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1. Use of digital measurement of medication adherence and lung function to guide the management of uncontrolled asthma (INCA Sun): a multicentre, single-blinded, randomised clinical trial

Author

Lombard, Lorna, Walsh, Joanne, Plunkett, Sinead, McCartan, Thomas A, Hale, Elaine Mac, Greene, Garrett, Mulvey, Christopher, Mokoka, Matshediso C, van Boven, Job F M, Cushen, Breda, Sulaiman, Imran, Brennan, Vincent, Kerr, Patrick J, Reilly, Richard B, Hughes, Cian, Kent, Brian D, Jackson, David J, Butler, Marcus, Counihan, Ian, Hayes, James, Faul, John, Kelly, Martin, Convery, Rory, Nanzer, Alexandra M, Fitzgerald, J Mark, Murphy, Desmond M, Heaney, Liam G, and Costello, Richard W

Published
2023
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4. Cytosponge-trefoil factor 3 versus usual care to identify Barrett's oesophagus in a primary care setting: a multicentre, pragmatic, randomised controlled trial

Author

Bagewadi, Abhay, Patrick, Abigail, Shenoy, Achuth, Redmond, Aisling, Muddu, Ajay, Northrop, Alex, Groves, Alice, Shiner, Alice, Heer, Amardeep, Takhar, Amrit, Bowles, Amy, Jarman, Andrea, Wong, Angela, Lucas, Angie, Gibbons, Anita, Dhar, Anjan, Curry, Anji, Lalonde, Anna, Swinburn, Anna, Turner, Anne, Lydon, Anne-Marie, Gunstone, Anthony, Lee, Arlene, Nambi, Arul, Ariyarathenam, Arun, Elden, Ashley, Wilson, Ashley, Donepudi, Balaji, Campbell, Barbara, Uszycka, Basia, Bowers, Ben, Coghill, Ben, de Quadros, Bruno, Cheah, Calvin, Bratten, Carla, Brown, Carly, Moorbey, Chantelle, Clisby, Charles, Gordon, Charles, Schramm, Chris, Castle, Chris, Newark, Chris, Norris, Chrissie, A'Court, Christine, Graham, Claire, Fletcher, Clare, Grocott, Clare, Rees, Colin, Bakker, Corinne, Paschalides, Costas, Vickery, Craig, Schembri, Damian, Morris, Danielle, Hagan, Daryl, Cronk, David, Goddard, David, Graham, David, Phillips, Dean, Prabhu, Deeksha, Kejariwal, Deepak, Garg, Dhirendra, Lonsdale, Diane, Butterworth, Dianne, Clements, Donna, Bradman, Drew, Blake, Duncan, Mather, Elizabeth, O'Farrell, Ewan, Markowetz, Florian, Adams, Fran, Pesola, Francesca, Forbes, Gareth, Taylor, Gary, Collins, Glenn, Irvine, Gordon, Fourie, Gysbert, Doyle, Harriet, Barnes, Heather, Bowyer, Helen, Whiting, Helen, Beales, Ian, Binnian, Ian, Bremner, Ian, Jennings, Ian, Troiceanu, Ilona, Modelell, Ines, Emmerson, Ingrid, Ortiz, Jacobo, Lilley, Jacqueline, Harvey, Jacquelyn, Vicars, Jacqui, Takhar, Jagjit, Larcombe, James, Bornschein, Jan, Aldegather, Jehad, Johnson, Jenny, Ducker, Jill, Skinner, Jo, Dash, Joanne, Walsh, Joanne, Miralles, Jose, Ridgway, Josephine, Ince, Julia, Kennedy, Julie, Hampson, Kat, Milne, Kate, Ellerby, Katherine, Priddis, Katherine, Rainsbury, Kathy, Powell, Kelly, Gunner, Kerry, Ragunath, Krish, Knox, Kyle, Baseley, Laura, White, Lauren, Lovat, Laurence, Berney, Lee, Crockett, Lindsay, Murray, Lisa, Westwood, Lisa, Chalkley, Lisa, Leggett, Loraine, Dale, Louise, Scovell, Louise, Brooks, Lucy, Saunders, Lucy, Owen, Lydia, Dilwershah, Maria, Baldry, Marie, Corcoran, Marie, Roy, Marie, Macedo, Mario, Attah, Mark, Anson, Mary-Jo, Rutter, Matt, Wallard, Matthew, Gaw, Matthew, Hunt, Matthew, Lea-Hagerty, Megan, Penacerrada, Melchizedek, Bianchi, Michele, Baker-Moffatt, Michelle, Czajkowski, Michelle, Sleeth, Michelle, Brewer, Nick, Wooding, Nick, Todd, Nicky, Millen, Nicola, Zolle, Olga, Whitehead, Orla, Ojechi, Patrick, Moore, Patrick, Banim, Paul, Spellar, Paula, Bhandari, Pradeep, Kant, Prashant, Nixon, Rachel, Russell, Rebecca, Roberts, Rebekah, Skule, Rene, West, Richard, Fox, Robin, Beesley, Ruth, Gibbins, Ruth, Osborne, Ruth, Thiagarajan, S, Bastiman, Sally, Warburton, Samantha, Pai, Samir, Leith-Russell, Sarah, Utting, Sarah, Watson, Sarah, Wytrykowski, Sarah, Singh, Satish, Malhotra, Shalini, Woods, Sharon, Conway, Shaun, Mateer, Sherrie, Macrae, Shona, Singh, Shruti, Fourie, Simona, Campbell, Siobhan, Parslow-Williams, Siobhan, Goel, Sonica, Dellar, Stephen, Jones, Stephen, Knight, Steve, Mackay-Thomas, Stuart, Mukherjee, Stuti, Allen, Sue, Henry, Suzanne, Evans, Tara, Leighton, Theresa, Bray, Tim, Shackleton, Tom, Santosh, Vanaja, Glover, Vicki, Chandraraj, Vijay, Elson, Will, Briggs, William, Barron, Zoe, Khan, Zohrah, Fitzgerald, Rebecca C, di Pietro, Massimiliano, O'Donovan, Maria, Maroni, Roberta, Muldrew, Beth, Debiram-Beecham, Irene, Gehrung, Marcel, Offman, Judith, Tripathi, Monika, Smith, Samuel G, Aigret, Benoit, Walter, Fiona M, Rubin, Greg, and Sasieni, Peter

Published
2020
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5. Feeding difficulties in children with food allergies: An EAACI Task Force Report

Author

Hill, Sarah‐Anne, primary, Nurmatov, Ulugbek, additional, DunnGalvin, Audrey, additional, Reese, Imke, additional, Vieira, Mario C., additional, Rommel, Natalie, additional, Dupont, Christophe, additional, Venter, Carina, additional, Cianferoni, Antonella, additional, Walsh, Joanne, additional, Yonamine, Glauce, additional, Beauregard, Alexia, additional, Meyer, Rosan, additional, and Vazquez‐Ortiz, Marta, additional

Published
2024
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6. Plugging the Patient Evidence Gap: What Patients with Swallowing Disorders Post-Stroke Say about Thickened Liquids

Author

McCurtin, Arlene, Healy, Chiara, Kelly, Linda, Murphy, Fiona, Ryan, Jean, and Walsh, Joanne

Abstract

Background: Oropharyngeal dysphagia post-stroke is well known, with its presence increasing the risk of poor outcomes in particular aspiration and aspiration pneumonia. Management to minimize the risk of aspiration and improve swallow safety post-stroke includes the treatment of thickened liquids (TL), an established bolus modification intervention. Despite widespread use, there is a lack of robust empirical evidence and minimal patient evidence as to the experience and acceptability of using thickeners by people who experience dysphagia after a stroke. Aims: To explore people with swallowing disorders post-stroke experiences of and acceptability regarding the bolus modification treatment of thickened liquids. Methods & Procedures: A qualitative, descriptive study exploring the experiences of individuals given TL after their stroke. A purposive sample of 14 adults was obtained with data collection and generation through the medium of individual semi-structured interviews. Inductive thematic analysis was used to analyse the data. Outcomes & Results: Three overarching themes of "uncertainty," "an unpleasant experience" and "a trade-off" were identified. These themes highlight that participants disliked TL and this dislike may have impacted clinically in terms of adherence, hydration and quality of life. Lack of sensory appeal was important in framing patient dislike. Participants' involvement in and understanding of reasons for prescription of TL was poor leading to uncertainty regarding the treatment. Notwithstanding, some participants felt it was necessary for their stroke recovery. Conclusions & Implications: TL can be considered a burdensome treatment from multiple perspectives including product palatability, treatment uncertainty and treatment adherence issues. Despite intensely disliking this treatment, some patients ultimately understand why the treatment is prescribed. Improvements in product palatability are required in order to improve adherence and patient quality of life. Consideration of other treatment options and newer products to manage aspiration post-stroke is also warranted.

Published
2018
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10. Use of digital measurement of medication adherence and lung function to guide the management of uncontrolled asthma (INCA Sun): a multicentre, single-blinded, randomised clinical trial

Author

Hale, Elaine Mac, primary, Greene, Garrett, additional, Mulvey, Christopher, additional, Mokoka, Matshediso C, additional, van Boven, Job F M, additional, Cushen, Breda, additional, Sulaiman, Imran, additional, Brennan, Vincent, additional, Kerr, Patrick J, additional, Reilly, Richard B, additional, Hughes, Cian, additional, Kent, Brian D, additional, Jackson, David J, additional, Butler, Marcus, additional, Counihan, Ian, additional, Hayes, James, additional, Faul, John, additional, Kelly, Martin, additional, Convery, Rory, additional, Nanzer Kelly, Alexandra M, additional, Fitzgerald, J Mark, additional, Murphy, Desmond M, additional, Heaney, Liam G, additional, Costello, Richard W, additional, Lombard, Lorna, additional, Walsh, Joanne, additional, Plunkett, Sinead, additional, and McCartan, Thomas A, additional

Published
2023
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14. A Comparison of Coping Strategies Used by Parents of Children with Disabilities and Parents of Children without Disabilities

Author

Paster, Angela, Brandwein, David, and Walsh, Joanne

Abstract

The purpose of this study was to determine whether coping strategies differ in parents of children with disabilities and parents of children without disabilities. Participants consisted of 112 parents, including 50 parents of children with disabilities and 62 parents of children without disabilities. It was hypothesized that coping strategies would be different between the two parent groups. It was also hypothesized that parents of children with disabilities would Seek Social Support and utilize Planful Problem Solving more often than parents of children without disabilities. Coping strategies employed were significantly different between the groups. Seeking Social Support was a more commonly used method of coping among parents of children with disabilities, as was Escape Avoidance and Positive Reappraisal. (Contains 3 tables.)

Published
2009
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17. The Impact of Biofeedback on Self-Efficacy in Adults with Asthma: A Cross-Sectional Descriptive Survey

Author

Walsh,Joanne, Moore,Zena, Mac Hale,Elaine, Greene,Garrett, Doyle,Frank, Costello,Richard W, Murray,Bridget, Walsh,Joanne, Moore,Zena, Mac Hale,Elaine, Greene,Garrett, Doyle,Frank, Costello,Richard W, and Murray,Bridget

Abstract

Joanne Walsh,1,2 Zena Moore,1 Elaine Mac Hale,2 Garrett Greene,2 Frank Doyle,3 Richard W Costello,2 Bridget Murray1 1School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland; 2Clinical Research Centre, Beaumont Hospital and RCSI University of Medicine and Health Sciences, Dublin, Ireland; 3Department of Health Psychology, RCSI University of Medicine and Health Sciences, Dublin, IrelandCorrespondence: Richard W Costello, Clinical Research Centre, Beaumont Hospital and RCSI University of Medicine and Health Sciences, Dublin, Ireland, Tel +353 01-8093761, Email rcostello@rcsi.comPurpose: Educating patients to self-manage chronic diseases such as asthma is a key role for nurses. The success of this education is often limited by low patient self-efficacy. In this study, we hypothesized that the self-efficacy of patients could be enhanced if their education was based on biofeedback of their own self-management, following a nurse led educational intervention.Patients and Methods: Patients with severe and uncontrolled asthma from one centre who participated in an eight-month, nurse-led asthma education and dose adjustment Randomised Control Trial (RCT) were studied (NCT02307669). Inhaler adherence and technique of use were objectively assessed using a validated digital device. The data recorded on this device was used as the basis for the individualised biofeedback. The Asthma Self-efficacy Questionnaire was used to assess self-efficacy.Results: A total of 88 participants (44 in each group) completed the asthma self-efficacy questionnaire at the end of the study. The mean overall level of self-efficacy was high across both groups; 91 (8.7), with both biofeedback and standard care groups having similarly high levels of self-efficacy, biofeedback group: 89 (10) and standard care group 93 (6). Self-efficacy was not related to objective measures of adherence at either the start of the study, 68 (26), p=0.23, or the end of the study, 58

Published
2022

18. Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

Author

Guest, Claire, Dewhirst, Sarah Y, Lindsay, Steve W, Allen, David J, Aziz, Sophie, Baerenbold, Oliver, Bradley, John, Chabildas, Unnati, Chen-Hussey, Vanessa, Clifford, Samuel, Cottis, Luke, Dennehy, Jessica, Foley, Erin, Gezan, Salvador A, Gibson, Tim, Greaves, Courtenay K, Kleinschmidt, Immo, Lambert, Sébastien, Last, Anna, Morant, Steve, Parker, Josephine E A, Pickett, John, Quilty, Billy J, Rooney, Ann, Shah, Manil, Somerville, Mark, Squires, Chelci, Walker, Martin, Logan, James G, Jones, Robert, Assis, Ana, Borthwick, Ewan, Caton, Laura, Edwards, Rachel, Heal, Janette, Hill, David, Jahan, Nazifa, Johnson, Cecelia, Kaye, Angela, Kirkpatrick, Emily, Kisha, Sarah, Ledeatte Williams, Zaena, Moar, Robert, Owonibi, Tolulope, Purcell, Benjamin, Rixson, Christopher, Spencer, Freya, Stefanidis, Anastasios, Stewart, Sophie, Tytheridge, Scott, Wakley, Sian, Wildman, Shanice, Aziz, Catherine, Care, Helen, Curtis, Emily, Dowse, Claire, Makepeace, Alan, Oultram, Sally-Anne, Smith, Jayde, Shenton, Fiona, Hutchins, Harry, Mart, Robert, Cartwright, Jo-anne, Forsey, Miranda, Goodsell, Kerry, Kittridge, Lauren, Nicholson, Anne, Ramos, Angelo, Ritches, Joanne, Setty, Niranjan, Vertue, Mark, Bergstrom, Malin, Chaudhary, Zain, De Wilton, Angus, Gaskell, Kate, Houlihan, Catherine, Jones, Imogen, Margaritis, Marios, Miralhes, Patricia, Owens, Leah, Rampling, Tommy, Rickman, Hannah, Boffito, Marta, Fernandez, Candida, Cotterell, Bryony, Guerdette, Anne-Marie, Tsaknis, George, Turns, Margaret, Walsh, Joanne, Frankland, Lisa, West, Raha, Holland, Maureen, Keenan, Natalie, Wassall, Helen, Young, Megan, Rangeley, Jade, Saalmink, Gwendolyn, Adlakha, Sanjay, Buckley, Philip, Allsop, Lynne, Smith, Susan, Sowter, Donna, Campbell, Alison, Jones, Julie, Laird, Steve, O’Toole, Sarah, Ryan, Courteney, Evans, Jessica, Rand, James, Schumacher, Natasha, Hazelton, Tracey, Dodgson, Andrew, Glasgow, Susannah, Kadiu, Denise, Lopuszansky, Orianne, Oommen, Anu, Prabhu, Joshi, Pursell, Molly, Turner, Jane, Walton, Hollie, Andrews, Robert, Cruickshank, Irena, Thompson, Catherine, Wainwright, Tania, Roebuck, Alun, Lawrence, Tara, Netherton, Kimberley, Hewitt, Claire, Shephardson, Sarah, Crasto, Winston Andrew, Lake, Judith, Musanhu, Rosemary, Walker, Rebecca, Burns, Karen, Higham, Andrew, Le Bas, Julie, Mackenzie, Nicola, Thatcher, Hilary, Beadle, Shannen, Buckley, Sarah, Castle, Gail, Fletcher, Aimee, Holbrook, Sara, Kane, Patricia, Lindley, Kate, Lowry, Tracey, Lupton, Stephanie, Oddy, Sharon, Slater, Lynda, Sylvester, Martin, Agwuh, Kenneth, Maxwell, Veronica, Ryder, Stephen, Topham, Kirsty, Egbuniwe, Obi, Matthews, Rebecca, Arenas-Pinto, Alejandro, Prymas, Paulina, Severn, Abigail, Shaw, Amber, Begum, Safia, Lenton, Daniel, Scriven, James, Leeman, Lucy, Rudge, Karen, Storr, Emma, Alvarez, Ana, Forster, Kate, Hind, Daniel, Cook, Natalie, Peeling, Rosanna, Carey, Peter, Wilson, Anne, and Davis, Jane

Subjects
Volatile Organic Compounds, Dogs, SARS-CoV-2, fungi, Animals, COVID-19, Humans, Mass Screening, General Medicine, Asymptomatic Infections, Sensitivity and Specificity
Abstract

Background A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. Methods Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. Results About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95–100) to 100% and specificity from 99% (95% CI 97–100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76–87) to 94% (95% CI 89–98) and specificity ranging from 76% (95% CI 70–82) to 92% (95% CI 88–96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. Conclusions People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people. Trial Registration NCT04509713 (clinicaltrials.gov).

Published
2022

22. The Impact of Biofeedback on Self-efficacy in Adults with Asthma

Author

Walsh, Joanne

Subjects
Adult, Primary (Preventative) [Clinical Nursing], Biofeedback, Psychology, 111002 Clinical Nursing: Primary (Preventative), FOS: Health sciences, Preventative health care, Asthma, Health Literacy
Abstract

Objective: To examine self-efficacy among adults with asthma, following a nurse led educational intervention using biofeedback of treatment use.Method: The research design employed was a quantitative, non-experimental, cross sectional, descriptive survey. An adapted version of a previously validated closed-ended questionnaire known as the Asthma Self-Efficacy Questionnaire was used as the data collection instrument. A total of 88 participants, completed the asthma self-efficacy questionnaire once. Data was analysed using descriptive and inferential statistics. data analysis was undertaken.Results: Both study groups had highest mean attempted (82.4; SD: 21) and actual adherence (68; SD: 26) in month 1 and lowest in month 8 mean (attempted adherence: 68.9; SD: 33, mean actual adherence 57.8; SD: 32). The control group reported feeling the most confident in communicating with their healthcare provider and the intervention group felt most confident in knowing about the use of their inhalers. Despite the control group reporting the highest mean level of overall self-efficacy compared to the intervention group, they showed a statistically significant decline in mean actual adherence with a mean difference of 19.60 (p=0.003) between month 1 and month 8.Conclusion: In this study, a high level of perceived self-efficacy did not reflect actual behaviour when compared to objective measures of self-management such as adherence. The findings demonstrate that biofeedback when incorporated into asthma self-management strategies, provides a personalised and objective overview of asthma management, resulting in a more accurate assessment of self-efficacy that appears to be more representative of actual behaviour. This information can be used to deliver personalised asthma and self-management education, ensuring effective asthma management.

Published
2021
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23. The effectiveness of continuous respiratory rate monitoring in predicting hypoxic and pyrexic events: a retrospective cohort study

Author

McCartan, Thomas A, primary, Worrall, Amy P, additional, Conluain, Ruán Ó, additional, Alaya, Fátimah, additional, Mulvey, Christopher, additional, MacHale, Elaine, additional, Brennan, Vincent, additional, Lombard, Lorna, additional, Walsh, Joanne, additional, Murray, Myles, additional, Costello, Richard W, additional, and Greene, Garrett, additional

Published
2021
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24. Prevalence of Suppressed Morning Serum Cortisol and Its Relationship With Cumulative Glucocorticoid Exposure in a Moderate-Severe Asthma Patient Cohort

Author

Martin-Grace*, Julie, primary, Brennan*, Vincent, additional, Mulvey, Christopher, additional, Greene, Garrett, additional, Collier, Geraldine, additional, Cartan, Thomas Mc, additional, Lombard, Lorna, additional, Walsh, Joanne, additional, Plunkett, Sinead, additional, Hale, Elaine Mac, additional, Sherlock, Mark, additional, and Costello, Richard, additional

Published
2021
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25. Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19:The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial

Author

Angus, Derek C, Derde, Lennie, Al-Beidh, Farah, Annane, Djillali, Arabi, Yaseen, Beane, Abigail, van Bentum-Puijk, Wilma, Berry, Lindsay, Bhimani, Zahra, Bonten, Marc, Bradbury, Charlotte, Brunkhorst, Frank, Buxton, Meredith, Buzgau, Adrian, Cheng, Allen C, de Jong, Menno, Detry, Michelle, Estcourt, Lise, Fitzgerald, Mark, Goossens, Herman, Green, Cameron, Haniffa, Rashan, Higgins, Alisa M, Horvat, Christopher, Hullegie, Sebastiaan J, Kruger, Peter, Lamontagne, Francois, Lawler, Patrick R, Linstrum, Kelsey, Litton, Edward, Lorenzi, Elizabeth, Marshall, John, McAuley, Daniel, McGlothin, Anna, McGuinness, Shay, McVerry, Bryan, Montgomery, Stephanie, Mouncey, Paul, Murthy, Srinivas, Nichol, Alistair, Parke, Rachael, Parker, Jane, Rowan, Kathryn, Sanil, Ashish, Santos, Marlene, Saunders, Christina, Seymour, Christopher, Turner, Anne, van de Veerdonk, Frank, Venkatesh, Balasubramanian, Zarychanski, Ryan, Berry, Scott, Lewis, Roger J, McArthur, Colin, Webb, Steven A, Gordon, Anthony C, Writing Committee for the REMAP-CAP Investigators, Angus, Derek, Cheng, Allen, De Jong, Menno, Gordon, Anthony, Lawler, Patrick, Webb, Steve, Campbell, Lewis, Forbes, Andrew, Gattas, David, Heritier, Stephane, Higgins, Lisa, Peake, Sandra, Presneill, Jeffrey, Seppelt, Ian, Trapani, Tony, Young, Paul, Bagshaw, Sean, Daneman, Nick, Ferguson, Niall, Misak, Cheryl, Hullegie, Sebastiaan, Pletz, Mathias, Rohde, Gernot, Rowan, Kathy, Alexander, Brian, Basile, Kim, Girard, Timothy, Huang, David, Vates, Jennifer, Beasley, Richard, Fowler, Robert, McGloughlin, Steve, Morpeth, Susan, Paterson, David, Venkatesh, Bala, Uyeki, Tim, Baillie, Kenneth, Duffy, Eamon, Fowler, Rob, Hills, Thomas, Orr, Katrina, Patanwala, Asad, Tong, Steve, Netea, Mihai, Bihari, Shilesh, Carrier, Marc, Fergusson, Dean, Goligher, Ewan, Haidar, Ghady, Hunt, Beverley, Kumar, Anand, Laffan, Mike, Lawless, Patrick, Lother, Sylvain, McCallum, Peter, Middeldopr, Saskia, McQuilten, Zoe, Neal, Matthew, Pasi, John, Schutgens, Roger, Stanworth, Simon, Turgeon, Alexis, Weissman, Alexandra, Adhikari, Neill, Anstey, Matthew, Brant, Emily, de Man, Angelique, Lamonagne, Francois, Masse, Marie-Helene, Udy, Andrew, Arnold, Donald, Begin, Phillipe, Charlewood, Richard, Chasse, Michael, Coyne, Mark, Cooper, Jamie, Daly, James, Gosbell, Iain, Harvala-Simmonds, Heli, Hills, Tom, MacLennan, Sheila, Menon, David, McDyer, John, Pridee, Nicole, Roberts, David, Shankar-Hari, Manu, Thomas, Helen, Tinmouth, Alan, Triulzi, Darrell, Walsh, Tim, Wood, Erica, Calfee, Carolyn, O’Kane, Cecilia, Shyamsundar, Murali, Sinha, Pratik, Thompson, Taylor, Young, Ian, Bihari, Shailesh, Hodgson, Carol, Laffey, John, McAuley, Danny, Orford, Neil, Neto, Ary, Lewis, Roger, McGlothlin, Anna, Miller, Eliza, Singh, Vanessa, Zammit, Claire, van Bentum Puijk, Wilma, Bouwman, Wietske, Mangindaan, Yara, Parker, Lorraine, Peters, Svenja, Rietveld, Ilse, Raymakers, Kik, Ganpat, Radhika, Brillinger, Nicole, Markgraf, Rene, Ainscough, Kate, Brickell, Kathy, Anjum, Aisha, Lane, Janis-Best, Richards-Belle, Alvin, Saull, Michelle, Wiley, Daisy, Bion, Julian, Connor, Jason, Gates, Simon, Manax, Victoria, van der Poll, Tom, Reynolds, John, van Beurden, Marloes, Effelaar, Evelien, Schotsman, Joost, Boyd, Craig, Harland, Cain, Shearer, Audrey, Wren, Jess, Clermont, Giles, Garrard, William, Kalchthaler, Kyle, King, Andrew, Ricketts, Daniel, Malakoutis, Salim, Marroquin, Oscar, Music, Edvin, Quinn, Kevin, Cate, Heidi, Pearson, Karen, Collins, Joanne, Hanson, Jane, Williams, Penny, Jackson, Shane, Asghar, Adeeba, Dyas, Sarah, Sutu, Mihaela, Murphy, Sheenagh, Williamson, Dawn, Mguni, Nhlanhla, Potter, Alison, Porter, David, Goodwin, Jayne, Rook, Clare, Harrison, Susie, Williams, Hannah, Campbell, Hilary, Lomme, Kaatje, Williamson, James, Sheffield, Jonathan, van’t Hoff, Willian, McCracken, Phobe, Young, Meredith, Board, Jasmin, Mart, Emma, Knott, Cameron, Smith, Julie, Boschert, Catherine, Affleck, Julia, Ramanan, Mahesh, D’Souza, Ramsy, Pateman, Kelsey, Shakih, Arif, Cheung, Winston, Kol, Mark, Wong, Helen, Shah, Asim, Wagh, Atul, Simpson, Joanne, Duke, Graeme, Chan, Peter, Cartner, Brittney, Hunter, Stephanie, Laver, Russell, Shrestha, Tapaswi, Regli, Adrian, Pellicano, Annamaria, McCullough, James, Tallott, Mandy, Kumar, Nikhil, Panwar, Rakshit, Brinkerhoff, Gail, Koppen, Cassandra, Cazzola, Federica, Brain, Matthew, Mineall, Sarah, Fischer, Roy, Biradar, Vishwanath, Soar, Natalie, White, Hayden, Estensen, Kristen, Morrison, Lynette, Smith, Joanne, Cooper, Melanie, Health, Monash, Shehabi, Yahya, Al-Bassam, Wisam, Hulley, Amanda, Whitehead, Christina, Lowrey, Julie, Gresha, Rebecca, Walsham, James, Meyer, Jason, Harward, Meg, Venz, Ellen, Williams, Patricia, Kurenda, Catherine, Smith, Kirsy, Smith, Margaret, Garcia, Rebecca, Barge, Deborah, Byrne, Deborah, Byrne, Kathleen, Driscoll, Alana, Fortune, Louise, Janin, Pierre, Yarad, Elizabeth, Hammond, Naomi, Bass, Frances, Ashelford, Angela, Waterson, Sharon, Wedd, Steve, McNamara, Robert, Buhr, Heidi, Coles, Jennifer, Schweikert, Sacha, Wibrow, Bradley, Rauniyar, Rashmi, Myers, Erina, Fysh, Ed, Dawda, Ashlish, Mevavala, Bhaumik, Litton, Ed, Ferrier, Janet, Nair, Priya, Buscher, Hergen, Reynolds, Claire, Santamaria, John, Barbazza, Leanne, Homes, Jennifer, Smith, Roger, Murray, Lauren, Brailsford, Jane, Forbes, Loretta, Maguire, Teena, Mariappa, Vasanth, Smith, Judith, Simpson, Scott, Maiden, Matthew, Bone, Allsion, Horton, Michelle, Salerno, Tania, Sterba, Martin, Geng, Wenli, Depuydt, Pieter, De Waele, Jan, De Bus, Liesbet, Fierens, Jan, Bracke, Stephanie, Reeve, Brenda, Dechert, William, Chassé, Michaël, Carrier, François Martin, Boumahni, Dounia, Benettaib, Fatna, Ghamraoui, Ali, Bellemare, David, Cloutier, Ève, Francoeur, Charles, Lamontagne, François, D’Aragon, Frédérick, Carbonneau, Elaine, Leblond, Julie, Vazquez-Grande, Gloria, Marten, Nicole, Wilson, Maggie, Albert, Martin, Serri, Karim, Cavayas, Alexandros, Duplaix, Mathilde, Williams, Virginie, Rochwerg, Bram, Karachi, Tim, Oczkowski, Simon, Centofanti, John, Millen, Tina, Duan, Erick, Tsang, Jennifer, Patterson, Lisa, English, Shane, Watpool, Irene, Porteous, Rebecca, Miezitis, Sydney, McIntyre, Lauralyn, Brochard, Laurent, Burns, Karen, Sandhu, Gyan, Khalid, Imrana, Binnie, Alexandra, Powell, Elizabeth, McMillan, Alexandra, Luk, Tracy, Aref, Noah, Andric, Zdravko, Cviljevic, Sabina, Đimoti, Renata, Zapalac, Marija, Mirković, Gordan, Baršić, Bruno, Kutleša, Marko, Kotarski, Viktor, Vujaklija Brajković, Ana, Babel, Jakša, Sever, Helena, Dragija, Lidija, Kušan, Ira, Vaara, Suvi, Pettilä, Leena, Heinonen, Jonna, Kuitunen, Anne, Karlsson, Sari, Vahtera, Annukka, Kiiski, Heikki, Ristimäki, Sanna, Azaiz, Amine, Charron, Cyril, Godement, Mathieu, Geri, Guillaume, Vieillard-Baron, Antoine, Pourcine, Franck, Monchi, Mehran, Luis, David, Mercier, Romain, Sagnier, Anne, Verrier, Nathalie, Caplin, Cecile, Siami, Shidasp, Aparicio, Christelle, Vautier, Sarah, Jeblaoui, Asma, Fartoukh, Muriel, Courtin, Laura, Labbe, Vincent, Leparco, Cécile, Muller, Grégoire, Nay, Mai-Anh, Kamel, Toufik, Benzekri, Dalila, Jacquier, Sophie, Mercier, Emmanuelle, Chartier, Delphine, Salmon, Charlotte, Dequin, PierreFrançois, Schneider, Francis, Morel, Guillaume, L’Hotellier, Sylvie, Badie, Julio, Berdaguer, Fernando Daniel, Malfroy, Sylvain, Mezher, Chaouki, Bourgoin, Charlotte, Megarbane, Bruno, Voicu, Sebastian, Deye, Nicolas, Malissin, Isabelle, Sutterlin, Laetitia, Guitton, Christophe, Darreau, Cédric, Landais, Mickaël, Chudeau, Nicolas, Robert, Alain, Moine, Pierre, Heming, Nicholas, Maxime, Virginie, Bossard, Isabelle, Nicholier, Tiphaine Barbarin, Colin, Gwenhael, Zinzoni, Vanessa, Maquigneau, Natacham, Finn, André, Kreß, Gabriele, Hoff, Uwe, Friedrich Hinrichs, Carl, Nee, Jens, Hagel, Stefan, Ankert, Juliane, Kolanos, Steffi, Bloos, Frank, Petros, Sirak, Pasieka, Bastian, Kunz, Kevin, Appelt, Peter, Schütze, Bianka, Kluge, Stefan, Nierhaus, Axel, Jarczak, Dominik, Roedl, Kevin, Weismann, Dirk, Frey, Anna, Klinikum Neukölln, Vivantes, Reill, Lorenz, Distler, Michael, Maselli, Astrid, Bélteczki, János, Magyar, István, Fazekas, Ágnes, Kovács, Sándor, Szőke, Viktória, Szigligeti, Gábor, Leszkoven, János, Collins, Daniel, Breen, Patrick, Frohlich, Stephen, Whelan, Ruth, McNicholas, Bairbre, Scully, Michael, Casey, Siobhan, Kernan, Maeve, Doran, Peter, O’Dywer, Michael, Smyth, Michelle, Hayes, Leanne, Hoiting, Oscar, Peters, Marco, Rengers, Els, Evers, Mirjam, Prinssen, Anton, Bosch Ziekenhuis, Jeroen, Simons, Koen, Rozendaal, Wim, Polderman, F, de Jager, P, Moviat, M, Paling, A, Salet, A, Rademaker, Emma, Peters, Anna Linda, de Jonge, E, Wigbers, J, Guilder, E, Butler, M, Cowdrey, Keri-Anne, Newby, Lynette, Chen, Yan, Simmonds, Catherine, McConnochie, Rachael, Ritzema Carter, Jay, Henderson, Seton, Van Der Heyden, Kym, Mehrtens, Jan, Williams, Tony, Kazemi, Alex, Song, Rima, Lai, Vivian, Girijadevi, Dinu, Everitt, Robert, Russell, Robert, Hacking, Danielle, Buehner, Ulrike, Williams, Erin, Browne, Troy, Grimwade, Kate, Goodson, Jennifer, Keet, Owen, Callender, Owen, Martynoga, Robert, Trask, Kara, Butler, Amelia, Schischka, Livia, Young, Chelsea, Lesona, Eden, Olatunji, Shaanti, Robertson, Yvonne, José, Nuno, Amaro dos Santos Catorze, Teodoro, de Lima Pereira, Tiago Nuno Alfaro, Neves Pessoa, Lucilia Maria, Castro Ferreira, Ricardo Manuel, Pereira Sousa Bastos, Joana Margarida, Aysel Florescu, Simin, Stanciu, Delia, Zaharia, Miahela Florentina, Kosa, Alma Gabriela, Codreanu, Daniel, Marabi, Yaseen, Al Qasim, Eman, Moneer Hagazy, Mohamned, Al Swaidan, Lolowa, Arishi, Hatim, Muñoz-Bermúdez, Rosana, Marin-Corral, Judith, Salazar Degracia, Anna, Parrilla Gómez, Francisco, Mateo López, Maria Isabel, Rodriguez Fernandez, Jorge, Cárcel Fernández, Sheila, Carmona Flores, Rosario, León López, Rafael, de la Fuente Martos, Carmen, Allan, Angela, Polgarova, Petra, Farahi, Neda, McWilliam, Stephen, Hawcutt, Daniel, Rad, Laura, O’Malley, Laura, Whitbread, Jennifer, Kelsall, Olivia, Wild, Laura, Thrush, Jessica, Wood, Hannah, Austin, Karen, Donnelly, Adrian, Kelly, Martin, O’Kane, Sinéad, McClintock, Declan, Warnock, Majella, Johnston, Paul, Gallagher, Linda Jude, Mc Goldrick, Clare, Mc Master, Moyra, Strzelecka, Anna, Jha, Rajeev, Kalogirou, Michael, Ellis, Christine, Krishnamurthy, Vinodh, Deelchand, Vashish, Silversides, Jon, McGuigan, Peter, Ward, Kathryn, O’Neill, Aisling, Finn, Stephanie, Phillips, Barbara, Mullan, Dee, Oritz-Ruiz de Gordoa, Laura, Thomas, Matthew, Sweet, Katie, Grimmer, Lisa, Johnson, Rebekah, Pinnell, Jez, Robinson, Matt, Gledhill, Lisa, Wood, Tracy, Morgan, Matt, Cole, Jade, Hill, Helen, Davies, Michelle, Antcliffe, David, Templeton, Maie, Rojo, Roceld, Coghlan, Phoebe, Smee, Joanna, Mackay, Euan, Cort, Jon, Whileman, Amanda, Spencer, Thomas, Spittle, Nick, Kasipandian, Vidya, Patel, Amit, Allibone, Suzanne, Genetu, Roman Mary, Ramali, Mohamed, Ghosh, Alison, Bamford, Peter, London, Emily, Cawley, Kathryn, Faulkner, Maria, Jeffrey, Helen, Smith, Tim, Brewer, Chris, Gregory, Jane, Limb, James, Cowton, Amanda, O’Brien, Julie, Nikitas, Nikitas, Wells, Colin, Lankester, Liana, Pulletz, Mark, Birch, Jenny, Wiseman, Sophie, Horton, Sarah, Alegria, Ana, Turki, Salah, Elsefi, Tarek, Crisp, Nikki, Allen, Louise, McCullagh, Iain, Robinson, Philip, Hays, Carole, Babio-Galan, Maite, Stevenson, Hannah, Khare, Divya, Pinder, Meredith, Selvamoni, Selvin, Gopinath, Amitha, Pugh, Richard, Menzies, Daniel, Mackay, Callum, Allan, Elizabeth, Davies, Gwyneth, Puxty, Kathryn, McCue, Claire, Cathcart, Susanne, Hickey, Naomi, Ireland, Jane, Yusuff, Hakeem, Isgro, Graziella, Brightling, Chris, Bourne, Michelle, Craner, Michelle, Watters, Malcolm, Prout, Rachel, Davies, Louisa, Pegler, Suzannah, Kyeremeh, Lynsey, Arbane, Gill, Wilson, Karen, Gomm, Linda, Francia, Federica, Brett, Stephen, Sousa Arias, Sonia, Elin Hall, Rebecca, Budd, Joanna, Small, Charlotte, Birch, Janine, Collins, Emma, Henning, Jeremy, Bonner, Stephen, Hugill, Keith, Cirstea, Emanuel, Wilkinson, Dean, Karlikowski, Michal, Sutherland, Helen, Wilhelmsen, Elva, Woods, Jane, North, Julie, Sundaran, Dhinesh, Hollos, Laszlo, Coburn, Susan, Walsh, Joanne, Turns, Margaret, Hopkins, Phil, Smith, John, Noble, Harriet, Depante, Maria Theresa, Clarey, Emma, Laha, Shondipon, Verlander, Mark, Williams, Alexandra, Huckle, Abby, Hall, Andrew, Cooke, Jill, Gardiner-Hill, Caroline, Maloney, Carolyn, Qureshi, Hafiz, Flint, Neil, Nicholson, Sarah, Southin, Sara, Nicholson, Andrew, Borgatta, Barbara, Turner-Bone, Ian, Reddy, Amie, Wilding, Laura, Chamara Warnapura, Loku, Agno Sathianathan, Ronan, Golden, David, Hart, Ciaran, Jones, Jo, Bannard-Smith, Jonathan, Henry, Joanne, Birchall, Katie, Pomeroy, Fiona, Quayle, Rachael, Makowski, Arystarch, Misztal, Beata, Ahmed, Iram, KyereDiabour, Thyra, Naiker, Kevin, Stewart, Richard, Mwaura, Esther, Mew, Louise, Wren, Lynn, Willams, Felicity, Innes, Richard, Doble, Patricia, Hutter, Joanne, Shovelton, Charmaine, Plumb, Benjamin, Szakmany, Tamas, Hamlyn, Vincent, Hawkins, Nancy, Lewis, Sarah, Dell, Amanda, Gopal, Shameer, Ganguly, Saibal, Smallwood, Andrew, Harris, Nichola, Metherell, Stella, Lazaro, Juan Martin, Newman, Tabitha, Fletcher, Simon, Nortje, Jurgens, Fottrell-Gould, Deirdre, Randell, Georgina, Zaman, Mohsin, Elmahi, Einas, Jones, Andrea, Hall, Kathryn, Mills, Gary, Ryalls, Kim, Bowler, Helen, Sall, Jas, Bourne, Richard, Borrill, Zoe, Duncan, Tracey, Lamb, Thomas, Shaw, Joanne, Fox, Claire, Moreno Cuesta, Jeronimo, Xavier, Kugan, Purohit, Dharam, Elhassan, Munzir, Bakthavatsalam, Dhanalakshmi, Rowland, Matthew, Hutton, Paula, Bashyal, Archana, Davidson, Neil, Hird, Clare, Chhablani, Manish, Phalod, Gunjan, Kirkby, Amy, Archer, Simon, Netherton, Kimberley, Reschreiter, Henrik, Camsooksai, Julie, Patch, Sarah, Jenkins, Sarah, Pogson, David, Rose, Steve, Daly, Zoe, Brimfield, Lutece, Claridge, Helen, Parekh, Dhruv, Bergin, Colin, Bates, Michelle, Dasgin, Joanne, McGhee, Christopher, Sim, Malcolm, Hay, Sophie Kennedy, Henderson, Steven, Phull, Mandeep-Kaur, Zaidi, Abbas, Pogreban, Tatiana, Rosaroso, Lace Paulyn, Harvey, Daniel, Lowe, Benjamin, Meredith, Megan, Ryan, Lucy, Hormis, Anil, Walker, Rachel, Collier, Dawn, Kimpton, Sarah, Oakley, Susan, Rooney, Kevin, Rodden, Natalie, Hughes, Emma, Thomson, Nicola, McGlynn, Deborah, Walden, Andrew, Jacques, Nicola, Coles, Holly, Tilney, Emma, Vowell, Emma, Schuster-Bruce, Martin, Pitts, Sally, Miln, Rebecca, Purandare, Laura, Vamplew, Luke, Spivey, Michael, Bean, Sarah, Burt, Karen, Moore, Lorraine, Day, Christopher, Gibson, Charly, Gordon, Elizabeth, Zitter, Letizia, Keenan, Samantha, Baker, Evelyn, Cherian, Shiney, Cutler, Sean, Roynon-Reed, Anna, Harrington, Kate, Raithatha, Ajay, Bauchmuller, Kris, Ahmad, Norfaizan, Grecu, Irina, Trodd, Dawn, Martin, Jane, Wrey Brown, Caroline, Arias, Ana-Marie, Craven, Thomas, Hope, David, Singleton, Jo, Clark, Sarah, Rae, Nicola, Welters, Ingeborg, Hamilton, David Oliver, Williams, Karen, Waugh, Victoria, Shaw, David, Puthucheary, Zudin, Martin, Timothy, Santos, Filipa, Uddin, Ruzena, Somerville, Alastair, Tatham, Kate Colette, Jhanji, Shaman, Black, Ethel, Dela Rosa, Arnold, Howle, Ryan, Tully, Redmond, Drummond, Andrew, Dearden, Joy, Philbin, Jennifer, Munt, Sheila, Vuylsteke, Alain, Chan, Charles, Victor, Saji, Matsa, Ramprasad, Gellamucho, Minerva, Creagh-Brown, Ben, Tooley, Joe, Montague, Laura, De Beaux, Fiona, Bullman, Laetitia, Kersiake, Ian, Demetriou, Carrie, Mitchard, Sarah, Ramos, Lidia, White, Katie, Donnison, Phil, Johns, Maggie, Casey, Ruth, Mattocks, Lehentha, Salisbury, Sarah, Dark, Paul, Claxton, Andrew, McLachlan, Danielle, Slevin, Kathryn, Lee, Stephanie, Hulme, Jonathan, Joseph, Sibet, Kinney, Fiona, Senya, Ho Jan, Oborska, Aneta, Kayani, Abdul, Hadebe, Bernard, Orath Prabakaran, Rajalakshmi, Nichols, Lesley, Thomas, Matt, Worner, Ruth, Faulkner, Beverley, Gendall, Emma, Hayes, Kati, Hamilton-Davies, Colin, Chan, Carmen, Mfuko, Celina, Abbass, Hakam, Mandadapu, Vineela, Leaver, Susannah, Forton, Daniel, Patel, Kamal, Paramasivam, Elankumaran, Powell, Matthew, Gould, Richard, Wilby, Elizabeth, Howcroft, Clare, Banach, Dorota, Fernández de Pinedo Artaraz, Ziortza, Cabreros, Leilani, White, Ian, Croft, Maria, Holland, Nicky, Pereira, Rita, Zaki, Ahmed, Johnson, David, Jackson, Matthew, Garrard, Hywel, Juhaz, Vera, Roy, Alistair, Rostron, Anthony, Woods, Lindsey, Cornell, Sarah, Pillai, Suresh, Harford, Rachel, Rees, Tabitha, Ivatt, Helen, Sundara Raman, Ajay, Davey, Miriam, Lee, Kelvin, Barber, Russell, Chablani, Manish, Brohi, Farooq, Jagannathan, Vijay, Clark, Michele, Purvis, Sarah, Wetherill, Bill, Dushianthan, Ahilanandan, Cusack, Rebecca, de Courcy-Golder, Kim, Smith, Simon, Jackson, Susan, Attwood, Ben, Parsons, Penny, Page, Valerie, Zhao, Xiao Bei, Oza, Deepali, Rhodes, Jonathan, Anderson, Tom, Morris, Sheila, Xia Le Tai, Charlotte, Thomas, Amy, Keen, Alexandra, Digby, Stephen, Cowley, Nicholas, Southern, David, Reddy, Harsha, Campbell, Andy, Watkins, Claire, Smuts, Sara, Touma, Omar, Barnes, Nicky, Alexander, Peter, Felton, Tim, Ferguson, Susan, Sellers, Katharine, Bradley-Potts, Joanne, Yates, David, Birkinshaw, Isobel, Kell, Kay, Marshall, Nicola, Carr-Knott, Lisa, Writing Committee for the REMAP-CAP Investigators, Menon, David [0000-0002-3228-9692], Apollo - University of Cambridge Repository, Medical Microbiology and Infection Prevention, and AII - Infectious diseases

Subjects
Male, Hydrocortisone, Anti-Inflammatory Agents, lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], 01 natural sciences, law.invention, 0302 clinical medicine, Randomized controlled trial, law, Adrenal Cortex Hormones, Clinical endpoint, Medicine, 030212 general & internal medicine, Hydrocortisone/administration & dosage, Original Investigation, 2. Zero hunger, Mortality rate, Shock, Covid19, General Medicine, Middle Aged, Intensive care unit, 3. Good health, Intensive Care Units, Treatment Outcome, Early Termination of Clinical Trials, Corticosteroid, Female, Coronavirus Infections, medicine.drug, Adult, medicine.medical_specialty, Respiration, Artificial/statistics & numerical data, medicine.drug_class, Anti-Inflammatory Agents/administration & dosage, Pneumonia, Viral, UNCOVER, Adrenal Cortex Hormones/therapeutic use, 03 medical and health sciences, Betacoronavirus, All institutes and research themes of the Radboud University Medical Center, Internal medicine, Humans, 0101 mathematics, Adverse effect, Pandemics, business.industry, SARS-CoV-2, 010102 general mathematics, COVID-19, Odds ratio, Coronavirus Infections/drug therapy, Pneumonia, Viral/drug therapy, Respiration, Artificial, COVID-19 Drug Treatment, Shock/drug therapy, Human medicine, business
Abstract

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited.Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19.Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020.Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108).Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%).Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively.Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions.Trial Registration: ClinicalTrials.gov Identifier: NCT02735707.

Published
2020
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27. Cytosponge-trefoil factor 3 versus usual care to identify Barrett's oesophagus in a primary care setting: a multicentre, pragmatic, randomised controlled trial

Author

Fitzgerald, Rebecca C, primary, di Pietro, Massimiliano, additional, O'Donovan, Maria, additional, Maroni, Roberta, additional, Muldrew, Beth, additional, Debiram-Beecham, Irene, additional, Gehrung, Marcel, additional, Offman, Judith, additional, Tripathi, Monika, additional, Smith, Samuel G, additional, Aigret, Benoit, additional, Walter, Fiona M, additional, Rubin, Greg, additional, Sasieni, Peter, additional, Bagewadi, Abhay, additional, Patrick, Abigail, additional, Shenoy, Achuth, additional, Redmond, Aisling, additional, Muddu, Ajay, additional, Northrop, Alex, additional, Groves, Alice, additional, Shiner, Alice, additional, Heer, Amardeep, additional, Takhar, Amrit, additional, Bowles, Amy, additional, Jarman, Andrea, additional, Wong, Angela, additional, Lucas, Angie, additional, Gibbons, Anita, additional, Dhar, Anjan, additional, Curry, Anji, additional, Lalonde, Anna, additional, Swinburn, Anna, additional, Turner, Anne, additional, Lydon, Anne-Marie, additional, Gunstone, Anthony, additional, Lee, Arlene, additional, Nambi, Arul, additional, Ariyarathenam, Arun, additional, Elden, Ashley, additional, Wilson, Ashley, additional, Donepudi, Balaji, additional, Campbell, Barbara, additional, Uszycka, Basia, additional, Bowers, Ben, additional, Coghill, Ben, additional, de Quadros, Bruno, additional, Cheah, Calvin, additional, Bratten, Carla, additional, Brown, Carly, additional, Moorbey, Chantelle, additional, Clisby, Charles, additional, Gordon, Charles, additional, Schramm, Chris, additional, Castle, Chris, additional, Newark, Chris, additional, Norris, Chrissie, additional, A'Court, Christine, additional, Graham, Claire, additional, Fletcher, Clare, additional, Grocott, Clare, additional, Rees, Colin, additional, Bakker, Corinne, additional, Paschalides, Costas, additional, Vickery, Craig, additional, Schembri, Damian, additional, Morris, Danielle, additional, Hagan, Daryl, additional, Cronk, David, additional, Goddard, David, additional, Graham, David, additional, Phillips, Dean, additional, Prabhu, Deeksha, additional, Kejariwal, Deepak, additional, Garg, Dhirendra, additional, Lonsdale, Diane, additional, Butterworth, Dianne, additional, Clements, Donna, additional, Bradman, Drew, additional, Blake, Duncan, additional, Mather, Elizabeth, additional, O'Farrell, Ewan, additional, Markowetz, Florian, additional, Adams, Fran, additional, Pesola, Francesca, additional, Forbes, Gareth, additional, Taylor, Gary, additional, Collins, Glenn, additional, Irvine, Gordon, additional, Fourie, Gysbert, additional, Doyle, Harriet, additional, Barnes, Heather, additional, Bowyer, Helen, additional, Whiting, Helen, additional, Beales, Ian, additional, Binnian, Ian, additional, Bremner, Ian, additional, Jennings, Ian, additional, Troiceanu, Ilona, additional, Modelell, Ines, additional, Emmerson, Ingrid, additional, Ortiz, Jacobo, additional, Lilley, Jacqueline, additional, Harvey, Jacquelyn, additional, Vicars, Jacqui, additional, Takhar, Jagjit, additional, Larcombe, James, additional, Bornschein, Jan, additional, Aldegather, Jehad, additional, Johnson, Jenny, additional, Ducker, Jill, additional, Skinner, Jo, additional, Dash, Joanne, additional, Walsh, Joanne, additional, Miralles, Jose, additional, Ridgway, Josephine, additional, Ince, Julia, additional, Kennedy, Julie, additional, Hampson, Kat, additional, Milne, Kate, additional, Ellerby, Katherine, additional, Priddis, Katherine, additional, Rainsbury, Kathy, additional, Powell, Kelly, additional, Gunner, Kerry, additional, Ragunath, Krish, additional, Knox, Kyle, additional, Baseley, Laura, additional, White, Lauren, additional, Lovat, Laurence, additional, Berney, Lee, additional, Crockett, Lindsay, additional, Murray, Lisa, additional, Westwood, Lisa, additional, Chalkley, Lisa, additional, Leggett, Loraine, additional, Dale, Louise, additional, Scovell, Louise, additional, Brooks, Lucy, additional, Saunders, Lucy, additional, Owen, Lydia, additional, Dilwershah, Maria, additional, Baldry, Marie, additional, Corcoran, Marie, additional, Roy, Marie, additional, Macedo, Mario, additional, Attah, Mark, additional, Anson, Mary-Jo, additional, Rutter, Matt, additional, Wallard, Matthew, additional, Gaw, Matthew, additional, Hunt, Matthew, additional, Lea-Hagerty, Megan, additional, Penacerrada, Melchizedek, additional, Bianchi, Michele, additional, Baker-Moffatt, Michelle, additional, Czajkowski, Michelle, additional, Sleeth, Michelle, additional, Brewer, Nick, additional, Wooding, Nick, additional, Todd, Nicky, additional, Millen, Nicola, additional, Zolle, Olga, additional, Whitehead, Orla, additional, Ojechi, Patrick, additional, Moore, Patrick, additional, Banim, Paul, additional, Spellar, Paula, additional, Bhandari, Pradeep, additional, Kant, Prashant, additional, Nixon, Rachel, additional, Russell, Rebecca, additional, Roberts, Rebekah, additional, Skule, Rene, additional, West, Richard, additional, Fox, Robin, additional, Beesley, Ruth, additional, Gibbins, Ruth, additional, Osborne, Ruth, additional, Thiagarajan, S, additional, Bastiman, Sally, additional, Warburton, Samantha, additional, Pai, Samir, additional, Leith-Russell, Sarah, additional, Utting, Sarah, additional, Watson, Sarah, additional, Wytrykowski, Sarah, additional, Singh, Satish, additional, Malhotra, Shalini, additional, Woods, Sharon, additional, Conway, Shaun, additional, Mateer, Sherrie, additional, Macrae, Shona, additional, Singh, Shruti, additional, Fourie, Simona, additional, Campbell, Siobhan, additional, Parslow-Williams, Siobhan, additional, Goel, Sonica, additional, Dellar, Stephen, additional, Jones, Stephen, additional, Knight, Steve, additional, Mackay-Thomas, Stuart, additional, Mukherjee, Stuti, additional, Allen, Sue, additional, Henry, Suzanne, additional, Evans, Tara, additional, Leighton, Theresa, additional, Bray, Tim, additional, Shackleton, Tom, additional, Santosh, Vanaja, additional, Glover, Vicki, additional, Chandraraj, Vijay, additional, Elson, Will, additional, Briggs, William, additional, Barron, Zoe, additional, and Khan, Zohrah, additional

Published
2020
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28. Aeroelastic Sizing for High-Speed Research (HSR) Longitudinal Control Alternatives Project (LCAP)

Author

Walsh, Joanne L, Dunn, H. J, Stroud, W. Jefferson, Barthelemy, J.-F, Weston, Robert P, Martin, Carl J, and Bennett, Robert M

Subjects
Aircraft Design, Testing And Performance
Abstract

The Longitudinal Control Alternatives Project (LCAP) compared three high-speed civil transport configurations to determine potential advantages of the three associated longitudinal control concepts. The three aircraft configurations included a conventional configuration with a layout having a horizontal aft tail, a configuration with a forward canard in addition to a horizontal aft tail, and a configuration with only a forward canard. The three configurations were aeroelastically sized and were compared on the basis of operational empty weight (OEW) and longitudinal control characteristics. The sized structure consisted of composite honeycomb sandwich panels on both the wing and the fuselage. Design variables were the core depth of the sandwich and the thicknesses of the composite material which made up the face sheets of the sandwich. Each configuration was sized for minimum structural weight under linear and nonlinear aeroelastic loads subject to strain, buckling, ply-mixture, and subsonic and supersonic flutter constraints. This report describes the methods that were used and the results that were generated for the aeroelastic sizing of the three configurations.

Published
2005

29. Optimizing tuning masses for helicopter rotor blade vibration reduction and comparison with test data

Author

Pritchard, Jocelyn I., Adelman, Howard M., Walsh, Joanne L., and Wilbur, Matthew L.

Subjects
Rotors (Helicopters) -- Aerodynamics, Tuning (Electronics) -- Analysis, Vibration -- Control, Aerospace and defense industries, Business, Science and technology
Abstract

An optimization methodology was developed to methodically position tuning masses along a rotor blade span and to reduce vibratory response. The methodology was used with a one-sixth Mach-scaled rotor blade model, with the positioning of three masses and six masses in different instances. The 3d, 4th and 5th harmonics of shear between 4-8% were lowered by the three-mass system, and the shear harmonics between 24-34% were lowered by the optimized six-mass system.

Published
1993

30. Stacking-sequence optimization for buckling of laminated plates by integer programming

Author

Haftka, Rafael T. and Walsh, Joanne L.

Subjects
Plates (Engineering) -- Research, Composite materials -- Research, Airframes -- Research, Aerospace and defense industries, Business
Abstract

The use of a standard integer-programming technique in stacking-sequence designing of composite laminated plates is discussed. The buckling characteristics of graphite/epoxy laminates having constant thickness and subjected to uniaxial and biaxial loadings were evaluated using the LINDO computer program and ply-orientation-identity design variables. Results indicated that the technique is capable of treating stacking-sequence design problems as a simple linear formula.

Published
1992

31. MOESM1 of An update to the Milk Allergy in Primary Care guideline

Author

Fox, Adam, Brown, Trevor, Walsh, Joanne, Venter, Carina, Meyer, Rosan, Nowak-Wegrzyn, Anna, Levin, Michael, Spawls, Hannah, Beatson, Jolene, Marie-Therese Lovis, Vieira, Mario, and Fleischer, David

Abstract

Additional file 1. Initial fact sheet for infants with symptoms of a possible mild to moderate non-IgE mediated allergy whilst being exclusively or partly breastfed.

Published
2019
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32. Sensitivity of Lumped Constraints Using the Adjoint Method

Author

Akgun, Mehmet A, Haftka, Raphael T, Wu, K. Chauncey, and Walsh, Joanne L

Subjects
Aircraft Design, Testing And Performance
Abstract

Adjoint sensitivity calculation of stress, buckling and displacement constraints may be much less expensive than direct sensitivity calculation when the number of load cases is large. Adjoint stress and displacement sensitivities are available in the literature. Expressions for local buckling sensitivity of isotropic plate elements are derived in this study. Computational efficiency of the adjoint method is sensitive to the number of constraints and, therefore, the method benefits from constraint lumping. A continuum version of the Kreisselmeier-Steinhauser (KS) function is chosen to lump constraints. The adjoint and direct methods are compared for three examples: a truss structure, a simple HSCT wing model, and a large HSCT model. These sensitivity derivatives are then used in optimization.

Published
1999

33. Optimization Issues with Complex Rotorcraft Comprehensive Analysis

Author

Walsh, Joanne L, Young, Katherine C, Tarzanin, Frank J, Hirsh, Joel E, and Young, Darrell K

Subjects
Computer Programming And Software
Abstract

This paper investigates the use of the general purpose automatic differentiation (AD) tool called Automatic Differentiation of FORTRAN (ADIFOR) as a means of generating sensitivity derivatives for use in Boeing Helicopter's proprietary comprehensive rotor analysis code (VII). ADIFOR transforms an existing computer program into a new program that performs a sensitivity analysis in addition to the original analysis. In this study both the pros (exact derivatives, no step-size problems) and cons (more CPU, more memory) of ADIFOR are discussed. The size (based on the number of lines) of the VII code after ADIFOR processing increased by 70 percent and resulted in substantial computer memory requirements at execution. The ADIFOR derivatives took about 75 percent longer to compute than the finite-difference derivatives. However, the ADIFOR derivatives are exact and are not functions of step-size. The VII sensitivity derivatives generated by ADIFOR are compared with finite-difference derivatives. The ADIFOR and finite-difference derivatives are used in three optimization schemes to solve a low vibration rotor design problem.

Published
1998

34. Application of response surface techniques to helicopter rotor blade optimization procedure

Author

Henderson, Joseph Lynn, Walsh, Joanne L, and Young, Katherine C

Subjects
Aircraft Design, Testing And Performance
Abstract

In multidisciplinary optimization problems, response surface techniques can be used to replace the complex analyses that define the objective function and/or constraints with simple functions, typically polynomials. In this work a response surface is applied to the design optimization of a helicopter rotor blade. In previous work, this problem has been formulated with a multilevel approach. Here, the response surface takes advantage of this decomposition and is used to replace the lower level, a structural optimization of the blade. Problems that were encountered and important considerations in applying the response surface are discussed. Preliminary results are also presented that illustrate the benefits of using the response surface.

Published
1995

35. Automatic differentiation evaluated as a tool for rotorcraft design and optimization

Author

Walsh, Joanne L and Young, Katherine C

Subjects
Aircraft Design, Testing And Performance
Abstract

This paper investigates the use of automatic differentiation (AD) as a means for generating sensitivity analyses in rotorcraft design and optimization. This technique transforms an existing computer program into a new program that performs sensitivity analysis in addition to the original analysis. The original FORTRAN program calculates a set of dependent (output) variables from a set of independent (input) variables, the new FORTRAN program calculates the partial derivatives of the dependent variables with respect to the independent variables. The AD technique is a systematic implementation of the chain rule of differentiation, this method produces derivatives to machine accuracy at a cost that is comparable with that of finite-differencing methods. For this study, an analysis code that consists of the Langley-developed hover analysis HOVT, the comprehensive rotor analysis CAMRAD/JA, and associated preprocessors is processed through the AD preprocessor ADIFOR 2.0. The resulting derivatives are compared with derivatives obtained from finite-differencing techniques. The derivatives obtained with ADIFOR 2.0 are exact within machine accuracy and do not depend on the selection of step-size, as are the derivatives obtained with finite-differencing techniques.

Published
1995

36. Integrated aerodynamic/dynamic/structural optimization of helicopter rotor blades using multilevel decomposition

Author

Walsh, Joanne L, Young, Katherine C, Pritchard, Jocelyn I, Adelman, Howard M, and Mantay, Wayne R

Subjects
Aircraft Design, Testing And Performance
Abstract

This paper describes an integrated aerodynamic/dynamic/structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general-purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of global quantities (stiffness, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic designs are performed at a global level and the structural design is carried out at a detailed level with considerable dialog and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several examples.

Published
1995

38. A strategy for electronic dissemination of NASA Langley technical publications

Author

Roper, Donna G, Mccaskill, Mary K, Holland, Scott D, Walsh, Joanne L, Nelson, Michael L, Adkins, Susan L, Ambur, Manjula Y, and Campbell, Bryan A

Subjects
Documentation And Information Science
Abstract

To demonstrate NASA Langley Research Center's relevance and to transfer technology to external customers in a timely and efficient manner, Langley has formed a working group to study and recommend a course of action for the electronic dissemination of technical reports (EDTR). The working group identified electronic report requirements (e.g., accessibility, file format, search requirements) of customers in U.S. industry through numerous site visits and personal contacts. Internal surveys were also used to determine commonalities in document preparation methods. From these surveys, a set of requirements for an electronic dissemination system was developed. Two candidate systems were identified and evaluated against the set of requirements: the Full-Text Electronic Documents System (FEDS), which is a full-text retrieval system based on the commercial document management package Interleaf, and the Langley Technical Report Server (LTRS), which is a Langley-developed system based on the publicly available World Wide Web (WWW) software system. Factors that led to the selection of LTRS as the vehicle for electronic dissemination included searching and viewing capability, current system operability, and client software availability for multiple platforms at no cost to industry. This report includes the survey results, evaluations, a description of the LTRS architecture, recommended policy statement, and suggestions for future implementations.

Published
1994

39. Multilevel decomposition approach to integrated aerodynamic/dynamic/structural optimization of helicopter rotor blades

Author

Walsh, Joanne L, Young, Katherine C, Pritchard, Jocelyn I, Adelman, Howard M, and Mantay, Wayne R

Subjects
Aircraft Design, Testing And Performance
Abstract

This paper describes an integrated aerodynamic, dynamic, and structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of local quantities (stiffnesses, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic design is performed at a global level and the structural design is carried out at a detailed level with considerable dialogue and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several cases.

Published
1994

40. Recent advances in multidisciplinary optimization of rotorcraft

Author

Adelman, Howard M, Walsh, Joanne L, and Pritchard, Jocelyn I

Subjects
Aircraft Design, Testing And Performance
Abstract

A joint activity involving NASA and Army researchers at NASA LaRC to develop optimization procedures to improve the rotor blade design process by integrating appropriate disciplines and accounting for all of the important interactions among the disciplines is described. The disciplines involved include rotor aerodynamics, rotor dynamics, rotor structures, airframe dynamics, and acoustics. The work is focused on combining these five key disciplines in an optimization procedure capable of designing a rotor system to satisfy multidisciplinary design requirements. Fundamental to the plan is a three-phased approach. In phase 1, the disciplines of blade dynamics, blade aerodynamics, and blade structure are closely coupled while acoustics and airframe dynamics are decoupled and are accounted for as effective constraints on the design for the first three disciplines. In phase 2, acoustics is integrated with the first three disciplines. Finally, in phase 3, airframe dynamics is integrated with the other four disciplines. Representative results from work performed to date are described. These include optimal placement of tuning masses for reduction of blade vibratory shear forces, integrated aerodynamic/dynamic optimization, and integrated aerodynamic/dynamic/structural optimization. Examples of validating procedures are described.

Published
1992

41. Aerodynamic performance optimization of a rotor blade using a neural network as the analysis

Author

Lamarsh, William J., II, Walsh, Joanne L, and Rogers, James L

Subjects
Aircraft Design, Testing And Performance
Abstract

This paper describes the use of neural networks as a replacement for rotor analyses in a conventional aerodynamic performance optimization procedure. The optimization procedure minimizes an objective function, a linear combination of horsepower required for hover, forward flight, and maneuver. The design variables are pretwist, taper initiation, taper ratio, and blade root chord. Constraints consist of limits on horsepower required (for hover, forward flight, and maneuver), stall, trim, and minimum tip chord. Neural network analyses agree with conventional analyses.

Published
1992

42. Integrated multidisciplinary rotorcraft optimization research at the NASA Langley Research Center

Author

Adelman, Howard M, Mantay, Wayne R, Walsh, Joanne L, and Pritchard, Jocelyn I

Subjects
Aeronautics (General)
Abstract

NASA-Langley and U.S. Army researchers have developed optimization procedures for improving helicopter rotor blade design processes through more extensive integration of the requisite disciplines. These disciplines encompass rotor aerodynamics, rotor dynamics, rotor structures, airframe dynamics, and acoustics. In the first phase of this integrated optimization approach, acoustics and airframe aerodynamics are decoupled and accounted for as effective constraints on the design for the first three disciplines. In phase two, acoustics is integrated with the first three disciplines; in phase three, airframe dynamics are integrated with the other four disciplines. Representative results are presented from recent work on blade shear force reduction and aerodynamic/dynamic optimization.

Published
1992

43. Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades

Author

Walsh, Joanne L, Lamarsh, William J., II, and Adelman, Howard M

Subjects
Aircraft Design, Testing And Performance
Abstract

A fully integrated aerodynamic/dynamic optimization procedure is described for helicopter rotor blades. The procedure combines performance and dynamic analyses with a general purpose optimizer. The procedure minimizes a linear combination of power required (in hover, forward flight, and maneuver) and vibratory hub shear. The design variables include pretwist, taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses, and tuning mass locations. Aerodynamic constraints consist of limits on power required in hover, forward flight and maneuvers; airfoil section stall; drag divergence Mach number; minimum tip chord; and trim. Dynamic constraints are on frequencies, minimum autorotational inertia, and maximum blade weight. The procedure is demonstrated for two cases. In the first case, the objective function involves power required (in hover, forward flight and maneuver) and dynamics. The second case involves only hover power and dynamics. The designs from the integrated procedure are compared with designs from a sequential optimization approach in which the blade is first optimized for performance and then for dynamics. In both cases, the integrated approach is superior.

Published
1992

44. Recent advances in integrated multidisciplinary optimization of rotorcraft

Author

Adelman, Howard M, Walsh, Joanne L, and Pritchard, Jocelyn I

Subjects
Aircraft Design, Testing And Performance
Abstract

A joint activity involving NASA and Army researchers at NASA LaRC to develop optimization procedures to improve the rotor blade design process by integrating appropriate disciplines and accounting for all of the important interactions among the disciplines is described. The disciplines involved include rotor aerodynamics, rotor dynamics, rotor structures, airframe dynamics, and acoustics. The work is focused on combining these five key disciplines in an optimization procedure capable of designing a rotor system to satisfy multidisciplinary design requirements. Fundamental to the plan is a three-phased approach. In phase 1, the disciplines of blade dynamics, blade aerodynamics, and blade structure are closely coupled while acoustics and airframe dynamics are decoupled and are accounted for as effective constraints on the design for the first three disciplines. In phase 2, acoustics is integrated with the first three disciplines. Finally, in phase 3, airframe dynamics is integrated with the other four disciplines. Representative results from work performed to date are described. These include optimal placement of tuning masses for reduction of blade vibratory shear forces, integrated aerodynamic/dynamic optimization, and integrated aerodynamic/dynamic/structural optimization. Examples of validating procedures are described.

Published
1992

45. Optimizing tuning masses for helicopter rotor blade vibration reduction including computed airloads and comparison with test data

Author

Pritchard, Jocelyn I, Adelman, Howard M, Walsh, Joanne L, and Wilbur, Matthew L

Subjects
Aircraft Design, Testing And Performance
Abstract

The development and validation of an optimization procedure to systematically place tuning masses along a rotor blade span to minimize vibratory loads are described. The masses and their corresponding locations are the design variables that are manipulated to reduce the harmonics of hub shear for a four-bladed rotor system without adding a large mass penalty. The procedure incorporates a comprehensive helicopter analysis to calculate the airloads. Predicting changes in airloads due to changes in design variables is an important feature of this research. The procedure was applied to a one-sixth, Mach-scaled rotor blade model to place three masses and then again to place six masses. In both cases the added mass was able to achieve significant reductions in the hub shear. In addition, the procedure was applied to place a single mass of fixed value on a blade model to reduce the hub shear for three flight conditions. The analytical results were compared to experimental data from a wind tunnel test performed in the Langley Transonic Dynamics Tunnel. The correlation of the mass location was good and the trend of the mass location with respect to flight speed was predicted fairly well. However, it was noted that the analysis was not entirely successful at predicting the absolute magnitudes of the fixed system loads.

Published
1992

46. Performance optimization of helicopter rotor blades

Author

Walsh, Joanne L

Subjects
Aircraft Design, Testing And Performance
Abstract

As part of a center-wide activity at NASA Langley Research Center to develop multidisciplinary design procedures by accounting for discipline interactions, a performance design optimization procedure is developed. The procedure optimizes the aerodynamic performance of rotor blades by selecting the point of taper initiation, root chord, taper ratio, and maximum twist which minimize hover horsepower while not degrading forward flight performance. The procedure uses HOVT (a strip theory momentum analysis) to compute the horse power required for hover and the comprehensive helicopter analysis program CAMRAD to compute the horsepower required for forward flight and maneuver. The optimization algorithm consists of the general purpose optimization program CONMIN and approximate analyses. Sensitivity analyses consisting of derivatives of the objective function and constraints are carried out by forward finite differences. The procedure is applied to a test problem which is an analytical model of a wind tunnel model of a utility rotor blade.

Published
1991

48. Stacking-sequence optimization for buckling of laminated plates by integer programming

Author

Haftka, Raphael T and Walsh, Joanne L

Subjects
Structural Mechanics
Abstract

Integer-programming formulations for the design of symmetric and balanced laminated plates under biaxial compression are presented. Both maximization of buckling load for given total thickness, and the minimization of total thickness subject to a buckling constraint are formulated. The design variables that define the stacking sequence of the laminate are zero-one integers. It is shown that the formulation results in a linear optimization problem that can be solved on readily aviable software. Constraints on the stacking sequence such as a limit on the number of contiguous plies of the same orientation and limits on in-plane stiffnesses are easily accommodated. Examples are presented for graphite-epoxy plates under uniaxial and biaxial compression using a commercial software package based on the branch-and-bound algorithm.

Published
1991

49. NRF2 regulates the glutamine transporter Slc38a3 (SNAT3) in kidney in response to metabolic acidosis

Author

Lister, Adam, primary, Bourgeois, Soline, additional, Imenez Silva, Pedro H., additional, Rubio-Aliaga, Isabel, additional, Marbet, Philippe, additional, Walsh, Joanne, additional, Shelton, Luke M., additional, Keller, Bettina, additional, Verrey, Francois, additional, Devuyst, Olivier, additional, Giesbertz, Pieter, additional, Daniel, Hannelore, additional, Goldring, Christopher E., additional, Copple, Ian M., additional, Wagner, Carsten A., additional, and Odermatt, Alex, additional

Published
2018
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