is associated with longer prehospital on-scene times.
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Supples, Michael W., Vaizer, Julia, Liao, Mark, Faris, Gregory W., O'Donnell, Daniel P., and Glober, Nancy K.
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Background: Obesity is a growing epidemic associated with higher rates of metabolic disease, heart disease and all-cause mortality. Heavier patients may require more advanced resources and specialized equipment. We hypothesize that increasing patient weight will be associated with longer prehospital on-scene times.Methods: We reviewed electronic patient care records for patients transported by two urban 9-1-1 emergency medical services (EMS) agencies. We collected age, sex, estimated patient weight, vital signs (systolic blood pressure, heart rate, pulse oximetry), provider impression, method of moving patient to ambulance, and on-scene times. We selected patients with time-sensitive diagnoses of stroke, ST-segment elevation myocardial infarction (STEMI), and trauma and compared on-scene times for patients who weighed above or below 300 pounds. We performed descriptive statistics, Mann-Whitney U tests for continuous variables and Chi-square tests for discrete variables. We constructed a generalized linear model to determine the effect of patient weight adjusted for covariates.Results: For a three-year period (May 1, 2018 to April 30, 2021) 48,203 patients were transported with an EMS impression of stroke, ST-segment elevation myocardial infarction (STEMI), and trauma. 23,654 (49.1%) patients were female, and the median age was 52 (IQR 34-68) years. The median weight was 175.0 (IQR 150.0-205.0) pounds. Patients above a dichotomous weight categorization of 300 pounds experienced a longer median scene time with any time-sensitive diagnosis (12.6 versus 11.9 min p < 0.001), STEMI (16.0 versus 13.1 min, p = 0.014) and blunt trauma (12.6 versus 11.9 min, p < 0.001)). They were more likely to be hypoxic (p < 0.001) and more likely to experience cardiac arrest (p < 0.001). They were less likely to walk to the ambulance (22.1% versus 32.2%, p < 0.001).Conclusion: Patient weight above 300 pounds was associated with significantly longer on-scene time. These patients were more likely to be hypoxic, sustain a cardiac arrest, and less likely to walk to the ambulance. [ABSTRACT FROM AUTHOR]- Published
- 2022
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18. Functional movement screen did not predict musculoskeletal injury among emergency medical services professionals.
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Supples, Michael W., Brichler, Kevin P., Glober, Nancy K., Lardaro, Thomas A., and O'Donnell, Daniel P.
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INJURY risk factors ,SKELETAL muscle injuries ,EVALUATION of medical care ,ANALYSIS of variance ,WORKERS' compensation ,FUNCTIONAL status ,MEDICAL screening ,PHYSICAL fitness ,FUNCTIONAL assessment ,RISK assessment ,PHYSICAL activity ,CLINICAL medicine ,DESCRIPTIVE statistics ,REPEATED measures design ,RECEIVER operating characteristic curves ,SENSITIVITY & specificity (Statistics) ,DATA analysis software ,ODDS ratio ,LOGISTIC regression analysis - Abstract
BACKGROUND: Emergency Medical Services (EMS) professionals frequently experience job-related injuries, most commonly overexertion or movement injuries. Data on injury reduction in EMS professionals is limited. The Functional Movement Screen (FMS) is a movement analysis tool suggested to predict musculoskeletal injury, but it has not previously been evaluated for EMS professionals. OBJECTIVE: To evaluate the effectiveness of the FMS to predict musculoskeletal injury among EMS professionals. METHODS: In October 2014, EMS professionals employed in an urban third-service EMS agency volunteered to participate in FMS administered by certified screeners. Age, sex, height and weight were recorded. After screening, participants were instructed on exercises to correct movement deficiencies. We reviewed recorded injuries from 2013 to 2016. We performed descriptive statistics. With logistic regression modeling, we described factors that predicted musculoskeletal injury. We generated a receiver operating curve (ROC) for FMS prediction of musculoskeletal injury. RESULTS: 147 of 240 full-time employees participated in the FMS. Participants' mean age was 33.7 years (SD = 9.6) and the majority (65%) were male. The median initial FMS score was 14 (IQR 11–16). Area under the ROC curve was 0.603 (p = 0.213) for FMS ability to predict any musculoskeletal injury within two years. Female sex was associated higher odds of injury (OR 3.98, 95% CI 1.61–9.80). Increasing age, body mass index (BMI) category, and FMS score≤14 did not predict musculoskeletal injury. CONCLUSION: The FMS did not predict musculoskeletal injury among EMS professionals. [ABSTRACT FROM AUTHOR]
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- 2022
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19. A novel emergency medical services protocol to improve treatment time for large vessel occlusion strokes.
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Glober, Nancy, Supples, Michael, Persaud, Sarah, Kim, David, Liao, Mark, Glidden, Michele, O'Donnell, Dan, Tainter, Christopher, Boustani, Malaz, and Alexander, Andreia
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- *
EMERGENCY medical services , *MEDICAL protocols , *AMBULANCES , *ENDOVASCULAR surgery , *TRANSPORTATION of patients , *EMAIL , *STROKE patients - Abstract
In many systems, patients with large vessel occlusion (LVO) strokes experience delays in transport to thrombectomy-capable centers. This pilot study examined use of a novel emergency medical services (EMS) protocol to expedite transfer of patients with LVOs to a comprehensive stroke center (CSC). From October 1, 2020 to February 22, 2021, Indianapolis EMS piloted a protocol, in which paramedics, after transporting a patient with a possible stroke remained at the patient's bedside until released by the emergency department or neurology physician. In patients with possible LVO, EMS providers remained at the bedside until the clinical assessment and CT angiography (CTA) were complete. If indicated, the paramedics at bedside transferred the patient, via the same ambulance, to a nearby thrombectomy-capable CSC with which an automatic transfer agreement had been arranged. This five-month mixed methods study included case-control assessment of use of the protocol, number of transfers, safety during transport, and time saved in transfer compared to emergent transfers via conventional interfacility transfer agencies. In qualitative analysis EMS providers, and ED physicians and neurologists at both sending and receiving institutions, completed e-mail surveys on the process, and offered suggestions for process improvement. Responses were coded with an inductive content analysis approach. The protocol was used 42 times during the study period; four patients were found to have LVOs and were transferred to the CSC. There were no adverse events. Median time from decision-to-transfer to arrival at the CSC was 27.5 minutes (IQR 24.5–29.0), compared to 314.5 minutes (IQR 204.0–459.3) for acute non-stroke transfers during the same period. Major themes of provider impressions included: incomplete awareness of the protocol, smooth process, challenges when a stroke alert was activated after EMS left the hospital, greater involvement of EMS in patient care, and comments on communication and efficiency. This pilot study demonstrated the feasibility, safety, and efficiency of a novel approach to expedite endovascular therapy for patients with LVOs. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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20. Examination of physician characteristics in opioid prescribing in the emergency department.
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Glober, Nancy K., Brown, Ian, and Sebok-Syer, Stefanie S.
- Abstract
Aim: We aimed to better understand variation in opioid prescribing practices by investigating physician factors at one academic suburban Emergency Department (ED).Methods: We retrospectively reviewed the electronic medical records of all patients given opioid prescriptions in the Stanford Health Care ED from 2009 to 2018. We described the variation in opioid prescriptions over time from 2009 to 2018, then dove deeper into a single year (July 1, 2017 to July 1, 2018). We described the number and type of opioid prescriptions at discharge and variation in attending physician opioid prescribing patterns using independent t-tests and a Fischer's exact test.Results: From 2009 to 2018, 657,037 patient visits occurred; 92,612 (14.1%) opioid prescriptions were written. Opioid prescriptions increased from 2009, peaked in 2015, then decreased. Individual providers wrote opioid prescriptions for 1 to 17% of their discharged patients. There was no significant difference in opioid prescribing based on provider gender (p = 0.456), fellow or attending status (p = 0.390), residency completed at Stanford Hospital (p = 0.593), residency completed within California (p = 0.493), or residency completed after 2010 (p = 0.589). Of the 371 providers who wrote opioid prescriptions from 2009 through 2018, 120 wrote prescriptions for patients who had already received at least three opioid prescriptions in the same year from the same department.Conclusion: This study could inform policymakers by describing patterns of variation in opioid prescribing over time and between providers. Although we did see significant differences in prescribing patterns from one provider to the next, those were not explained by the factors we examined. Further studies could investigate factors such as provider experience with pain and addiction, bias regarding particular pathologies, and concern around patient satisfaction scores. [ABSTRACT FROM AUTHOR]- Published
- 2021
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21. Out-of-hospital cardiac arrest volumes and characteristics during the COVID-19 pandemic.
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Glober, Nancy K., Supples, Michael, Faris, Greg, Arkins, Thomas, Christopher, Shawn, Fulks, Tyler, Rayburn, David, Weinstein, Elizabeth, Liao, Mark, O'Donnell, Daniel, and Lardaro, Thomas
- Abstract
Aim: The COVID-19 pandemic has significantly impacted Emergency Medical Services (EMS) operations throughout the country. Some studies described variation in total volume of out-of-hospital cardiac arrests (OHCA) during the pandemic. We aimed to describe the changes in volume and characteristics of OHCA patients and resuscitations in one urban EMS system.Methods: We performed a retrospective cohort analysis of all recorded atraumatic OHCA in Marion County, Indiana, from January 1, 2019 to June 30, 2019 and from January 1, 2020 to June 30, 2020. We described patient, arrest, EMS response, and survival characteristics. We performed paired and unpaired t-tests to evaluate the changes in those characteristics during COVID-19 as compared to the prior year. Data were matched by month to control for seasonal variation.Results: The total number of arrests increased from 884 in 2019 to 1034 in 2020 (p = 0.016). Comparing 2019 to 2020, there was little difference in age [median 62 (IQR 59-73) and 60 (IQR 47-72), p = 0.086], gender (38.5% and 39.8% female, p = 0.7466, witness to arrest (44.3% and 39.6%, p = 0.092), bystander AED use (10.1% and 11.4% p = 0.379), bystander CPR (48.7% and 51.4%, p = 0.242). Patients with a shockable initial rhythm (19.2% and 15.4%, p = 0.044) both decreased in 2020, and response time increased by 18 s [6.0 min (IQR 4.5-7.7) and 6.3 min (IQR 4.7-8.0), p = 0.008]. 47.7% and 54.8% (p = 0.001) of OHCA patients died in the field, 19.7% and 19.3% (p = 0.809) died in the Emergency Department, 21.8% and 18.5% (p = 0.044) died in the hospital, 10.8% and 7.4% (p = 0.012) were discharged from the hospital, and 9.3% and 5.9% (p = 0.005) were discharged with Cerebral Performance Category score ≤ 2.Conclusion: Total OHCA increased during the COVID-19 pandemic when compared with the prior year. Although patient characteristics were similar, initial shockable rhythm, and proportion of patients who died in the hospital decreased during the pandemic. Further investigation will explore etiologies of those findings. [ABSTRACT FROM AUTHOR]- Published
- 2021
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22. Validation of the NUE Rule to Predict Futile Resuscitation of Out-of-Hospital Cardiac Arrest.
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Glober, Nancy K., Lardaro, Thomas, Christopher, Shawn, Tainter, Christopher R., Weinstein, Elizabeth, and Kim, David
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CARDIOPULMONARY resuscitation ,HEALTH policy ,PREDICTIVE tests ,PATIENT selection ,AGE distribution ,RESEARCH methodology ,RETROSPECTIVE studies ,BYSTANDER CPR ,TREATMENT effectiveness ,MEDICAL protocols ,CARDIAC arrest ,SURVIVAL analysis (Biometry) ,EMERGENCY medical services ,DESCRIPTIVE statistics ,QUALITY assurance ,ELECTROCARDIOGRAPHY ,DEFIBRILLATORS ,LONGITUDINAL method - Abstract
We validated the NUE rule, using three criteria (Non-shockable initial rhythm, Unwitnessed arrest, Eighty years or older) to predict futile resuscitation of patients with out-of-hospital cardiac arrest (OHCA). We performed a retrospective cohort analysis of all recorded OHCA in Marion County, Indiana, from January 1, 2014 to December 31, 2019. We described patient, arrest, and emergency medical services (EMS) response characteristics, and assessed the performance of the NUE rule in identifying patients unlikely to survive to hospital discharge. From 2014 to 2019, EMS responded to 4370 patients who sustained OHCA. We excluded 329 (7.5%) patients with incomplete data. Median patient age was 62 years (IQR 49 − 73), 1599 (39.6%) patients were female, and 1728 (42.8%) arrests were witnessed. The NUE rule identified 290 (7.2%) arrests, of whom none survived to hospital discharge. In external validation, the NUE rule (Non-shockable initial rhythm, Unwitnessed arrest, Eighty years or older) correctly identified 7.2% of OHCA patients unlikely to survive to hospital discharge. The NUE rule could be used in EMS protocols and policies to identify OHCA patients very unlikely to benefit from aggressive resuscitation. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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23. Can we predict which COVID‐19 patients will need transfer to intensive care within 24 hours of floor admission?
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Wang, Alfred Z., Ehrman, Robert, Bucca, Antonino, Croft, Alexander, Glober, Nancy, Holt, Daniel, Lardaro, Thomas, Musey, Paul, Peterson, Kelli, Schaffer, Jason, Trigonis, Russell, Hunter, Benton R., and Pines, Jesse M.
- Subjects
INTENSIVE care units ,RESEARCH ,GLOMERULAR filtration rate ,COVID-19 ,ACQUISITION of data methodology ,CONFIDENCE intervals ,MULTIPLE regression analysis ,PATIENTS ,MEDICAL cooperation ,RETROSPECTIVE studies ,HOSPITAL admission & discharge ,HOSPITAL care ,CRITICAL care medicine ,MEDICAL records ,LEUKOCYTE count ,STATISTICAL models ,ODDS ratio ,EMERGENCY medicine - Abstract
Background: Patients with COVID‐19 can present to the emergency department (ED) at any point during the spectrum of illness, making it difficult to predict what level of care the patient will ultimately require. Admission to a ward bed, which is subsequently upgraded within hours to an intensive care unit (ICU) bed, represents an inability to appropriately predict the patient's course of illness. Predicting which patients will require ICU care within 24 hours would allow admissions to be managed more appropriately. Methods: This was a retrospective study of adults admitted to a large health care system, including 14 hospitals across the state of Indiana. Included patients were aged ≥ 18 years, were admitted to the hospital from the ED, and had a positive polymerase chain reaction (PCR) test for COVID‐19. Patients directly admitted to the ICU or in whom the PCR test was obtained > 3 days after hospital admission were excluded. Extracted data points included demographics, comorbidities, ED vital signs, laboratory values, chest imaging results, and level of care on admission. The primary outcome was a combination of either death or transfer to ICU within 24 hours of admission to the hospital. Data analysis was performed by logistic regression modeling to determine a multivariable model of variables that could predict the primary outcome. Results: Of the 542 included patients, 46 (10%) required transfer to ICU within 24 hours of admission. The final composite model, adjusted for age and admission location, included history of heart failure and initial oxygen saturation of <93% plus either white blood cell count > 6.4 or glomerular filtration rate < 46. The odds ratio (OR) for decompensation within 24 hours was 5.17 (95% confidence interval [CI] = 2.17 to 12.31) when all criteria were present. For patients without the above criteria, the OR for ICU transfer was 0.20 (95% CI = 0.09 to 0.45). Conclusions: Although our model did not perform well enough to stand alone as a decision guide, it highlights certain clinical features that are associated with increased risk of decompensation. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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24. Characteristics of COVID‐19 patients with bacterial coinfection admitted to the hospital from the emergency department in a large regional healthcare system.
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Lardaro, Thomas, Wang, Alfred Z., Bucca, Antonino, Croft, Alexander, Glober, Nancy, Holt, Daniel B., Musey, Paul I., Peterson, Kelli D., Trigonis, Russell A., Schaffer, Jason T., and Hunter, Benton R.
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COVID-19 ,MIXED infections ,LEUKOCYTE count ,HOSPITAL emergency services ,BLOOD urea nitrogen - Abstract
Introduction: The rate of bacterial coinfection with SARS‐CoV‐2 is poorly defined. The decision to administer antibiotics early in the course of SARS‐CoV‐2 infection depends on the likelihood of bacterial coinfection. Methods: We performed a retrospective chart review of all patients admitted through the emergency department with confirmed SARS‐CoV‐2 infection over a 6‐week period in a large healthcare system in the United States. Blood and respiratory culture results were abstracted and adjudicated by multiple authors. The primary outcome was the rate of bacteremia. We secondarily looked to define clinical or laboratory features associated with bacteremia. Results: There were 542 patients admitted with confirmed SARS‐CoV‐2 infection, with an average age of 62.8 years. Of these, 395 had blood cultures performed upon admission, with six true positive results (1.1% of the total population). An additional 14 patients had positive respiratory cultures treated as true pathogens in the first 72 h. Low blood pressure and elevated white blood cell count, neutrophil count, blood urea nitrogen, and lactate were statistically significantly associated with bacteremia. Clinical outcomes were not statistically significantly different between patients with and without bacteremia. Conclusions: We found a low rate of bacteremia in patients admitted with confirmed SARS‐CoV‐2 infection. In hemodynamically stable patients, routine antibiotics may not be warranted in this population. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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25. Prehospital sedation with ketamine vs. midazolam: Repeat sedation, intubation, and hospital outcomes.
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Holland, Dustin, Glober, Nancy, Christopher, Shawn, Zahn, Evan, Lardaro, Thomas, and O'Donnell, Dan
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- 2020
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26. Variations in the California Emergency Medical Services Response to Opioid Use Disorder.
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Glober, Nancy K., Hern, Gene, McBride, Owen, and Mercer, Mary P.
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ANALGESICS , *CRITICAL care medicine , *DRUG overdose , *EMERGENCY medical services , *EMERGENCY medicine , *MEDICAL protocols , *MEETINGS , *NALOXONE , *NARCOTICS , *POLICE , *QUALITY assurance , *QUESTIONNAIRES , *SUBSTANCE abuse , *VOLUNTARY health agencies , *PATIENT refusal of treatment , *DESCRIPTIVE statistics - Abstract
Introduction: Opioids contributed to over 300,000 deaths in the United States in the past 10 years. Most research on drug use occurs in clinics or hospitals; few studies have evaluated the impact of opioid use on emergency medical services (EMS) or the EMS response to opioid use disorder (OUD). This study describes the perceived burden of disease, data collection, and interventions in California local EMS agencies (LEMSA). Methods: We surveyed medical directors of all 33 California LEMSAs with 25 multiple-choice and free-answer questions. Results were collected in RedCap and downloaded into Excel (Microsoft Corporation, Redmond WA). This study was exempt from review by the Alameda Health System - Highland Hospital Institutional Review Board. Results: Of the 33 California LEMSAs, 100% responded, all indicating that OUD significantly affects their patients. Most (91%) had specific protocols directing care of those patients and repeat naloxone dosing. After naloxone administration, none permitted release to law enforcement custody, 6% permitted patient refusal of care, and 45% directed base hospital contact for refusal of care. Few protocols directed screening or treatment of OUD or withdrawal symptoms. Regular data collection occurred in 76% of LEMSAs, with only 48% linking EMS data with hospital or coroner outcomes. In only 30% did the medical director oversee regular quality improvement meetings. Of respondents, 64% were aware of public health agency-based outreach programs and 42% were aware of emergency department BRIDGE programs (Medication Assisted Treatment and immediate referral). Only 9% oversaw naloxone kit distribution (all under the medical director), and 6% had EMS-based outreach programs. In almost all (94%), law enforcement officers carried naloxone and administered it anywhere from a few times a year to greater than 200 in one LEMSA. Conclusion: This study represents an important description of EMS medical directors' approaches to the impact of OUD as well as trends in protocols and interventions to treat and prevent overdoses. Through this study, we can better understand the variable response to patients with OUD across California. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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27. The DAGMAR Score: D-dimer assay-guided moderation of adjusted risk. Improving specificity of the D-dimer for pulmonary embolism.
- Author
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Glober, Nancy, Tainter, Christopher R., Brennan, Jesse, Darocki, Mark, Klingfus, Morgan, Choi, Michelle, Derksen, Brenna, Rudolf, Frances, Wardi, Gabriel, Castillo, Edward, and Chan, Theodore
- Abstract
We generated a novel scoring system to improve the test characteristics of D-dimer in patients with suspected PE (pulmonary emboli). Electronic Medical Record data were retrospectively reviewed on Emergency Department (ED) patients 18 years or older for whom a D-dimer and imaging were ordered between June 4, 2012 and March 30, 2016. Symptoms (dyspnea, unilateral leg swelling, hemoptysis), age, vital signs, medical history (cancer, recent surgery, medications, history of deep vein thrombosis or PE, COPD, smoking), laboratory values (quantitative D-dimer, platelets, and mean platelet volume (MPV)), and imaging results (CT, VQ) were collected. Points were designated to factors that were significant in two multiple regression analyses, for PE or positive D-dimer. Points predictive of PE were designated positive values and points predictive of positive D-dimer, irrespective of presence of PE, were designated negative values. The DAGMAR (D-dimer Assay-Guided Moderation of Adjusted Risk) score was developed using age and platelet adjustment and points for factors associated with PE and elevated D-dimer. Of 8486 visits reviewed, 3523 were unique visits with imaging, yielding 2253 (26.5%) positive D-dimers. 3501 CT scans and 156 VQ scans were completed, detecting 198 PE. In our cohort, a DAGMAR Score < 2 equated to overall PE risk < 1.2%. Specificity improved (38% to 59%) without compromising sensitivity (94% to 96%). Use of the DAGMAR Score would have reduced CT scans from 2253 to 1556 and lead to fewer false negative results. By considering factors that affect D-dimer and also PE, we improved specificity without compromising sensitivity. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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28. Use of the d-dimer for Detecting Pulmonary Embolism in the Emergency Department.
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Glober, Nancy, Tainter, Christopher R., Brennan, Jesse, Darocki, Mark, Klingfus, Morgan, Choi, Michelle, Derksen, Brenna, Rudolf, Frances, Wardi, Gabriel, Castillo, Edward, and Chan, Theodore
- Abstract
Background: Assessment for pulmonary embolism (PE) in the emergency department (ED) remains complex, involving clinical decision tools, blood tests, and imaging.Objective: Our objective was to examine the test characteristics of the high-sensitivity d-dimer for the diagnosis of PE at our institution and evaluate use of the d-dimer and factors associated with a falsely elevated d-dimer.Methods: We retrospectively collected data on adult patients evaluated with a d-dimer and computed tomography (CT) pulmonary angiogram or ventilation perfusion scan at two EDs between June 4, 2012 and March 30, 2016. We collected symptoms (dyspnea, unilateral leg swelling, hemoptysis), vital signs, and medical and social history (cancer, recent surgery, medications, history of deep vein thrombosis or PE, chronic obstructive pulmonary disease, smoking). We calculated test characteristics, including sensitivity, specificity, and likelihood ratios for the assay using conventional threshold and with age adjustment, and performed a univariate analysis.Results: We found 3523 unique visits with d-dimer and imaging, detecting 198 PE. Imaging was pursued on 1270 patients with negative d-dimers, revealing 9 false negatives, and d-dimer was sent on 596 patients for whom negative Pulmonary Embolism Rule-Out Criteria (PERC) were documented with 2% subsequent radiographic detection of PE. The d-dimer showed a sensitivity of 95.7% (95% confidence interval [CI] 91-98%), specificity of 40.0% (95% CI 38-42%), negative likelihood ratio of 0.11 (95% CI 0.06-0.21), and positive likelihood ratio of 1.59 (95% CI 1.53-1.66) for the radiographic detection of PE. With age adjustment, 347 of the 2253 CT scans that were pursued in patients older than 50 years with an elevated d-dimer could have been avoided without missing any additional PE. Many risk factors, such as age, history of PE, recent surgery, shortness of breath, tachycardia and hypoxia, elevated the d-dimer, regardless of the presence of PE.Conclusions: Many patients with negative d-dimer and PERC still received imaging. Our data support the use of age adjustment, and perhaps adjustment for other factors seen in patients evaluated for PE. [ABSTRACT FROM AUTHOR]- Published
- 2018
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29. Acute Stroke: Current Evidence-based Recommendations for Prehospital Care.
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Glober, Nancy K., Sporer, Karl A., Guluma, Kama Z., Serra, John P., Barger, Joe A., Brown, John F., Gilbert, Gregory H., Koenig, Kristi L., Rudnick, Eric M., and Salvucci, Angelo A.
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BLOOD sugar analysis , *HEART physiology , *EMERGENCY medical services , *EMERGENCY medicine , *MEDICAL protocols , *PATIENT monitoring , *STROKE , *SYSTEMATIC reviews , *EVIDENCE-based medicine - Abstract
Introduction: In the United States, emergency medical services (EMS) protocols vary widely across jurisdictions. We sought to develop evidence-based recommendations for the prehospital evaluation and treatment of a patient with a suspected stroke and to compare these recommendations against the current protocols used by the 33 EMS agencies in the state of California. Methods: We performed a literature review of the current evidence in the prehospital treatment of a patient with a suspected stroke and augmented this review with guidelines from various national and international societies to create our evidence-based recommendations. We then compared the stroke protocols of each of the 33 EMS agencies for consistency with these recommendations. The specific protocol components that we analyzed were the use of a stroke scale, blood glucose evaluation, use of supplemental oxygen, patient positioning, 12-lead electrocardiogram (ECG) and cardiac monitoring, fluid assessment and intravenous access, and stroke regionalization. Results: Protocols across EMS agencies in California varied widely. Most used some sort of stroke scale with the majority using the Cincinnati Prehospital Stroke Scale (CPSS). All recommended the evaluation of blood glucose with the level for action ranging from 60 to 80mg/dL. Cardiac monitoring was recommended in 58% and 33% recommended an ECG. More than half required the direct transport to a primary stroke center and 88% recommended hospital notification. Conclusion: Protocols for a patient with a suspected stroke vary widely across the state of California. The evidence-based recommendations that we present for the prehospital diagnosis and treatment of this condition may be useful for EMS medical directors tasked with creating and revising these protocols. [ABSTRACT FROM AUTHOR]
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- 2016
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30. Rapid Electrocardiogram Evolution in a Dialysis Patient.
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Glober, Nancy, Burns, Boyd D., and Tainter, Christopher R.
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HEMODIALYSIS patients , *ELECTROCARDIOGRAPHY , *CHRONIC kidney failure , *DIABETES , *EMERGENCY medical services , *ABDOMINAL pain , *VENTRICULAR tachycardia , *HYPERKALEMIA , *DISEASE complications , *DIAGNOSIS ,CHRONIC kidney failure complications - Published
- 2016
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31. Endothelial cells downregulate apolipoprotein D expression in mural cells through paracrine secretion and Notch signaling.
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Pajaniappan, Mohanasundari, Glober, Nancy K., Kennard, Simone, Hua Liu, Ning Zhao, and Lilly, Brenda
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VASCULAR endothelium , *CELL communication , *BLOOD vessels , *APOLIPOPROTEINS , *GLYCOPROTEINS - Abstract
Endothelial and mural cell interactions are vitally important for proper formation and function of blood vessels. These two cell types communicate to regulate multiple aspects of vessel function. In studying genes regulated by this interaction, we identified apolipoprotein D (APOD) as one gene that is downregulated in mural cells by coculture with endothelial cells. APOD is a secreted glycoprotein that has been implicated in governing stress response, lipid metabolism, and aging. Moreover, APOD is known to regulate smooth muscle cells and is found in abundance within atherosclerotic lesions. Our data show that the regulation of APOD in mural cells is bimodal. Paracrine secretion by endothelial cells causes partial downregulation of APOD expression. Additionally, cell contact-dependent Notch signaling plays a role. NOTCH3 on mural cells promotes the downregulation of APOD, possibly through interaction with the JAGGED-1 ligand on endothelial cells. Our results show that NOTCH3 contributes to the downregulation of APOD and by itself is sufficient to attenuate APOD transcript expression. In examining the consequence of decreased APOD expression in mural cells, we show that APOD negatively regulates cell adhesion. APOD attenuates adhesion by reducing focal contacts; however, it has no effect on stress fiber formation. These data reveal a novel mechanism in which endothelial cells control neighboring mural cells through the downregulation of APOD, which, in turn, influences mural cell function by modulating adhesion. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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32. Second Place: Adjusting D-dimer for Platelets to Improve Specificity for Pulmonary Emboli.
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Glober, Nancy K., Tainter, C.R., Brennan, J., Darocki, M., Klingfus, M., Choi, M., Derksen, B., Rudolf, F., Wardi, G., Castillo, E., and Chan, T.
- Subjects
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FIBRIN fragment D , *BLOOD platelets , *IMMUNOSPECIFICITY , *PULMONARY embolism , *VENOUS thrombosis - Published
- 2018
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33. Fluid Resuscitation and Progression to Renal Replacement Therapy in Patients With COVID-19.
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Holt, Daniel B., Lardaro, Thomas, Wang, Alfred Z., Musey, Paul I., Trigonis, Russell, Bucca, Antonino, Croft, Alexander, Glober, Nancy, Peterson, Kelli, Schaffer, Jason T., Hunter, Benton R., and Musey, Paul I Jr
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SARS-CoV-2 , *COVID-19 , *RENAL replacement therapy , *CORONAVIRUS diseases , *COVID-19 treatment - Abstract
Background: Coronavirus disease 2019 (COVID-19) is associated with respiratory symptoms and renal effects. Data regarding fluid resuscitation and kidney injury in COVID-19 are lacking, and understanding this relationship is critical.Objectives: To determine if there is an association between fluid volume administered in 24 h and development of renal failure in COVID-19 patients.Methods: Retrospective chart review; 14 hospitals in Indiana. Included patients were adults admitted between March 11, 2020 and April 13, 2020 with a positive test for severe acute respiratory syndrome coronavirus 2 within 3 days of admission. Patients requiring renal replacement therapy prior to admission were excluded. Volumes and types of resuscitative intravenous fluids in the first 24 h were obtained with demographics, medical history, and other objective data. The primary outcome was initiation of renal replacement therapy. Logistic regression modeling was utilized in creating multivariate models for determining factors associated with the primary outcome.Results: The fluid volume received in the first 24 h after hospital admission was associated with initiation of renal replacement therapy in two different multivariate logistic regression models. An odds ratio of 1.42 (95% confidence interval 1.01-1.99) was observed when adjusting for age, heart failure, obesity, creatinine, bicarbonate, and total fluid volume. An odds ratio of 1.45 (95% confidence interval 1.02-2.05) was observed when variables significant in univariate analysis were adjusted for.Conclusions: Each liter of intravenous fluid administered to patients with COVID-19 in the first 24 h of presentation was independently associated with an increased risk for initiation of renal replacement therapy, supporting judicious fluid administration in patients with this disease. [ABSTRACT FROM AUTHOR]- Published
- 2022
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