12 results on '"Skibsted S."'
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2. Efficacy and Safety of Degludec versus Glargine in Type 2 Diabetes
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Marso, Steven P., McGuire, Darren K., Zinman, Bernard, Poulter, Neil R., Emerson, Scott S., Pieber, Thomas R., Pratley, Richard E., Haahr, Poul-Martin, Lange, Martin, Brown-Frandsen, Kirstine, Moses, Alan, Skibsted, Simon, Kvist, Kajsa, Buse, John B., Hansen, Liliana, Rabol, Rasmus, Bethel, Angelyn, de Lemos, James A., Senn, Stephen, Schwamm, Lee, Timmis, Adam, Alshekhlee, Amer, Bach, Richard G., Bertram, Therese M., Chaitman, Bernard R., Cruz-Flores, Salvador, Eckstein, Jane F., Feen, Eliahu S., Fisher, Simon J., Gosselin, Gilbert, Lim, Michael J., Rosenzweig, James L., Sherwin, Robert S., Tobin, Garry S., Vetrovec, George, Ziemer, David C., Daugaard, Gedske, Ozlem, Er, Pollak, Michael, Sengelov, Lisa, Ahn, Yu-Bae, Amod, Aslam, Benroubi, Mariana, Boonyavarakul, Apussanee, Canecki-Varzic, Silvija, Consoli, Agostino, Eliaschewitz, Freddy G., Franek, Edward, Jinnouchi, Hideaki, Khokhlov, Alexander L., Mithal, Ambrish, Philis-Tsimikas, Athena, Ong, Tiong Kiam, Pop, Lavinia, Reyna, Leobardo Sauque, Rodbard, Helena W., Roula, Daoud, Saravanan, Ponnusamy, Sesti, Giorgio, Sorli, Christopher, Sposetti, Georgina, Tinahones-Madueno, Francisco-Jose, Woo, Vincent, Berard, Lori, Caballero Mateos, Irene, Cooper, Alison, DeLuca, Clarisa, Fillary, Julie, Gan, Diyana Gan Abdul Rahman, Goondiwala, Amina, Tapia, Jessica, Busch, Robert, Chaykin, Louis, Hollander, Priscilla, Karounos, Dennis, Lane, Wendy, Lingvay, Ildiko, Odugbesan, A. Ola, Ohlson-Elliott, Theresa, Ovalle, Fernando, Pantalone, Kevin M., Phelps, Spencer, Pop-Busui, Rodica, Robertson, David, Shlesinger, Yshay, Silver, Robert, Warren, Mark, Arrar, Mohammed, Belkheir, Hassani, Benabbas, Youcef, Benferhat, Schahrazed, Benoun, Fatima, Bensalem, Samia, Bourezane, Samia, Elaoufi, Selmane Rafik, Gourine, Mouna, Hadjhabib, Mohamed, Hadjhamou, Fouzia, Hakem, Jamila, Kaouache, Nadjib, Khensal, Sabrina, Kitouni, Yacine, Lakhal, Lamine, Lezzar, Al-Kassem, Meftah, Imane Aicha, Mehenni, Lahouaria, Nouri, Nassim, Oumerzouk, Fatma, Sakouhi, Mounia, Talha, Kheira, Zemri, Zouaouia, Caeiro, Gabriela, Calveyra, Martha, Cluigt, Natalia, Geronazzo, Veronica, Lema, Silvina, Monferran, Pablo Martinez, Oviedo, Alejandra, Pozzi, Jose, Sala, Jorgelina, Schindler, Ana, Sernia, Virginia, Sicer, Maximiliano, Solis, Silvana, Varela, Patricia, Waitman, Jorge, Augusto, Gustavo Akerman, Alves, Breno, Alves, Erica, Barbosa, Luciana, Bona, Renata, Borges, Joao, Bosco, Adriana, Canani, Luis, Castro, Denise, Cavalcante, Lilian, Cechin, Laura, Cercato, Cintia, Chrisman, Carlos, Chrisman, Carolina, Cintra, Rebecca, Costa, Giselle, Fontenele, Ana Paula, Forti, Adriana, Francisco, Beatriz, Franco, Denise, Genestreti, Paulo, Gross, Jorge, Haddad, Ludmila, Halpern, Alfredo, Hissa, Miguel, Leanca, Camila, Leao, Beatriz, Marinho, Ana Elisabeth, Miranda, Isabela, Nunes, Adriano, Palhares, Fabiana, Poco, Christiani, Salles, Joao, Sampaio, Celia, Santomauro Jr., Augusto, Sartori, Carla, Sena, Rita, da Silva, Marina, da Silva, Paula, Siqueira, Katia, Turatti, Luiz, Valenti, Adriana, Vasques, Enio, Vencio, Sergio, Visconti, Guilherme, Belanger, Andre, Chilvers, Martyn, Dumas, Richard, Hurd, Carmen, Lafleche, Francois, Lam, Paul, Martinho, Valdemar, Palardy, Jean, Peterson, Sean, Raff, Errol, Sinnaeve, Linda, Sullivan, David, Baraban, Vedrana, Blaslov, Kristina, Bulum, Tomislav, Varzic, Silvija Canecki, Berkovic, Maja Cigrovski, Balen, Marica Jandric, Kruljac, Ivan, Lesnjakovic, Silvija, Lucijanic, Tomo, Majic, Ana, Rahelic, Dario, Smircic-Duvnjak, Lea, Vrkljan, Milan, Zibar, Karin, Zukanovic, Sidbela, Athanasopoulou, Elpida, Benis, Apostolos, Benroubi, Marian, Diakoumopoulou, Evanthia, Doupis, John, Griva, Theodora, Kazakos, Kyriakos, Kypraios, Nikolaos, Liatis, Stavros, Makrilakis, Konstantinos, Markou, Athina, Pagkalos, Emmanouil, Papanastasiou, Labrini, Piaditis, George, Sampanis, Christos, Siami, Evangelia, Tentolouris, Nikolaos, Tsiama, Vasiliki, Zografou, Ioanna, Adling, Pravishal, Ali, Mazher, Anjana, R. M., Arora, Arpita, Ayyar, Vageesh, Banerjee, Suvro, Bantwal, Ganapathi, Bhagwat, Nikhil, Bhattacharyya, Arpandev, Bhawarlal, Anandakumar, Chakraborty, Rabin, Chatterjee, Sanjay, Chaudhury, Tirthankar, Chowdhury, Subhankar, Damelia, Priyal, Das, Mrinal, Das, Sambit, Desai, Ankush, Devru, Nandani, Farooqui, Khalid, Ganie, Mohd Ashraf, George, Belinda, Ghosh, Debasis, Girithara, Gopalakrishnan, Haribhai, Zinzuwadia, Jain, Nupur, Jassar, Amandeep, Joshi, Ameya, Kale, Dilip, Kale, Shailaja, Kelwade, Jayant, Khatri, Ashok, Kulshreshtha, Bindu, Majumder, Bikas, Manikath, Neeraj, Margekar, Shubhalaxmi, Marwah, Tiven, Mathew, Vivek, Moulick, N. D., Mutha, Abhay, Muthu, Ramuu, Nalawade, Asmitha, Narvencar, Kedareshwar, Pandit, Kaushik, Pangtey, Ghanshyam, Parthasarathy, B., Phatak, Sanjeev, Prakash, Anupam, Prusty, Pitambar, Radhakrishnan, Chandni, Raja, Indu, Ravikumar, Ranjani, Saboo, Banshi, Sachdeva, Isha, Sangeetha, S., Selvam, Kasthuri, Sethi, Bipin, Shah, Asha, Shah, Harshal K., Shah, Priyanshu, Shah, Rushit, Shaikh, Sajid, Shaikh, Shehla, Singh, K. P., Sivagnanam, Nallaperumal, Tandon, Nikhil, Thonangi, Rajesh, Thulaseedharan, N., Toraskar, Kedar, Varthakavi, Premalatha, Yadav, Sameer, Andreozz, Francesco, Arturi, Franco, Baldassarre, Maria Pompea Antonia, Barone, Milena, Bonura, Alice, Bucciarelli, Loredana, Buscemi, Chiara, Buscemi, Silvio, Carnovale, Anna, Caselli, Chiara, Ceci, Gloria, Cimino, Elena, Colella, Alessandra, Crippa, Valentina, Cufone, Sabrina, D'Erasmo, Laura, De Remigis, Alessandra, Di Bartolo, Paolo, Dionisio, Rosa, Disoteo, Olga, Fabietti, Marcella, Febo, Fabrizio, Filetti, Sebastiano, Genovese, Stefano, Ginestra, Federica, Grancini, Valeria, Grieco, Elvira, Magistro, Andrea, Maranghi, Marianna, Monti, Lucilla, Orsi, Emanuela, Pellicano, Francesca, Pergolini, Daniela, Perticone, Francesca, Piatti, PierMarco, Pizzi, Gianluigi, Rondinelli, Maurizio, Rossi, Margherita, Rubino, Mariangela, Setola, Emanuela, Succurro, Elena, Tarquini, Giovanna, Tiseo, Giusy, Tonolo, Giancarlo, Villa, Valentina, Hong, Stefania Vo, 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Fong, Alan Yean Yip, Ghapar, Abd Kahar Abd, Hanafi, Norliza, Abu Hassan, Muhammad Radzi, Iberahim, Rahimah, Khiew, Ning Zan, Lee, Gary Chin Keong, Lingam, Rakesh, Long, Richard Chay Shien, Mahendran, Kauthaman A., Oon, Yen Yee, Said, Asri, Sothiratnam, Radhakrishna, Sridhar, Ganiga Srinivasiah, Suan, Mohd Azri Mohd, Tan, Sian Kong, Tey, Julian Hock Chuan, Undok, Abdul Wahab, Waid, Anuar, Vijayasingham, Shalini, Voon, Chi Yen, Yew, Kuan Leong, Yong, Lit Sin, Aguilar-Parra, Lilia, Aguirre, Karen, Alvarez-Carrillo, Rebeca, Arechavaleta-Granell, Maria, Baez-Campos, Aurelia, Bayram Llamas, Edmundo Alfredo, Calvo-Vargas, Cesar, Cedano-Limon, Marielena, Beltran Jaramillo, Teresita de Jesus, de Jesus Marin-Lopez, Jose, Ruiz Cornejo, Maria de Jesus, Juarez Cervantes, Maria del Carmen, Estevez de Alba, Isabel, Estrada Garcia, Raul, Estrada, Suellen, Garza-Vergara, Marisol, Gonzalez-Perez, Rosa, Gonzalez-Valencia, Carlos, Guerrero-Reyes, Rosalinda, Haro Ortiz, Jose Alfredo, Hernandez Salazar, 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Bostick, Janette, Boucher, Loria, Braddy, Dalton, Breton, Cristian, Broxton, Bridgitte, Burke, Deborah, Cabielles, Tania, Calderon, Juan, Canaan, Yamil, Canelon, Gustavo, Cardona, Jose, Castillo-Gonzalez, Sheila, Castro, Kenia, Chacon, Jessica, Chehade, Joe, Church, Lara, Cohen, Lisa, Crews, Erin, Cromer, Carol, Cromer, Michael, Cuello, Jorge, Cusi, Kenneth, de Leon, Karel, Delgado, Edmund, Delima, Selma, Denson, Janneral, DeVries, Benjamin, Diaz, Maritza, Digon, Beningo, Dowell, Michelle, Dunn, Kelly, Ellington, Dickson, Emerick, George, Encarnacion, Frances, Ezell, Leslie, Fahmie, Ashley, Fernandez, Noel, Figueredo, Rommel, Flores, Patricia, Fonseca, Juan, Freeman, Alain, Freeman, Erica, Frias, Juan, Garcia, Lazaro, Geronilla, Giovanni, Hamilton, Maxine, Hanabergh, Rodolfo, Hanson, Lenita, Harris, Kim, Hasan, Syed, Hendrick, Mary, Hicks, Melissa, Hoffman, Lara, Holt, Antoniwa, Hutchinson, Rachel, Iparraguirre, Harold, Janicka, Ania, Janovitz, Richard, Jones, Dawn, Jones, Sarah, Kaplun, Polina, Kelijman, Mirtha, Kersey, Angela, Khan, Ashraf, Khan, Misal, Kirk, Gregory, Konwai, Ada, Kovar, Meghan, Kutner, Mark, Lafaille, Jimmy, Leach, Dana, Levenson, David, Lewy-Alterbaum, Lorena, Light, Timothy, Lo, Margaret, Loman, Lusiana, Long, Sarah, Love, Jeremy, Madera, Arelis, Magee, James, Mallitz, Michelle, Marquez, Farid, Marshall, Richard, Mary, James, Mateu, Janet, McKenzie, Leanna, McKenzie, Rona, McKenzie, Wilfred, Mendelson, Reina, Mendez, Janet, Merlos, Ledis, Mierau, Jennifer, Miller, Robert, Miranda-Palma, Bresta, Montoya, Carla, Navarro, Eduardo, Neller, Christian, Nossa, Janette, Osborn, Tracey, Patel, Bijal, Penabad, Jesus, Perez, Gilberto, Pineda, Daniel, Pinero-Pilona, Antonio, Platt, George, Prestwood, Elfrida, Quintero, Luis, Rengifo, Rodney, Reyes, Dannier, Rivas, Aolani, Rivero, Mileydis, Robinson, Sonya, Robison, Dawn, Rodriguez, Dalier, Rodriguez, Ivete, Rodriguez, Toni, Rosario, Cristobal, Rosenwasser, Rebecca, Roura, Miguel, Schramm, Erich, Schramm, Rashi, Schweppe, Laurie, Segel, Scott, Sepulveda, Maria, Sheets, Lori, Shields, Karen, Smith, Krystal, Smith, Meridith, Somerstein, Claudia, Solano, Maria, Suarez, Manuel, Suarez, Omar, Suarez, Rogelio, Suarez, Victor, Sultan, Senan, Sutton, David, Szilagyi, Istvan, Thorne, Margaret, Travaglini, Stefano, Trevino, Miguel, Uriarte, Oscar, Vizcaino, Luis, Wadud, Khurram, Wehmeier, Kent, Weinstein, Debra, Welch, Celeste, White, Alexander, White, Basil, Williams, Tammy, Williamson, Cathi, Zwick, Lisa, Adams, Cynthia, Arkin, David, Bode, Bruce, Bonner, Sheila, Bonner, Wes, Bowen, Danielle, Bradley, Brooke, Bradley, Paul, Brennan, Elizaebeth, Brown, Cynthia, Brown, De'Aira, Burgess, Elizabeth, Collett, Taryn, Cook, Jennifer, Corse, Steven, Dukes, Kaitlyn, Durrence, Jenna, Dutton, Robin, Estes, Susie, Falcone, Laura, Gaskin, Katherine, Goswami, Ketan, Hart, Mari, Jester, Donni, Kies, Staci, Leiker, Abigail, Lu, Peter, Mahakala, Aparna, Maher, Maxine, Miller, Alan, Miller, Todd, Munoz, Miguel, Musto, Michele, Odugbesan, Adeniyi, Olson, Darin, Osborn, Nicole, Pace, Kerri, Pajkovic, Gordana, Paulus, Carolyn, Phillips, Lawrence, Reddick, William, Reed, John, Rowe, Shasta, Sadler, Barney, Sapp, Tabby, Savoye, Louise, Shore, David, Shuman, Susan, Sittle, Jessica, Standard, Shana, Wardell, Karla, Workman, Amy, Carreira, Tabitha, Fitz-Patrick, David, Hean, Vutheary, Limcolioc, April, Nihei, Megan, Park, Youngmee, Pau, Cindy, Shannon, Lindsey, Trotter, Alanna, Williams, Sierra, Coughenour, Tiffany, Liljenquist, John, Vance, Carl, Williams, Jonathan, Agaiby, Eva, Agaiby, John, Argo, Kelly, Azad, Nasrin, Backus, Arnetta, Bartlett, Andrew, Bellucci-Jackson, Jennifer, Boncu, Otilia, Brodt, Pamela, Carlson, Hope, Collins, Kathleen, Delibasic, Maja, Dor, Isaac, Dukett, Mary, Emanuele, Nicholas, Gabel, Faith, Giardino, Alexandra, Hoelzer, David, Hsieh, Annming, Jayaramaiah, Poornima, Kearney, Tracy, Kelley, Kathleen, Kravarusic, Jelena, Lawless, Andrea, Maheshwari, Hiralal, Martin, Elonia, McCall, Linda, Mikell, Frank, Nallala, Deepika, Said, Sufyan, Sakla, Nadia, Schiferi, Joanna, Smith, Patricia, Stokes, John, Sugimoto, Danny, Sugimoto, Michael, Swan, Delores, Alfata, Sarab, Beavins, Jill, Bogardus, Christine, Bogardus, Donald, Calvert, Brittany, Calvin, Krystle, Carlton, Kristin, Condit, Jonathan, Cornett, George, Covert, Carissa, Dela Llana, Alexander, Dixon, Rosie, Felker, Dean, Fogle, Sara, Harris, Leslie, Hoebeke, Roberta E., Silva, Ricardo, Johnson, Tabitha, Johnston, Kelly, King, Jennifer, Lang, Amber, Maynard, Kenneth, McDaniel, Vickie, McGrath, Caitlin, Moretto, Thomas, Ndukwu, Ikeadi, O'Connor, Leo, O'Connor, Thomas, Oliver, Tonya, Overmars, Stephanie, Patel, Minesh, Patterson, Geraldine, Powell, Talessa, Raphael, Annmarie, Raphael, Rod, Reedy, Mark, Reyes, Hubert S., Ridge, Terry, Rutherford, Mary S., Sebastian, Mike, Shaeffer, Laura, Sheets, Michelle, Thomas, Karen, Trueblood, Nancy, Tucker, Tiffany, White, Adrian, Wooldridge, Evan, Wright, Alison, Zapinski, Dawn, Cieloha, Megan, Coker, Clarine, Fletcher, Cherisa, Kay, Jennifer, Larsen, Katelyn, Marar, Isam, Motz, Zachary, Paul, Jamie, Pokorski, Alan, Rabadi-Marar, Diana, Scheideler, Kylie, Sobczyk, Jill, Sweeney, Kolbie, Brink, Nathalie, Buth, Dennis, Grimes, Katlyn, Lakin, Gregory, Loewen, Barbara, Secrist, Neal, Simon, William, Steere, Diane, Talbot, Kimberly, Wheeler, Christina, Adams, Derick, Armour, Wilhelmina, Asher, Whitney, Bays, Harold, Behl, Noreen, Conn, Brittany, Dye, Lyndsey, Earl, Jennifer, Farris, Neil, Figueroa, Sergio, Fruit, Olivia, Hobbs, Coury, Howard, Kristen, Jackson, Tamara, Kain, Angela, Karounos, Chrisi, Keiran, Sarah, Kennedy, Ryan, King, Kara, Krishnasamy, Sathya, Lambdin, Winona, Micklewright, Gezina, Mokshagundam, Sri, Mooney, Stephen, Moore, Melanie, Morgan, Denise, Morley, Robert, Newsome, Stacy, Nichols, Jennifer, Nims, Diana, Nockerts, Nichole, Oremus, Ruth, Ritchey, Barbara Michelle, Shelton, Christina, Smith, Danielle, Walker, Debra, Weiter, Kathleen, Whitehead, Rebecca, Wood, Robert, Woodruff, Robin, Young, J. DeShay, Alper, Arnold, Berry, Martha, Gauthier, Nadine, Griffin, Catina, Haynes, Elvia, Lawrence, Jereta, McCormack, Teraina (Terry), Midyett, Catherine, Rangaraj, Uma, Smith, William, Stricker, Makenzie, Tandron, Ileana, Vargas, Ramon, Wagner, Michele, Albert, Marie, Baillargeon, Gail, Stephens, John, Aroda, Vanita, Dempsey, Michael, Junker, Herman, Lopez, Meyling, Shepard, Marc, Ciccarelli, Carol, Datu, Joycelyn, Gallagher-Dorval, Karen, Hartigan, Celia, Kowaloff, Edward, Lock, John-Paul, Rosano, Nina, Shaw, Bernadette, Thompson, Michael, Abbo, Camerin, Anton, Salwan, Baker, Lydia, Bhan, Arti, Blackmer, Karla, Boley, Bryon, Hyde, Desirae, Bonema, John, Carlin, Tamara, Curran, Nancy, Dandois, Bethani, Dermyer, Abigail, Dubeck, Janet, Elkins, Jennifer, Ferris, Pamela, Fraser, Neil, Kozma, Julia Gargan, Gasparovich, David, Grodman, Robert, Hampson, Jaison, Hewitt, Mitzie, Hiatt, Judy, Hopkins, Ashley, Howard, Jennifer, Joliat, John, Joliat, Jonathan, Kalt, Steven, Katzman, Steven, Kogelman, Jesse, Kruger, Davida, Kurkowski, April, Lambert, Jodi, Levcovici, Elisabeta, Lockwood, Karen, Lumberg, Michael, McMullen, Lindsay, Meyers, Peter, Minto, Carrie, Nelson, Tina, Neuner, Gregory, Nurmi, Kevin, Perkins, Judy, Plunkett, Cynthia, Ruoff, Gary, Samantray, Julie, Sawah, Ahmed, Secord, Samantha, Seyoum, Berhane, Sherman, Sarah, Simpson, Michael, Sinkoff, Mark, Skoney, Joseph, Small, Jami, Szerlong, Heidi, Tinetti, Timothy, Toomey, Adam, VanDerWerff, Holly, Whitehouse, Fred, Wilkins, Raechelle, Wolf, Stephanie, Yarows, Steven, Eldred, Edward, Hester, Kim, Huling, Randall, Moore, Kerry, Preslar, Kristina, Proge, Nakia, Smith, Lora, Young, Gerri, Bybee, Kevin, Carr, George, Case, Christopher, Forker, Alan, Goeke, Lindsay, Kocher, Darlene, Lin, Oscar, Littlefield, James, Mendoza, Robinson, Moore, Michelle, Oberhaus, Barbara, Reed, Larry, Rutherford, Cheryl, Windsor, Sheryl, Ackerman, Kim, Christensen, Reed, De Souza, Jose, Douglas, Kirsten, Hrncirik, Therese, Lefkowicz, Magen, McDonnell, Lindsey, Severa, Larry, Tjaden, Jeffrey, Ward, Laura, Addison, Shayla, Anderson, Robert, Baker, Claire, Baum, Robert, Belitz, Jenny, Boerner, Shannon, Desouza, Cyrus, Drincic, Andjela, Fitzgibbons, William, Francis, Jessica, Garvin, Maureen, Goldner, Whitney, Goodrich, Melissa, Grubbs, Lisa, Hatcher, Theresa, Hochstein, Renee, Hunter, Christine, Keuchel, Janet, Konigsberg, Sarah, Kuechenmeister, Lisa, Mack, Lynn, McKnight, Thomas, Mensik, Hanna, Mitchell, Jan, Moeller, Jennifer, Montanez, Edward, Montoya, Paul, Nemecek, Tiffany, Neumeister, Amy, Newcomb, Jefferey, Pracht, Shelby, Radke, Kaitlin, Rahman, Jennifer, Renter, Samantha, Reynolds, Jill, Grace Rodriguez, Maria, Satorie, Jessica, Schiermann, Lynnda, Severson, Megan, Shivaswamy, Vijay, Snyder, Janice, Tillson, Renee, Vasey, Andrew, Wahl, Timothy, Weeks, William, Wolf, Thomas, Springer, Brian, Mitchell, Janice, Woodruff, Mark, Alvarez, Isis, Bowers, Rachel, Cala-Anaya, Franklin, Castellanos-Rodriguez, Rudiber, Christopher, Janice, Concepcion, Gelvis, Fernandez, Dareana, Ghanem, Paula, Harmon, Nicolette, Kaiser, Heather, Kaplan, Westbrook, Klugh, Serena, Le, Nicholas, Lehrner, Stephanie, Lopez, Robert, Martin, Olga, Martinez, Tina, Martini, Nicole, Molin, Clifford, Nakhle, Samer, Newsome, Yvonne, Neylon, Ellen, Olsen, Jarvis, Palal, Betsy, Philander, Peter, Rosario, Adriana Ruiz, Sanchez, Mary, Sierra-Garcia, Miguel, Smith, Myesha, Trenche, Samuel, Trippi, Danna, Vernetti, Nicholas, Villegas, Laura, White, Briana, Wilcox, Simmon, Carmolli-Cornette, Lisa, Chuang, Ellie, Hoffman, Sheila, Laliberte, Alison, Marshall, Lisa, Meroski, Pamela, O'Neil, Christine, Shippey, Jeanette, Spector, Linda, Cam, Jenny, Chen, Tina, Damle, Jagadish, Holman, Rebecca, Liu, Jenny, Milazzo, Carmelo, Quach, Brenda, Yang, Bin, Ahmad, Basharat, Aro, Dominic, Azeem, Fouzal, Bonaccorso, Antoinette, Brancato, Lenore, Brautigam, Donald, Charatz, Seth, Chiarot, June, Cunningham, Linda, DiCerbo, Nancy, DiGiovanna, Michael, Dluge-Aungst, Dawn, Dolbeer, Traci, Donovan, Daniel, Duma, Carol, Ellwood, Rebecca, Freedman, Zachary, Gorman, Timothy, Gunther, David, Hogan, Eileen, Hoffman, Lisa, Kapoor, Anoop, Kelly, Michelle, Kitchen, Timothy, Lam, David, Levister, Camilla, Levy, Carol, Lopez, Carlos, Lyons-Patterson, Jane, Makam, Sashi, Mauceri, Diana, McClary, Carole, Murak, Daniel, Nolan, Kelly, Nunez, Carlos, Elliott, Theresa Ohlson, Ostrowski, Phillip, Pallai, Ajish, Porges, Andrew, Rodes, Alfredo, Rosenberg, Richard, Rubel, Lance, Serrano, Lelanie, Shannon, Daniel, Sinha, Rabi, Snyder, Brian, Sy, Claude, Tang, Rocky, Varavenkataraman, Raghupathy, Winterberger, Amy, Allen, Stacy, Anderson, Marshall, Asbill, Mark, Beasley, Amanda, Bennett, Chrisitian, Bergamo, Katherine, Bilbro, Scott, Brannock, Sarah, Brown, Laura, Bryant, Danielle, Buck, Genena, Burdette, Tara, Buse, John, Butler, Laura, Caldwell, Ronald, Cannon, Daniel, Cannon, Kevin, Carter, Casey, Cherry, Amanda, Comer, Zoe, Crittenden, Susan, Cuffee, Juanita, Culmer, Dawn, Daly-Barnes, Kristie, Evensen, Erick, Saavedra, Rubin, Dean, John, Dezube, Milana, Diner, Jaime, Dodis, Regina, Dostou, Jean, Duclos, Michelle, Elmore, Miles, Ferree, Kristin, Foster, Leslie, Fuller, Gail, Garner, Susan, Godwin, Sarah, Goley, April, Gore, Whitney, Green, Brenda, Harris, Elizabeth, Harris, Joy, Harris, Rebecca, Hatharasinghe, Roger, Herring, Charles, Hill, Chernessa, Holmes, Robert, Hutchens, Erin, Jennings, Stuart, Kaake, Ashley, Kaygun, Handan, Kirkman, Marian Sue, Kretzschmar, Heather, Lang, Jaqueline, Largay, Joseph, Lewis, Melissa, Long, Maria Ann, Lucas, Kathryn, McCall, Roshanda, McDaniel, Catrina, McGinnis, Lee Ann, Mogabgab, Edward, Montero, Manuel, Montgomery, Richard, Moran, Joseph, Morgan, Katie, Motley, Taylor, Narendran, Mahendra, Narron, Brooke, Newman, Walter, Nicholson, John, Norris, Stephanie, Norton, Jennifer, Nowell, Erin, O'Brien, Heather, Otero, Heydin, Perkins, Victoria, Quesada, Reyna, Raad, George, Raley, Rosalie, Raman, Sujatha, Rappaport, Jonathan, Ray, Robert, Ray, Shannon, Rice, Lucian, Roberts, Christine, Ross, Kaylee, Scanlon, Erica, Schroder, Derek, Schulte, Michelle, Schultzaberger, Richard, Shoffner, Sylvia, Skrzynski, Michael, Smith, Brian, Smith, Holly, Smith, Jennifer, Smith, Justin, Smith, Kendall, Smith, Leigh, Sparks, Jeffrey, Speece, Jessica, Spivey, Jessica, Stebich, Andrew, Temple, Karen, Tessena, Boryana, Thompson, Casey, Traylor, Henry, Turner, Juliana, Turner, Sh-Rhonda, Van Cleeff, Martin, Vukojicic, Katarina, Wanchick, Leslie, Weinrib, Stephen, Werner, Devorah, Wishon, Candace, Wilson, Jonathan, Wilson, Stephanie, Wright, Heather, Young, Laura, Brown, Jamie, Lillestol, Michael, Allen, Mary Jo, Barker, Bruce, Bedel, Gary, Beuerlein, Shawn, Bhati, Amar, Breslaw, Nicole, Brown, April, Burtch, Brian, Check, Ellen, Chiodi, Shawnie, Colosimo, Linda, Cope, Kathie, Coressel, Denise, Cramer, Bethany, Crowel, Amy, Daboul, Nizar, DeLong, Rebecca, Dirkes, William, Farooki, Zia, Fixler, Don, Friedhof, Linda, Griffen, Julie, Hagan, Anjani, Haney, Kevin, Harris, Kira, Hoffmann, Amanda, Hsieh, Ronald, Ismail-Beigi, Faramarz, Kelso, Kim, Khamousia, Nidaa, Kremer, Gerald, Krol, Adrienne, Kulow, Tanya, Kuttler, Mary, Lee, Andy, Leksan, Jane, McElroy-Marcus, Susan, McNeil, Donald, Mulligan, Guy, Pantalone, Kevin, Perrino, Frank, Peysha, Lori, Pickett, Alicia, Pritchard, James, Provanzana, Kathleen, Rancitelli, Philip, Raupp, Randa, Schnall, Adrian, Siddiqui, Jaweriah, Smith, Cynthia, Sood, Ajay, Sortor, Jennifer, Spinelli, Kristin, Taylor, Diana, Thornton, Aaron, Tiktin, Margaret, Twine, Melissa, Urbank, Ann, Voegele, Francis, Wang, Lawrence, Weiss, Daniel, Werner, Amanda, Williams, Daniel, Wilson, Charles, Aberle, Teresa, Arcon-Rios, Sirlys, Arodak, Bassam, Aziz, Ammara, Bixler, Lacey, Brisko, Carolann, Cannon, Melanie, Choe, James, Connery, Lisa, Elkosseifi, Myriam, George, Aja, Hummer, Kimberly, LaBryer, Lauren, Lane, James, Lim, Jonea, Mirza, Lubna, Mittadodla, Saritha, Priest, Diane, Rao, Veitla, Ricci, Cassie, Salazar, Jennifer, Shaw, Jessica, St John, Jennifer, Talsania, Mitali, Joarder, Farahnaz, Riddle, Matthew, Yuen, Kevin, Ahmed, Intekhab, Austin, Barry, Bagnata, Linda, Bartkowiak, Anthony, Batrus, Jennifer, Baumgarten, Thomas, Belejchak, Paula, Belucher, Christine, Benton, Julia, Berger, Peter, Biscoveanu, Mihaela, Blasick, Abbie, Blyler, Kay, Casey, Linda, Chandler, Bronell, Chapman, Kim, Chou, Brian, Cisar, Jennifer, Clatterbuck, Tabetha, Cook, Richard, Cook II, Richard, Dardi, Inderpreet, DeSalle, Dina, Edevane, Shari, Elliott, Kelsey, Everhart, Brian, Furlong, Kevin J., Geishauser, Jennifer, Goisse, Marcy, Goodman, Yvonne, Gordon, Murray, Gutowski, Catherine, Harris, Ronald, Hu, Ying, Hull, Jenny, Iachini, Kathleen, Iannamorelli, Anthony, Jabbour, Serge, Jackson, Timothy, Jameson, Brian, Janosov, Paula, Jordan, Terry, Kennedy, John, Kurtzer, Gayle, Labuda, Joseph, LaRochelle, Anne, Levinson, Lawrence, Madder, Robert, Marconnet, Kari, Mareda, Patricia, Michael, Donna, Michaels, Debra, Miller, Cathy, Miller, Jeffrey, Minnock, Ann, Mitchell, Michele, Moturi, Hemlata, Moyer, Heather, Nagelberg, Steven, Oliver, Michael, Pepe, Constance, Porter, Patricia, Posey, Dora, Purighalla, Raman, Redmond, Jessica, Reiling, Joseph, Reiling, Susan, Rine, Kathy, Rust, William, Sandutch, Jill, Shaffer, Anette, Shalongo, Annette, Shawley, Sarah, Shonk, Pamela, Singh, Vinay, Slucki, Andrea, Spiller, Kellie, Springer, Nina, Sprouse, Heidi, Sun, Albert, Taylor, Judith, Watine, Robert, Watkinson, Jean, Weaver, Nancy, Wenocur, Howard, Yeoman, Gary, Ackermann, Jeremy, Averill, Nathan, Ballard, Thomas, Byars, William, Carter, Caroline, Castay, Cathleen, Cheek, Shonda, Collins, Jessica, Collins-Jamison, Traci, Cooksey, Erin, Crockford, Nicole, Cutshall, Christopher, Dar, Moahad, Drake, Almond, Dugat, Kameron, Durham, John, Durham, Nancy, Eagerton, Donald, Edmonds, Nataish, Farmer III, Joseph, Fogarty, Charles, Gatlin, June, Geisberg, Harry, Grossman, Margaret, Harrell, Wendy, Harrison, John, Harrison, Kristina, Heincelman, Sarah, Holladay, Lauren, Huberty, Chad, Isakov, Ingrid, Jordan, Norma, Kunkel, Michael, Laserna, Susan, Lawrence, Amanda, Lawrence, Andrew, Livingston, Yolanda, Lloyd, Eric, Lopez, Lina, Manuel, Joyce, Mayfield, Ronald, McDonald, Margaret, Morgan, Caroleann, Munn, Joanie, Murphy, Lauren, Nicol, Brent, Nicol, Philip, Norris, Rebecca, Phillips, Bunny, Reeves, Angela, Roach, Jacqueline, Ross, Irene, Santi, Jeffrey, Saunders, Jacqueline, Shugart, Henry, Singleton, Monnieque, Slee, Erin, Smalley, Kathleen, Smith, Kristy, Sofley, Carl, Solheim, Vesna, Spell, Samantha, Tanenberg, Robert, Whitley, Chester, Widel, Michele, Wilhoit, Gordon, Wood, Ruth, Wright, Jaime, Wulf, Alissa, Zapkowski, Julie, Adams, Kristi, Adams, Roberta, Ananthula, Parvati, Bass, Caitlin, Bates, Chad, Baum, Howard, Bays, Anna, Beck, Teresa, Bilbrey, Brett, Brown, Ashley, Carlton, Anna, Carter, Jenny, Cheri, Laura, Childress, Richard, Cloud, Mark, Cohen, A. Jay, Coulter, Alice, Dotson, Kimberly, Green, Christopher, Helton, Ashley, Houstrup, Jody, Huffman, David, Hurst, Fred, Hurst, James, Jantzi, Curtis, Jerkins, Randy, Jerkins, Terri, Jones, Mandy, Kelley, Shane, Kroulek, Judy, Lawhun, Dennis, Lee, Marcus, Louthan, James, Manning, Rickey, May, Michael, Means, Katherine, Monroe, Jeanabe, Morawski, Emily, Morin, David, Morris, Barbara, Murray, John, Nichols, Robert, Olivi, Stacie, Pennington, Kristina, Poe, Jenny, Pour, Omid Rad, Reeves, Michael, Reeves, Pamela, Root, Connie, Rose, Karen, Rutledge, Lily, Ryan, Eugene, Schwartz, Patricia, Shafer, Jennifer, Singh, Chitra, Smith, Ashley, Snow, Rodney, Stamps, Henry, Staton, Peggy, Stevens, Jeffrey, Stovall, Cathy, Sturgeon, Rodney, Tapp, John, Tapp, Stacy, Warner, Carla, Weiss, Melissa, White, Judith, Wierum, Craig, Williams, Konya, Wright, Tiffanie, Abreu, Marconi, Agarwal, Vandana, Ahmad, Aftab, Ahmad, Asma, Ahmed, Faiz, Ahn, Lily, Alaoui, Rachid, Alarcon, Stephanie, Allison, Dale, Annamalai, Rajasekaran, Arain, Khadija, Arellano, Angelica, Arispe, Barbara, Aronoff, Stephen, Arroyo, Jesus, Bachand, Trista, Bailey, Becky, Bajaj, Chris, Banarer, Salomon, Barnella, Christine, Bartz, Mary, Bates, Jason, Becerra, Karen, Benavides, Melissa, Bhuchar, Subodh, Blevins, Thomas, Bonazzi, Amanda, Brinson, Cynthia, Brown, Timiki, Buckley, Brandee, Bunker, Christy, Burbano, Jose, Busch, Vickey, Bustamante, Rosibel, Calais, Natalee, Camacho, Allison, Camp, Shana, Canada, Nancy, Canchola, Daniel, Cano, Monica, Cardenas, Veronica, Carl, Leighton, Casaubon, Luis, Castaneda, Jessica, Castro, Cindy, Ceniceros, Diego, Chaicha-Brom, Tira, Chaudhuri, Sudipta, Chavez-Velazquez, Alberto, Chen, Jocelyn, Cheyne, Theresa, Chilka, Sapna, Chionh, Kristen, Chunawala, Imran, Cope, Pamela, Cotton, Sonja, Daudjee, Munib, Davidson, Tichina, De Loa, Geraldine, Deniega, Ann, Dillow, Naco, Dominguez, Michael, Dorfman, Steven, Doshi, Ankur, Dunnam, Theresa, Dutta, Papiya, Eiler-Prater, Megann, Eizensmits, Christina, Espinosa, Valerie, Evans, Charles, Frisinger, Cathy, Ganta, Shylesh, Galtney, Kirsten, Gammon, John, Garcia, Analisa Marie, Garcia, Ronald, Glaspie, Tina, Golici, Laura, Gonzalez, Julian, Grant, David, Green, Carrie, Groce, Lindsay, Guevara, Stephanie, Gutierrez, Mary, Hance, Cortney, Harden, Tiffany, Harrison, Lindsay, Herrera, Carlos, Hettler, Evelyn, Holcomb, Michelle, Hoste, Laura, Hudson, Tina, Huerta, Leticia, Jain, Mahendra, Jayaram, Preeth, Johnson, Monique, Johnson, Olivia, Jones, Laurie, Joseph, John, Juarez, Misty, Karimjee, Najmuddin, Kephart, Ashley, Khan, Armgan, Khan, Jalil, Killian, Michael, Kniffen, Wendy, Koops, Maureen, Kwan, Louise, La, John, Laabs, Jessica, Ledesma, Gilbert, Lee, Monica, Leuck, Kathryn, Livingston, Carrie, Llanas, Teresa, Longoria, Andy, Lorta, Jesse, Lothringer, Larry, MacAdams, Maria, MacAdams, Michael, MacGillivray, Brian, Mageno, Melissa, Malek, Megan, Manley, Meghan, Marler, Karen, Martinez, Cristina, Massie, Jodi, Mateos, Glenda, Mathew, Reena, Maya, Claudia, Mazour, Meg, Mbogua, Caroline, McClendon, Robert, McMullen, Deirdre, Meisner, Carl, Menchaca, Ethelina, Meyers, Linda, Miklius, Audrey, Milam, Alissa, Milam, Ronnie, Miller, Tracey, Mize, Lisa, Moczygemba, Roger, Nafegar, Alexandria, Naidu, Jayaram, Nair, Archana, Nguyen, Alison, Nielsen, Douglas, Nisnisan, Josier, Nisnisan, Maria, Oden, Wendy, Orsak, Jessica, Ozer, Kerem, Papacostea, Olivia, Panchal, Ravi, Parikh, Manish, Parthiban, Beneta, Perrin, Brittney, Perkins, Harvonya, Poduri, Madhuri, Pop, Laurentiu, Portales, Nancy, Porter-Tucci, Linda, Postell, Sharon, Pucillo, Ronald, Reddy, Madhavi, Reddy, Rajneesh, Almodovar, Ramon Gilberto Reyes, Richard, Timiki, Richardson, Michele, Riesch, Louie, Rivera-Flores, Samuel, Rizvi, Abid, Rizvi, Samana, Rodas, Kristen, Rodgers, Mark, Roe, Erin, Rogers, Rebecca, Romero, Reina, Rooker-Morris, Leslie, Rosenstock, Julio, Sachson, Richard, Salcedo, Alfredo, Schill, Joseph, Schuh, Kristy, Schuster, Randall, Scribner, Anita, Shah, Arunkumar, Shaw, Stephanie, Shirley, Jefferey, Shook, Mindy, Siddiqui, Muhammad, Sims, Jamie, Singh, Kamalpreet, Skero, Jennifer, Smejdir, Debra, Stacey, Amy, Strzinek, Robert, Tan, Anjanette, Tayabali, Khadija, Tercero, Almundena, Terry, Jessica, Tinney, Irene, Toe, Florence, Toler, Sara, Tripathy, Devjit, Tumyan, Anna, Ulrich, Michelle, Underwood, Krissandra, Valdez, Christina, Valdivieso, April, Vanderheiden, Anna, Vandre, Morgan, Vardeman, Megan, Vargas, Richard, Vilchis, Monica, Warne, Desiree, Wallace, Jeannine, Weindorff, Kathleen, Welch, Heather, Wellmon, Brandy, Werntz, Gary, White, Leta, Williams, Tearani, Wilson, Maria, Wilson, Robyn, Wilson, Ronald, Wood, John, Wright, David, Yeatts, Rhonda, Zahir, Rezwana, Crockett, Becky, Fawson, Ashlee, Graham, Timothy, Mitchell, Zachary, Quinn, Sally, Reddy, Deepika, Rhudy, Jackson, Rosen, Priscilla, Shakespear, Kaylynn, Simmons, Debra, Turner, Ashley, Vanderhoof, Erin, Wahl, Matthew, Zubair, Imran, Algus, Michael, Gilbert, Matthew, Gorson, David, Marney, Annis, Rodriguez, Juan Pablo Perdomo, Roth, Marilynn, Scofield, Sheilah, Thottan, Anna, Bittorf, Margaret, Botros, Emad, Carpenter, Jeanette, Dashiell, Carie, Dimascio-Johnson, Nancy, Fredrickson, Sonja, Gruenther, M., Hall, Lisa, Hartman, Aaron, Hiner, Janette, Kelley, Donna, Kimmel, Melissa, Larrick, Laura, Lawson, Marie, Levy, James, Lewis, Teneshia, Lovell, Charles, Newby, James, Nichols, Michelle, O'Donnell, Philip, Orengo, Sheyla, Pecsok, Thomas, Reiss, Lind, Tarkington, Phillip, Yeatts, Donald, Zieve, Franklin, Ahmed, Imtiaz, Ali, Sajid, Anwar, Sami, Cathcart, Harold, Dawson, Lucia, Floresca, Rolando, Galvan, Ashley, Gardner, Timothy, Halsey, Kami, Hashmi, Farrukh, Hegel, Jackie, Landon, Megan, Moulton, Katlynn, Nand, Cheta, Sakic, Amra, Sevey, Brandie, Wilson, Bruce, Ahl, Scott, Blank, Robert, Coly, Gerard, Hoenig, Jennifer, Jain, Rajeev, Joseph, Elizabeth, Kasprzak, Debra, Kisiel, Lalaine, Maaske, Maurene, Manadhar, Smriti, Patel, Shailendra, Sickler, Whitney, Grp, D.E.V.O.T.E. Study, Acibadem University Dspace, Marso S.P., Mcguire D.K., Zinman B., Poulter N.R., Emerson S.S., Pieber T.R., Pratley R.E., Haahr P.-M., Lange M., Brown-Frandsen K., Moses A., Skibsted S., Kvist K., Buse J.B., and Buscemi S
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Insulin degludec ,Blood Glucose ,Male ,medicine.medical_treatment ,DEVOTE Study Group ,Insulin Glargine ,Type 2 diabetes ,Kaplan-Meier Estimate ,030204 cardiovascular system & hematology ,law.invention ,diabetes ,insulin ,0302 clinical medicine ,Randomized controlled trial ,law ,Cardiovascular Disease ,GLUCOSE CONTROL ,11 Medical and Health Sciences ,RISK ,COMPLICATIONS ,OUTCOMES ,Incidence ,General Medicine ,Middle Aged ,Insulin, Long-Acting ,VARIABILITY ,Cardiovascular Diseases ,diabetes mellitus ,Female ,Life Sciences & Biomedicine ,Human ,medicine.drug ,medicine.medical_specialty ,030209 endocrinology & metabolism ,Aged ,Diabetes Mellitus, Type 2 ,Double-Blind Method ,Humans ,Hypoglycemia ,Hypoglycemic Agents ,Medicine (all) ,Bedtime ,Article ,EVENTS ,03 medical and health sciences ,HYPOGLYCEMIA ,Medicine, General & Internal ,Internal medicine ,Diabetes mellitus ,General & Internal Medicine ,medicine ,Intensive care medicine ,METAANALYSIS ,Science & Technology ,Hypoglycemic Agent ,Insulin glargine ,business.industry ,Insulin ,medicine.disease ,business ,BASAL INSULIN - Abstract
BACKGROUND Degludec is an ultralong-Acting, once-daily basal insulin that is approved for use in adults, adolescents, and children with diabetes. Previous open-label studies have shown lower day-To-day variability in the glucose-lowering effect and lower rates of hypoglycemia among patients who received degludec than among those who received basal insulin glargine. However, data are lacking on the cardiovascular safety of degludec. METHODS We randomly assigned 7637 patients with type 2 diabetes to receive either insulin degludec (3818 patients) or insulin glargine U100 (3819 patients) once daily between dinner and bedtime in a double-blind, treat-To-Target, event-driven cardiovascular outcomes trial. The primary composite outcome in the time-To-event analysis was the first occurrence of an adjudicated major cardiovascular event (death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke) with a prespecified noninferiority margin of 1.3. Adjudicated severe hypoglycemia, as defined by the American Diabetes Association, was the prespecified, multiplicity-Adjusted secondary outcome. RESULTS Of the patients who underwent randomization, 6509 (85.2%) had established cardiovascular disease, chronic kidney disease, or both. At baseline, the mean age was 65.0 years, the mean duration of diabetes was 16.4 years, and the mean (±SD) glycated hemoglobin level was 8.4±1.7%; 83.9% of the patients were receiving insulin. The primary outcome occurred in 325 patients (8.5%) in the degludec group and in 356 (9.3%) in the glargine group (hazard ratio, 0.91; 95% confidence interval, 0.78 to 1.06; P
- Published
- 2017
3. Leukocyte Transcriptional Response in Sepsis.
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Skibsted S, Bhasin MK, Henning DJ, Jaminet SC, Lewandowski J, Kirkegaard H, Aird WC, and Shapiro NI
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- Adult, Aged, Female, Humans, Male, Middle Aged, Prospective Studies, RNA, Messenger metabolism, Real-Time Polymerase Chain Reaction, Inflammation genetics, Inflammation metabolism, Leukocytes metabolism, Sepsis genetics, Sepsis metabolism
- Abstract
Background: The complex host response to sepsis is incompletely understood. The aim of this investigation is to use leukocyte RNA sequencing to characterize biological functions, cellular pathways, and key regulatory molecules driving sepsis pathophysiology., Methods: This was a prospective, observational study of emergency department patients with sepsis, at an urban, academic, tertiary care center. In the derivation cohort, we collected blood at enrollment and 90 days after hospital discharge allowing each patient to serve as an internal control. We performed RNA sequencing to quantify transcriptional expression changes during sepsis and non-sepsis states. We then performed unsupervised and supervised analyses, as well as functional and pathway analyses. We selected the top down and upregulated genes and key regulatory molecules for validation. Validation occurred in a cohort of septic and non-septic using real-time PCR., Results: The derivation cohort included 5 patients, and RNA sequencing revealed 916 unique mRNA transcripts differentially expressed during sepsis. Among these, 673 (73%) genes were upregulated, and 243 (27%) were downregulated. Functional enrichment analysis revealed a highly dynamic downstream effect of the transcriptional activity during sepsis. Of the 43 functional cellular pathways activated during sepsis, the top pathways were closely associated with inflammation and response to infection. Validation occurred in 18 septic and 25 non-septic control patients, with 34/45 (76%) of identified genes validated. The regulatory analysis identified several key regulators of sepsis., Conclusions: Highly dynamic transcriptional activity occurs in leukocytes during sepsis, activating key cellular pathways and master regulatory molecules that drive the sepsis process.
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- 2019
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4. Efficacy and Safety of Degludec versus Glargine in Type 2 Diabetes.
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Marso SP, McGuire DK, Zinman B, Poulter NR, Emerson SS, Pieber TR, Pratley RE, Haahr PM, Lange M, Brown-Frandsen K, Moses A, Skibsted S, Kvist K, and Buse JB
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- Aged, Blood Glucose analysis, Cardiovascular Diseases epidemiology, Diabetes Mellitus, Type 2 blood, Diabetes Mellitus, Type 2 complications, Double-Blind Method, Female, Humans, Hypoglycemia chemically induced, Hypoglycemic Agents therapeutic use, Incidence, Insulin Glargine therapeutic use, Insulin, Long-Acting therapeutic use, Kaplan-Meier Estimate, Male, Middle Aged, Cardiovascular Diseases chemically induced, Diabetes Mellitus, Type 2 drug therapy, Hypoglycemic Agents adverse effects, Insulin Glargine adverse effects, Insulin, Long-Acting adverse effects
- Abstract
Background: Degludec is an ultralong-acting, once-daily basal insulin that is approved for use in adults, adolescents, and children with diabetes. Previous open-label studies have shown lower day-to-day variability in the glucose-lowering effect and lower rates of hypoglycemia among patients who received degludec than among those who received basal insulin glargine. However, data are lacking on the cardiovascular safety of degludec., Methods: We randomly assigned 7637 patients with type 2 diabetes to receive either insulin degludec (3818 patients) or insulin glargine U100 (3819 patients) once daily between dinner and bedtime in a double-blind, treat-to-target, event-driven cardiovascular outcomes trial. The primary composite outcome in the time-to-event analysis was the first occurrence of an adjudicated major cardiovascular event (death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke) with a prespecified noninferiority margin of 1.3. Adjudicated severe hypoglycemia, as defined by the American Diabetes Association, was the prespecified, multiplicity-adjusted secondary outcome., Results: Of the patients who underwent randomization, 6509 (85.2%) had established cardiovascular disease, chronic kidney disease, or both. At baseline, the mean age was 65.0 years, the mean duration of diabetes was 16.4 years, and the mean (±SD) glycated hemoglobin level was 8.4±1.7%; 83.9% of the patients were receiving insulin. The primary outcome occurred in 325 patients (8.5%) in the degludec group and in 356 (9.3%) in the glargine group (hazard ratio, 0.91; 95% confidence interval, 0.78 to 1.06; P<0.001 for noninferiority). At 24 months, the mean glycated hemoglobin level was 7.5±1.2% in each group, whereas the mean fasting plasma glucose level was significantly lower in the degludec group than in the glargine group (128±56 vs. 136±57 mg per deciliter, P<0.001). Prespecified adjudicated severe hypoglycemia occurred in 187 patients (4.9%) in the degludec group and in 252 (6.6%) in the glargine group, for an absolute difference of 1.7 percentage points (rate ratio, 0.60; P<0.001 for superiority; odds ratio, 0.73; P<0.001 for superiority). Rates of adverse events did not differ between the two groups., Conclusions: Among patients with type 2 diabetes at high risk for cardiovascular events, degludec was noninferior to glargine with respect to the incidence of major cardiovascular events. (Funded by Novo Nordisk and others; DEVOTE ClinicalTrials.gov number, NCT01959529 .).
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- 2017
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5. Number of organ dysfunctions predicts mortality in emergency department patients with suspected infection: a multicenter validation study.
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Jessen MK, Skibsted S, and Shapiro NI
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- Adult, Aged, Aged, 80 and over, Case-Control Studies, Female, Hospital Mortality, Humans, Infections complications, Logistic Models, Male, Middle Aged, Multiple Organ Failure etiology, Organ Dysfunction Scores, Prospective Studies, Retrospective Studies, Risk Assessment, Emergency Service, Hospital statistics & numerical data, Infections mortality, Multiple Organ Failure mortality
- Abstract
Objectives: The aim of this study was to validate the association between number of organ dysfunctions and mortality in emergency department (ED) patients with suspected infection., Methods: This study was conducted at two medical care center EDs. The internal validation set was a prospective cohort study conducted in Boston, USA. The external validation set was a retrospective case-control study conducted in Aarhus, Denmark. The study included adult patients (>18 years) with clinically suspected infection. Laboratory results and clinical data were used to assess organ dysfunctions. Inhospital mortality was the outcome measure. Multivariate logistic regression was used to determine the independent mortality odds for number and types of organ dysfunctions., Results: We enrolled 4952 (internal) and 483 (external) patients. The mortality rate significantly increased with increasing number of organ dysfunctions: internal validation: 0 organ dysfunctions: 0.5% mortality, 1: 3.6%, 2: 9.5%, 3: 17%, and 4 or more: 37%; external validation: 2.2, 6.7, 17, 41, and 57% mortality (both P<0.001 for trend). Age-adjusted and comorbidity-adjusted number of organ dysfunctions remained an independent predictor. The effect of specific types of organ dysfunction on mortality was most pronounced for hematologic [odds ratio (OR) 3.3 (95% confidence interval (CI) 2.0-5.4)], metabolic [OR 3.3 (95% CI 2.4-4.6); internal validation], and cardiovascular dysfunctions [OR 14 (95% CI 3.7-50); external validation]., Conclusion: The number of organ dysfunctions predicts sepsis mortality.
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- 2017
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6. Prediction of bacteremia in the emergency department: an external validation of a clinical decision rule.
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Jessen MK, Mackenhauer J, Hvass AM, Ellermann-Eriksen S, Skibsted S, Kirkegaard H, Schønheyder HC, and Shapiro NI
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- Adult, Aged, Bacteremia epidemiology, Bacteremia physiopathology, Case-Control Studies, Chi-Square Distribution, Databases, Factual, Denmark, Female, Hospitals, University, Humans, Male, Middle Aged, Outcome Assessment, Health Care, Predictive Value of Tests, ROC Curve, Retrospective Studies, Statistics, Nonparametric, Bacteremia blood, Blood microbiology, Blood-Borne Pathogens isolation & purification, Decision Support Systems, Clinical, Emergency Service, Hospital
- Abstract
Objective: The objective of this study was to validate a previously published clinical decision rule for predicting a positive blood culture in emergency department (ED) patients with suspected infection on the basis of major and minor criteria and a total score (Shapiro et al., J Emerg Med, 2008; 35:255-264)., Methods: This is a retrospective matched cohort study of adult ED patients with blood cultures obtained from 1 January 2011 through to 31 December 2011. ED patients with blood culture-confirmed bacteremia were matched 1 : 3 with patients with negative cultures. The outcome was 'true bacteremia'. Data on clinical history, comorbid illnesses, physical observations, and laboratory tests were used to evaluate the application of the clinical decision rule. We report the sensitivity, specificity, and area under the curve., Results: Among 1526 patients, 105 (6.9%) patients were classified with true bacteremia. The sensitivity of the prediction rule was 94% (95% confidence interval, 88-98%) and the specificity was 48% (95% confidence interval, 42-53%). The area under the receiver-operating characteristics curve was 0.83., Conclusion: The clinical decision rule performed well in our ED setting and is likely to be a useful supplement to clinical judgment.
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- 2016
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7. The reliability and validity of passive leg raise and fluid bolus to assess fluid responsiveness in spontaneously breathing emergency department patients.
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Duus N, Shogilev DJ, Skibsted S, Zijlstra HW, Fish E, Oren-Grinberg A, Lior Y, Novack V, Talmor D, Kirkegaard H, and Shapiro NI
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- Adult, Aged, Confidence Intervals, Emergency Service, Hospital, Female, Humans, Lifting, Male, Middle Aged, Monitoring, Physiologic instrumentation, Monitoring, Physiologic methods, Odds Ratio, Prospective Studies, Reproducibility of Results, Sodium Chloride administration & dosage, Stroke Volume physiology, Time Factors, Cardiac Output physiology, Fluid Therapy methods, Leg, Patient Positioning methods, Respiration
- Abstract
Purpose: We investigated the reproducibility of passive leg raise (PLR) and fluid bolus (BOLUS) using the Non-Invasive Cardiac Output Monitor (NICOM; Cheetah Medical, Tel Aviv, Israel) for assessment of fluid responsiveness (FR) in spontaneously breathing emergency department (ED) patients., Methods: Prospective, observational study of a convenience sample of adult ED patients receiving intravenous fluid bolus. We assessed stroke volume (SV) using NICOM and obtained results from PLR, where the head of the bed was changed from semirecumbent to supine while the patients' legs raised to 45° for 3 minutes. Fluid bolus was defined as 5 mL/kg normal saline infusion. Maximal increase in SV was recorded. Fluid responsiveness was defined as an increase of SV greater than 10% from baseline. We obtained 4 consecutive responses for each patient; PLR1, PLR2, BOLUS1 separated each by 10 minutes, and BOLUS2 initiated immediately after the end of BOLUS1. We calculated κ statistics, correlation coefficients, and odds ratios with 95% confidence interval and Bland-Altman plots., Results: We enrolled 109 patients enrolled in this study. The 2 PLRs were significantly correlated (r = 0.78, P < .001) with κ = 0.46 for FR (P < .001). The 2 BOLUSES less strongly correlated (r = 0.14, P = .001) and κ = 0.06 for FR (P < .001). Patients who were responsive to PLR1 had 9.5 (3.6-25) odds of being FR for PLR2, whereas those responsive to BOLUS1 had a 1.8 (0.76-4.3) increased odds of FR for BOLUS2., Conclusion: In conclusion, we have found PLR as measured by the NICOM to be a promising tool for the evaluation of SV responsiveness. It was feasible for use in the ED, and the data suggest that the PLR technique may be more reproducible than the fluid bolus technique for assessing volume responsiveness., (Copyright © 2014 Elsevier Inc. All rights reserved.)
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- 2015
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8. Transcriptomics may pave the biomarker road in sepsis*.
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Skibsted S and Shapiro NI
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- Humans, Decision Support Systems, Clinical, Intensive Care Units, Risk Assessment, Shock, Septic mortality, Shock, Septic physiopathology
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- 2014
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9. The microcirculation is preserved in emergency department low-acuity sepsis patients without hypotension.
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Filbin MR, Hou PC, Massey M, Barche A, Kao E, Bracey A, Skibsted S, Chang Y, and Shapiro NI
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- Adolescent, Adult, Aged, Biomarkers blood, Blood Flow Velocity, Case-Control Studies, Emergency Service, Hospital, Female, Humans, Hypotension etiology, Lactic Acid blood, Linear Models, Male, Middle Aged, Multivariate Analysis, Patient Acuity, Prospective Studies, Sepsis blood, Sepsis complications, Systemic Inflammatory Response Syndrome blood, Systemic Inflammatory Response Syndrome complications, Systemic Inflammatory Response Syndrome physiopathology, United States, Young Adult, Microcirculation, Sepsis physiopathology
- Abstract
Objectives: Microcirculatory dysfunction plays an important role in sepsis pathophysiology. Previous studies using sidestream dark-field (SDF) imaging have demonstrated microcirculatory flow abnormalities in patients with septic shock; however, the microcirculation is relatively unstudied in lower-acuity sepsis patients. The hypothesis was that patients with sepsis, but without hypotension, will demonstrate signs of flow abnormalities compared to noninfected control patients., Methods: This was a prospective, observational study in a convenience sample of patients with sepsis and noninfected controls, conducted in three urban, tertiary care emergency departments (EDs) in the United States. Sepsis was defined as suspected infection plus two or more systemic inflammatory response syndrome (SIRS) criteria; those with hypotension were excluded. Noninfected controls were ED patients without infection and without SIRS criteria. SDF imaging was obtained in all study patients during ED evaluation. Recommended microcirculatory flow parameters were measured, and the difference in these measures between sepsis patients and noninfected controls were calculated. The authors also correlated microcirculatory flow parameters with patient variables, including serum lactate., Results: A total of 106 patients were enrolled: 63 with sepsis and 43 noninfected controls. There were no differences in microcirculatory flow scores between sepsis patients and noninfected controls. Median microvascular flow index (MFI; with interquartile range [IQR] was 3.00 (IQR = 2.73 to 3.00) in sepsis patients versus 2.93 (IQR = 2.73 to 3.00) in control patients (p = 0.33), and mean proportion of perfused small vessels (PPV) was 91.5% (95% CI = 89.7% to 93.3%) versus 91.8% (95% CI = 89.7% to 93.9%), with a mean difference of 0.3% (95% CI = -2.5% to 3.1%; p = 0.84). Similarly, there were no significant differences in total vessel density, perfused vessel density, or heterogeneity index (HI). In the subset of infected patients for whom serum lactates were obtained (n % 37), MFI and PPV were negatively correlated with elevated serum lactate values: r = -0.32, p = 0.04; and r = -0.44, p < 0.01, respectively., Conclusions: Measureable microcirculatory flow abnormalities were not observed in patients with early sepsis in the absence of hypotension. However, microcirculatory abnormalities were correlated with elevated serum lactate in normotensive sepsis patients, supporting the notion that impaired microcirculatory flow is coupled with cellular distress., (© 2014 by the Society for Academic Emergency Medicine.)
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- 2014
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10. Bench-to-bedside review: future novel diagnostics for sepsis - a systems biology approach.
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Skibsted S, Bhasin MK, Aird WC, and Shapiro NI
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- Biomarkers analysis, Genomics, Humans, Risk Assessment, Critical Illness, Sepsis diagnosis, Systems Biology
- Abstract
The early, accurate diagnosis and risk stratification of sepsis remains an important challenge in the critically ill. Since traditional biomarker strategies have not yielded a gold standard marker for sepsis, focus is shifting towards novel strategies that improve assessment capabilities. The combination of technological advancements and information generated through the human genome project positions systems biology at the forefront of biomarker discovery. While previously available, developments in the technologies focusing on DNA, gene expression, gene regulatory mechanisms, protein and metabolite discovery have made these tools more feasible to implement and less costly, and they have taken on an enhanced capacity such that they are ripe for utilization as tools to advance our knowledge and clinical research. Medicine is in a genome-level era that can leverage the assessment of thousands of molecular signals beyond simply measuring selected circulating proteins. Genomics is the study of the entire complement of genetic material of an individual. Epigenetics is the regulation of gene activity by reversible modifications of the DNA. Transcriptomics is the quantification of the relative levels of messenger RNA for a large number of genes in specific cells or tissues to measure differences in the expression levels of different genes, and the utilization of patterns of differential gene expression to characterize different biological states of a tissue. Proteomics is the large-scale study of proteins. Metabolomics is the study of the small molecule profiles that are the terminal downstream products of the genome and consists of the total complement of all low-molecular-weight molecules that cellular processes leave behind. Taken together, these individual fields of study may be linked during a systems biology approach. There remains a valuable opportunity to deploy these technologies further in human research. The techniques described in this paper not only have the potential to increase the spectrum of diagnostic and prognostic biomarkers in sepsis, but they may also enable the discovery of new disease pathways. This may in turn lead us to improved therapeutic targets. The objective of this paper is to provide an overview and basic framework for clinicians and clinical researchers to better understand the 'omics technologies' to enhance further use of these valuable tools.
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- 2013
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11. The association of near infrared spectroscopy-derived StO2 measurements and biomarkers of endothelial activation in sepsis.
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Skibsted S, Arnold R, Sherwin R, Singh S, Lundy D, Nelson T, Puskarich MA, Trzeciak S, Jones AE, and Shapiro NI
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- Aged, Biomarkers blood, Female, Humans, Interleukin-6 blood, Male, Middle Aged, Oxygen Consumption physiology, Plasminogen Activator Inhibitor 1 blood, Systemic Inflammatory Response Syndrome physiopathology, Endothelium, Vascular physiopathology, Oxygen metabolism, Sepsis physiopathology, Shock, Septic physiopathology, Spectroscopy, Near-Infrared
- Abstract
Near infrared spectroscopy (NIRS) may be utilized in conjunction with a vascular occlusion test to quantify a tissue bed's ability to re-oxygenate by measuring continuous tissue oxygen saturation recovery rate. We hypothesize that NIRS recovery slope will be associated with expression of endothelial biomarkers, thus, making it a feasible bedside surrogate for assessing endothelial activation/dysfunction in patients with sepsis. A secondary analysis of a prospective, multicenter, observational study was done on a convenience sample of adult patients at four university emergency departments consisting of patients with septic shock, sepsis without shock and patients without infection. At enrollment we measured the NIRS-derived measurements and collected plasma to assay biomarkers of endothelial activation. 186 patients were enrolled in the study. The mean age was 63 (± 16) years with 60 % male gender. Univariate analysis assessing the linear relationship between the recovery slope with endothelial biomarkers, found a weak but statistical significant association between NIRS recovery slope and soluble fms-like tyrosine kinase-1 (sFLT-1) and tPAI-1 (r = -0.08, p < 0.0001 and r = -0.06, p = 0.002). When adjusting for diabetes, age and sequential organ failure assessment score at enrollment, only sFLT-1 persisted having a statistically significant association (r = -0.04, p = 0.01). We found a weak, but statistically significant relationship between NIRS-derived measurements and biomarkers of endothelial activation/dysfunction in patients with sepsis. This study fails to support the use of NIRS-derived measurements as a clinical or research tool to identify patients with endothelial cell activation/dysfunction and informs researchers that this is not a robust option for identifying this lesion at the bedside.
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- 2013
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12. Biomarkers of endothelial cell activation in early sepsis.
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Skibsted S, Jones AE, Puskarich MA, Arnold R, Sherwin R, Trzeciak S, Schuetz P, Aird WC, and Shapiro NI
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- Adult, Aged, E-Selectin blood, Female, Hospital Mortality, Humans, Intercellular Adhesion Molecule-1 blood, Male, Middle Aged, Plasminogen Activator Inhibitor 1 blood, Prospective Studies, Vascular Endothelial Growth Factor Receptor-1 blood, Biomarkers blood, Endothelial Cells metabolism, Sepsis blood, Sepsis metabolism
- Abstract
Purpose: The objective of this study was to investigate the association of endothelial-related markers with organ dysfunction and in-hospital mortality to validate our earlier findings in a multicenter study. We hypothesize that (i) endothelial biomarkers will be associated with organ dysfunction and mortality in sepsis and that (ii) soluble fms-like tyrosine kinase 1 (sFlt-1) holds promise as a novel prognostic marker in sepsis., Methods: This was a prospective, multicenter, observational study of a convenience sample of emergency department (ED) patients with a suspected infection presenting to one of four urban, academic medical center EDs between January 2009 and January 2010. We collected plasma while the patients were in the ED and subsequently assayed endothelial-related biomarkers, namely, sFlt-1, soluble E-selectin, soluble intercellular adhesion molecule 1, soluble vascular cell adhesion molecule 1, and plasminogen activator inhibitor 1 (PAI-1). Outcomes were organ dysfunction and in-hospital mortality., Results: We enrolled a total of 166 patients: 63 with sepsis (38%), 61 with severe sepsis (37%), and 42 with septic shock (25%). All endothelial biomarkers were significantly associated with sepsis severity, P < 0.002. We found a significant intercorrelation between all biomarkers, strongest between sFlt-1 and PAI-1 (r = 0.61, P < 0.001) and PAI-1 and soluble E-selectin and soluble intercellular adhesion molecule 1 (r = 0.49, P < 0.001). Among the endothelial biomarkers, sFlt-1 had the strongest association with Sequential Organ Failure Assessment score (r = 0.58, P < 0.001). Soluble fms-like tyrosine kinase 1 and PAI-1 had the highest area under the operating receiver characteristic curve for mortality of 0.87., Conclusions: This multicenter validation study confirms that markers of endothelial activation are associated with sepsis severity, organ dysfunction, and mortality in sepsis. This supports the hypothesis that the endothelium plays a central role in the pathophysiology of sepsis and may serve as a more accurate prediction tool and a target for therapies aimed at ameliorating endothelial cell dysfunction. In addition, sFLT-1 holds promise as a novel sepsis severity biomarker.
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- 2013
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