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1,234 results on '"Hurst, John R."'

5. Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis

Author

Pakzad, Ashkan, Xu, Mou-Cheng, Cheung, Wing Keung, Vermant, Marie, Goos, Tinne, De Sadeleer, Laurens J, Verleden, Stijn E, Wuyts, Wim A, Hurst, John R, and Jacob, Joseph

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Several chronic lung diseases, like idiopathic pulmonary fibrosis (IPF) are characterised by abnormal dilatation of the airways. Quantification of airway features on computed tomography (CT) can help characterise disease progression. Physics based airway measurement algorithms have been developed, but have met with limited success in part due to the sheer diversity of airway morphology seen in clinical practice. Supervised learning methods are also not feasible due to the high cost of obtaining precise airway annotations. We propose synthesising airways by style transfer using perceptual losses to train our model, Airway Transfer Network (ATN). We compare our ATN model with a state-of-the-art GAN-based network (simGAN) using a) qualitative assessment; b) assessment of the ability of ATN and simGAN based CT airway metrics to predict mortality in a population of 113 patients with IPF. ATN was shown to be quicker and easier to train than simGAN. ATN-based airway measurements were also found to be consistently stronger predictors of mortality than simGAN-derived airway metrics on IPF CTs. Airway synthesis by a transformation network that refines synthetic data using perceptual losses is a realistic alternative to GAN-based methods for clinical CT analyses of idiopathic pulmonary fibrosis. Our source code can be found at https://github.com/ashkanpakzad/ATN that is compatible with the existing open-source airway analysis framework, AirQuant., Comment: 11 Pages, 4 figures. Source code available: https://github.com/ashkanpakzad/ATN. Initial submission version, to be published in MICCAI Workshop on Deep Generative Models 2022

Published
2022

6. Evaluation of automated airway morphological quantification for assessing fibrosing lung disease

Author

Pakzad, Ashkan, Cheung, Wing Keung, Quan, Kin, Mogulkoc, Nesrin, Van Moorsel, Coline H. M., Bartholmai, Brian J., Van Es, Hendrik W., Ezircan, Alper, Van Beek, Frouke, Veltkamp, Marcel, Karwoski, Ronald, Peikert, Tobias, Clay, Ryan D., Foley, Finbar, Braun, Cassandra, Savas, Recep, Sudre, Carole, Doel, Tom, Alexander, Daniel C., Wijeratne, Peter, Hawkes, David, Hu, Yipeng, Hurst, John R, and Jacob, Joseph

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Abnormal airway dilatation, termed traction bronchiectasis, is a typical feature of idiopathic pulmonary fibrosis (IPF). Volumetric computed tomography (CT) imaging captures the loss of normal airway tapering in IPF. We postulated that automated quantification of airway abnormalities could provide estimates of IPF disease extent and severity. We propose AirQuant, an automated computational pipeline that systematically parcellates the airway tree into its lobes and generational branches from a deep learning based airway segmentation, deriving airway structural measures from chest CT. Importantly, AirQuant prevents the occurrence of spurious airway branches by thick wave propagation and removes loops in the airway-tree by graph search, overcoming limitations of existing airway skeletonisation algorithms. Tapering between airway segments (intertapering) and airway tortuosity computed by AirQuant were compared between 14 healthy participants and 14 IPF patients. Airway intertapering was significantly reduced in IPF patients, and airway tortuosity was significantly increased when compared to healthy controls. Differences were most marked in the lower lobes, conforming to the typical distribution of IPF-related damage. AirQuant is an open-source pipeline that avoids limitations of existing airway quantification algorithms and has clinical interpretability. Automated airway measurements may have potential as novel imaging biomarkers of IPF severity and disease extent., Comment: 14 pages, 8 Figures, for associated source code, see https://github.com/ashkanpakzad/AirQuant

Published
2021

10. The challenges of deploying artificial intelligence models in a rapidly evolving pandemic

Author

Hu, Yipeng, Jacob, Joseph, Parker, Geoffrey JM, Hawkes, David J, Hurst, John R, and Stoyanov, Danail

Subjects
Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks. The gaze of these networks has in recent years turned increasingly towards applications in healthcare. It was perhaps inevitable that COVID-19, a global disease propagating health and economic devastation, should capture the attention and resources of the world's computer scientists in academia and industry. The potential for AI to support the response to the pandemic has been proposed across a wide range of clinical and societal challenges, including disease forecasting, surveillance and antiviral drug discovery. This is likely to continue as the impact of the pandemic unfolds on the world's people, industries and economy but a surprising observation on the current pandemic has been the limited impact AI has had to date in the management of COVID-19. This correspondence focuses on exploring potential reasons behind the lack of successful adoption of AI models developed for COVID-19 diagnosis and prognosis, in front-line healthcare services. We highlight the moving clinical needs that models have had to address at different stages of the epidemic, and explain the importance of translating models to reflect local healthcare environments. We argue that both basic and applied research are essential to accelerate the potential of AI models, and this is particularly so during a rapidly evolving pandemic. This perspective on the response to COVID-19, may provide a glimpse into how the global scientific community should react to combat future disease outbreaks more effectively., Comment: Accepted in Nature Machine Intelligence

Published
2020
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16. Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Author

Raman, Betty, McCracken, Celeste, Cassar, Mark P, Moss, Alastair J, Finnigan, Lucy, Samat, Azlan Helmy A, Ogbole, Godwin, Tunnicliffe, Elizabeth M, Alfaro-Almagro, Fidel, Menke, Ricarda, Xie, Cheng, Gleeson, Fergus, Lukaschuk, Elena, Lamlum, Hanan, McGlynn, Kevin, Popescu, Iulia A, Sanders, Zeena-Britt, Saunders, Laura C, Piechnik, Stefan K, Ferreira, Vanessa M, Nikolaidou, Chrysovalantou, Rahman, Najib M, Ho, Ling-Pei, Harris, Victoria C, Shikotra, Aarti, Singapuri, Amisha, Pfeffer, Paul, Manisty, Charlotte, Kon, Onn M, Beggs, Mark, O'Regan, Declan P, Fuld, Jonathan, Weir-McCall, Jonathan R, Parekh, Dhruv, Steeds, Rick, Poinasamy, Krisnah, Cuthbertson, Dan J, Kemp, Graham J, Semple, Malcolm G, Horsley, Alexander, Miller, Christopher A, O'Brien, Caitlin, Shah, Ajay M, Chiribiri, Amedeo, Leavy, Olivia C, Richardson, Matthew, Elneima, Omer, McAuley, Hamish J C, Sereno, Marco, Saunders, Ruth M, Houchen-Wolloff, Linzy, Greening, Neil J, Bolton, Charlotte E, Brown, Jeremy S, Choudhury, Gourab, Diar Bakerly, Nawar, Easom, Nicholas, Echevarria, Carlos, Marks, Michael, Hurst, John R, Jones, Mark G, Wootton, Daniel G, Chalder, Trudie, Davies, Melanie J, De Soyza, Anthony, Geddes, John R, Greenhalf, William, Howard, Luke S, Jacob, Joseph, Man, William D-C, Openshaw, Peter J M, Porter, Joanna C, Rowland, Matthew J, Scott, Janet T, Singh, Sally J, Thomas, David C, Toshner, Mark, Lewis, Keir E, Heaney, Liam G, Harrison, Ewen M, Kerr, Steven, Docherty, Annemarie B, Lone, Nazir I, Quint, Jennifer, Sheikh, Aziz, Zheng, Bang, Jenkins, R Gisli, Cox, Eleanor, Francis, Susan, Halling-Brown, Mark, Chalmers, James D, Greenwood, John P, Plein, Sven, Hughes, Paul J C, Thompson, A A Roger, Rowland-Jones, Sarah L, Wild, James M, Kelly, Matthew, Treibel, Thomas A, Bandula, Steven, Aul, Raminder, Miller, Karla, Jezzard, Peter, Smith, Stephen, Nichols, Thomas E, McCann, Gerry P, Evans, Rachael A, Wain, Louise V, Brightling, Christopher E, Neubauer, Stefan, Baillie, J K, Shaw, Alison, Hairsine, Brigid, Kurasz, Claire, Henson, Helen, Armstrong, Lisa, Shenton, Liz, Dobson, H, Dell, Amanda, Lucey, Alice, Price, Andrea, Storrie, Andrew, Pennington, Chris, Price, Claire, Mallison, Georgia, Willis, Gemma, Nassa, Heeah, Haworth, Jill, Hoare, Michaela, Hawkings, Nancy, Fairbairn, Sara, Young, Susan, Walker, S, Jarrold, I, Sanderson, Amy, David, C, Chong-James, K, Zongo, O, James, W Y, Martineau, A, King, Bernie, Armour, C, McAulay, D, Major, E, McGinness, Jade, McGarvey, L, Magee, N, Stone, Roisin, Drain, S, Craig, T, Bolger, A, Haggar, Ahmed, Lloyd, Arwel, Subbe, Christian, Menzies, Daniel, Southern, David, McIvor, Emma, Roberts, K, Manley, R, Whitehead, Victoria, Saxon, W, Bularga, A, Mills, N L, El-Taweel, Hosni, Dawson, Joy, Robinson, Leanne, Saralaya, Dinesh, Regan, Karen, Storton, Kim, Brear, Lucy, Amoils, S, Bermperi, Areti, Elmer, Anne, Ribeiro, Carla, Cruz, Isabel, Taylor, Jessica, Worsley, J, Dempsey, K, Watson, L, Jose, Sherly, Marciniak, S, Parkes, M, McQueen, Alison, Oliver, Catherine, Williams, Jenny, Paradowski, Kerry, Broad, Lauren, Knibbs, Lucy, Haynes, Matthew, Sabit, Ramsey, Milligan, L, Sampson, Claire, Hancock, Alyson, Evenden, Cerys, Lynch, Ceri, Hancock, Kia, Roche, Lisa, Rees, Meryl, Stroud, Natalie, Thomas-Woods, T, Heller, S, Robertson, E, Young, B, Wassall, Helen, Babores, M, Holland, Maureen, Keenan, Natalie, Shashaa, Sharlene, Price, Carly, Beranova, Eva, Ramos, Hazel, Weston, Heather, Deery, Joanne, Austin, Liam, Solly, Reanne, Turney, Sharon, Cosier, Tracey, Hazelton, Tracy, Ralser, M, Wilson, Ann, Pearce, Lorraine, Pugmire, S, Stoker, Wendy, McCormick, W, Dewar, A, Arbane, Gill, Kaltsakas, G, Kerslake, Helen, Rossdale, J, Bisnauthsing, Karen, Aguilar Jimenez, Laura A, Martinez, L M, Ostermann, Marlies, Magtoto, Murphy M, Hart, Nicholas, Marino, Philip, Betts, Sarah, Solano, Teresa S, Arias, Ava Maria, Prabhu, A, Reed, Annabel, Wrey Brown, Caroline, Griffin, Denise, Bevan, Emily, Martin, Jane, Owen, J, Alvarez Corral, Maria, Williams, Nick, Payne, Sheila, Storrar, Will, Layton, Alison, Lawson, Cathy, Mills, Clare, Featherstone, James, Stephenson, Lorraine, Burdett, Tracy, Ellis, Y, Richards, A, Wright, C, Sykes, D L, Brindle, K, Drury, Katie, Holdsworth, L, Crooks, M G, Atkin, Paul, Flockton, Rachel, Thackray-Nocera, Susannah, Mohamed, Abdelrahman, Taylor, Abigail, Perkins, Emma, Ross, Gavin, McGuinness, Heather, Tench, Helen, Phipps, Janet, Loosley, Ronda, Wolf-Roberts, Rebecca, Coetzee, S, Omar, Zohra, Ross, Alexandra, Card, Bethany, Carr, Caitlin, King, Clara, Wood, Chloe, Copeland, D, Calvelo, Ellen, Chilvers, Edwin R, Russell, Emily, Gordon, Hussain, Nunag, Jose Lloyd, Schronce, J, March, Katherine, Samuel, Katherine, Burden, L, Evison, Lynsey, McLeavey, Laura, Orriss-Dib, Lorna, Tarusan, Lawrence, Mariveles, Myril, Roy, Maura, Mohamed, Noura, Simpson, Neil, Yasmin, Najira, Cullinan, P, Daly, Patrick, Haq, Sulaimaan, Moriera, Silvia, Fayzan, Tamanah, Munawar, Unber, Nwanguma, Uchechi, Lingford-Hughes, A, Altmann, Danny, Johnston, D, Mitchell, J, Valabhji, J, Price, L, Molyneaux, P L, Thwaites, Ryan S, Walsh, S, Frankel, A, Lightstone, L, Wilkins, M, Willicombe, M, McAdoo, S, Touyz, R, Guerdette, Anne-Marie, Warwick, Katie, Hewitt, Melanie, Reddy, R, White, Sonia, McMahon, A, Hoare, Amy, Knighton, Abigail, Ramos, Albert, Te, Amelie, Jolley, Caroline J, Speranza, Fabio, Assefa-Kebede, Hosanna, Peralta, Ida, Breeze, Jonathon, Shevket, K, Powell, Natassia, Adeyemi, Oluwaseun, Dulawan, Pearl, Adrego, Rita, Byrne, S, Patale, Sheetal, Hayday, A, Malim, M, Pariante, C, Sharpe, C, Whitney, J, Bramham, K, Ismail, K, Wessely, S, Nicholson, T, Ashworth, Andrew, Humphries, Amy, Tan, Ai Lyn, Whittam, Beverley, Coupland, C, Favager, Clair, Peckham, D, Wade, Elaine, Saalmink, Gwen, Clarke, Jude, Glossop, Jodie, Murira, Jennifer, Rangeley, Jade, Woods, Janet, Hall, Lucy, Dalton, Matthhew, Window, Nicola, Beirne, Paul, Hardy, Tim, Coakley, G, Turtle, Lance, Berridge, Anthony, Cross, Andy, Key, Angela L, Rowe, Anna, Allt, Ann Marie, Mears, Chloe, Malein, Flora, Madzamba, Gladys, Hardwick, H E, Earley, Joanne, Hawkes, Jenny, Pratt, James, Wyles, J, Tripp, K A, Hainey, Kera, Allerton, Lisa, Lavelle-Langham, L, Melling, Lucy, Wajero, Lilian O, Poll, L, Noonan, Matthew J, French, N, Lewis-Burke, N, Williams-Howard, S A, Cooper, Shirley, Kaprowska, Sabina, Dobson, S L, Marsh, Sophie, Highett, Victoria, Shaw, V, Beadsworth, M, Defres, S, Watson, Ekaterina, Tiongson, Gerlynn F, Papineni, Padmasayee, Gurram, Sambasivarao, Diwanji, Shalin N, Quaid, Sheena, Briggs, A, Hastie, Claire, Rogers, Natalie, Stensel, D, Bishop, L, McIvor, K, Rivera-Ortega, P, Al-Sheklly, B, Avram, Cristina, Faluyi, David, Blaikely, J, Piper Hanley, K, Radhakrishnan, K, Buch, M, Hanley, N A, Odell, Natasha, Osbourne, Rebecca, Stockdale, Sue, Felton, T, Gorsuch, T, Hussell, T, Kausar, Zunaira, Kabir, T, McAllister-Williams, H, Paddick, S, Burn, D, Ayoub, A, Greenhalgh, Alan, Sayer, A, Young, A, Price, D, Burns, G, MacGowan, G, Fisher, Helen, Tedd, H, Simpson, J, Jiwa, Kasim, Witham, M, Hogarth, Philip, West, Sophie, Wright, S, McMahon, Michael J, Neill, Paula, Dougherty, Andrew, Morrow, A, Anderson, David, Grieve, D, Bayes, Hannah, Fallon, K, Mangion, K, Gilmour, L, Basu, N, Sykes, R, Berry, C, McInnes, I B, Donaldson, A, Sage, E K, Barrett, Fiona, Welsh, B, Bell, Murdina, Quigley, Jackie, Leitch, Karen, Macliver, L, Patel, Manish, Hamil, R, Deans, Andrew, Furniss, J, Clohisey, S, Elliott, Anne, Solstice, A R, Deas, C, Tee, Caroline, Connell, David, Sutherland, Debbie, George, J, Mohammed, S, Bunker, Jenny, Holmes, Katie, Dipper, A, Morley, Anna, Arnold, David, Adamali, H, Welch, H, Morrison, Leigh, Stadon, Louise, Maskell, Nick, Barratt, Shaney, Dunn, Sarah, Waterson, Samuel, Jayaraman, Bhagy, Light, Tessa, Selby, N, Hosseini, A, Shaw, Karen, Almeida, Paula, Needham, Robert, Thomas, Andrew K, Matthews, Laura, Gupta, Ayushman, Nikolaidis, Athanasios, Dupont, Catherine, Bonnington, J, Chrystal, Melanie, Greenhaff, P L, Linford, S, Prosper, Sabrina, Jang, W, Alamoudi, Asma, Bloss, Angela, Megson, Clare, Nicoll, Debby, Fraser, Emily, Pacpaco, Edmund, Conneh, Florence, Ogg, G, McShane, H, Koychev, Ivan, Chen, Jin, Pimm, John, Ainsworth, Mark, Pavlides, M, Sharpe, M, Havinden-Williams, May, Petousi, Nayia, Talbot, Nick, Carter, Penny, Kurupati, Prathiba, Dong, T, Peng, Yanchun, Burns, A, Kanellakis, N, Korszun, A, Connolly, B, Busby, J, Peto, T, Patel, B, Nolan, C M, Cristiano, Daniele, Walsh, J A, Liyanage, Kamal, Gummadi, Mahitha, Dormand, N, Polgar, Oliver, George, P, Barker, R E, Patel, Suhani, Gibbons, M, Matila, Darwin, Jarvis, Hannah, Lim, Lai, Olaosebikan, Olaoluwa, Ahmad, Shanaz, Brill, Simon, Mandal, S, Laing, C, Michael, Alice, Reddy, A, Johnson, C, Baxendale, H, Parfrey, H, Mackie, J, Newman, J, Pack, Jamie, Parmar, J, Paques, K, Garner, Lucie, Harvey, Alice, Summersgill, C, Holgate, D, Hardy, E, Oxton, J, Pendlebury, Jessica, McMorrow, L, Mairs, N, Majeed, N, Dark, P, Ugwuoke, R, Knight, Sean, Whittaker, S, Strong-Sheldrake, Sophia, Matimba-Mupaya, Wadzanai, Chowienczyk, P, Pattenadk, Dibya, Hurditch, E, Chan, Flora, Carborn, H, Foot, H, Bagshaw, J, Hockridge, J, Sidebottom, J, Lee, Ju Hee, Birchall, K, Turner, Kim, Haslam, L, Holt, L, Milner, L, Begum, M, Marshall, M, Steele, N, Tinker, N, Ravencroft, Phillip, Butcher, Robyn, Misra, S, Coburn, Zach, Fairman, Alexandra, Ford, Amber, Holbourn, Ailsa, Howell, Alice, Lawrie, Allan, Lye, Alison, Mbuyisa, Angeline, Zawia, Amira, Holroyd-Hind, B, Thamu, B, Clark, Cameron, Jarman, Claire, Norman, C, Roddis, C, Foote, David, Lee, Elvina, Ilyas, F, Stephens, G, Newell, Helen, Turton, Helena, Macharia, Irene, Wilson, Imogen, Cole, Joby, McNeill, J, Meiring, J, Rodger, J, Watson, James, Chapman, Kerry, Harrington, Kate, Chetham, Luke, Hesselden, L, Nwafor, Lorenza, Dixon, Myles, Plowright, Megan, Wade, Phillip, Gregory, Rebecca, Lenagh, Rebecca, Stimpson, R, Megson, Sharon, Newman, Tom, Cheng, Yutung, Goodwin, Camelia, Heeley, Cheryl, Sissons, D, Sowter, D, Gregory, Heidi, Wynter, Inez, Hutchinson, John, Kirk, Jill, Bennett, Kaytie, Slack, Katie, Allsop, Lynne, Holloway, Leah, Flynn, Margaret, Gill, Mandy, Greatorex, M, Holmes, Megan, Buckley, Phil, Shelton, Sarah, Turner, Sarah, Sewell, Terri Ann, Whitworth, V, Lovegrove, Wayne, Tomlinson, Johanne, Warburton, Louise, Painter, Sharon, Vickers, Carinna, Redwood, Dawn, Tilley, Jo, Palmer, Sue, Wainwright, Tania, Breen, G, Hotopf, M, Dunleavy, A, Teixeira, J, Ali, Mariam, Mencias, Mark, Msimanga, N, Siddique, Sulman, Samakomva, T, Tavoukjian, Vera, Forton, D, Ahmed, R, Cook, Amanda, Thaivalappil, Favas, Connor, Lynda, Rees, Tabitha, McNarry, M, Williams, N, McCormick, Jacqueline, McIntosh, Jerome, Vere, Joanne, Coulding, Martina, Kilroy, Susan, Turner, Victoria, Butt, Al-Tahoor, Savill, Heather, Fraile, Eva, Ugoji, Jacinta, Landers, G, Lota, Harpreet, Portukhay, Sofiya, Nasseri, Mariam, Daniels, Alison, Hormis, Anil, Ingham, Julie, Zeidan, Lisa, Osborne, Lynn, Chablani, Manish, Banerjee, A, David, A, Pakzad, A, Rangelov, B, Williams, B, Denneny, E, Willoughby, J, Xu, M, Mehta, P, Batterham, R, Bell, R, Aslani, S, Lilaonitkul, W, Checkley, A, Bang, Dongchun, Basire, Donna, Lomas, D, Wall, E, Plant, Hannah, Roy, K, Heightman, M, Lipman, M, Merida Morillas, Marta, Ahwireng, Nyarko, Chambers, R C, Jastrub, Roman, Logan, S, Hillman, T, Botkai, A, Casey, A, Neal, A, Newton-Cox, A, Cooper, B, Atkin, C, McGee, C, Welch, C, Wilson, D, Sapey, E, Qureshi, H, Hazeldine, J, Lord, J M, Nyaboko, J, Short, J, Stockley, J, Dasgin, J, Draxlbauer, K, Isaacs, K, Mcgee, K, Yip, K P, Ratcliffe, L, Bates, M, Ventura, M, Ahmad Haider, N, Gautam, N, Baggott, R, Holden, S, Madathil, S, Walder, S, Yasmin, S, Hiwot, T, Jackson, T, Soulsby, T, Kamwa, V, Peterkin, Z, Suleiman, Z, Chaudhuri, N, Wheeler, H, Djukanovic, R, Samuel, R, Sass, T, Wallis, T, Marshall, B, Childs, C, Marouzet, E, Harvey, M, Fletcher, S, Dickens, C, Beckett, P, Nanda, U, Daynes, E, Charalambou, A, Yousuf, A J, Lea, A, Prickett, A, Gooptu, Bibek, Hargadon, Beverley, Bourne, Charlotte, Christie, C, Edwardson, C, Lee, D, Baldry, E, Stringer, E, Woodhead, F, Mills, G, Arnold, H, Aung, H, Qureshi, I N, Finch, J, Skeemer, J, Hadley, K, Khunti, Kamlesh, Carr, Liesel, Ingram, L, Aljaroof, M, Bakali, M, Bakau, M, Baldwin, M, Bourne, Michelle, Pareek, Manish, Soares, M, Tobin, Martin, Armstrong, Natalie, Brunskill, Nigel, Goodman, N, Cairns, P, Haldar, Pranab, McCourt, P, Dowling, R, Russell, Richard, Diver, Sarah, Edwards, Sarah, Glover, Sarah, Parker, S, Siddiqui, Salman, Ward, T J C, Mcnally, T, Thornton, T, Yates, Tom, Ibrahim, W, Monteiro, Will, Thickett, D, Wilkinson, D, Broome, M, McArdle, P, Upthegrove, R, Wraith, D, Langenberg, C, Summers, C, Bullmore, E, Heeney, J L, Schwaeble, W, Sudlow, C L, Adeloye, D, Newby, D E, Rudan, I, Shankar-Hari, M, Thorpe, M, Pius, R, Walmsley, S, McGovern, A, Ballard, C, Allan, L, Dennis, J, Cavanagh, J, Petrie, J, O'Donnell, K, Spears, M, Sattar, N, MacDonald, S, Guthrie, E, Henderson, M, Guillen Guio, Beatriz, Zhao, Bang, Lawson, C, Overton, Charlotte, Taylor, Chris, Tong, C, Mukaetova-Ladinska, Elizabeta, Turner, E, Pearl, John E, Sargant, J, Wormleighton, J, Bingham, Michelle, Sharma, M, Steiner, Mike, Samani, Nilesh, Novotny, Petr, Free, Rob, Allen, R J, Finney, Selina, Terry, Sarah, Brugha, Terry, Plekhanova, Tatiana, McArdle, A, Vinson, B, Spencer, L G, Reynolds, W, Ashworth, M, Deakin, B, Chinoy, H, Abel, K, Harvie, M, Stanel, S, Rostron, A, Coleman, C, Baguley, D, Hufton, E, Khan, F, Hall, I, Stewart, I, Fabbri, L, Wright, L, Kitterick, P, Morriss, R, Johnson, S, Bates, A, Antoniades, C, Clark, D, Bhui, K, Channon, K M, Motohashi, K, Sigfrid, L, Husain, M, Webster, M, Fu, X, Li, X, Kingham, L, Klenerman, P, Miiler, K, Carson, G, Simons, G, Huneke, N, Calder, P C, Baldwin, D, Bain, S, Lasserson, D, Daines, L, Bright, E, Stern, M, Crisp, P, Dharmagunawardena, R, Reddington, A, Wight, A, Bailey, L, Ashish, A, Robinson, E, Cooper, J, Broadley, A, Turnbull, A, Brookes, C, Sarginson, C, Ionita, D, Redfearn, H, Elliott, K, Barman, L, Griffiths, L, Guy, Z, Gill, Rhyan, Nathu, Rashmita, Harris, Edward, Moss, P, Finnigan, J, Saunders, Kathryn, Saunders, Peter, Kon, S, Kon, Samantha S, O'Brien, Linda, Shah, K, Shah, P, Richardson, Emma, Brown, V, Brown, M, Brown, Jo, Brown, J, Brown, Ammani, Brown, Angela, Choudhury, N, Jones, S, Jones, H, Jones, L, Jones, I, Jones, G, Jones, Heather, Jones, Don, Davies, Ffyon, Davies, Ellie, Davies, Kim, Davies, Gareth, Davies, Gwyneth A, Howard, K, Porter, Julie, Rowland, J, Rowland, A, Scott, Kathryn, Singh, Suver, Singh, Claire, Thomas, S, Thomas, Caradog, Lewis, Victoria, Lewis, J, Lewis, D, Harrison, P, Francis, C, Francis, R, Hughes, Rachel Ann, Hughes, Joan, Hughes, A D, Thompson, T, Kelly, S, Smith, D, Smith, Nikki, Smith, Andrew, Smith, Jacqui, Smith, Laurie, Smith, Susan, Evans, Teriann, Evans, Ranuromanana I, Evans, D, Evans, R, Evans, H, and Evans, J

Published
2023
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18. Reproducibility of an airway tapering measurement in CT with application to bronchiectasis

Author

Quan, Kin, Tanno, Ryutaro, Shipley, Rebecca J., Brown, Jeremy S., Jacob, Joseph, Hurst, John R., and Hawkes, David J.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Purpose: This paper proposes a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on CT. We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure. Methods: We generate a spline from the centreline of an airway to measure the area and arclength at contiguous intervals. The tapering measurement is the gradient of the linear regression between area in log space and arclength. The reproducibility of the measure was assessed by analysing different radiation doses, voxel sizes and reconstruction kernel on single timepoint and longitudinal CT scans and by evaluating the effct of airway bifurcations. Results: Using 74 airways from 10 CT scans, we show a statistical difference, p = 3.4 $\times$ 10$^{-4}$ in tapering between healthy airways (n = 35) and those affected by bronchiectasis (n = 39). The difference between the mean of the two populations was 0.011mm$^{-1}$ and the difference between the medians of the two populations was 0.006mm$^{-1}$. The tapering measurement retained a 95\% confidence interval of $\pm$0.005mm$^{-1}$ in a simulated 25 mAs scan and retained a 95% confidence of $\pm$0.005mm$^{-1}$ on simulated CTs up to 1.5 times the original voxel size. Conclusion: We have established an estimate of the precision of the tapering measurement and estimated the effect on precision of simulated voxel size and CT scan dose. We recommend that the scanner calibration be undertaken with the phantoms as described, on the specific CT scanner, radiation dose and reconstruction algorithm that is to be used in any quantitative studies. Our code is available at https://github.com/quan14/AirwayTaperingInCT, Comment: 55 pages, 18 figures, The manuscript was originally published in Journal of Medical Imaging

Published
2019
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19. Tapering Analysis of Airways with Bronchiectasis

Author

Quan, Kin, Shipley, Rebecca J., Tanno, Ryutaro, McPhillips, Graeme, Vavourakis, Vasileios, Edwards, David, Jacob, Joseph, Hurst, John R., and Hawkes, David J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics, Quantitative Biology - Quantitative Methods
Abstract

Bronchiectasis is the permanent dilation of airways. Patients with the disease can suffer recurrent exacerbations, reducing their quality of life. The gold standard to diagnose and monitor bronchiectasis is accomplished by inspection of chest computed tomography (CT) scans. A clinician examines the broncho-arterial ratio to determine if an airway is brochiectatic. The visual analysis assumes the blood vessel diameter remains constant, although this assumption is disputed in the literature. We propose a simple measurement of tapering along the airways to diagnose and monitor bronchiectasis. To this end, we constructed a pipeline to measure the cross-sectional area along the airways at contiguous intervals, starting from the carina to the most distal point observable. Using a phantom with calibrated 3D printed structures, the precision and accuracy of our algorithm extends to the sub voxel level. The tapering measurement is robust to bifurcations along the airway and was applied to chest CT images acquired in clinical practice. The result is a statistical difference in tapering rate between airways with bronchiectasis and controls. Our code is available at https://github.com/quan14/AirwayTaperingInCT., Comment: 12 pages, 7 figures. Previously submitted for SPIE Medical Imaging, 2018, Houston, Texas, United States

Published
2019
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20. Determinants of recovery from post-COVID-19 dyspnoea: analysis of UK prospective cohorts of hospitalised COVID-19 patients and community-based controls

Author

Abel, K., Adamali, H., Adeloye, D., Adeyemi, O., Adrego, R., Aguilar Jimenez, L.A., Ahmad, S., Ahmad Haider, N., Ahmed, R., Ahwireng, N., Ainsworth, M., Al-Sheklly, B., Alamoudi, A., Ali, M., Aljaroof, M., All, A.M., Allan, L., Allen, R.J., Allerton, L., Allsop, L., Almeida, P., Altmann, D., Alvarez Corral, M., Amoils, S., Anderson, D., Antoniades, C., Arbane, G., Arias, A., Armour, C., Armstrong, L., Armstrong, N., Arnold, D., Arnold, H., Ashish, A., Ashworth, A., Ashworth, M., Aslani, S., Assefa-Kebede, H., Atkin, C., Atkin, P., Aul, R., Aung, H., Austin, L., Avram, C., Ayoub, A., Babores, M., Baggott, R., Bagshaw, J., Baguley, D., Bailey, L., Baillie, J.K., Bain, S., Bakali, M., Bakau, M., Baldry, E., Baldwin, D., Baldwin, M., Ballard, C., Banerjee, A., Bang, B., Barker, R.E., Barman, L., Barratt, S., Barrett, F., Basire, D., Basu, N., Bates, M., Bates, A., Batterham, R., Baxendale, H., Bayes, H., Beadsworth, M., Beckett, P., Beggs, M., Begum, M., Beirne, P., Bell, D., Bell, R., Bennett, K., Beranova, E., Bermperi, A., Berridge, A., Berry, C., Betts, S., Bevan, E., Bhui, K., Bingham, M., Birchall, K., Bishop, L., Bisnauthsing, K., Blaikely, J., Bloss, A., Bolger, A., Bolton, C.E., Bonnington, J., Botkai, A., Bourne, C., Bourne, M., Bramham, K., Brear, L., Breen, G., Breeze, J., Briggs, A., Bright, E., Brightling, C.E., Brill, S., Brindle, K., Broad, L., Broadley, A., Brookes, C., Broome, M., Brown, A., Brown, J., Brown, J.S., Brown, M., Brown, V., Brugha, T., Brunskill, N., Buch, M., Buckley, P., Bularga, A., Bullmore, E., Burden, L., Burdett, T., Burn, D., Burns, G., Burns, A., Busby, J., Butcher, R., Butt, A., Byrne, S., Cairns, P., Calder, P.C., Calvelo, E., Carborn, H., Card, B., Carr, C., Carr, L., Carson, G., Carter, P., Casey, A., Cassar, M., Cavanagh, J., Chablani, M., Chalder, T., Chalmers, J.D., Chambers, R.C., Chan, F., Channon, K.M., Chapman, K., Charalambou, A., Chaudhuri, N., Checkley, A., Chen, J., Cheng, Y., Chetham, L., Childs, C., Chilvers, E.R., Chinoy, H., Chiribiri, A., Chong-James, K., Choudhury, G., Choudhury, N., Chowienczyk, P., Christie, C., Chrystal, M., Clark, D., Clark, C., Clarke, J., Clohisey, S., Coakley, G., Coburn, Z., Coetzee, S., Cole, J., Coleman, C., Conneh, F., Connell, D., Connolly, B., Connor, L., Cook, A., Cooper, B., Cooper, J., Cooper, S., Copeland, D., Cosier, T., Coulding, M., Coupland, C., Cox, E., Craig, T., Crisp, P., Cristiano, D., Crooks, M.G., Cross, A., Cruz, I., Cullinan, P., Cuthbertson, D., Daines, L., Dalton, M., Daly, P., Daniels, A., Dark, P., Dasgin, J., David, A., David, C., Davies, E., Davies, F., Davies, G., Davies, G.A., Davies, K., Davies, M.J., Dawson, J., Daynes, E., De Soyza, A., Deakin, B., Deans, A., Deas, C., Deery, J., Defres, S., Dell, A., Dempsey, K., Denneny, E., Dennis, J., Dewar, A., Dharmagunawardena, R., Diar-Bakerly, N., Dickens, C., Dipper, A., Diver, S., Diwanji, S.N., Dixon, M., Djukanovic, R., Dobson, H., Dobson, S.L., Docherty, A.B., Donaldson, A., Dong, T., Dormand, N., Dougherty, A., Dowling, R., Drain, S., Draxlbauer, K., Drury, K., drury, H.J.C., Dulawan, P., Dunleavy, A., Dunn, S., Dupont, C., Earley, J., Easom, N., Echevarria, C., Edwards, S., Edwardson, C., El-Taweel, H., Elliott, A., Elliott, K., Ellis, Y., Elmer, A., Elneima, O., Evans, D., Evans, H., Evans, J., Evans, R., Evans, R.A., Evans, R.I., Evans, T., Evenden, C., Evison, L., Fabbri, L., Fairbairn, S., Fairman, A., Fallon, K., Faluyi, D., Favager, C., Fayzan, T., Featherstone, J., Felton, T., Finch, J., Finney, S., Finnigan, J., Finnigan, L., Fisher, H., Fletcher, S., Flockton, R., Flynn, M., Foot, H., Foote, D., Ford, A., Forton, D., Fraile, E., Francis, C., Francis, R., Francis, S., Frankel, A., Fraser, E., Free, R., French, N., Fu, X., Fuld, J., Furniss, J., Garner, L., Gautam, N., Geddes, J.R., George, J., George, P., Gibbons, M., Gill, M., Gilmour, L., Gleeson, F., Glossop, J., Glover, S., Goodman, N., Goodwin, C., Gooptu, B., Gordon, H., Gorsuch, T., Greatorex, M., Greenhaff, P.L., Greenhalf, W., Greenhalgh, A., Greening, N.J., Greenwood, J., Gregory, H., Gregory, R., Grieve, D., Griffin, D., Griffiths, L., Guerdette, A.-M., Guio, B. Guillen, Gummadi, M., Gupta, A., Gurram, S., Guthrie, E., Guy, Z., Henson, H.H., Hadley, K., Haggar, A., Hainey, K., Hairsine, B., Haldar, P., Hall, I., Hall, L., Halling-Brown, M., Hamil, R., Hancock, A., Hancock, K., Hanley, N.A., Haq, S., Hardwick, H.E., Hardy, E., Hardy, T., Hargadon, B., Harrington, K., Harris, E., Harris, V.C., Harrison, E.M., Harrison, P., Hart, N., Harvey, A., Harvey, M., Harvie, M., Haslam, L., Havinden-Williams, M., Hawkes, J., Hawkings, N., Haworth, J., Hayday, A., Haynes, M., Hazeldine, J., Hazelton, T., Heaney, L.G., Heeley, C., Heeney, J.L., Heightman, M., Heller, S., Henderson, M., Hesselden, L., Hewitt, M., Highett, V., Hillman, T., Hiwot, T., Ho, L.P., Hoare, A., Hoare, M., Hockridge, J., Hogarth, P., Holbourn, A., Holden, S., Holdsworth, L., Holgate, D., Holland, M., Holloway, L., Holmes, K., Holmes, M., Holroyd-Hind, B., Holt, L., Hormis, A., Horsley, A., Hosseini, A., Hotopf, M., Houchen-Wolloff, L., Howard, K., Howard, L.S., Howell, A., Hufton, E., Hughes, A.D., Hughes, J., Hughes, R., Humphries, A., Huneke, N., Hurditch, E., Hurst, J., Husain, M., Hussell, T., Hutchinson, J., Ibrahim, W., Ilyas, F., Ingham, J., Ingram, L., Ionita, D., Isaacs, K., Ismail, K., Jackson, T., Jacob, J., James, W.Y., Jang, W., Jarman, C., Jarrold, I., Jarvis, H., Jastrub, R., Jayaraman, B., Jenkins, R.G., Jezzard, P., Jiwa, K., Johnson, C., Johnson, S., Johnston, D., Jolley, C.J., Jones, D., Jones, G., Jones, H., Jones, I., Jones, L., Jones, M.G., Jones, S., Jose, S., Kabir, T., Kaltsakas, G., Kamwa, V., Kanellakis, N., s. Kaprowska, Kausar, Z., Keenan, N., Kelly, S., Kemp, G., Kerr, S., Kerslake, H., Key, A.L., Khan, F., Khunti, K., Kilroy, S., King, B., King, C., Kingham, L., Kirk, J., Kitterick, P., Klenerman, P., Knibbs, L., Knight, S., Knighton, A., Kon, O., Kon, S., Kon, S.S., Koprowska, S., Korszun, A., Koychev, I., Kurasz, C., Kurupati, P., Laing, C., Lamlum, H., Landers, G., Langenberg, C., Lasserson, D., Lavelle-Langham, L., Lawrie, A., Lawson, C., Layton, A., Lea, A., Leavy, O.C., Lee, D., Lee, J.-H., Lee, E., Leitch, K., Lenagh, R., Lewis, D., Lewis, J., Lewis, K.E., Lewis, V., Lewis-Burke, N., Li, X., Light, T., Lightstone, L., Lilaonitkul, W., Lim, L., Linford, S., Lingford-Hughes, A., Lipman, M., Liyanage, K., Lloyd, A., Logan, S., Lomas, D., Lone, N.I., Loosley, R., Lord, J.M., Lota, H., Lovegrove, W., Lucey, A., Lukaschuk, E., Lye, A., Lynch, C., MacDonald, S., MacGowan, G., Macharia, I., Mackie, J., Macliver, L., Madathil, S., Madzamba, G., Magee, N., Magtoto, M.M., Mairs, N., Majeed, N., Major, E., Malein, F., Malim, M., Mallison, G., D-C. Man, W., Mandal, S., Mangion, K., Manisty, C., Manley, R., March, K., Marciniak, S., Marino, P., Mariveles, M., Marks, M., Marouzet, E., Marsh, S., Marshall, B., Marshall, M., Martin, J., Martineau, A., Martinez, L.M., Maskell, N., Matila, D., Matimba-Mupaya, W., Matthews, L., Mbuyisa, A., McAdoo, S., McAllister-Williams, H., McArdle, A., McArdle, P., McAulay, D., McCann, G.P., McCormick, J., McCormick, W., McCourt, P., McGarvey, L., McGee, C., Mcgee, K., McGinness, J., McGlynn, K., McGovern, A., McGuinness, H., McInnes, I.B., McIntosh, J., McIvor, E., McIvor, K., McLeavey, L., McMahon, A., McMahon, M.J., McMorrow, L., Mcnally, T., McNarry, M., McNeill, J., McQueen, A., McShane, H., Mears, C., Megson, C., Megson, S., Mehta, P., Meiring, J., Melling, L., Mencias, M., Menzies, D., Merida Morillas, M., Michael, A., Miller, C., Milligan, L., Mills, C., Mills, G., Mills, N.L., Milner, L., Misra, S., Mitchell, J., Mohamed, A., Mohamed, N., Mohammed, S., Molyneaux, P.L., Monteiro, W., Moriera, S., Morley, A., Morrison, L., Morriss, R., Morrow, A., Moss, A.J., Moss, P., Motohashi, K., Msimanga, N., Mukaetova-Ladinska, E., Munawar, U., Murira, J., Nanda, U., Nassa, H., Nasseri, M., Neal, A., Needham, R., Neill, P., Neubauer, S., Newby, D.E., Newell, H., Newman, T., Newman, J., Newton-Cox, A., Nicholson, T., Nicoll, D., Nikolaidis, A., Nolan, C.M., Noonan, M.J., Norman, C., Novotny, P., Nunag, J., Nwafor, L., Nwanguma, U., Nyaboko, J., O'Brien, C., O'Donnell, K., O'Regan, D., O'Brien, L., Odell, N., Ogg, G., Olaosebikan, O., Oliver, C., Omar, Z., Openshaw, P.J.M., Orriss-Dib, L., Osborne, L., Osbourne, R., Ostermann, M., Overton, C., Owen, J., Oxton, J., Pack, J., Pacpaco, E., Paddick, S., Painter, S., Pakzad, A., Palmer, S., Papineni, P., Paques, K., Paradowski, K., Pareek, M., Parekh, D., Parfrey, H., Pariante, C., Parker, S., Parkes, M., Parmar, J., Patale, S., Patel, B., Patel, M., Patel, S., Pattenadk, D., Pavlides, M., Payne, S., Pearce, L., Pearl, J.E., Peckham, D., Pendlebury, J., Peng, Y., Pennington, C., Peralta, I., Perkins, E., Peterkin, Z., Peto, T., Petousi, N., Petrie, J., Pfeffer, P., Phipps, J., Pimm, J., Hanley, K. Piper, Pius, R., Plant, H., Plein, S., Plekhanova, T., Plowright, M., Poinasamy, K., Polgar, O., Poll, L., Porter, J.C., Porter, J., Portukhay, S., Powell, N., Prabhu, A., Pratt, J., Price, A., Price, C., Price, D., Price, L., Prickett, A., Propescu, J., Prosper, S., Pugmire, S., Quaid, S., Quigley, J., Quint, J., Qureshi, H., Qureshi, I.N., Radhakrishnan, K., Rahman, N.M., Ralser, M., Raman, B., Ramos, A., Ramos, H., Rangeley, J., Rangelov, B., Ratcliffe, L., Ravencroft, P., Reddington, A., Reddy, R., Reddy, A., Redfearn, H., Redwood, D., Reed, A., Rees, M., Rees, T., Regan, K., Reynolds, W., Ribeiro, C., Richards, A., Richardson, E., Richardson, M., Rivera-Ortega, P., Roberts, K., Robertson, E., Robinson, E., Robinson, L., Roche, L., Roddis, C., Rodger, J., Ross, A., Ross, G., Rossdale, J., Rostron, A., Rowe, A., Rowland, A., Rowland, J., Rowland, M.J., Rowland-Jones, S.L., Roy, K., Roy, M., Rudan, I., Russell, R., Russell, E., Saalmink, G., Sabit, R., Sage, E.K., Samakomva, T., Samani, N., Sampson, C., Samuel, K., Samuel, R., Sanderson, A., Sapey, E., Saralaya, D., Sargant, J., Sarginson, C., Sass, T., Sattar, N., Saunders, K., Saunders, R.M., Saunders, P., Saunders, L.C., Savill, H., Saxon, W., Sayer, A., Schronce, J., Schwaeble, W., Scott, J.T., Scott, K., Selby, N., Semple, M.G., Sereno, M., Sewell, T.A., Shah, A., Shah, K., Shah, P., Shankar-Hari, M., Sharma, M., Sharpe, C., Sharpe, M., Shashaa, S., Shaw, A., Shaw, K., Shaw, V., Sheikh, A., Shelton, S., Shenton, L., Shevket, K., Shikotra, A., Short, J., Siddique, S., Siddiqui, S., Sidebottom, J., Sigfrid, L., Simons, G., Simpson, J., Simpson, N., Singapuri, A., Singh, C., Singh, S., Singh, S.J., Sissons, D., Skeemer, J., Slack, K., Smith, A., Smith, D., Smith, S., Smith, J., Smith, L., Soares, M., Solano, T.S., Solly, R., Solstice, A.R., Soulsby, T., Southern, D., Sowter, D., Spears, M., Spencer, L.G., Speranza, F., Stadon, L., Stanel, S., Steele, N., Steiner, M., Stensel, D., Stephens, G., Stephenson, L., Stern, M., Stewart, I., Stimpson, R., Stockdale, S., Stockley, J., Stoker, W., Stone, R., Storrar, W., Storrie, A., Storton, K., Stringer, E., Strong-Sheldrake, S., Stroud, N., Subbe, C., Sudlow, C.L., Suleiman, Z., Summers, C., Summersgill, C., Sutherland, D., Sykes, D.L., Sykes, R., Talbot, N., Tan, A.L., Tarusan, L., Tavoukjian, V., Taylor, A., Taylor, C., Taylor, J., Te, A., Tedd, H., Tee, C.J., Teixeira, J., Tench, H., Terry, S., Thackray-Nocera, S., Thaivalappil, F., Thamu, B., Thickett, D., Thomas, C., Thomas, D.C., Thomas, S., Thomas, A.K., Thomas-Woods, T., Thompson, T., Thompson, A.A.R., Thornton, T., Thorpe, M., Thwaites, R.S., Tilley, J., Tinker, N., Tiongson, G.F., Tobin, M., Tomlinson, J., Tong, C., Toshner, M., Touyz, R., Tripp, K.A., Tunnicliffe, E., Turnbull, A., Turner, E., Turner, S., Turner, V., Turner, K., Turney, S., Turtle, L., Turton, H., Ugoji, J., Ugwuoke, R., Upthegrove, R., Valabhji, J., Ventura, M., Vere, J., Vickers, C., Vinson, B., Wade, E., Wade, P., Wain, L.V., Wainwright, T., Wajero, L.O., Walder, S., Walker, S., Wall, E., Wallis, T., Walmsley, S., Walsh, J.A., Walsh, S., Warburton, L., Ward, T.J.C., Warwick, K., Wassall, H., Waterson, S., Watson, E., Watson, L., Watson, J., McCall, J. Weir, Welch, C., Welch, H., Welsh, B., Wessely, S., West, S., Weston, H., Wheeler, H., White, S., Whitehead, V., Whitney, J., Whittaker, S., Whittam, B., Whitworth, V., Wight, A., Wild, J., Wilkins, M., Wilkinson, D., Williams, B., Williams, N., Williams, J., Williams-Howard, S.A., Willicombe, M., Willis, G., Willoughby, J., Wilson, A., Wilson, D., Wilson, I., Window, N., Witham, M., Wolf-Roberts, R., Wood, C., Woodhead, F., Woods, J., Wootton, D.G., Wormleighton, J., Worsley, J., Wraith, D., Brown, C. Wrey, Wright, C., Wright, L., Wright, S., Wyles, J., Wynter, I., Xu, M., Yasmin, N., Yasmin, S., Yates, T., Yip, K.P., Young, B., Young, S., Young, A., Yousuf, A.J., Zawia, A., Zeidan, L., Zhao, B., Zheng, B., Zongo, O., Zheng, Bang, Vivaldi, Giulia, Daines, Luke, Leavy, Olivia C., Richardson, Matthew, Elneima, Omer, McAuley, Hamish J.C., Shikotra, Aarti, Singapuri, Amisha, Sereno, Marco, Saunders, Ruth M., Harris, Victoria C., Houchen-Wolloff, Linzy, Greening, Neil J., Pfeffer, Paul E., Hurst, John R., Brown, Jeremy S., Shankar-Hari, Manu, Echevarria, Carlos, De Soyza, Anthony, Harrison, Ewen M., Docherty, Annemarie B., Lone, Nazir, Quint, Jennifer K., Chalmers, James D., Ho, Ling-Pei, Horsley, Alex, Marks, Michael, Poinasamy, Krishna, Raman, Betty, Heaney, Liam G., Wain, Louise V., Evans, Rachael A., Brightling, Christopher E., Martineau, Adrian, and Sheikh, Aziz

Published
2023
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21. Identification of key opportunities for optimising the management of high-risk COPD patients in the UK using the CONQUEST quality standards: an observational longitudinal study

Author

Halpin, David M.G., Dickens, Andrew P., Skinner, Derek, Murray, Ruth, Singh, Mukesh, Hickman, Katherine, Carter, Victoria, Couper, Amy, Evans, Alexander, Pullen, Rachel, Menon, Shruti, Morris, Tamsin, Muellerova, Hana, Bafadhel, Mona, Chalmers, James, Devereux, Graham, Gibson, Martin, Hurst, John R., Jones, Rupert, Kostikas, Konstantinos, Quint, Jennifer, Singh, Dave, van Melle, Marije, Wilkinson, Tom, and Price, David

Published
2023
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23. Disease Progression Modeling in Chronic Obstructive Pulmonary Disease

Author

Young, Alexandra L, Bragman, Felix JS, Rangelov, Bojidar, Han, MeiLan K, Galbán, Craig J, Lynch, David A, Hawkes, David J, Alexander, Daniel C, Hurst, John R, Crapo, James D, Silverman, Edwin K, Make, Barry J, Regan, Elizabeth A, Beaty, Terri, Begum, Ferdouse, Castaldi, Peter J, Cho, Michael, DeMeo, Dawn L, Boueiz, Adel R, Foreman, Marilyn G, Halper-Stromberg, Eitan, Hayden, Lystra P, Hersh, Craig P, Hetmanski, Jacqueline, Hobbs, Brian D, Hokanson, John E, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Parker, Margaret M, Qiao, Dandi, Wan, Emily S, Won, Sungho, Sakornsakolpat, Phuwanat, Prokopenko, Dmitry, Al Qaisi, Mustafa, Coxson, Harvey O, Gray, Teresa, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Newell, John D, Ross, James C, Estepar, Raul San Jose, Schroeder, Joyce, Sieren, Jered, Stinson, Douglas, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Washko, George, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Moore, Camille, Strand, Matt, Hughes, John, Kinney, Gregory, Pratte, Katherine, Young, Kendra A, Bhatt, Surya, Bon, Jessica, Martinez, Carlos, Murray, Susan, Soler, Xavier, Bowler, Russell P, Kechris, Katerina, Banaei-Kashani, Farnoush, Curtis, Jeffrey L, Martinez, Carlos H, Pernicano, Perry G, Hanania, Nicola, Alapat, Philip, Atik, Mustafa, Bandi, Venkata, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Nachiappan, Arun, Parulekar, Amit, Barr, R Graham, Austin, John, D’Souza, Belinda, Pearson, Gregory DN, Rozenshtein, Anna, Thomashow, Byron, MacIntyre, Neil, McAdams, H Page, Washington, Lacey, McEvoy, Charlene, and Tashjian, Joseph

Subjects
Biomedical Imaging, Lung, Clinical Research, Chronic Obstructive Pulmonary Disease, Aetiology, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, 2.1 Biological and endogenous factors, Respiratory, Aged, Disease Progression, Female, Humans, Male, Middle Aged, Models, Theoretical, Pulmonary Disease, Chronic Obstructive, Tomography, X-Ray Computed, clustering, CT imaging, emphysema, bronchitis, chronic obstructive pulmonary disease, COPDGene Investigators, Medical and Health Sciences, Respiratory System
Abstract

Rationale: The decades-long progression of chronic obstructive pulmonary disease (COPD) renders identifying different trajectories of disease progression challenging.Objectives: To identify subtypes of patients with COPD with distinct longitudinal progression patterns using a novel machine-learning tool called "Subtype and Stage Inference" (SuStaIn) and to evaluate the utility of SuStaIn for patient stratification in COPD.Methods: We applied SuStaIn to cross-sectional computed tomography imaging markers in 3,698 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-4 patients and 3,479 controls from the COPDGene (COPD Genetic Epidemiology) study to identify subtypes of patients with COPD. We confirmed the identified subtypes and progression patterns using ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) data. We assessed the utility of SuStaIn for patient stratification by comparing SuStaIn subtypes and stages at baseline with longitudinal follow-up data.Measurements and Main Results: We identified two trajectories of disease progression in COPD: a "Tissue→Airway" subtype (n = 2,354, 70.4%), in which small airway dysfunction and emphysema precede large airway wall abnormalities, and an "Airway→Tissue" subtype (n = 988, 29.6%), in which large airway wall abnormalities precede emphysema and small airway dysfunction. Subtypes were reproducible in ECLIPSE. Baseline stage in both subtypes correlated with future FEV1/FVC decline (r = -0.16 [P

Published
2020

27. Predictors of specialist care referrals (SCR) following emergency department review or hospital admission in adults with previous acute COVID-19: a prospective UK cohort study.

Author

Saigal, Anita, Xiao, Songyuan, Siddique, Owais, Naran, Prasheena, Bintalib, Heba M, Niklewicz, Camila Nagoda, Seligmann, George, Naidu, Sindhu Bhaarrati, Shah, Amar J, Ogbonnaya, Chibueze, Hurst, John R, Lipman, Marc Ci, and Mandal, Swapna

Subjects
MENTAL health services, POST-acute COVID-19 syndrome, MEDICAL sciences, PUBLIC health, SYMPTOM burden
Abstract

Background: Long-COVID research to date focuses on outcomes in non-hospitalised vs. hospitalised survivors. However Emergency Department attendees (post-ED) presenting with acute COVID-19 may experience less supported recovery compared to people admitted and discharged from hospital (post-hospitalised group, PH). Objective: We evaluated outcomes and predictors of specialty care referrals (SCR) in those with ongoing symptomatic Long-COVID, comparing post-ED and PH adults. Methods: This prospective observational cohort study evaluates 800 PH and 484 post-ED adults from a single hospital in London, United Kingdom. Participants had either confirmed laboratory-positive SARS-CoV-2 infection or clinically suspected acute COVID-19 and were offered post-COVID clinical follow-up at approximately six weeks after their ED attendance or inpatient discharge, to assess ongoing symptoms and support recovery. Multiple logistic regression determined associations with specialist care referrals (SCR) to respiratory, cardiology, physiotherapy (including chest physiotherapy), and mental health services. Results: Presence of at least one Long-COVID symptom was lower in adults attending ED services with acute COVID-19 compared to those hospitalised (70.1% post-ED vs. 79.5% PH adults, p < 0.001). Total number of Long-COVID symptoms was associated with increased SCR in all patients (adjusted odds ratio (aOR) = 1.26, 95%CI:1.16, 1.36, p < 0.001), with post-ED adults more likely to need a SCR overall (aOR = 1.82, 95%CI:1.19, 2.79, p = 0.006). Post-ED adults had higher SCR to both physiotherapy (aOR = 2.59, 95%CI:1.35, 4.96, p = 0.004) and mental health services (aOR = 3.84, 95%CI:2.00, 7.37, p < 0.001), with pre-existing mental illness linked to the latter (aOR = 4.08, 95%CI:1.07, 15.6, p = 0.04). Conclusions: We demonstrate greater specialist care referrals to mental health and physiotherapy services in patients attending the ED and discharged with acute COVID-19, compared to those admitted, despite lower ongoing COVID-19 symptom burden. Total number of symptoms, pre-existing co-morbidity such as smoking status, cardiac co-morbidities, and mental health illnesses may predict those requiring healthcare input. This information may enable better post-COVID support for ED attendees, a distinct group who should not be neglected when preparing for future pandemics. Trial registration: This study had HRA approval (20/HRA/4928). [ABSTRACT FROM AUTHOR]

Published
2025
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30. Illness Representations of Chronic Obstructive Pulmonary Disease (COPD) to Inform Health Education Strategies and Research Design-Learning from Rural Uganda

Author

Nagourney, Emily M., Robertson, Nicole M., Rykiel, Natalie, Siddharthan, Trishul, Alupo, Patricia, Encarnacion, Marysol, Kirenga, Bruce J., Kalyesubula, Robert, Quaderi, Shumonta A., Hurst, John R., Checkley, William, and Pollard, Suzanne L.

Abstract

More than 90% of chronic obstructive pulmonary disease (COPD)-related deaths occur in low- and middle-income countries; however, few studies have examined the illness experiences of individuals living with and providing treatment for COPD in these settings. This study characterizes illness representations for COPD in Nakaseke, Uganda from the perspectives of health care providers, village health teams and community members (CMs) with COPD. We conducted 40 in-depth, semi-structured interviews (16 health care providers, 12 village health teams and 12 CMs, aged 25-80 years). Interviews were analyzed using inductive coding, and the Illness Representations Model guided our analysis. Stakeholder groups showed concordance in identifying causal mechanisms of COPD, but showed disagreement in reasons for care seeking behaviors and treatment preferences. CMs did not use a distinct label to differentiate COPD from other respiratory illnesses, and described both the physical and social consequences of COPD. Local representations can inform development of adapted educational and self-management tools for COPD.

Published
2020
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32. Effectiveness-implementation of COPD case finding and self-management action plans in low- and middle-income countries: global excellence in COPD outcomes (GECo) study protocol

Author

Siddharthan, Trishul, Pollard, Suzanne L, Quaderi, Shumonta A, Mirelman, Andrew J, Cárdenas, Maria Kathia, Kirenga, Bruce, Rykiel, Natalie A, Miranda, J Jaime, Shrestha, Laxman, Chandyo, Ram K, Cattamanchi, Adithya, Michie, Susan, Barber, Julie, Checkley, William, and Hurst, John R

Subjects
Health Services and Systems, Health Sciences, Chronic Obstructive Pulmonary Disease, Lung, Clinical Trials and Supportive Activities, Clinical Research, Health Services, Comparative Effectiveness Research, Prevention, Respiratory, Adult, Cost-Benefit Analysis, Humans, Peak Expiratory Flow Rate, Pulmonary Disease, Chronic Obstructive, Randomized Controlled Trials as Topic, Self Care, Spirometry, Surveys and Questionnaires, COPD, COPD exacerbations, COPD case finding, COPD action plan, Non-communicable disease, Self-management, GECo Study Investigators, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Cardiovascular System & Hematology, General & Internal Medicine, Clinical sciences, Epidemiology, Health services and systems
Abstract

BackgroundChronic obstructive pulmonary disease (COPD) is the end result of a susceptible individual being exposed to sufficiently deleterious environmental stimuli. More than 90% of COPD-related deaths occur in low- and middle-income countries (LMICs). LMICs face unique challenges in managing COPD; for example, deficient primary care systems present challenges for proper diagnosis and management. Formal diagnosis of COPD requires quality-assured spirometry, which is often limited to urban health centres. Similarly, standard treatment options for COPD remain limited where few providers are trained to manage COPD. The Global Excellence in COPD Outcomes (GECo) studies aim to assess the performance of a COPD case-finding questionnaire with and without peak expiratory flow (PEF) to diagnose COPD, and inform the effectiveness and implementation of COPD self-management Action Plans in LMIC settings. The ultimate goal is to develop simple, low-cost models of care that can be implemented in LMICs. This study will be carried out in Nepal, Peru and Uganda, three distinct LMIC settings.Methods/designWe aim to assess the diagnostic accuracy of a simple questionnaire with and without PEF to case-find COPD (GECo1), and examine the effectiveness, cost-effectiveness and implementation of a community-health-worker-supported self-management Action Plan strategy for managing exacerbations of COPD (GECo2). To achieve the first aim, we will enrol a randomly selected sample of up to 10,500 adults aged ≥ 40 years across our three sites, with the goal to enrol 240 participants with moderate-to-severe COPD in to GECo2. We will apply two case-finding questionnaires (Lung Function Questionnaire and CAPTURE) with and without PEF and compare performance against spirometry. We will report ROC areas, sensitivity and specificity. Individuals who are identified as having COPD grades B-D will be invited to enrol in an effectiveness-implementation hybrid randomised trial of a multi-faceted COPD self-management Action Plan intervention delivered by CHWs. The intervention group will receive (1) COPD education, (2) facilitated-self management Action Plans for COPD exacerbations and (3) monthly visits by community health workers. The control group will receive COPD education and standard of care treatment provided by local health providers. Beginning at baseline, we will measure quality of life with the EuroQol-5D (EQ-5D) and St. George's Respiratory Questionnaire (SGRQ) every 3 months over a period of 1 year. The primary endpoint is SGRQ at 12 months. Quality-adjusted life years (QALYs) using the Short-Form 36 version 2 will also be calculated. We will additionally assess the acceptability and feasibility of implementing COPD Action Plans in each setting among providers and individuals with COPD.DiscussionThis study should provide evidence to inform the use of pragmatic models of COPD diagnosis and management in LMIC settings.Trial registrationNCT03359915 (GECo1). Registered on 2 December 2017 and NCT03365713 (GECo2). Registered on 7 December 2017. Trial acronym: Global Excellence in COPD Outcomes (GECo1; GECo2).

Published
2018

36. MACE in COPD: addressing cardiopulmonary risk

Author

Hurst, John R, primary, Gale, Chris P, additional, Hurst, John R., additional, Bhutani, Mohit, additional, Bourbeau, Jean, additional, Han, MeiLan, additional, Hawkins, Nathaniel M., additional, Lam, Carolyn S.P., additional, Marciniuk, Darcy D., additional, Price, David, additional, Stolz, Daiana, additional, Zieroth, Shelley, additional, and Gale, Chris P., additional

Published
2024
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37. The long-term sequelae of COVID-19: an international consensus on research priorities for patients with pre-existing and new-onset airways disease

Author

Adeloye, Davies, Elneima, Omer, Daines, Luke, Poinasamy, Krisnah, Quint, Jennifer K, Walker, Samantha, Brightling, Chris E, Siddiqui, Salman, Hurst, John R, Chalmers, James D, Pfeffer, Paul E, Novotny, Petr, Drake, Thomas M, Abdollahi, Mohammad, Agarwal, Dhiraj, Al-Lehebi, Riyad, Barnes, Peter J, Bayry, Jagadeesh, Bonay, Marcel, Bont, Louis J, Bourdin, Arnaud, Brown, Thomas, Caramori, Gaetano, Chan, Amy Hai Yan, Dockrell, David H, Doe, Simon, Duckers, Jamie, D'Urzo, Anthony, Ekström, Magnus, Esteban, Cristóbal, Greene, Catherine M, Gupta, Atul, Ingram, Jennifer L, Khoo, Ee Ming, Ko, Fanny Wai San, Koppelman, Gerard H, Lipworth, Brian J, Lisspers, Karin, Loebinger, Michael, Lopez-Campos, Jose Luis, Maddocks, Matthew, Mannino, David, Martinez-Garcia, Miguel A, Mcnamara, Renae, Miravitlles, Marc, Ndarukwa, Pisirai, Pooler, Alison, Rhee, Chin Kook, Schwarz, Peter, Shaw, Dominick, Steiner, Michael, Tai, Andrew, Ulrik, Charlotte Suppli, Walker, Paul, Williams, Michelle C, Heaney, Liam G, Rudan, Igor, Sheikh, Aziz, and De Soyza, Anthony

Published
2021
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38. Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study

Author

Abel, K, Adamali, H, Adeloye, D, Adeyemi, O, Adeyemi, F, Ahmad, S, Ahmed, R, Ainsworth, M, Al-Sheklly, B, Alamoudi, A, Aljaroof, M, Allan, L, Allen, R, Alli, A, Altmann, D, Anderson, D, Andrews, M, Angyal, A, Antoniades, C, Arbane, G, Armour, C, Armstrong, N, Armstrong, L, Arnold, H, Arnold, D, Ashworth, M, Ashworth, A, Assefa-Kebede, H, Atkin, P, Atkins, H, Atkins, A, Aul, R, Avram, C, Baggott, R, Baguley, D, Baillie, J K, Bain, S, Bakali, M, Bakau, M, Baldry, E, Baldwin, D, Ballard, C, Bambrough, J, Barker, R E, Barratt, S, Barrett, F, Basire, D, Basu, N, Batterham, R, Baxendale, H, Bayes, H, Bayley, M, Beadsworth, M, Beirne, P, Bell, R, Bell, D, Berry, C, Betts, S, Bhui, K, Bishop, L, Blaikely, J, Bloomfield, C, Bloss, A, Bolger, A, Bolton, C E, Bonnington, J, Botkai, A, Bourne, M, Bourne, C, Bradley, E, Bramham, K, Brear, L, Breen, G, Breeze, J, Briggs, A, Bright, E, Brightling, C E, Brill, S, Brindle, K, Broad, L, Broome, M, Brown, J S, Brown, M, Brown, J, Brown, R, Brown, V, Brown, A, Brugha, T, Brunskill, N, Buch, M, Bularga, A, Bullmore, E, Burn, D, Burns, G, Busby, J, Buttress, A, Byrne, S, Cairns, P, Calder, P C, Calvelo, E, Card, B, Carr, L, Carson, G, Carter, P, Cavanagh, J, Chalder, T, Chalmers, J D, Chambers, R C, Channon, K, Chapman, K, Charalambou, A, Chaudhuri, N, Checkley, A, Chen, J, Chetham, L, Chilvers, E R, Chinoy, H, Chong-James, K, Choudhury, N, Choudhury, G, Chowdhury, P, Chowienczyk, P, Christie, C, Clark, D, Clark, C, Clarke, J, Clift, P, Clohisey, S, Coburn, Z, Cole, J, Coleman, C, Connell, D, Connolly, B, Connor, L, Cook, A, Cooper, B, Coupland, C, Craig, T, Crisp, P, Cristiano, D, Crooks, M G, Cross, A, Cruz, I, Cullinan, P, Daines, L, Dalton, M, Dark, P, Dasgin, J, David, A, David, C, Davies, M, Davies, G, Davies, K, Davies, F, Davies, G A, Daynes, E, De Silva, T, De Soyza, A, Deakin, B, Deans, A, Defres, S, Dell, A, Dempsey, K, Dennis, J, Dewar, A, Dharmagunawardena, R, Diar Bakerly, N, Dipper, A, Diver, S, Diwanji, S N, Dixon, M, Djukanovic, R, Dobson, H, Dobson, C, Dobson, S L, Docherty, A B, Donaldson, A, Dong, T, Dormand, N, Dougherty, A, Dowling, R, Drain, S, Dulawan, P, Dunleavy, A, Dunn, S, Easom, N, Echevarria, C, Edwards, S, Edwardson, C, Elliott, B, Elliott, A, Ellis, Y, Elmer, A, Elneima, O, Evans, R A, Evans, J, Evans, H, Evans, D, Evans, R I, Evans, R, Evans, T, Fabbri, L, Fairbairn, S, Fairman, A, Fallon, K, Faluyi, D, Favager, C, Felton, T, Finch, J, Finney, S, Fisher, H, Fletcher, S, Flockton, R, Foote, D, Ford, A, Forton, D, Francis, R, Francis, S, Francis, C, Frankel, A, Fraser, E, Free, R, French, N, Fuld, J, Furniss, J, Garner, L, Gautam, N, Geddes, J R, George, P M, George, J, Gibbons, M, Gilmour, L, Gleeson, F, Glossop, J, Glover, S, Goodman, N, Gooptu, B, Gorsuch, T, Gourlay, E, Greenhaff, P, Greenhalf, W, Greenhalgh, A, Greening, N J, Greenwood, J, Greenwood, S, Gregory, R, Grieve, D, Gummadi, M, Gupta, A, Gurram, S, Guthrie, E, Hadley, K, Haggar, A, Hainey, K, Haldar, P, Hall, I, Hall, L, Halling-Brown, M, Hamil, R, Hanley, N A, Hardwick, H E, Hardy, E, Hargadon, B, Harrington, K, Harris, V, Harrison, E M, Harrison, P, Hart, N, Harvey, A, Harvey, M, Harvie, M, Havinden-Williams, M, Hawkes, J, Hawkings, N, Haworth, J, Hayday, A, Heaney, L G, Heeney, J L, Heightman, M, Heller, S, Henderson, M, Hesselden, L, Hillman, T, Hingorani, A, Hiwot, T, Ho, L P, Hoare, A, Hoare, M, Hogarth, P, Holbourn, A, Holdsworth, L, Holgate, D, Holmes, K, Holroyd-Hind, B, Horsley, A, Hosseini, A, Hotopf, M, Houchen, L, Howard, L, Howell, A, Hufton, E, Hughes, A, Hughes, J, Hughes, R, Humphries, A, Huneke, N, Hurst, J R, Hurst, R, Husain, M, Hussell, T, Ibrahim, W, Ient, A, Ingram, L, Ismail, K, Jackson, T, Jacob, J, James, W Y, Janes, S, Jarvis, H, Jayaraman, B, Jenkins, R G, Jezzard, P, Jiwa, K, Johnson, S, Johnson, C, Johnston, D, Jolley, C, Jolley, C J, Jones, I, Jones, S, Jones, D, Jones, H, Jones, G, Jones, M, Jose, S, Kabir, T, Kaltsakas, G, Kamwa, V, Kar, P, Kausar, Z, Kelly, S, Kerr, S, Key, A L, Khan, F, Khunti, K, King, C, King, B, Kitterick, P, Klenerman, P, Knibbs, L, Knight, S, Knighton, A, Kon, O M, Kon, S, Kon, S S, Korszun, A, Kotanidis, C, Koychev, I, Kurupati, P, Kwan, J, Laing, C, Lamlum, H, Landers, G, Langenberg, C, Lasserson, D, Lawrie, A, Lea, A, Leavy, O C, Lee, D, Lee, E, Leitch, K, Lenagh, R, Lewis, K, Lewis, V, Lewis, K E, Lewis, J, Lewis-Burke, N, Light, T, Lightstone, L, Lim, L, Linford, S, Lingford-Hughes, A, Lipman, M, Liyanage, K, Lloyd, A, Logan, S, Lomas, D, Lone, N I, Loosley, R, Lord, J M, Lota, H, Lucey, A, MacGowan, G, Macharia, I, Mackay, C, Macliver, L, Madathil, S, Madzamba, G, Magee, N, Mairs, N, Majeed, N, Major, E, Malim, M, Mallison, G, Man, W, Mandal, S, Mangion, K, Mansoori, P, Marciniak, S, Mariveles, M, Marks, M, Marshall, B, Martineau, A, Maskell, N, Matila, D, Matthews, L, Mayet, J, McAdoo, S, McAllister-Williams, H, McArdle, P, McArdle, A, McAulay, D, McAuley, H J C, McAuley, D F, McCafferty, K, McCann, G P, McCauley, H, McCourt, P, Mcgarvey, L, McGinness, J, McGovern, A, McGuinness, H, McInnes, I B, McIvor, K, McIvor, E, McMahon, A, McMahon, M J, McMorrow, L, Mcnally, T, McNarry, M, McQueen, A, McShane, H, Megson, S, Meiring, J, Menzies, D, Michael, A, Michael, B D, Milligan, L, Mills, N, Mitchell, J, Mohamed, A, Molyneaux, P L, Monteiro, W, Morley, A, Morrison, L, Morriss, R, Morrow, A, Moss, A, Moss, A J, Moss, P, Mukaetova-Ladinska, E, Munawar, U, Murali, E, Murira, J, Nassa, H, Neill, P, Neubauer, S, Newby, D, Newell, H, Newton Cox, A, Nicholson, T, Nicoll, D, Nolan, C M, Noonan, M J, Novotny, P, Nunag, J, Nyaboko, J, O'Brien, L, Odell, N, Ogg, G, Olaosebikan, O, Oliver, C, Omar, Z, Openshaw, P J M, Osbourne, R, Ostermann, M, Overton, C, Oxton, J, Pacpaco, E, Paddick, S, Papineni, P, Paradowski, K, Pareek, M, Parekh, D, Parfrey, H, Pariante, C, Parker, S, Parkes, M, Parmar, J, Parvin, R, Patale, S, Patel, B, Patel, S, Patel, M, Pathmanathan, B, Pavlides, M, Pearl, J E, Peckham, D, Pendlebury, J, Peng, Y, Pennington, C, Peralta, I, Perkins, E, Peto, T, Petousi, N, Petrie, J, Pfeffer, P, Phipps, J, Pimm, J, Piper Hanley, K, Pius, R, Plein, S, Plekhanova, T, Poinasamy, K, Polgar, O, Poll, L, Porter, J C, Portukhay, S, Powell, N, Price, L, Price, D, Price, A, Price, C, Prickett, A, Quaid, S, Quigley, J, Quint, J, Qureshi, H, Rahman, N, Rahman, M, Ralser, M, Raman, B, Ramos, A, Rangeley, J, Rees, T, Regan, K, Richards, A, Richardson, M, Rivera-Ortega, P, Robertson, E, Rodgers, J, Ross, G, Rossdale, J, Rostron, A, Routen, A, Rowland, A, Rowland, M J, Rowland, J, Rowland-Jones, S L, Roy, K, Rudan, I, Russell, R, Russell, E, Sabit, R, Sage, E K, Samani, N, Samuel, R, Sapey, E, Saralaya, D, Saratzis, A, Sargeant, J, Sass, T, Sattar, N, Saunders, K, Saunders, R, Saxon, W, Sayer, A, Schwaeble, W, Scott, J, Scott, K, Selby, N, Semple, M G, Sereno, M, Shah, K, Shah, A, Shah, P, Sharma, M, Sharpe, M, Sharpe, C, Shaw, V, Sheikh, A, Shevket, K, Shikotra, A, Short, J, Siddiqui, S, Sigfrid, L, Simons, G, Simpson, J, Singapuri, A, Singh, S J, Singh, C, Singh, S, Skeemer, J, Smith, I, Smith, J, Smith, L, Smith, A, Soares, M, Southern, D, Spears, M, Spencer, L G, Speranza, F, Stadon, L, Stanel, S, Steiner, M, Stensel, D, Stern, M, Stewart, I, Stockley, J, Stone, R, Storrie, A, Storton, K, Stringer, E, Subbe, C, Sudlow, C, Suleiman, Z, Summers, C, Summersgill, C, Sutherland, D, Sykes, D L, Sykes, R, Talbot, N, Tan, A L, Taylor, C, Taylor, A, Te, A, Tedd, H, Tee, C J, Tench, H, Terry, S, Thackray-Nocera, S, Thaivalappil, F, Thickett, D, Thomas, D, Thomas, D C, Thomas, A K, Thompson, A A R, Thompson, T, Thornton, T, Thwaites, R S, Tobin, M, Toingson, G F, Tong, C, Toshner, M, Touyz, R, Tripp, K A, Tunnicliffe, E, Turner, E, Turtle, L, Turton, H, Ugwuoke, R, Upthegrove, R, Valabhji, J, Vellore, K, Wade, E, Wain, L V, Wajero, L O, Walder, S, Walker, S, Wall, E, Wallis, T, Walmsley, S, Walsh, S, Walsh, J A, Watson, L, Watson, J, Watson, E, Welch, C, Welch, H, Welsh, B, Wessely, S, West, S, Wheeler, H, Whitehead, V, Whitney, J, Whittaker, S, Whittam, B, Wild, J, Wilkins, M, Wilkinson, D, Williams, N, Williams, B, Williams, J, Williams-Howard, S A, Willicombe, M, Willis, G, Wilson, D, Wilson, I, Window, N, Witham, M, Wolf-Roberts, R, Woodhead, F, Woods, J, Wootton, D, Worsley, J, Wraith, D, Wright, L, Wright, C, Wright, S, Xie, C, Yasmin, S, Yates, T, Yip, K P, Young, B, Young, S, Young, A, Yousuf, A J, Yousuf, A, Zawia, A, Zhao, B, Zongo, O, Evans, Rachael A, McAuley, Hamish J C, Harrison, Ewen M, Shikotra, Aarti, Singapuri, Amisha, Sereno, Marco, Elneima, Omer, Docherty, Annemarie B, Lone, Nazir I, Leavy, Olivia C, Daines, Luke, Baillie, J Kenneth, Brown, Jeremy S, Chalder, Trudie, De Soyza, Anthony, Diar Bakerly, Nawar, Easom, Nicholas, Geddes, John R, Greening, Neil J, Hart, Nick, Heaney, Liam G, Heller, Simon, Howard, Luke, Hurst, John R, Jacob, Joseph, Jenkins, R Gisli, Jolley, Caroline, Kerr, Steven, Kon, Onn M, Lewis, Keir, Lord, Janet M, McCann, Gerry P, Neubauer, Stefan, Openshaw, Peter J M, Parekh, Dhruv, Pfeffer, Paul, Rahman, Najib M, Raman, Betty, Richardson, Matthew, Rowland, Matthew, Semple, Malcolm G, Shah, Ajay M, Singh, Sally J, Sheikh, Aziz, Thomas, David, Toshner, Mark, Chalmers, James D, Ho, Ling-Pei, Horsley, Alex, Marks, Michael, Poinasamy, Krisnah, Wain, Louise V, and Brightling, Christopher E

Published
2021
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39. Improving lung health in low-income and middle-income countries: from challenges to solutions

Author

Meghji, Jamilah, Mortimer, Kevin, Agusti, Alvar, Allwood, Brian W, Asher, Innes, Bateman, Eric D, Bissell, Karen, Bolton, Charlotte E, Bush, Andrew, Celli, Bartolome, Chiang, Chen-Yuan, Cruz, Alvaro A, Dinh-Xuan, Anh-Tuan, El Sony, Asma, Fong, Kwun M, Fujiwara, Paula I, Gaga, Mina, Garcia-Marcos, Luis, Halpin, David M G, Hurst, John R, Jayasooriya, Shamanthi, Kumar, Ajay, Lopez-Varela, Maria V, Masekela, Refiloe, Mbatchou Ngahane, Bertrand H, Montes de Oca, Maria, Pearce, Neil, Reddel, Helen K, Salvi, Sundeep, Singh, Sally J, Varghese, Cherian, Vogelmeier, Claus F, Walker, Paul, Zar, Heather J, and Marks, Guy B

Published
2021
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42. Introduction

Author

Fabre, Aurelie, primary, Hurst, John R., additional, and Ramjug, Sheila, additional

Published
2021
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43. Current Practices and Considerations in Lung Biopsy for Suspected Granulomatous-Lymphocytic Interstitial Lung Disease: A Clinician Survey.

Author

Bintalib, Heba M., Davidsen, Jesper Rømhild, Van de Ven, Annick A.J.M., Goddard, Sarah, Burns, Siobhan O., Warnatz, Klaus, and Hurst, John R.

Subjects
LUNG radiography, BIOPSY, CONSENSUS (Social sciences), GRANULOMA, MEDICAL personnel, RESEARCH funding, COMPUTED tomography, INTERSTITIAL lung diseases, CHEST X rays, SURVEYS, IMMUNOHISTOCHEMISTRY, PHYSICIAN practice patterns, NEEDLE biopsy, EXPERTISE
Abstract

Introduction: This study explores clinicians' diagnostic practices and perceptions in the context of granulomatous-lymphocytic interstitial lung disease (GLILD), a pulmonary manifestation of common variable immunodeficiency disorder. The aim was to gain valuable insights into key aspects, such as the utilization of radiological features for diagnostic purposes, indications for lung biopsy, preferred biopsy techniques, and the relative importance of different histopathological findings in confirming GLILD. Method: A survey targeting expert clinicians was conducted, focusing on their experiences, practices, and attitudes towards lung biopsy in suspected GLILD cases. Results: The survey revealed that the majority of respondents accepted high-resolution computed tomography as a sufficient alternative to biopsy for making a probable GLILD diagnosis in most patients. There was a consensus among most respondents that the presence of extrapulmonary granulomatous disease is adequate for making a diagnosis of GLILD where the chest imaging and clinical picture are consistent. When a biopsy was recommended, there was notable variation in the preferred initial biopsy technique, with 35% favouring transbronchial biopsy. Conclusion: Our findings underscore the complexity of diagnosing GLILD, indicating varied clinician opinions on the necessity and efficacy of lung biopsies. They highlight the need for further research and the development of consistent diagnostic criteria and management protocols, ultimately aiming to enhance the accuracy and safety of GLILD diagnosis and treatment strategies. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Investigating pulmonary and non-infectious complications in common variable immunodeficiency disorders: a UK national multi-centre study.

Author

Bintalib, Heba M., Grigoriadou, Sofia, Patel, Smita Y., Mutlu, Leman, Sooriyakumar, Kavitha, Vaitla, Prashantha, McDermott, Elizabeth, Drewe, Elizabeth, Steele, Cathal, Ahuja, Manisha, Garcez, Tomaz, Gompels, Mark, Grammatikos, Alexandros, Herwadkar, Archana, Ayub, Rehana, Halliday, Neil, Burns, Siobhan O., Hurst, John R., and Goddard, Sarah

Subjects
COMMON variable immunodeficiency, INTERSTITIAL lung diseases, DISEASE complications, LUNG diseases, GASTROINTESTINAL diseases, BRONCHIECTASIS
Abstract

Background: Common Variable Immunodeficiency Disorders (CVID) encompass a spectrum of immunodeficiency characterised by recurrent infections and diverse non-infectious complications (NICs). This study aimed to describe the clinical features and variation in NICs in CVID with and without interstitial lung disease (ILD) from a large UK national registry population. Methods: Retrospective, cross-sectional data from a UK multicentre database (previously known as UKPIN), categorising patients into those with CVID-ILD and those with NICs related to CVID but without pulmonary involvement (CVID-EP; EP= extra-pulmonary involvement only). Results: 129 patients were included. Chronic lung diseases, especially CVID-ILD, are prominent complications in complex CVID, occurring in 62% of the cohort. Bronchiectasis was common (64% of the cohort) and associated with greater pulmonary function impairment in patients with CVID-ILD compared to those without bronchiectasis. Lymphadenopathy and the absence of gastrointestinal diseases were significant predictors of ILD in complex CVID. Although the presence of liver disease did not differ significantly between the groups, nearly half of the CVID-ILD patients were found to have liver disease. Patients with CVID-ILD were more likely to receive immunosuppressive treatments such as rituximab and mycophenolate mofetil than the CVID-EP group, indicating greater need for treatment and risk of complications. Conclusion: This study highlights the significant burden of CVID-ILD within the CVID population with NICs only. The lungs emerged as the most frequently affected organ, with ILD and bronchiectasis both highly prevalent. These findings emphasise the necessity of a comprehensive and multidisciplinary approach in managing CVID patients, considering their susceptibility to various comorbidities and complications. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Operational Modeling with Health Economics to Support Decision Making for COPD Patients

Author

Yakutcan, Usame, Demir, Eren, Hurst, John R., Taylor, Paul C., and Ridsdale, Heidi A.

Subjects
Medical care, Cost of -- Evaluation, Lung diseases, Obstructive -- Care and treatment, Decision-making -- Health aspects, Business, Health care industry
Abstract

Objective: To assess the impact of interventions for improving the management of chronic obstructive pulmonary disease (COPD), specifically increased use of pulmonary rehabilitation (PR) on patient outcomes and cost-benefit analysis. Data Sources: We used the national Hospital Episode Statistics (HES) datasets in England, local data and experts from the hospital setting, National Prices and National Tariffs, reports and the literature around the effectiveness of PR programs. Study Design: The COPD pathway was modeled using discrete event simulation (DES) to capture the patient pathway to an adequate level of detail as well as randomness in the real world. DES was further enhanced by the integration of a health economic model to calculate the net benefit and cost of treating COPD patients based on key sets of interventions. Data Collection/Extraction Methods: A total of 150 input parameters and 75 distributions were established to power the model using the HES dataset, outpatient activity data from the hospital and community services, and the literature. Principal Findings: The simulation model showed that increasing referral to PR (by 10%, 20%, or 30%) would be cost-effective (with a benefit-cost ratio of 5.81, 5.95, and 5.91, respectively) by having a positive impact on patient outcomes and operational metrics. Number of deaths, admissions, and bed days decreased (ie, by 3.56 patients, 4.90 admissions, and 137.31 bed days for a 30% increase in PR referrals) as well as quality of life increased (ie, by 5.53 QALY among 1540 patients for the 30% increase). Conclusions: No operational model, either statistical or simulation, has previously been developed to capture the COPD patient pathway within a hospital setting. To date, no model has investigated the impact of PR on COPD services, such as operations, key performance, patient outcomes, and cost-benefit analysis. The study will support policies around extending availability of PR as a major intervention. KEYWORDS COPD, cost-benefit analysis, decision support toolkit, discrete event simulation, health economics, patient flow modelling, pulmonary rehabilitation, What Is Known on This Topic * Prior studies showed the effectiveness of pulmonary rehabilitation, comparing patient outcomes, and costs. * The quantifiable impact of re-designing COPD (chronic obstructive pulmonary [...]

Published
2021
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