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2. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

Author

Maddali, Manoj V, Churpek, Matthew, Pham, Tai, Rezoagli, Emanuele, Zhuo, Hanjing, Zhao, Wendi, He, June, Delucchi, Kevin L, Wang, Chunxue, Wickersham, Nancy, McNeil, J Brennan, Jauregui, Alejandra, Ke, Serena, Vessel, Kathryn, Gomez, Antonio, Hendrickson, Carolyn M, Kangelaris, Kirsten N, Sarma, Aartik, Leligdowicz, Aleksandra, Liu, Kathleen D, Matthay, Michael A, Ware, Lorraine B, Laffey, John G, Bellani, Giacomo, Calfee, Carolyn S, Sinha, Pratik, Rios, Fernando, Van Haren, Frank, Sottiaux, T, Lora, Fredy S, Azevedo, Luciano C, Depuydt, P, Fan, Eddy, Bugedo, Guillermo, Qiu, Haibo, Gonzalez, Marcos, Silesky, Juan, Cerny, Vladimir, Nielsen, Jonas, Jibaja, Manuel, Pham, Tài, Wrigge, Hermann, Matamis, Dimitrios, Ranero, Jorge Luis, Hashemian, SM, Amin, Pravin, Clarkson, Kevin, Kurahashi, Kiyoyasu, Villagomez, Asisclo, Zeggwagh, Amine Ali, Heunks, Leo M, Laake, Jon Henrik, Palo, Jose Emmanuel, do Vale Fernandes, Antero, Sandesc, Dorel, Arabi, Yaasen, Bumbasierevic, Vesna, Nin, Nicolas, Lorente, Jose A, Larsson, Anders, Piquilloud, Lise, Abroug, Fekri, McAuley, Daniel F, McNamee, Lia, Hurtado, Javier, Bajwa, Ed, Démpaire, Gabriel, Francois, Guy M, Sula, Hektor, Nunci, Lordian, Cani, Alma, Zazu, Alan, Dellera, Christian, Insaurralde, Carolina S, Alejandro, Risso V, Daldin, Julio, Vinzio, Mauricio, Fernandez, Ruben O, Cardonnet, Luis P, Bettini, Lisandro R, Bisso, Mariano Carboni, Osman, Emilio M, Setten, Mariano G, Lovazzano, Pablo, Alvarez, Javier, Villar, Veronica, Milstein, Cesar, Pozo, Norberto C, Grubissich, Nicolas, Plotnikow, Gustavo A, Vasquez, Daniela N, Ilutovich, Santiago, Tiribelli, Norberto, Chena, Ariel, Pellegrini, Carlos A, Saenz, María G, Estenssoro, Elisa, Brizuela, Matias, and Gianinetto, Hernan

Subjects
Lung, Clinical Research, Acute Respiratory Distress Syndrome, Rare Diseases, Respiratory, Good Health and Well Being, Acute Lung Injury, Humans, Machine Learning, Positive-Pressure Respiration, Respiratory Distress Syndrome, Retrospective Studies, LUNG SAFE Investigators and the ESICM Trials Group, Clinical Sciences, Public Health and Health Services, Other Medical and Health Sciences
Abstract

BackgroundTwo acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS.MethodsIn this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable.FindingsThe primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p

Published
2022

3. Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials

Author

Sinha, Pratik, Kerchberger, V Eric, Willmore, Andrew, Chambers, Julia, Zhuo, Hanjing, Abbott, Jason, Jones, Chayse, Wickersham, Nancy, Wu, Nelson, Neyton, Lucile, Langelier, Charles R, Mick, Eran, He, June, Jauregui, Alejandra, Churpek, Matthew M, Gomez, Antonio D, Hendrickson, Carolyn M, Kangelaris, Kirsten N, Sarma, Aartik, Leligdowicz, Aleksandra, Delucchi, Kevin L, Liu, Kathleen D, Russell, James A, Matthay, Michael A, Walley, Keith R, Ware, Lorraine B, and Calfee, Carolyn S

Published
2023
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4. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

Author

Rios, Fernando, Van Haren, Frank, Sottiaux, T, Lora, Fredy S, Azevedo, Luciano C, Depuydt, P, Fan, Eddy, Bugedo, Guillermo, Qiu, Haibo, Gonzalez, Marcos, Silesky, Juan, Cerny, Vladimir, Nielsen, Jonas, Jibaja, Manuel, Pham, Tài, Wrigge, Hermann, Matamis, Dimitrios, Ranero, Jorge Luis, Hashemian, S. M, Amin, Pravin, Clarkson, Kevin, Bellani, Giacomo, Kurahashi, Kiyoyasu, Villagomez, Asisclo, Zeggwagh, Amine Ali, Heunks, Leo M, Laake, Jon Henrik, Palo, Jose Emmanuel, do Vale Fernandes, Antero, Sandesc, Dorel, Arabi, Yaasen, Bumbasierevic, Vesna, Nin, Nicolas, Lorente, Jose A, Larsson, Anders, Piquilloud, Lise, Abroug, Fekri, McAuley, Daniel F, McNamee, Lia, Hurtado, Javier, Bajwa, Ed, Démpaire, Gabriel, Francois, Guy M, Sula, Hektor, Nunci, Lordian, Cani, Alma, Zazu, Alan, Dellera, Christian, Insaurralde, Carolina S, Alejandro, Risso V, Daldin, Julio, Vinzio, Mauricio, Fernandez, Ruben O, Cardonnet, Luis P, Bettini, Lisandro R, Bisso, Mariano Carboni, Osman, Emilio M, Setten, Mariano G, Lovazzano, Pablo, Alvarez, Javier, Villar, Veronica, Milstein, Cesar, Pozo, Norberto C, Grubissich, Nicolas, Plotnikow, Gustavo A, Vasquez, Daniela N, Ilutovich, Santiago, Tiribelli, Norberto, Chena, Ariel, Pellegrini, Carlos A, Saenz, María G, Estenssoro, Elisa, Brizuela, Matias, Gianinetto, Hernan, Gomez, Pablo E, Cerrato, Valeria I, Bezzi, Marco G, Borello, Silvina A, Loiacono, Flavia A, Fernandez, Adriana M, Knowles, Serena, Reynolds, Claire, Inskip, Deborah M, Miller, Jennene J, Kong, Jing, Whitehead, Christina, Bihari, Shailesh, Seven, Aylin, Krstevski, Amanda, Rodgers, Helen J, Millar, Rebecca T, Mckenna, Toni E, Bailey, Irene M, Hanlon, Gabrielle C, Aneman, Anders, Lynch, Joan M, Azad, Raman, Neal, John, Woods, Paul W, Roberts, Brigit L, Kol, Mark R, Wong, Helen S, Riss, Katharina C, Staudinger, Thomas, Wittebole, Xavier, Berghe, Caroline, Bulpa, Pierre A, Dive, Alain M, Verstraete, Rik, Lebbinck, Herve, Depuydt, Pieter, Vermassen, Joris, Meersseman, Philippe, Ceunen, Helga, Rosa, Jonas I, Beraldo, Daniel O, Piras, Claudio, Ampinelli, Adenilton M R, Nassar Jr, Antonio P, Mataloun, Sergio, Moock, Marcelo, Thompson, Marlus M, Gonçalves, Claudio H, Antônio, Ana Carolina P, Ascoli, Aline, Biondi, Rodrigo S, Fontenele, Danielle C, Nobrega, Danielle, Sales, Vanessa M, Shindhe, Suresh, Ismail, Dk Maizatul Aiman B Pg Hj, Laffey, John, Beloncle, Francois, Davies, Kyle G, Cirone, Rob, Manoharan, Venika, Ismail, Mehvish, Goligher, Ewan C, Jassal, Mandeep, Nishikawa, Erin, Javeed, Areej, Curley, Gerard, Rittayamai, Nuttapol, Parotto, Matteo, Ferguson, Niall D, Mehta, Sangeeta, Knoll, Jenny, Pronovost, Antoine, Canestrini, Sergio, Bruhn, Alejandro R, Garcia, Patricio H, Aliaga, Felipe A, Farías, Pamela A, Yumha, Jacob S, Ortiz, Claudia A, Salas, Javier E, Saez, Alejandro A, Vega, Luis D, Labarca, Eduardo F, Martinez, Felipe T, Carreño, Nicolás G, Lora, Pilar, Liu, Haitao, Liu, Ling, Tang, Rui, Luo, Xiaoming, An, Youzhong, Zhao, Huiying, Gao, Yan, Zhai, Zhe, Ye, Zheng L, Wang, Wei, Li, Wenwen, Li, Qingdong, Zheng, Ruiqiang, Yu, Wenkui, Shen, Juanhong, Li, Xinyu, Yu, Tao, Lu, Weihua, Wu, Ya Q, Huang, Xiao B, He, Zhenyang, Lu, Yuanhua, Han, Hui, Zhang, Fan, Sun, Renhua, Wang, Hua X, Qin, Shu H, Zhu, Bao H, Zhao, Jun, Liu, Jian, Li, Bin, Liu, Jing L, Zhou, Fa C, Li, Qiong J, Zhang, Xing Y, Li-Xin, Zhou, Xin-Hua, Qiang, Jiang, Liangyan, Gao, Yuan N, Zhao, Xian Y, Li, Yuan Y, Li, Xiao L, Wang, Chunting, Yao, Qingchun, Yu, Rongguo, Chen, Kai, Shao, Huanzhang, Qin, Bingyu, Huang, Qing Q, Zhu, Wei H, Hang, Ai Y, Hua, Ma X, Li, Yimin, Xu, Yonghao, Di, Yu D, Ling, Long L, Qin, Tie H, Wang, Shou H, Qin, Junping, Han, Yi, Zhou, Suming, Vargas, Monica P, Silesky Jimenez, Juan I, González Rojas, Manuel A, Solis-Quesada, Jaime E, Ramirez-Alfaro, Christian M, Máca, Jan, Sklienka, Peter, Gjedsted, Jakob, Christiansen, Aage, Villamagua, Boris G, Llano, Miguel, Burtin, Philippe, Buzancais, Gautier, Beuret, Pascal, Pelletier, Nicolas, Mortaza, Satar, Mercat, Alain, Chelly, Jonathan, Jochmans, Sébastien, Terzi, Nicolas, Daubin, Cédric, Carteaux, Guillaume, de Prost, Nicolas, Chiche, Jean-Daniel, Daviaud, Fabrice, Pham, Tai, Fartoukh, Muriel, Barberet, Guillaume, Biehler, Jerome, Dellamonica, Jean, Doyen, Denis, Arnal, Jean-Michel, Briquet, Anais, Hraiech, Sami, Papazian, Laurent, Follin, Arnaud, Roux, Damien, Messika, Jonathan, Kalaitzis, Evangelos, Dangers, Laurence, Combes, Alain, Au, Siu-Ming, Béduneau, Gaetan, Carpentier, Dorothée, Zogheib, Elie H, Dupont, Herve, Ricome, Sylvie, Santoli, Francesco L, Besset, Sebastien L, Michel, Philippe, Gelée, Bruno, Danin, Pierre-Eric, Goubaux, Bernard, Crova, Philippe J, Phan, Nga T, Berkelmans, Frantz, Badie, Julio C, Tapponnier, Romain, Gally, Josette, Khebbeb, Samy, Herbrecht, Jean-Etienne, Schneider, Francis, Declercq, Pierre-Louis M, Rigaud, Jean-Philippe, Duranteau, Jacques, Harrois, Anatole, Chabanne, Russell, Marin, Julien, Bigot, Charlene, Thibault, Sandrine, Ghazi, Mohammed, Boukhazna, Messabi, Ould Zein, Salem, Richecoeur, Jack R, Combaux, Daniele M, Grelon, Fabien, Le Moal, Charlene, Sauvadet, Elise P, Robine, Adrien, Lemiale, Virginie, Reuter, Danielle, Dres, Martin, Demoule, Alexandre, Goldgran-Toledano, Dany, Baboi, Loredana, Guérin, Claude, Lohner, Ralph, Kraßler, Jens, Schäfer, Susanne, Zacharowski, Kai D, Meybohm, Patrick, Reske, Andreas W, Simon, Philipp, Hopf, Hans-Bernd F, Schuetz, Michael, Baltus, Thomas, Papanikolaou, Metaxia N, Papavasilopoulou, Theonymfi G, Zacharas, Giannis A, Ourailogloy, Vasilis, Mouloudi, Eleni K, Massa, Eleni V, Nagy, Eva O, Stamou, Electra E, Kiourtzieva, Ellada V, Oikonomou, Marina A, Avila, Luis E, Cortez, Cesar A, Citalán, Johanna E, Jog, Sameer A, Sable, Safal D, Shah, Bhagyesh, Gurjar, Mohan, Baronia, Arvind K, Memon, Mohammedfaruk, Muthuchellappan, Radhakrishnan, Ramesh, Venkatapura J, Shenoy, Anitha, Unnikrishnan, Ramesh, Dixit, Subhal B, Rhayakar, Rachana V, Ramakrishnan, Nagarajan, Bhardwaj, Vallish K, Mahto, Heera L, Sagar, Sudha V, Palaniswamy, Vijayanand, Ganesan, Deeban, Mohammadreza Hashemian, Seyed, Jamaati, Hamidreza, Heidari, Farshad, Meaney, Edel A, Nichol, Alistair, Knapman, Karl M, O'Croinin, Donall, Dunne, Eimhin S, Breen, Dorothy M, Clarkson, Kevin P, Jaafar, Rola F, Dwyer, Rory, Amir, Fahd, Ajetunmobi, Olaitan O, O'Muircheartaigh, Aogan C, Black, Colin S, Treanor, Nuala, Collins, Daniel V, Altaf, Wahid, Zani, Gianluca, Fusari, Maurizio, Spadaro, Savino, Volta, Carlo A, Graziani, Romano, Brunettini, Barbara, Palmese, Salvatore, Formenti, Paolo, Umbrello, Michele, Lombardo, Andrea, Pecci, Elisabetta, Botteri, Marco, Savioli, Monica, Protti, Alessandro, Mattei, Alessia, Schiavoni, Lorenzo, Tinnirello, Andrea, Todeschini, Manuel, Giarratano, Antonino, Cortegiani, Andrea, Sher, Sara, Rossi, Anna, Antonelli, Massimo M, Montini, Luca M, Casalena, Paolo, Scafetti, Sergio, Panarello, Giovanna, Occhipinti, Giovanna, Patroniti, Nicolò, Pozzi, Matteo, Biscione, Roberto R, Poli, Michela M, Raimondi, Ferdinando, Albiero, Daniela, Crapelli, Giulia, Beck, Eduardo, Pota, Vincenzo, Schiavone, Vincenzo, Molin, Alexandre, Tarantino, Fabio, Monti, Giacomo, Frati, Elena, Mirabella, Lucia, Cinnella, Gilda, Fossali, Tommaso, Colombo, Riccardo, Terragni, Pierpaolo, Pattarino, Ilaria, Mojoli, Francesco, Braschi, Antonio, Borotto, Erika E, Cracchiolo, Andrea N, Palma, Daniela M, Raponi, Francesco, Foti, Giuseppe, Vascotto, Ettore R, Coppadoro, Andrea, Brazzi, Luca, Floris, Leda, Iotti, Giorgio A, Venti, Aaron, Yamaguchi, Osamu, Takagi, Shunsuke, Maeyama, Hiroki N, Watanabe, Eizo, Yamaji, Yoshihiro, Shimizu, Kazuyoshi, Shiozaki, Kyoko, Futami, Satoru, Ryosuke, Sekine, Saito, Koji, Kameyama, Yoshinobu, Ueno, Keiko, Izawa, Masayo, Okuda, Nao, Suzuki, Hiroyuki, Harasawa, Tomofumi, Nasu, Michitaka, Takada, Tadaaki, Ito, Fumihito, Nunomiya, Shin, Koyama, Kansuke, Abe, Toshikazu, Andoh, Kohkichi, Kusumoto, Kohei, Hirata, Akira, Takaba, Akihiro, Kimura, Hiroyasu, Matsumoto, Shuhei, Higashijima, Ushio, Honda, Hiroyuki, Aoki, Nobumasa, Imai, Hiroshi, Ogino, Yasuaki, Mizuguchi, Ichiko, Ichikado, Kazuya, Nitta, Kenichi, Mochizuki, Katsunori, Hashida, Tomoaki, Tanaka, Hiroyuki, Nakamura, Tomoyuki, Niimi, Daisuke, Ueda, Takeshi, Kashiwa, Yozo, Uchiyama, Akinori, Sabelnikovs, Olegs, Oss, Peteris, Haddad, Youssef, Liew, Kong Y, Ñamendys-Silva, Silvio A, Jarquin-Badiola, Yves D, Sanchez-Hurtado, Luis A, Gomez-Flores, Saira S, Marin, Maria C, Villagomez, Asisclo J, Lemus, Jordana S, Fierro, Jonathan M, Cervantes, Mavy Ramirez, Mejia, Francisco Javier Flores, Gonzalez, Daniel R, Dector, Dulce M, Estrella, Claudia R, Sanchez-Medina, Jorge R, Ramirez-Gutierrez, Alvaro, George, Fernando G, Aguirre, Janet S, Buensuseso, Juan A, Poblano, Manuel, Dendane, Tarek, Balkhi, Hicham, Elkhayari, Mina, Samkaoui, Nacer, Ezzouine, Hanane, Benslama, Abdellatif, Amor, Mourad, Maazouzi, Wajdi, Cimic, Nedim, Beck, Oliver, Bruns, Monique M, Schouten, Jeroen A, Rinia, Myra, Raaijmakers, Monique, Van Wezel, Hellen M, Heines, Serge J, Buise, Marc P, Simonis, Fabienne D, Schultz, Marcus J, Goodson, Jennifer C, rowne, Troy S B, Navarra, Leanlove, Hunt, Anna, Hutchison, Robyn A, Bailey, Mathew B, Newby, Lynette, Mcarthur, Colin, Kalkoff, Michael, Mcleod, Alex, Casement, Jonathan, Hacking, Danielle J, Andersen, Finn H, Dolva, Merete S, Laake, Jon H, Barratt-Due, Andreas, Noremark, Kim Andre L, Søreide, Eldar, Sjøbø, Brit Å, Guttormsen, Anne B, Yoshido, Hector H Leon, Aguilar, Ronald Zumaran, Oscanoa, Fredy A Montes, Alisasis, Alain U, Robles, Joanne B, Pasanting-Lim, Rossini Abbie B, Tan, Beatriz C, Andruszkiewicz, Pawel, Jakubowska, Karina, Cox, Cristina M, Alvarez, António M, Oliveira, Bruno S, Montanha, Gustavo M, Barros, Nelson C, Pereira, Carlos S, Messias, António M, Monteiro, Jorge M, Araujo, Ana M, Catorze, Nuno T, Marum, Susan M, Bouw, Maria J, Gomes, Rui M, Brito, Vania A, Castro, Silvia, Estilita, Joana M, Barros, Filipa M, Serra, Isabel M, Martinho, Aurelia M, Tomescu, Dana R, Marcu, Alexandra, Bedreag, Ovidiu H, Papurica, Marius, Corneci, Dan E, Negoita, Silvius Ioan, Grigoriev, Evgeny, Gritsan, Alexey I, Gazenkampf, Andrey A, Almekhlafi, Ghaleb, Albarrak, Mohamad M, Mustafa, Ghanem M, Maghrabi, Khalid A, Salahuddin, Nawal, Aisa, Tharwat M, Al Jabbary, Ahmed S, Tabhan, Edgardo, Arabi, Yaseen M, Trinidad, Olivia A, Al Dorzi, Hasan M, Tabhan, Edgardo E, Bolon, Stefan, Smith, Oliver, Mancebo, Jordi, Aguirre-Bermeo, Hernan, Lopez-Delgado, Juan C, Esteve, Francisco, Rialp, Gemma, Forteza, Catalina, De Haro, Candelaria, Artigas, Antonio, Albaiceta, Guillermo M, De Cima-Iglesias, Sara, Seoane-Quiroga, Leticia, Ceniceros-Barros, Alexandra, Ruiz-Aguilar, Antonio L, Claraco-Vega, Luis M, Soler, Juan Alfonso, Lorente, Maria del Carmen, Hermosa, Cecilia, Gordo, Federico, Prieto-González, Miryam, López-Messa, Juan B, Perez, Manuel P, Pere, Cesar P, Allue, Raquel Montoiro, Roche-Campo, Ferran, Ibañez-Santacruz, Marcos, Temprano, Susana, Pintado, Maria C, De Pablo, Raul, Gómez, Pilar Ricart Aroa, Ruiz, Silvia Rodriguez, Moles, Silvia Iglesias, Jurado, Mª Teresa, Arizmendi, Alfons, Piacentini, Enrique A, Franco, Nieves, Honrubia, Teresa, Perez Cheng, Meisy, Perez Losada, Elena, Blanco, Javier, Yuste, Luis J, Carbayo-Gorriz, Cecilia, Cazorla-Barranquero, Francisca G, Alonso, Javier G, Alda, Rosa S, Algaba, Ángela, Navarro, Gonzalo, Cereijo, Enrique, Diaz-Rodriguez, Esther, Marcos, Diego Pastor, Montero, Laura Alvarez, Para, Luis Herrera, Sanchez, Roberto Jimenez, Blasco Navalpotro, Miguel Angel, Abad, Ricardo Diaz, Montiel González, Raquel, Toribio, Dácil Parrilla, Castro, Alejandro G, Artiga, Maria Jose D, Penuelas, Oscar, Roser, Tomas P, Olga, Moreno F, Curto, Elena Gallego, Sánchez, Rocío Manzano, Imma, Vallverdu P, Elisabet, Garcia M, Claverias, Laura, Magret, Monica, Pellicer, Ana M, Rodriguez, Lucia L, Sánchez-Ballesteros, Jesús, González-Salamanca, Ángela, Jimenez, Antonio G, Huerta, Francisco P, Diaz, Juan Carlos J Sotillo, Lopez, Esther Bermejo, Moya, David D Llinares, Alfonso, Alec A Tallet, Eugenio Luis, Palazon Sanchez, Cesar, Palazon Sanchez, Rafael, Sánchez I, Virgilio, Corcoles G, Recio, Noelia N, Adamsson, Richard O, Rylander, Christian C, Holzgraefe, Bernhard, Broman, Lars M, Wessbergh, Joanna, Persson, Linnea, Schiöler, Fredrik, Kedelv, Hans, Tibblin, Anna Oscarsson, Appelberg, Henrik, Hedlund, Lars, Helleberg, Johan, Eriksson, Karin E, Glietsch, Rita, Larsson, Niklas, Nygren, Ingela, Nunes, Silvia L, Morin, Anna-Karin, Kander, Thomas, Adolfsson, Anne, Zender, Hervé O., Leemann-Refondini, Corinne, Elatrous, Souheil, Bouchoucha, Slaheddine, Chouchene, Imed, Ouanes, Islem, Ben Souissi, Asma, Kamoun, Salma, Demirkiran, Oktay, Aker, Mustafa, Erbabacan, Emre, Ceylan, Ilkay, Girgin, Nermin Kelebek, Ozcelik, Menekse, Ünal, Necmettin, Meco, Basak Ceyda, Akyol, Onat O, Derman, Suleyman S, Kennedy, Barry, Parhar, Ken, Srinivasa, Latha, McAuley, Danny, Steinberg, Jack, Hopkins, Phil, Mellis, Clare, Stansil, Frank, Kakar, Vivek, Hadfield, Dan, Brown, Christine, Vercueil, Andre, Bhowmick, Kaushik, Humphreys, Sally K, Ferguson, Andrew, Mckee, Raymond, Raj, Ashok S, Fawkes, Danielle A, Watt, Philip, Twohey, Linda, Thomas, Rajeev R Jha Matthew, Morton, Alex, Kadaba, Varsha, Smith, Mark J, Hormis, Anil P, Kannan, Santhana G, Namih, Miriam, Reschreiter, Henrik, Camsooksai, Julie, Kumar, Alek, Rugonfalvi, Szabolcs, Nutt, Christopher, Oneill, Orla, Seasman, Colette, Dempsey, Ged, Scott, Christopher J, Ellis, Helen E, Mckechnie, Stuart, Hutton, Paula J, Di Tomasso, Nora N, Vitale, Michela N, Griffin, Ruth O, Dean, Michael N, Cranshaw, Julius H, Willett, Emma L, Ioannou, Nicholas, Gillis, Sarah, Csabi, Peter, Macfadyen, Rosaleen, Dawson, Heidi, Preez, Pieter D, Williams, Alexandra J, Boyd, Owen, De Gordoa, Laura Ortiz-Ruiz, Bramall, Jon, Symmonds, Sophie, Chau, Simon K, Wenham, Tim, Szakmany, Tamas, Toth-Tarsoly, Piroska, Mccalman, Katie H, Alexander, Peter, Stephenson, Lorraine, Collyer, Thomas, Chapman, Rhiannon, Cooper, Raphael, Allan, Russell M, Sim, Malcolm, Wrathall, David W, Irvine, Donald A, Zantua, Kim S, Adams, John C, Burtenshaw, Andrew J, Sellors, Gareth P, Welters, Ingeborg D, Williams, Karen E, Hessell, Robert J, Oldroyd, Matthew G, Battle, Ceri E, Pillai, Suresh, Kajtor, Istvan, Sivashanmugave, Mageswaran, Okane, Sinead C, Donnelly, Adrian, Frigyik, Aniko D, Careless, Jon P, May, Martin M, Stewart, Richard, Trinder, T John, Hagan, Samantha J, Wise, Matt P, Cole, Jade M, MacFie, Caroline C, Dowling, Anna T, Nuñez, Edgardo, Pittini, Gustavo, Rodriguez, Ruben, Imperio, María C, Santos, Cristina, França, Ana G., Ebeid, Alejandro, Deicas, Alberto, Serra, Carolina, Uppalapati, Aditya, Kamel, Ghassan, Banner-Goodspeed, Valerie M, Beitler, Jeremy R, Mukkera, Satyanarayana Reddy, Kulkarni, Shreedhar, Lee, Jarone, Mesar, Tomaz, Shinn Iii, John O, Gomaa, Dina, Tainter, Christopher, Cowley, R Adams, Yeatts, Dale J, Warren, Jessica, Lanspa, Michael J, Miller, Russel R, Grissom, Colin K, Brown, Samuel M, Bauer, Philippe R, Gosselin, Ryan J, Kitch, Barrett T, Cohen, Jason E, Beegle, Scott H, Gueret, Renaud M, Tulaimat, Aiman, Choudry, Shazia, Stigler, William, Batra, Hitesh, Huff, Nidhi G, Lamb, Keith D, Oetting, Trevor W, Mohr, Nicholas M, Judy, Claine, Saito, Shigeki, Kheir, Fayez M, Schlichting, Adam B, Delsing, Angela, Elmasri, Mary, Crouch, Daniel R, Ismail, Dina, Blakeman, Thomas C, Dreyer, Kyle R, Baron, Rebecca M, Grijalba, Carolina Quintana, Hou, Peter C, Seethala, Raghu, Aisiku, Imo, Henderson, Galen, Frendl, Gyorgy, Hou, Sen-Kuang, Owens, Robert L, Schomer, Ashley, Bumbasirevic, Vesna, Jovanovic, Bojan, Surbatovic, Maja, Veljovic, Milic, Maddali, Manoj V, Churpek, Matthew, Rezoagli, Emanuele, Zhuo, Hanjing, Zhao, Wendi, He, June, Delucchi, Kevin L, Wang, Chunxue, Wickersham, Nancy, McNeil, J Brennan, Jauregui, Alejandra, Ke, Serena, Vessel, Kathryn, Gomez, Antonio, Hendrickson, Carolyn M, Kangelaris, Kirsten N, Sarma, Aartik, Leligdowicz, Aleksandra, Liu, Kathleen D, Matthay, Michael A, Ware, Lorraine B, Laffey, John G, Calfee, Carolyn S, and Sinha, Pratik

Published
2022
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6. The relationship between plasma lipid peroxidation products and primary graft dysfunction after lung transplantation is modified by donor smoking and reperfusion hyperoxia

Author

Diamond, Joshua M, Porteous, Mary K, Roberts, L Jackson, Wickersham, Nancy, Rushefski, Melanie, Kawut, Steven M, Shah, Rupal J, Cantu, Edward, Lederer, David J, Chatterjee, Shampa, Lama, Vibha N, Bhorade, Sangeeta, Crespo, Maria, McDyer, John, Wille, Keith, Orens, Jonathan, Weinacker, Ann, Arcasoy, Selim, Shah, Pali D, Wilkes, David S, Hage, Chadi, Palmer, Scott M, Snyder, Laurie, Calfee, Carolyn S, Ware, Lorraine B, Christie, Jason D, and Group, for the Lung Transplant Outcomes

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Lung, Tobacco Smoke and Health, Tobacco, Organ Transplantation, Clinical Research, Transplantation, Respiratory, Adult, Biomarkers, Female, Follow-Up Studies, Humans, Hyperoxia, Lipid Peroxidation, Lung Transplantation, Male, Postoperative Complications, Primary Graft Dysfunction, Reperfusion Injury, Retrospective Studies, Smoking, Time Factors, Tissue Donors, lung transplantation, lipid peroxidation, primary graft dysfunction, F2-isoprostane, isofuran, ischemia reperfusion, Lung Transplant Outcomes Group, Cardiorespiratory Medicine and Haematology, Surgery, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundDonor smoking history and higher fraction of inspired oxygen (FIO2) at reperfusion are associated with primary graft dysfunction (PGD) after lung transplantation. We hypothesized that oxidative injury biomarkers would be elevated in PGD, with higher levels associated with donor exposure to cigarette smoke and recipient hyperoxia at reperfusion.MethodsWe performed a nested case-control study of 72 lung transplant recipients from the Lung Transplant Outcomes Group cohort. Using mass spectroscopy, F2-isoprostanes and isofurans were measured in plasma collected after transplantation. Cases were defined in 2 ways: grade 3 PGD present at day 2 or day 3 after reperfusion (severe PGD) or any grade 3 PGD (any PGD).ResultsThere were 31 severe PGD cases with 41 controls and 35 any PGD cases with 37 controls. Plasma F2-isoprostane levels were higher in severe PGD cases compared with controls (28.6 pg/ml vs 19.8 pg/ml, p = 0.03). Plasma F2-isoprostane levels were higher in severe PGD cases compared with controls (29.6 pg/ml vs 19.0 pg/ml, p = 0.03) among patients reperfused with FIO2 >40%. Among recipients of lungs from donors with smoke exposure, plasma F2-isoprostane (38.2 pg/ml vs 22.5 pg/ml, p = 0.046) and isofuran (66.9 pg/ml vs 34.6 pg/ml, p = 0.046) levels were higher in severe PGD compared with control subjects.ConclusionsPlasma levels of lipid peroxidation products are higher in patients with severe PGD, in recipients of lungs from donors with smoke exposure, and in recipients exposed to higher Fio2 at reperfusion. Oxidative injury is an important mechanism of PGD and may be magnified by donor exposure to cigarette smoke and hyperoxia at reperfusion.

Published
2016

8. Body Composition and Mortality after Adult Lung Transplantation in the United States

Author

Singer, Jonathan P, Peterson, Eric R, Snyder, Mark E, Katz, Patricia P, Golden, Jeffrey A, D’Ovidio, Frank, Bacchetta, Matthew, Sonett, Joshua R, Kukreja, Jasleen, Shah, Lori, Robbins, Hilary, Van Horn, Kristin, Shah, Rupal J, Diamond, Joshua M, Wickersham, Nancy, Sun, Li, Hays, Steven, Arcasoy, Selim M, Palmer, Scott M, Ware, Lorraine B, Christie, Jason D, and Lederer, David J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Obesity, Organ Transplantation, Nutrition, Lung, Transplantation, Evaluation of treatments and therapeutic interventions, 6.4 Surgery, Cancer, Metabolic and endocrine, Oral and gastrointestinal, Stroke, Cardiovascular, Good Health and Well Being, Body Composition, Body Mass Index, Cohort Studies, Cross-Sectional Studies, Female, Humans, Leptin, Lung Diseases, Lung Transplantation, Male, Middle Aged, Retrospective Studies, Sarcopenia, Survival Rate, United States, obesity, sarcopenia, adiposity, leptin, biomarker, Medical and Health Sciences, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

RationaleObesity and underweight are contraindications to lung transplantation based on their associations with mortality in studies performed before implementation of the lung allocation score (LAS)-based organ allocation system in the United States Objectives: To determine the associations of body mass index (BMI) and plasma leptin levels with survival after lung transplantation.MethodsWe used multivariable-adjusted regression models to examine associations between BMI and 1-year mortality in 9,073 adults who underwent lung transplantation in the United States between May 2005 and June 2011, and plasma leptin and mortality in 599 Lung Transplant Outcomes Group study participants. We measured body fat and skeletal muscle mass using whole-body dual X-ray absorptiometry in 142 adult lung transplant candidates.Measurements and main resultsAdjusted mortality rates were similar among normal weight (BMI 18.5-24.9 kg/m(2)), overweight (BMI 25.0-29.9), and class I obese (BMI 30-34.9) transplant recipients. Underweight (BMI < 18.5) was associated with a 35% increased rate of death (95% confidence interval, 10-66%). Class II-III obesity (BMI ≥ 35 kg/m(2)) was associated with a nearly twofold increase in mortality (hazard ratio, 1.9; 95% confidence interval, 1.3-2.8). Higher leptin levels were associated with increased mortality after transplant surgery performed without cardiopulmonary bypass (P for interaction = 0.03). A BMI greater than or equal to 30 kg/m(2) was 26% sensitive and 97% specific for total body fat-defined obesity.ConclusionsA BMI of 30.0-34.9 kg/m(2) is not associated with 1-year mortality after lung transplantation in the LAS era, perhaps because of its low sensitivity for obesity. The association between leptin and mortality suggests the need to validate alternative methods to measure obesity in candidates for lung transplantation. A BMI greater than or equal to 30 kg/m(2) may no longer contraindicate lung transplantation.

Published
2014

9. Biomarkers of lung epithelial injury and inflammation distinguish severe sepsis patients with acute respiratory distress syndrome.

Author

Ware, Lorraine, Koyama, Tatsuki, Zhao, Zhiguo, Janz, David, Wickersham, Nancy, Bernard, Gordon, May, Addison, Calfee, Carolyn, and Matthay, Michael

Subjects
Aged, Biomarkers, Case-Control Studies, Critical Care, Diagnosis, Differential, Female, Humans, Lung Injury, Male, Middle Aged, Respiratory Distress Syndrome, Retrospective Studies, Sepsis
Abstract

INTRODUCTION: Despite recent modifications, the clinical definition of the acute respiratory distress syndrome (ARDS) remains non-specific, leading to under-diagnosis and under-treatment. This study was designed to test the hypothesis that a biomarker panel would be useful for biologic confirmation of the clinical diagnosis of ARDS in patients at risk of developing ARDS due to severe sepsis. METHODS: This was a retrospective case control study of 100 patients with severe sepsis and no evidence of ARDS compared to 100 patients with severe sepsis and evidence of ARDS on at least two of their first four ICU days. A panel that included 11 biomarkers of inflammation, fibroblast activation, proteolytic injury, endothelial injury, and lung epithelial injury was measured in plasma from the morning of ICU day two. A backward elimination model building strategy on 1,000 bootstrapped data was used to select the best performing biomarkers for further consideration in a logistic regression model for diagnosis of ARDS. RESULTS: Using the five best-performing biomarkers (surfactant protein-D (SP-D), receptor for advanced glycation end-products (RAGE), interleukin-8 (IL-8), club cell secretory protein (CC-16), and interleukin-6 (IL-6)) the area under the receiver operator characteristic curve (AUC) was 0.75 (95% CI: 0.7 to 0.84) for the diagnosis of ARDS. The AUC improved to 0.82 (95% CI: 0.77 to 0.90) for diagnosis of severe ARDS, defined as ARDS present on all four of the first four ICU days. CONCLUSIONS: Abnormal levels of five plasma biomarkers including three biomarkers generated by lung epithelium (SP-D, RAGE, CC-16) provided excellent discrimination for diagnosis of ARDS in patients with severe sepsis. Altered levels of plasma biomarkers may be useful biologic confirmation of the diagnosis of ARDS in patients with sepsis, and also potentially for selecting patients for clinical trials that are designed to reduce lung epithelial injury.

Published
2013

10. Androgen receptor signaling promotes Treg suppressive function during allergic airway inflammation

Author

Gandhi, Vivek D., Cephus, Jacqueline-Yvonne, Norlander, Allison E., Chowdhury, Nowrin U., Zhang, Jian, Ceneviva, Zachary J., Tannous, Elie, Polosukhin, Vasiliy V., Putz, Nathan D., Wickersham, Nancy, Singh, Amrit, Ware, Lorraine B., Bastarache, Julie A., Shaver, Ciara M., Chu, Hong Wei, Peebles, R. Stokes, Jr., and Newcomb, Dawn C.

Subjects
Suppressor cells -- Health aspects, Inflammation -- Development and progression, Cellular signal transduction -- Research, Asthma -- Development and progression, Health care industry
Abstract

Women have higher prevalence of asthma compared with men. In asthma, allergic airway inflammation is initiated by IL-33 signaling through ST2, leading to increased IL-4, IL-5, and IL-13 production and eosinophil infiltration. [Foxp3.sup.+] Tregs suppress and [ST2.sup.+] Tregs promote allergic airway inflammation. Clinical studies showed that the androgen dehydroepiandrosterone (DHEA) reduced asthma symptoms in patients, and mouse studies showed that androgen receptor (AR) signaling decreased allergic airway inflammation. Yet the impact of AR signaling on lung Tregs remains unclear. Using AR-deficient and Foxp3 fate-mapping mice, we determined that AR signaling increased Treg suppression during Alternaria extract (Alt Ext; allergen) challenge by stabilizing [Foxp3.sup.+] Tregs and limiting the number of [ST2.sup.+] ex-Tregs and IL-[13.sup.+] Th2 cells and ex-Tregs. AR signaling also decreased Alt Ext-induced [ST2.sup.+] Tregs in mice by limiting expression of Gata2, a transcription factor for ST2, and by decreasing Alt Ext-induced IL-33 production from murine airway epithelial cells. We confirmed our findings in human cells where 5[alpha]-dihydrotestosterone (DHT), an androgen, decreased IL-33-induced ST2 expression in lung Tregs and decreased Alt Ext-induced IL-33 secretion in human bronchial epithelial cells. Our findings showed that AR signaling stabilized Treg suppressive function, providing a mechanism for the sex difference in asthma., Introduction A sex bias exists in many autoimmune diseases and chronic inflammatory disorders, including asthma. Before puberty, males have increased asthma prevalence compared with females. After puberty, females have increased [...]

Published
2022
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11. A panel of lung injury biomarkers enhances the definition of primary graft dysfunction (PGD) after lung transplantation

Author

Shah, Rupal J, Bellamy, Scarlett L, Localio, A Russell, Wickersham, Nancy, Diamond, Joshua M, Weinacker, Ann, Lama, Vibha N, Bhorade, Sangeeta, Belperio, John A, Crespo, Maria, Demissie, Ejigayehu, Kawut, Steven M, Wille, Keith M, Lederer, David J, Lee, James C, Palmer, Scott M, Orens, Jonathan, Reynolds, John, Shah, Ashish, Wilkes, David S, Ware, Lorraine B, and Christie, Jason D

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Acute Respiratory Distress Syndrome, Rare Diseases, Lung, Transplantation, Organ Transplantation, Good Health and Well Being, Adult, Biomarkers, Female, Humans, Lung Injury, Lung Transplantation, Male, Middle Aged, Primary Graft Dysfunction, Prospective Studies, primary graft dysfunction, lung transplantation, biomarkers, acute lung injury, Cardiorespiratory Medicine and Haematology, Surgery, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundWe aimed to identify combinations of biomarkers to enhance the definition of primary graft dysfunction (PGD) for translational research.MethodsBiomarkers reflecting lung epithelial injury (soluble receptor for advance glycation end products [sRAGE] and surfactant protein-D [SP-D]), coagulation cascade (plasminogen activator inhibitor-1 [PAI-1] and protein C), and cell adhesion (intracellular adhesion molecule-1 [ICAM-1]) were measured in the plasma of 315 individuals derived from the Lung Transplant Outcomes Group cohort at 6 and 24 hours after transplantation. We assessed biomarker utility in 2 ways: first, we tested the discrimination of grade 3 PGD within 72 hours; second, we tested the predictive utility of plasma biomarkers for 90-day mortality.ResultsPGD developed in 86 of 315 individuals (27%). Twenty-patients (8%) died within 90 days of transplantation, of which 16 (70%) had PGD. Biomarkers measured at 24 hours had greater discrimination than at 6 hours. Individually, sRAGE (area under the curve [AUC], 0.71) and PAI-1 (AUC, 0.73) had the best discrimination of PGD. The combinations of sRAGE with PAI-1 (AUC, 0.75), PAI-1 with ICAM-1 (AUC, 0.75), and PAI-1 with SP-D (AUC, 0.76) had the best discrimination. Combinations of greater than 2 biomarkers did not significantly enhance discrimination of PGD. ICAM-1 with PAI-1 (AUC, 0.72) and ICAM-1 with sRAGE (AUC, 0.72) had the best prediction for 90-day mortality. The addition of ICAM-1, PAI-1, or sRAGE to the concurrent clinical PGD grade significantly improved the prediction of 90-day mortality (p < 0.001 each).ConclusionsMeasurement of the combination of a marker of impaired fibrinolysis with an epithelial injury or cell adhesion marker had the best discrimination for PGD and prediction for early death and may provide an alternative outcome useful in future research.

Published
2012

14. External validation of a biomarker and clinical prediction model for hospital mortality in acute respiratory distress syndrome

Author

Zhao, Zhiguo, Wickersham, Nancy, Kangelaris, Kirsten N., May, Addison K., Bernard, Gordon R., Matthay, Michael A., and Calfee, Carolyn S.

Subjects
Medical research, Medicine, Experimental, Mortality -- United Kingdom, Surface active agents, Acute respiratory distress syndrome -- Patient outcomes -- Prognosis, Health care industry
Abstract

Purpose Mortality prediction in ARDS is important for prognostication and risk stratification. However, no prediction models have been independently validated. A combination of two biomarkers with age and APACHE III was superior in predicting mortality in the NHLBI ARDSNet ALVEOLI trial. We validated this prediction tool in two clinical trials and an observational cohort. Methods The validation cohorts included 849 patients from the NHLBI ARDSNet Fluid and Catheter Treatment Trial (FACTT), 144 patients from a clinical trial of sivelestat for ARDS (STRIVE), and 545 ARDS patients from the VALID observational cohort study. To evaluate the performance of the prediction model, the area under the receiver operating characteristic curve (AUC), model discrimination, and calibration were assessed, and recalibration methods were applied. Results The biomarker/clinical prediction model performed well in all cohorts. Performance was better in the clinical trials with an AUC of 0.74 (95% CI 0.70-0.79) in FACTT, compared to 0.72 (95% CI 0.67-0.77) in VALID, a more heterogeneous observational cohort. The AUC was 0.73 (95% CI 0.70-0.76) when FACTT and VALID were combined. Conclusion We validated a mortality prediction model for ARDS that includes age, APACHE III, surfactant protein D, and interleukin-8 in a variety of clinical settings. Although the model performance as measured by AUC was lower than in the original model derivation cohort, the biomarker/clinical model still performed well and may be useful for risk assessment for clinical trial enrollment, an issue of increasing importance as ARDS mortality declines, and better methods are needed for selection of the most severely ill patients for inclusion., Author(s): Zhiguo Zhao [sup.1] [sup.2], Nancy Wickersham [sup.3], Kirsten N. Kangelaris [sup.4], Addison K. May [sup.5], Gordon R. Bernard [sup.3], Michael A. Matthay [sup.6] [sup.7], Carolyn S. Calfee [sup.6] [sup.7], [...]

Published
2017
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16. Secretory Cells are the Primary Source of pIgR in Small Airways

Author

Blackburn, Jessica B, primary, Schaff, Jacob A, additional, Gutor, Sergey, additional, Du, Rui-Hong, additional, Nichols, David, additional, Sherrill, Taylor, additional, Gutierrez, Austin J, additional, Xin, Matthew K., additional, Wickersham, Nancy, additional, Zhang, Yong, additional, Holtzman, Michael J., additional, Ware, Lorraine B, additional, Banovich, Nicholas E, additional, Kropski, Jonathan A, additional, Blackwell, Timothy S, additional, and Richmond, Bradley W, additional

Published
2022
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17. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis.

Author

UCL - SSS/IREC/MEDA - Pôle de médecine aiguë, UCL - (SLuc) Service de soins intensifs, UCL - (MGD) Services des soins intensifs, Maddali, Manoj V, Churpek, Matthew, Pham, Tai, Rezoagli, Emanuele, Zhuo, Hanjing, Zhao, Wendi, He, June, Delucchi, Kevin L, Wang, Chunxue, Wickersham, Nancy, McNeil, J Brennan, Jauregui, Alejandra, Ke, Serena, Vessel, Kathryn, Gomez, Antonio, Hendrickson, Carolyn M, Kangelaris, Kirsten N, Sarma, Aartik, Leligdowicz, Aleksandra, Liu, Kathleen D, Matthay, Michael A, Ware, Lorraine B, Laffey, John G, Bellani, Giacomo, Calfee, Carolyn S, Sinha, Pratik, LUNG SAFE Investigators and the ESICM Trials Group, Wittebole, Xavier, Berghe, Caroline, Bulpa, Pierre, Dive, Alain-Michel, UCL - SSS/IREC/MEDA - Pôle de médecine aiguë, UCL - (SLuc) Service de soins intensifs, UCL - (MGD) Services des soins intensifs, Maddali, Manoj V, Churpek, Matthew, Pham, Tai, Rezoagli, Emanuele, Zhuo, Hanjing, Zhao, Wendi, He, June, Delucchi, Kevin L, Wang, Chunxue, Wickersham, Nancy, McNeil, J Brennan, Jauregui, Alejandra, Ke, Serena, Vessel, Kathryn, Gomez, Antonio, Hendrickson, Carolyn M, Kangelaris, Kirsten N, Sarma, Aartik, Leligdowicz, Aleksandra, Liu, Kathleen D, Matthay, Michael A, Ware, Lorraine B, Laffey, John G, Bellani, Giacomo, Calfee, Carolyn S, Sinha, Pratik, LUNG SAFE Investigators and the ESICM Trials Group, Wittebole, Xavier, Berghe, Caroline, Bulpa, Pierre, and Dive, Alain-Michel

Abstract

BACKGROUND: Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. METHODS: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the

Published
2022

18. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

Author

V Maddali, Manoj, Churpek, Matthew, Pham, Tai, Rezoagli, Emanuele, Zhuo, Hanjing, Zhao, Wendi, He, June, L Delucchi, Kevin, Wang, Chunxue, Wickersham, Nancy, Brennan McNeil, J, Jauregui, Alejandra, Ke, Serena, Vessel, Kathryn, Gomez, Antonio, M Hendrickson, Carolyn, N Kangelaris, Kirsten, Sarma, Aartik, Leligdowicz, Aleksandra, D Liu, Kathleen, A Matthay, Michael, B Ware, Lorraine, G Laffey, John, Bellani, Giacomo, S Calfee, Carolyn, Sinha, Pratik, SAFE Investigators and the ESICM Trials Group, Lung, Montini, Luca, Luca Montini (ORCID:0000-0003-4602-5134), V Maddali, Manoj, Churpek, Matthew, Pham, Tai, Rezoagli, Emanuele, Zhuo, Hanjing, Zhao, Wendi, He, June, L Delucchi, Kevin, Wang, Chunxue, Wickersham, Nancy, Brennan McNeil, J, Jauregui, Alejandra, Ke, Serena, Vessel, Kathryn, Gomez, Antonio, M Hendrickson, Carolyn, N Kangelaris, Kirsten, Sarma, Aartik, Leligdowicz, Aleksandra, D Liu, Kathleen, A Matthay, Michael, B Ware, Lorraine, G Laffey, John, Bellani, Giacomo, S Calfee, Carolyn, Sinha, Pratik, SAFE Investigators and the ESICM Trials Group, Lung, Montini, Luca, and Luca Montini (ORCID:0000-0003-4602-5134)

Abstract

Background: Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. Methods: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the

Published
2022

19. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

Author

Maddali, M, Churpek, M, Pham, T, Rezoagli, E, Zhuo, H, Zhao, W, He, J, Delucchi, K, Wang, C, Wickersham, N, Mcneil, J, Jauregui, A, Ke, S, Vessel, K, Gomez, A, Hendrickson, C, Kangelaris, K, Sarma, A, Leligdowicz, A, Liu, K, Matthay, M, Ware, L, Laffey, J, Bellani, G, Calfee, C, Sinha, P, Maddali, Manoj V, Churpek, Matthew, Pham, Tai, Rezoagli, Emanuele, Zhuo, Hanjing, Zhao, Wendi, He, June, Delucchi, Kevin L, Wang, Chunxue, Wickersham, Nancy, McNeil, J Brennan, Jauregui, Alejandra, Ke, Serena, Vessel, Kathryn, Gomez, Antonio, Hendrickson, Carolyn M, Kangelaris, Kirsten N, Sarma, Aartik, Leligdowicz, Aleksandra, Liu, Kathleen D, Matthay, Michael A, Ware, Lorraine B, Laffey, John G, Bellani, Giacomo, Calfee, Carolyn S, Sinha, Pratik, Maddali, M, Churpek, M, Pham, T, Rezoagli, E, Zhuo, H, Zhao, W, He, J, Delucchi, K, Wang, C, Wickersham, N, Mcneil, J, Jauregui, A, Ke, S, Vessel, K, Gomez, A, Hendrickson, C, Kangelaris, K, Sarma, A, Leligdowicz, A, Liu, K, Matthay, M, Ware, L, Laffey, J, Bellani, G, Calfee, C, Sinha, P, Maddali, Manoj V, Churpek, Matthew, Pham, Tai, Rezoagli, Emanuele, Zhuo, Hanjing, Zhao, Wendi, He, June, Delucchi, Kevin L, Wang, Chunxue, Wickersham, Nancy, McNeil, J Brennan, Jauregui, Alejandra, Ke, Serena, Vessel, Kathryn, Gomez, Antonio, Hendrickson, Carolyn M, Kangelaris, Kirsten N, Sarma, Aartik, Leligdowicz, Aleksandra, Liu, Kathleen D, Matthay, Michael A, Ware, Lorraine B, Laffey, John G, Bellani, Giacomo, Calfee, Carolyn S, and Sinha, Pratik

Abstract

Background: Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. Methods: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the

Published
2022

20. Club cells are the primary source of pIgR in small airways

Author

Richmond, Bradley, primary, Blackburn, Jessica, additional, Schaff, Jacob, additional, Gutor, Sergey, additional, Nichols, David, additional, Sherrill, Taylor, additional, Wickersham, Nancy, additional, Ware, Lorraine, additional, Holtzman, Michael, additional, Gutierrez, Austin, additional, Banovich, Nicholas, additional, Lee, Jae-Woo, additional, and Blackwell, Timothy, additional

Published
2022
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21. Alveolar epithelial glycocalyx degradation mediates surfactant dysfunction and contributes to acute respiratory distress syndrome

Author

Rizzo, Alicia N., primary, Haeger, Sarah M., additional, Oshima, Kaori, additional, Yang, Yimu, additional, Wallbank, Alison M., additional, Jin, Ying, additional, Lettau, Marie, additional, McCaig, Lynda A., additional, Wickersham, Nancy E., additional, McNeil, J. Brennan, additional, Zakharevich, Igor, additional, McMurtry, Sarah A., additional, Langouët-Astrié, Christophe J., additional, Kopf, Katrina W., additional, Voelker, Dennis R., additional, Hansen, Kirk C., additional, Shaver, Ciara M., additional, Kerchberger, V. Eric, additional, Peterson, Ryan A., additional, Kuebler, Wolfgang M., additional, Ochs, Matthias, additional, Veldhuizen, Ruud A.W., additional, Smith, Bradford J., additional, Ware, Lorraine B., additional, Bastarache, Julie A., additional, and Schmidt, Eric P., additional

Published
2022
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22. Secretory cells are the primary source of pIgR in small airways

Author

Blackburn, Jessica B., primary, Schaff, Jacob A., additional, Gutor, Sergey, additional, Du, Rui-Hong, additional, Nichols, David, additional, Sherrill, Taylor, additional, Gutierrez, Austin J., additional, Xin, Matthew K., additional, Wickersham, Nancy, additional, Zhang, Yong, additional, Holtzman, Michael J., additional, Ware, Lorraine B, additional, Banovich, Nicholas E., additional, Kropski, Jonathan A., additional, Blackwell, Timothy S., additional, and Richmond, Bradley W., additional

Published
2021
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24. Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study

Author

Sinha, Pratik, primary, Delucchi, Kevin L, additional, Chen, Yue, additional, Zhuo, Hanjing, additional, Abbott, Jason, additional, Wang, Chunxue, additional, Wickersham, Nancy, additional, McNeil, J Brennan, additional, Jauregui, Alejandra, additional, Ke, Serena, additional, Vessel, Kathryn, additional, Gomez, Antonio, additional, Hendrickson, Carolyn M, additional, Kangelaris, Kirsten N, additional, Sarma, Aartik, additional, Leligdowicz, Aleksandra, additional, Liu, Kathleen D, additional, Matthay, Michael A, additional, Ware, Lorraine B, additional, and Calfee, Carolyn S, additional

Published
2021
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25. Additional file of Effect of balanced crystalloids versus saline on urinary biomarkers of acute kidney injury in critically ill adults

Author

Funke, Blake E., Jackson, Karen E., Self, Wesley H., Collins, Sean P., Saunders, Christina T., Wang, Li, Blume, Jeffrey D., Wickersham, Nancy, Brown, Ryan M., Casey, Jonathan D., Bernard, Gordon R., Rice, Todd W., Siew, Edward D., and Semler, Matthew W.

Abstract

Additional file of Effect of balanced crystalloids versus saline on urinary biomarkers of acute kidney injury in critically ill adults

Published
2021
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26. Additional file 5 of Angiopoietin-2 outperforms other endothelial biomarkers associated with severe acute kidney injury in patients with severe sepsis and respiratory failure

Author

Wen-Kuang Yu, J. Brennan McNeil, Wickersham, Nancy E., Shaver, Ciara M., Bastarache, Julie A., and Ware, Lorraine B.

Abstract

Additional file 5. Higher plasma angiopoietin-2 levels by quartile were associated with higher APACHE II scores. Data were summarized as boxplots where box encompassed 25‒75th percentile, error bars encompassed 10‒90th percentile and horizontal line showed median. Groups were compared by Kruskal-Wallis test. Post hoc analysis of groups comparison was performed using Mann-Whitney U test and Bonferroni correction.

Published
2021
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27. Additional file 2 of Angiopoietin-2 outperforms other endothelial biomarkers associated with severe acute kidney injury in patients with severe sepsis and respiratory failure

Author

Wen-Kuang Yu, J. Brennan McNeil, Wickersham, Nancy E., Shaver, Ciara M., Bastarache, Julie A., and Ware, Lorraine B.

Subjects
carbohydrates (lipids), animal structures, embryonic structures
Abstract

Additional file 2. Plasma levels of angiopoietin-2 were modestly associated with (a) plasma levels of endocan, (b) plasma levels of syndecan-1 and (c) plasma levels of sVE-cadherin. Plasma levels of endocan were modestly associated with (d) plasma levels of syndecan-1, but not associated with (e) plasma levels of sVE-cadherin. Plasma levels of sVE-cadherin were modestly associated with (f) plasma levels of syndecan-1. Collinearity of these four endothelial biomarkers was analyzed by Spearman rank correlation test.

Published
2021
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28. Additional file 1 of Effect of balanced crystalloids versus saline on urinary biomarkers of acute kidney injury in critically ill adults

Author

Funke, Blake E., Jackson, Karen E., Self, Wesley H., Collins, Sean P., Saunders, Christina T., Wang, Li, Blume, Jeffrey D., Wickersham, Nancy, Brown, Ryan M., Casey, Jonathan D., Bernard, Gordon R., Rice, Todd W., Siew, Edward D., and Semler, Matthew W.

Abstract

Additional file 1: Supplemental Methods. Table S1. Composition of the study fluids. Table S2. Coefficient of variation for urinary biomarkers. Table S3. Elixhauser comorbidity index. Table S4. Baseline laboratory values. Table S5. Volume of intravenous isotonic crystalloid by study group. Table S6. Volume of non-study intravenous crystalloid. Table S7. Laboratory values. Table S8. Multivariable model for urinary NGAL concentration. Table S9. Multivariable model for urinary KIM-1 concentration. Table S10. Multivariable model for Major Adverse Kidney Events within 30 days. Table S11. Highest stage of acute kidney injury developing after enrollment. Figure S1. Study group assignment during the trial. Figure S2. Flow of participants through the trial. Figure S3. Urinary biomarker levels at ED presentation and 36 h. The median (horizontal bar), interquartile range (colored box), 95% confidence interval (dashed line) for urinary NGAL and KIM-1 concentration at time of emergency department presentation (Day 0, 111 patients) and 36 h after hospital admission (Day 2, 261 patients) scaled to urinary creatinine concentration are displayed for patients in the balanced crystalloid group and saline group

Published
2021
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29. Additional file 3 of Angiopoietin-2 outperforms other endothelial biomarkers associated with severe acute kidney injury in patients with severe sepsis and respiratory failure

Author

Wen-Kuang Yu, J. Brennan McNeil, Wickersham, Nancy E., Shaver, Ciara M., Bastarache, Julie A., and Ware, Lorraine B.

Subjects
urologic and male genital diseases, female genital diseases and pregnancy complications
Abstract

Additional file 3. (a) Plasma levels of angiopoietin-2 were significantly lower in sepsis patients who did not develop AKI within the four study days compared to patients who had AKI on enrollment day or patients who developed AKI in the subsequent 72 hours after enrollment. (b) Plasma levels of angiopoietin-2 were significantly lower in sepsis patients without any AKI within the four study days compared to sepsis patients with persistent AKI at 48 or 72 hours since enrollment. (c) Among patients with AKI on enrolment day, plasma levels of angiopoietin-2 were significantly lower in patients with the resolution of AKI compared to patients without the resolution of AKI at 48 hours or 72 hours after enrollment. Data in panels a–c were summarized as boxplots where box encompassed 25‒75th percentile, error bars encompassed 10‒90th percentile and horizontal line showed median. Groups were compared by Kruskal-Wallis test (panels a and b) or Mann-Whitney U test (panel c). Post hoc analysis of groups comparison was performed using Mann-Whitney U test and Bonferroni correction (panels a and b).

Published
2021
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30. Additional file 1 of Angiopoietin-2 outperforms other endothelial biomarkers associated with severe acute kidney injury in patients with severe sepsis and respiratory failure

Author

Wen-Kuang Yu, J. Brennan McNeil, Wickersham, Nancy E., Shaver, Ciara M., Bastarache, Julie A., and Ware, Lorraine B.

Subjects
embryonic structures, urologic and male genital diseases
Abstract

Additional file 1. Receiver operator curves of plasma levels of angiopoietin-2, endocan, sVE-cadherin and syndecan-1 predicted the development of severe AKI within the four study days. AUC, area under the curve.

Published
2021
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31. Additional file 4 of Angiopoietin-2 outperforms other endothelial biomarkers associated with severe acute kidney injury in patients with severe sepsis and respiratory failure

Author

Wen-Kuang Yu, J. Brennan McNeil, Wickersham, Nancy E., Shaver, Ciara M., Bastarache, Julie A., and Ware, Lorraine B.

Abstract

Additional file 4. (a) The severity of AKI within the four study days was not associated with positive cumulative balance on enrollment day. The severity of AKI within the four study days was significantly associated with positive fluid balance in the subsequent (b) 24 hours, (c) 48 hours and (d) 72 hours since enrollment. Data in panels a-d were summarized as boxplots where box encompassed 25‒75th percentile, error bars encompassed 10‒90th percentile and horizontal line showed median. Groups were compared by Kruskal-Wallis test (panels a–d).

Published
2021
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32. Standardization of methods for sampling the distal airspace in mechanically ventilated patients using heat moisture exchange filter fluid

Author

Bastarache, Julie A., primary, McNeil, J. Brennan, additional, Plosa, Erin J., additional, Sucre, Jennifer S., additional, Kerchberger, V. Eric, additional, Habegger, Luke E., additional, Weddle, Elizabeth, additional, Sullivan, Briana, additional, Meegan, Jamie E., additional, Wickersham, Nancy E., additional, Shaver, Ciara M., additional, and Ware, Lorraine B., additional

Published
2021
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34. Effect of Balanced Crystalloids versus Saline on Urinary Biomarkers of Acute Kidney Injury in Critically Ill Adults

Author

Funke, Blake, primary, Jackson, Karen, additional, Self, Wesley, additional, Collins, Sean, additional, Saunders, Christina, additional, Wang, Li, additional, Blume, Jeffrey, additional, Wickersham, Nancy, additional, Brown, Ryan, additional, Casey, Jonathan, additional, Bernard, Gordon, additional, Rice, Todd, additional, Siew, Edward, additional, and Semler, Matthew, additional

Published
2020
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35. Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study.

Author

Sinha, Pratik, Delucchi, Kevin L., Yue Chen, Hanjing Zhuo, Abbott, Jason, Chunxue Wang, Wickersham, Nancy, McNeil, J. Brennan, Jauregui, Alejandra, Ke, Serena, Vessel, Kathryn, Gomez, Antonio, Hendrickson, Carolyn M., Kangelaris, Kirsten N., Sarma, Aartik, Leligdowicz, Aleksandra, Liu, Kathleen D., Matthay, Michael A., Ware, Lorraine B., and Calfee, Carolyn S.

Subjects
ADULT respiratory distress syndrome, NONINVASIVE ventilation, ARTIFICIAL respiration, ETIOLOGY of diseases, LATENT class analysis (Statistics), CD54 antigen, LONGITUDINAL method
Abstract

Rationale: Using latent class analysis (LCA), two subphenotypes of acute respiratory distress syndrome (ARDS) have consistently been identified in five randomised controlled trials (RCTs), with distinct biological characteristics, divergent outcomes and differential treatment responses to randomised interventions. Their existence in unselected populations of ARDS remains unknown. We sought to identify subphenotypes in observational cohorts of ARDS using LCA.Methods: LCA was independently applied to patients with ARDS from two prospective observational cohorts of patients admitted to the intensive care unit, derived from the Validating Acute Lung Injury markers for Diagnosis (VALID) (n=624) and Early Assessment of Renal and Lung Injury (EARLI) (n=335) studies. Clinical and biological data were used as class-defining variables. To test for concordance with prior ARDS subphenotypes, the performance metrics of parsimonious classifier models (interleukin 8, bicarbonate, protein C and vasopressor-use), previously developed in RCTs, were evaluated in EARLI and VALID with LCA-derived subphenotypes as the gold-standard.Results: A 2-class model best fit the population in VALID (p=0.0010) and in EARLI (p<0.0001). Class 2 comprised 27% and 37% of the populations in VALID and EARLI, respectively. Consistent with the previously described 'hyperinflammatory' subphenotype, Class 2 was characterised by higher proinflammatory biomarkers, acidosis and increased shock and worse clinical outcomes. The similarities between these and prior RCT-derived subphenotypes were further substantiated by the performance of the parsimonious classifier models in both cohorts (area under the curves 0.92-0.94). The hyperinflammatory subphenotype was associated with increased prevalence of chronic liver disease and neutropenia and reduced incidence of chronic obstructive pulmonary disease. Measurement of novel biomarkers showed significantly higher levels of matrix metalloproteinase-8 and markers of endothelial injury in the hyperinflammatory subphenotype, whereas, matrix metalloproteinase-9 was significantly lower.Conclusion: Previously described subphenotypes are generalisable to unselected populations of non-trauma ARDS. [ABSTRACT FROM AUTHOR]

Published
2022
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38. Haptoglobin-2 variant increases susceptibility to acute respiratory distress syndrome during sepsis

Author

Kerchberger, V. Eric, primary, Bastarache, Julie A., additional, Shaver, Ciara M., additional, Nagata, Hiromasa, additional, McNeil, J. Brennan, additional, Landstreet, Stuart R., additional, Putz, Nathan D., additional, Yu, Wen-Kuang, additional, Jesse, Jordan, additional, Wickersham, Nancy E., additional, Sidorova, Tatiana N., additional, Janz, David R., additional, Parikh, Chirag R., additional, Siew, Edward D., additional, and Ware, Lorraine B., additional

Published
2019
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39. Effect of balanced crystalloids versus saline on urinary biomarkers of acute kidney injury in critically ill adults.

Author

Funke, Blake E., Jackson, Karen E., Self, Wesley H., Collins, Sean P., Saunders, Christina T., Wang, Li, Blume, Jeffrey D., Wickersham, Nancy, Brown, Ryan M., Casey, Jonathan D., Bernard, Gordon R., Rice, Todd W., Siew, Edward D., Semler, Matthew W., for the SMART Investigators, Lindsell, Christopher J., Wanderer, Jonathan P., Stollings, Joanna L., SMART Investigators, and Pragmatic Critical Care Research Group

Subjects
ACUTE kidney failure, CRITICALLY ill, PHYSIOLOGIC salines, MANN Whitney U Test, ADULTS
Abstract

Background: Recent trials have suggested use of balanced crystalloids may decrease the incidence of major adverse kidney events compared to saline in critically ill adults. The effect of crystalloid composition on biomarkers of early acute kidney injury remains unknown.Methods: From February 15 to July 15, 2016, we conducted an ancillary study to the Isotonic Solutions and Major Adverse Renal Events Trial (SMART) comparing the effect of balanced crystalloids versus saline on urinary levels of neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) among 261 consecutively-enrolled critically ill adults admitted from the emergency department to the medical ICU. After informed consent, we collected urine 36 ± 12 h after hospital admission and measured NGAL and KIM-1 levels using commercially available ELISAs. Levels of NGAL and KIM-1 at 36 ± 12 h were compared between patients assigned to balanced crystalloids versus saline using a Mann-Whitney U test.Results: The 131 patients (50.2%) assigned to the balanced crystalloid group and the 130 patients (49.8%) assigned to the saline group were similar at baseline. Urinary NGAL levels were significantly lower in the balanced crystalloid group (median, 39.4 ng/mg [IQR 9.9 to 133.2]) compared with the saline group (median, 64.4 ng/mg [IQR 27.6 to 339.9]) (P < 0.001). Urinary KIM-1 levels did not significantly differ between the balanced crystalloid group (median, 2.7 ng/mg [IQR 1.5 to 4.9]) and the saline group (median, 2.4 ng/mg [IQR 1.3 to 5.0]) (P = 0.36).Conclusions: In this ancillary analysis of a clinical trial comparing balanced crystalloids to saline among critically ill adults, balanced crystalloids were associated with lower urinary concentrations of NGAL and similar urinary concentrations of KIM-1, compared with saline. These results suggest only a modest reduction in early biomarkers of acute kidney injury with use of balanced crystalloids compared with saline.Trial Registration: ClinicalTrials.gov number: NCT02444988 . Date registered: May 15, 2015. [ABSTRACT FROM AUTHOR]

Published
2021
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40. EXTERNAL VALIDATION OF A BIOMARKER AND CLINICAL PREDICTION MODEL FOR HOSPITAL MORTALITY IN ARDS

Author

Zhao, Zhiguo, Wickersham, Nancy, Kangelaris, Kirsten N., May, Addison, Bernard, Gordon, Matthay, Michael, Calfee, Carolyn S., Koyama, Tatsuki, and Ware, Lorraine B.

Subjects
Adult, Male, Respiratory Distress Syndrome, Acute Lung Injury, Interleukin-8, Reproducibility of Results, Middle Aged, Pulmonary Surfactant-Associated Protein D, Respiration, Artificial, Article, Cohort Studies, ROC Curve, Humans, Female, Hospital Mortality, Biomarkers, APACHE, Aged
Abstract

Mortality prediction in ARDS is important for prognostication and risk stratification. However, no prediction models have been independently validated. A combination of two biomarkers with age and APACHE III was superior in predicting mortality in the NHLBI ARDSNet ALVEOLI trial. We validated this prediction tool in two clinical trials and an observational cohort.The validation cohorts included 849 patients from the NHLBI ARDSNet Fluid and Catheter Treatment Trial (FACTT), 144 patients from a clinical trial of sivelestat for ARDS (STRIVE), and 545 ARDS patients from the VALID observational cohort study. To evaluate the performance of the prediction model, the area under the receiver operating characteristic curve (AUC), model discrimination, and calibration were assessed, and recalibration methods were applied.The biomarker/clinical prediction model performed well in all cohorts. Performance was better in the clinical trials with an AUC of 0.74 (95% CI 0.70-0.79) in FACTT, compared to 0.72 (95% CI 0.67-0.77) in VALID, a more heterogeneous observational cohort. The AUC was 0.73 (95% CI 0.70-0.76) when FACTT and VALID were combined.We validated a mortality prediction model for ARDS that includes age, APACHE III, surfactant protein D, and interleukin-8 in a variety of clinical settings. Although the model performance as measured by AUC was lower than in the original model derivation cohort, the biomarker/clinical model still performed well and may be useful for risk assessment for clinical trial enrollment, an issue of increasing importance as ARDS mortality declines, and better methods are needed for selection of the most severely ill patients for inclusion.

Published
2017

42. Cell-free hemoglobin promotes primary graft dysfunction through oxidative lung endothelial injury

Author

Shaver, Ciara M., primary, Wickersham, Nancy, additional, McNeil, J. Brennan, additional, Nagata, Hiromasa, additional, Miller, Adam, additional, Landstreet, Stuart R., additional, Kuck, Jamie L., additional, Diamond, Joshua M., additional, Lederer, David J., additional, Kawut, Steven M., additional, Palmer, Scott M., additional, Wille, Keith M., additional, Weinacker, Ann, additional, Lama, Vibha N., additional, Crespo, Maria M., additional, Orens, Jonathan B., additional, Shah, Pali D., additional, Hage, Chadi A., additional, Cantu, Edward, additional, Porteous, Mary K., additional, Dhillon, Gundeep, additional, McDyer, John, additional, Bastarache, Julie A., additional, Christie, Jason D., additional, and Ware, Lorraine B., additional

Published
2018
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47. Cell-free hemoglobin: a novel mediator of acute lung injury

Author

Shaver, Ciara M., primary, Upchurch, Cameron P., additional, Janz, David R., additional, Grove, Brandon S., additional, Putz, Nathan D., additional, Wickersham, Nancy E., additional, Dikalov, Sergey I., additional, Ware, Lorraine B., additional, and Bastarache, Julie A., additional

Published
2016
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