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2. Evolution of brain MRI lesions in paediatric myelinoligodendrocyte glycoprotein antibody- associated disease (MOGAD) and its relevance to disease course.

Author

Abdel-mannan, Omar, Champsas, Dimitrios, Tur, Carmen, Lee, Vanessa, Manivannan, Sharmila, Usman, Haroon, Skippen, Alison, Desai, Ishita, Chitre, Manali, Forsyth, Rob, Kneen, Rachel, Ram, Dipak, Ramdas, Sithara, Rossor, Thomas, West, Siobhan, Wright, Sukhvir, Palace, Jacqueline, Wassmer, Evangeline, Hemingway, Cheryl, and J. Lim, Ming

Subjects
NEUROMYELITIS optica, BRAIN damage, DISEASE complications, DISEASE progression, MEDICAL care, POSTVACCINAL encephalitis
Published
2024
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3. Evolution of brain MRI lesions in paediatric myelin-oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and its relevance to disease course

Author

Abdel-mannan, Omar, primary, Champsas, Dimitrios, additional, Tur, Carmen, additional, Lee, Vanessa, additional, Manivannan, Sharmila, additional, Usman, Haroon, additional, Skippen, Alison, additional, Desai, Ishita, additional, Chitre, Manali, additional, Forsyth, Rob, additional, Kneen, Rachel, additional, Ram, Dipak, additional, Ramdas, Sithara, additional, Rossor, Thomas, additional, West, Siobhan, additional, Wright, Sukhvir, additional, Palace, Jacqueline, additional, Wassmer, Evangeline, additional, Hemingway, Cheryl, additional, Lim, Ming J, additional, Mankad, Kshitij, additional, Ciccarelli, Olga, additional, and Hacohen, Yael, additional

Published
2023
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4. Evolution of brain MRI lesions in paediatric myelin-oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and its relevance to disease course

Author

Abdel-Mannan, Omar, Champsas, Dimitrios, Tur, Carmen, Lee, Vanessa, Manivannan, Sharmila, Usman, Haroon, Skippen, Alison, Desai, Ishita, Chitre, Manali, Forsyth, Rob, Kneen, Rachel, Ram, Dipak, Ramdas, Sithara, Rossor, Thomas, West, Siobhan, Wright, Sukhvir, Palace, Jacqueline, Wassmer, Evangeline, Hemingway, Cheryl, Lim, Ming J, Mankad, Kshitij, Ciccarelli, Olga, Hacohen, Yael, Abdel-Mannan, Omar, Champsas, Dimitrios, Tur, Carmen, Lee, Vanessa, Manivannan, Sharmila, Usman, Haroon, Skippen, Alison, Desai, Ishita, Chitre, Manali, Forsyth, Rob, Kneen, Rachel, Ram, Dipak, Ramdas, Sithara, Rossor, Thomas, West, Siobhan, Wright, Sukhvir, Palace, Jacqueline, Wassmer, Evangeline, Hemingway, Cheryl, Lim, Ming J, Mankad, Kshitij, Ciccarelli, Olga, and Hacohen, Yael

Abstract

Background: Lesion resolution is often observed in children with myelin-oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and asymptomatic lesions are less commonly reported in MOGAD than in multiple sclerosis (MS). Objective: We aimed to evaluate brain MRI changes over time in paediatric MOGAD. Methods: Retrospective study in eight UK paediatric neuroscience centres. Acute brain MRI and available follow-up MRIs were reviewed. Predictors for lesion dynamic were evaluated using multivariable regression and Kaplan-Meier survival analyses were used to predict risk of relapse, disability and MOG-Ab status. Results:200 children were included (MOGAD 97; MS 103). At first MRI post-attack, new symptomatic and asymptomatic lesions were seen more often in MS vs MOGAD (52/103 vs 28/97; p=0.002 and 37/103 vs 11/97; p<0.001); 83% of MOGAD patients showed at least one lesion’s resolution at 1st follow‐up scan, and 23% had normal MRI. Only 1 MS patient had single lesion resolution; none had normal MRI. Disappearing lesions in MOGAD were seen in 40% after the 2nd attack, 21% after 3rd attack and none after the 4th attack. New lesions at 1st follow-up scan were associated with increased likelihood of relapse (p=0.02) and persistent MOG-Ab serostatus (p=0.0016) compared to those with no new lesions. Plasma exchange was associated with increased likelihood of lesion resolution (p=0.01). Longer time from symptom onset to steroids was associated with increased likelihood of new lesions; 50% increase at 20 days (p=0.01). Conclusions and Relevance: These striking differences in lesion dynamics between MOGAD and MS suggest greater potential to repair. Early treatment with steroids and plasma exchange is associated with reduced likelihood of new lesions.

Published
2023

5. Machine learning models in predicting graft survival in kidney transplantation: meta-analysis

Author

Bharadhwaj Ravindhran, Pankaj Chandak, Nicole Schafer, Kaushal Kundalia, Woochan Hwang, Savvas Antoniadis, Usman Haroon, and Rhana Hassan Zakri

Subjects
General Medicine
Abstract

Background The variations in outcome and frequent occurrence of kidney allograft failure continue to pose important clinical and research challenges despite recent advances in kidney transplantation. The aim of this systematic review was to examine the current application of machine learning models in kidney transplantation and perform a meta-analysis of these models in the prediction of graft survival. Methods This review was registered with the PROSPERO database (CRD42021247469) and all peer-reviewed original articles that reported machine learning model-based prediction of graft survival were included. Quality assessment was performed by the criteria defined by Qiao and risk-of-bias assessment was performed using the PROBAST tool. The diagnostic performance of the meta-analysis was assessed by a meta-analysis of the area under the receiver operating characteristic curve and a hierarchical summary receiver operating characteristic plot. Results A total of 31 studies met the inclusion criteria for the review and 27 studies were included in the meta-analysis. Twenty-nine different machine learning models were used to predict graft survival in the included studies. Nine studies compared the predictive performance of machine learning models with traditional regression methods. Five studies had a high risk of bias and three studies had an unclear risk of bias. The area under the hierarchical summary receiver operating characteristic curve was 0.82 and the summary sensitivity and specificity of machine learning-based models were 0.81 (95 per cent c.i. 0.76 to 0.86) and 0.81 (95 per cent c.i. 0.74 to 0.86) respectively for the overall model. The diagnostic odds ratio for the overall model was 18.24 (95 per cent c.i. 11.00 to 30.16) and 29.27 (95 per cent c.i. 13.22 to 44.46) based on the sensitivity analyses. Conclusion Prediction models using machine learning methods may improve the prediction of outcomes after kidney transplantation by the integration of the vast amounts of non-linear data.

Published
2023

6. Evolution of brain MRI lesions in paediatric myelin-oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and its relevance to disease course

Author

Abdel-mannan, Omar, Champsas, Dimitrios, Tur, Carmen, Lee, Vanessa, Manivannan, Sharmila, Usman, Haroon, Skippen, Alison, Desai, Ishita, Chitre, Manali, Forsyth, Rob, Kneen, Rachel, Ram, Dipak, Ramdas, Sithara, Rossor, Thomas, West, Siobhan, Wright, Sukhvir, Palace, Jacqueline, Wassmer, Evangeline, Hemingway, Cheryl, Lim, Ming J, Mankad, Kshitij, Ciccarelli, Olga, and Hacohen, Yael

Abstract

BackgroundLesion resolution is often observed in children with myelin-oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and asymptomatic lesions are less commonly reported in MOGAD than in multiple sclerosis (MS).ObjectiveWe aimed to evaluate brain MRI changes over time in paediatric MOGAD.MethodsRetrospective study in eight UK paediatric neuroscience centres. Acute brain MRI and available follow-up MRIs were reviewed. Predictors for lesion dynamic were evaluated using multivariable regression and Kaplan-Meier survival analyses were used to predict risk of relapse, disability and MOG-Ab status.Results200 children were included (MOGAD 97; MS 103). At first MRI post attack, new symptomatic and asymptomatic lesions were seen more often in MS versus MOGAD (52/103 vs 28/97; p=0.002 and 37/103 vs 11/97; p<0.001); 83% of patients with MOGAD showed at least one lesion’s resolution at first follow‐up scan, and 23% had normal MRI. Only 1 patient with MS had single lesion resolution; none had normal MRI. Disappearing lesions in MOGAD were seen in 40% after the second attack, 21% after third attack and none after the fourth attack.New lesions at first follow-up scan were associated with increased likelihood of relapse (p=0.02) and persistent MOG-Ab serostatus (p=0.0016) compared with those with no new lesions. Plasma exchange was associated with increased likelihood of lesion resolution (p=0.01). Longer time from symptom onset to steroids was associated with increased likelihood of new lesions; 50% increase at 20 days (p=0.01).ConclusionsThese striking differences in lesion dynamics between MOGAD and MS suggest greater potential to repair. Early treatment with steroids and plasma exchange is associated with reduced likelihood of new lesions.

Published
2024
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8. Developing a Diagnostic Multivariable Prediction Model for Urinary Tract Cancer in Patients Referred with Haematuria: Results from the IDENTIFY Collaborative Study

Author

Sinan Khadhouri, Kevin M. Gallagher, Kenneth R. MacKenzie, Taimur T. Shah, Chuanyu Gao, Sacha Moore, Eleanor F. Zimmermann, Eric Edison, Matthew Jefferies, Arjun Nambiar, Thineskrishna Anbarasan, Miles P. Mannas, Taeweon Lee, Giancarlo Marra, Juan Gómez Rivas, Gautier Marcq, Mark A. Assmus, Taha Uçar, Francesco Claps, Matteo Boltri, Giuseppe La Montagna, Tara Burnhope, Nkwam Nkwam, Tomas Austin, Nicholas E. Boxall, Alison P. Downey, Troy A. Sukhu, Marta Antón-Juanilla, Sonpreet Rai, Yew-Fung Chin, Madeline Moore, Tamsin Drake, James S.A. Green, Beatriz Goulao, Graeme MacLennan, Matthew Nielsen, John S. McGrath, Veeru Kasivisvanathan, Aasem Chaudry, Abhishek Sharma, Adam Bennett, Adnan Ahmad, Ahmed Abroaf, Ahmed Musa Suliman, Aimee Lloyd, Alastair McKay, Albert Wong, Alberto Silva, Alexandre Schneider, Alison MacKay, Allen Knight, Alkiviadis Grigorakis, Amar Bdesha, Amy Nagle, Ana Cebola, Ananda Kumar Dhanasekaran, Andraž Kondža, André Barcelos, Andrea Benedetto Galosi, Andrea Ebur, Andrea Minervini, Andrew Russell, Andrew Webb, Ángel García de Jalón, Ankit Desai, Anna Katarzyna Czech, Anna Mainwaring, Anthony Adimonye, Arighno Das, Arnaldo Figueiredo, Arnauld Villers, Artur Leminski, Arvinda Chippagiri, Asim Ahmed Lal, Asıf Yıldırım, Athanasios Marios Voulgaris, Audrey Uzan, Aye Moh Moh Oo, Ayman Younis, Bachar Zelhof, Bashir Mukhtar, Ben Ayres, Ben Challacombe, Benedict Sherwood, Benjamin Ristau, Billy Lai, Brechtje Nellensteijn, Brielle Schreiter, Carlo Trombetta, Catherine Dowling, Catherine Hobbs, Cayo Augusto Estigarribia Benitez, Cédric Lebacle, Cherrie Wing Yin Ho, Chi-Fai Ng, Chloe Mount, Chon Meng Lam, Chris Blick, Christian Brown, Christopher Gallegos, Claire Higgs, Clíodhna Browne, Conor McCann, Cristina Plaza Alonso, Daniel Beder, Daniel Cohen, Daniel Gordon, Daniel Wilby, Danny Gordon, David Hrouda, David Hua Wu Lau, Dávid Karsza, David Mak, David Martin-Way, Denula Suthaharan, Dhruv Patel, Diego M Carrion, Donald Nyanhongo, Edward Bass, Edward Mains, Edwin Chau, Elba Canelon Castillo, Elizabeth Day, Elsayed Desouky, Emily Gaines, Emma Papworth, Emrah Yuruk, Enes Kilic, Eoin Dinneen, Erika Palagonia, Evanguelos Xylinas, Faizan Khawaja, Fernando Cimarra, Florian Bardet, Francesca Kum, Francesca Peters, Gábor Kovács, Geroge Tanasescu, Giles Hellawell, Giovanni Tasso, Gitte Lam, Giuseppe Pizzuto, Gordan Lenart, Günal Özgür, Hai Bi, Hannah Lyons, Hannah Warren, Hashim Ahmed, Helen Simpson, Helena Burden, Helena Gresty, Hernado Rios Pita, Holly Clarke, Hosam Serag, Howard Kynaston, Hugh Crawford-Smith, Hugh Mostafid, Hugo Otaola-Arca, Hui Fen Koo, Ibrahim Ibrahim, Idir Ouzaid, Ignacio Puche-Sanz, Igor Tomašković, Ilker Tinay, Iqbal Sahibzada, Isaac Thangasamy, Iván Revelo Cadena, Jacques Irani, Jakub Udzik, James Brittain, James Catto, James Green, James Tweedle, Jamie Borrego Hernando, Jamie Leask, Jas Kalsi, Jason Frankel, Jason Toniolo, Jay D. Raman, Jean Courcier, Jeevan Kumaradeevan, Jennifer Clark, Jennifer Jones, Jeremy Yuen-Chun Teoh, John Iacovou, John Kelly, John P. Selph, Jonathan Aning, Jon Deeks, Jonathan Cobley, Jonathan Olivier, Jonny Maw, José Antonio Herranz-Yagüe, Jose Ignacio Nolazco, Jose Manuel Cózar-Olmo, Joseph Bagley, Joseph Jelski, Joseph Norris, Joseph Testa, Joshua Meeks, Juan Hernandez, Juan Luis Vásquez, Karen Randhawa, Karishma Dhera, Katarzyna Gronostaj, Kathleen Houlton, Kathleen Lehman, Kathryn Gillams, Kelvin Adasonla, Kevin Brown, Kevin Murtagh, Kiki Mistry, Kim Davenport, Kosuke Kitamura, Laura Derbyshire, Laurence Clarke, Lawrie Morton, Levin Martinez, Louise Goldsmith, Louise Paramore, Luc Cormier, Lucio Dell'Atti, Lucy Simmons, Luis Martinez-Piñeiro, Luis Rico, Luke Chan, Luke Forster, Lulin Ma, Maria Camacho Gallego, Maria José Freire, Mark Emberton, Mark Feneley, Marta Viridiana Muñoz Rivero, Matea Pirša, Matteo Tallè, Matthew Crockett, Matthew Liew, Matthew Trail, Max Peters, Meghan Cooper, Meghana Kulkarni, Michael Ager, Ming He, Mo Li, Mohamed Omran Breish, Mohamed Tarin, Mohammed Aldiwani, Mudit Matanhelia, Muhammad Pasha, Mustafa Kaan Akalın, Nasreen Abdullah, Nathan Hale, Neha Gadiyar, Neil Kocher, Nicholas Bullock, Nicholas Campain, Nicola Pavan, Nihad Al-Ibraheem, Nikita Bhatt, Nishant Bedi, Nitin Shrotri, Niyati Lobo, Olga Balderas, Omar Kouli, Otakar Capoun, Pablo Oteo Manjavacas, Paolo Gontero, Paramananthan Mariappan, Patricio Garcia Marchiñena, Paul Erotocritou, Paul Sweeney, Paula Planelles, Peter Acher, Peter C. Black, Peter K Osei-Bonsu, Peter Østergren, Peter Smith, Peter-Paul Michiel Willemse, Piotr L. Chlosta, Qurrat Ul Ain, Rachel Barratt, Rachel Esler, Raihan Khalid, Ray Hsu, Remigiusz Stamirowski, Reshma Mangat, Ricardo Cruz, Ricky Ellis, Robert Adams, Robert Hessell, Robert J.A. Oomen, Robert McConkey, Robert Ritchie, Roberto Jarimba, Rohit Chahal, Rosado Mario Andres, Rosalyn Hawkins, Rotimi David, Rustom P. Manecksha, Sachin Agrawal, Syed Sami Hamid, Samuel Deem, Sanchia Goonewardene, Satchi Kuchibhotla Swami, Satoshi Hori, Shahid Khan, Shakeel Mohammud Inder, Shanthi Sangaralingam, Shekhar Marathe, Sheliyan Raveenthiran, Shigeo Horie, Shomik Sengupta, Sian Parson, Sidney Parker, Simon Hawlina, Simon Williams, Simone Mazzoli, Slawomir Grzegorz Kata, Sofia Pinheiro Lopes, Sónia Ramos, Sophie Rintoul-Hoad, Sorcha O'Meara, Steve Morris, Stacey Turner, Stefano Venturini, Stephanos Almpanis, Steven Joniau, Sunjay Jain, Susan Mallett, Sven Nikles, null Shahzad, Sylvia Yan, Tarq Toma, Teresa Cabañuz Plo, Thierry Bonnin, Tim Muilwijk, Tim Wollin, Timothy Shun Man Chu, Timson Appanna, Tom Brophy, Tom Ellul, Tomaž Smrkolj, Tracey Rowe, Troy Sukhu, Trushar Patel, Tullika Garg, Turhan Çaşkurlu, Uros Bele, Usman Haroon, Víctor Crespo-Atín, Victor Parejo Cortes, Victoria Capapé Poves, Vincent Gnanapragasam, Vineet Gauhar, Vinnie During, Vivek Kumar, Vojtech Fiala, Wasim Mahmalji, Wayne Lam, Yew Fung Chin, Yigit Filtekin, Yih Chyn Phan, Youssed Ibrahim, Zachary A Glaser, Zainal Adwin Abiddin, Zijian Qin, Zsuzsanna Zotter, Zulkifli Zainuddin, Khadhouri, Sinan, Gallagher, Kevin M., Mackenzie, Kenneth R., Shah, Taimur T., Gao, Chuanyu, Moore, Sacha, Zimmermann, Eleanor F., Edison, Eric, Jefferies, Matthew, Nambiar, Arjun, Anbarasan, Thineskrishna, Mannas, Miles P., Lee, Taeweon, Marra, Giancarlo, Gómez Rivas, Juan, Marcq, Gautier, Assmus, Mark A., Uçar, Taha, Claps, Francesco, Boltri, Matteo, La Montagna, Giuseppe, Burnhope, Tara, Nkwam, Nkwam, Austin, Toma, Boxall, Nicholas E., Downey, Alison P., Sukhu, Troy A., Antón-Juanilla, Marta, Rai, Sonpreet, Chin, Yew-Fung, Moore, Madeline, Drake, Tamsin, Green, James S. A., Goulao, Beatriz, Maclennan, Graeme, Nielsen, Matthew, Mcgrath, John S., Kasivisvanathan, Veeru, Chaudry, Aasem, Sharma, Abhishek, Bennett, Adam, Ahmad, Adnan, Abroaf, Ahmed, Suliman, Ahmed Musa, Lloyd, Aimee, Mckay, Alastair, Wong, Albert, Silva, Alberto, Schneider, Alexandre, Mackay, Alison, Knight, Allen, Grigorakis, Alkiviadi, Bdesha, Amar, Nagle, Amy, Cebola, Ana, Dhanasekaran, Ananda Kumar, Kondža, Andraž, Barcelos, André, Galosi, Andrea Benedetto, Ebur, Andrea, Minervini, Andrea, Russell, Andrew, Webb, Andrew, de Jalón, Ángel García, Desai, Ankit, Czech, Anna Katarzyna, Mainwaring, Anna, Adimonye, Anthony, Das, Arighno, Figueiredo, Arnaldo, Villers, Arnauld, Leminski, Artur, Chippagiri, Arvinda, Lal, Asim Ahmed, Yıldırım, Asıf, Voulgaris, Athanasios Mario, Uzan, Audrey, Oo, Aye Moh Moh, Younis, Ayman, Zelhof, Bachar, Mukhtar, Bashir, Ayres, Ben, Challacombe, Ben, Sherwood, Benedict, Ristau, Benjamin, Lai, Billy, Nellensteijn, Brechtje, Schreiter, Brielle, Trombetta, Carlo, Dowling, Catherine, Hobbs, Catherine, Benitez, Cayo Augusto Estigarribia, Lebacle, Cédric, Ho, Cherrie Wing Yin, Ng, Chi-Fai, Mount, Chloe, Lam, Chon Meng, Blick, Chri, Brown, Christian, Gallegos, Christopher, Higgs, Claire, Browne, Clíodhna, Mccann, Conor, Plaza Alonso, Cristina, Beder, Daniel, Cohen, Daniel, Gordon, Daniel, Wilby, Daniel, Gordon, Danny, Hrouda, David, Lau, David Hua Wu, Karsza, Dávid, Mak, David, Martin-Way, David, Suthaharan, Denula, Patel, Dhruv, Carrion, Diego M, Nyanhongo, Donald, Bass, Edward, Mains, Edward, Chau, Edwin, Canelon Castillo, Elba, Day, Elizabeth, Desouky, Elsayed, Gaines, Emily, Papworth, Emma, Yuruk, Emrah, Kilic, Ene, Dinneen, Eoin, Palagonia, Erika, Xylinas, Evanguelo, Khawaja, Faizan, Cimarra, Fernando, Bardet, Florian, Kum, Francesca, Peters, Francesca, Kovács, Gábor, Tanasescu, Geroge, Hellawell, Gile, Tasso, Giovanni, Lam, Gitte, Pizzuto, Giuseppe, Lenart, Gordan, Özgür, Günal, Bi, Hai, Lyons, Hannah, Warren, Hannah, Ahmed, Hashim, Simpson, Helen, Burden, Helena, Gresty, Helena, Rios Pita, Hernado, Clarke, Holly, Serag, Hosam, Kynaston, Howard, Crawford-Smith, Hugh, Mostafid, Hugh, Otaola-Arca, Hugo, Koo, Hui Fen, Ibrahim, Ibrahim, Ouzaid, Idir, Puche-Sanz, Ignacio, Tomašković, Igor, Tinay, Ilker, Sahibzada, Iqbal, Thangasamy, Isaac, Cadena, Iván Revelo, Irani, Jacque, Udzik, Jakub, Brittain, Jame, Catto, Jame, Green, Jame, Tweedle, Jame, Hernando, Jamie Borrego, Leask, Jamie, Kalsi, Ja, Frankel, Jason, Toniolo, Jason, Raman, Jay D., Courcier, Jean, Kumaradeevan, Jeevan, Clark, Jennifer, Jones, Jennifer, Teoh, Jeremy Yuen-Chun, Iacovou, John, Kelly, John, Selph, John P., Aning, Jonathan, Deeks, Jon, Cobley, Jonathan, Olivier, Jonathan, Maw, Jonny, Herranz-Yagüe, José Antonio, Nolazco, Jose Ignacio, Cózar-Olmo, Jose Manuel, Bagley, Joseph, Jelski, Joseph, Norris, Joseph, Testa, Joseph, Meeks, Joshua, Hernandez, Juan, Vásquez, Juan Lui, Randhawa, Karen, Dhera, Karishma, Gronostaj, Katarzyna, Houlton, Kathleen, Lehman, Kathleen, Gillams, Kathryn, Adasonla, Kelvin, Brown, Kevin, Murtagh, Kevin, Mistry, Kiki, Davenport, Kim, Kitamura, Kosuke, Derbyshire, Laura, Clarke, Laurence, Morton, Lawrie, Martinez, Levin, Goldsmith, Louise, Paramore, Louise, Cormier, Luc, Dell'Atti, Lucio, Simmons, Lucy, Martinez-Piñeiro, Lui, Rico, Lui, Chan, Luke, Forster, Luke, Ma, Lulin, Gallego, Maria Camacho, Freire, Maria José, Emberton, Mark, Feneley, Mark, Rivero, Marta Viridiana Muñoz, Pirša, Matea, Tallè, Matteo, Crockett, Matthew, Liew, Matthew, Trail, Matthew, Peters, Max, Cooper, Meghan, Kulkarni, Meghana, Ager, Michael, He, Ming, Li, Mo, Omran Breish, Mohamed, Tarin, Mohamed, Aldiwani, Mohammed, Matanhelia, Mudit, Pasha, Muhammad, Akalın, Mustafa Kaan, Abdullah, Nasreen, Hale, Nathan, Gadiyar, Neha, Kocher, Neil, Bullock, Nichola, Campain, Nichola, Pavan, Nicola, Al-Ibraheem, Nihad, Bhatt, Nikita, Bedi, Nishant, Shrotri, Nitin, Lobo, Niyati, Balderas, Olga, Kouli, Omar, Capoun, Otakar, Oteo Manjavacas, Pablo, Gontero, Paolo, Mariappan, Paramananthan, Marchiñena, Patricio Garcia, Erotocritou, Paul, Sweeney, Paul, Planelles, Paula, Acher, Peter, Black, Peter C., Osei-Bonsu, Peter K, Østergren, Peter, Smith, Peter, Willemse, Peter-Paul Michiel, Chlosta, Piotr L., Ul Ain, Qurrat, Barratt, Rachel, Esler, Rachel, Khalid, Raihan, Hsu, Ray, Stamirowski, Remigiusz, Mangat, Reshma, Cruz, Ricardo, Ellis, Ricky, Adams, Robert, Hessell, Robert, Oomen, Robert J. A., Mcconkey, Robert, Ritchie, Robert, Jarimba, Roberto, Chahal, Rohit, Andres, Rosado Mario, Hawkins, Rosalyn, David, Rotimi, Manecksha, Rustom P., Agrawal, Sachin, Hamid, Syed Sami, Deem, Samuel, Goonewardene, Sanchia, Swami, Satchi Kuchibhotla, Hori, Satoshi, Khan, Shahid, Mohammud Inder, Shakeel, Sangaralingam, Shanthi, Marathe, Shekhar, Raveenthiran, Sheliyan, Horie, Shigeo, Sengupta, Shomik, Parson, Sian, Parker, Sidney, Hawlina, Simon, Williams, Simon, Mazzoli, Simone, Grzegorz Kata, Slawomir, Pinheiro Lopes, Sofia, Ramos, Sónia, Rintoul-Hoad, Sophie, O'Meara, Sorcha, Morris, Steve, Turner, Stacey, Venturini, Stefano, Almpanis, Stephano, Joniau, Steven, Jain, Sunjay, Mallett, Susan, Nikles, Sven, Shahzad, Null, Yan, Sylvia, Toma, Tarq, Cabañuz Plo, Teresa, Bonnin, Thierry, Muilwijk, Tim, Wollin, Tim, Chu, Timothy Shun Man, Appanna, Timson, Brophy, Tom, Ellul, Tom, Smrkolj, Tomaž, Rowe, Tracey, Sukhu, Troy, Patel, Trushar, Garg, Tullika, Çaşkurlu, Turhan, Bele, Uro, Haroon, Usman, Crespo-Atín, Víctor, Parejo Cortes, Victor, Capapé Poves, Victoria, Gnanapragasam, Vincent, Gauhar, Vineet, During, Vinnie, Kumar, Vivek, Fiala, Vojtech, Mahmalji, Wasim, Lam, Wayne, Fung Chin, Yew, Filtekin, Yigit, Chyn Phan, Yih, Ibrahim, Youssed, Glaser, Zachary A, Abiddin, Zainal Adwin, Qin, Zijian, Zotter, Zsuzsanna, and Zainuddin, Zulkifli

Subjects
Renal cancer, Prostate cancer, Risk factors, Urology, Bladder cancer, Urothelial cancer, Risk factor, Urinary tract cancer, Haematuria, Risk Calculator
Abstract

Background: Patient factors associated with urinary tract cancer can be used to risk stratify patients referred with haematuria, prioritising those with a higher risk of cancer for prompt investigation. Objective: To develop a prediction model for urinary tract cancer in patients referred with haematuria. Design, setting, and participants: A prospective observational study was conducted in 10 282 patients from 110 hospitals across 26 countries, aged ≥16 yr and referred to secondary care with haematuria. Patients with a known or previous urological malignancy were excluded. Outcome measurements and statistical analysis: The primary outcomes were the presence or absence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC], and renal cancer). Mixed-effect multivariable logistic regression was performed with site and country as random effects and clinically important patient-level candidate predictors, chosen a priori, as fixed effects. Predictors were selected primarily using clinical reasoning, in addition to backward stepwise selection. Calibration and discrimination were calculated, and bootstrap validation was performed to calculate optimism. Results and limitations: The unadjusted prevalence was 17.2% (n = 1763) for bladder cancer, 1.20% (n = 123) for UTUC, and 1.00% (n = 103) for renal cancer. The final model included predictors of increased risk (visible haematuria, age, smoking history, male sex, and family history) and reduced risk (previous haematuria investigations, urinary tract infection, dysuria/suprapubic pain, anticoagulation, catheter use, and previous pelvic radiotherapy). The area under the receiver operating characteristic curve of the final model was 0.86 (95% confidence interval 0.85-0.87). The model is limited to patients without previous urological malignancy. Conclusions: This cancer prediction model is the first to consider established and novel urinary tract cancer diagnostic markers. It can be used in secondary care for risk stratifying patients and aid the clinician's decision-making process in prioritising patients for investigation. Patient summary: We have developed a tool that uses a person's characteristics to determine the risk of cancer if that person develops blood in the urine (haematuria). This can be used to help prioritise patients for further investigation.

Published
2022

9. Management of Urological Malignancy in Heart and Lung Transplant Recipients: An Irish National Cohort Study

Author

Bronagh Harrington, Nicholas J. Hegarty, Andrew Jones, D. Galvin, Robert A Keenan, Kiaran J O'Mally, Paul Ryan, Stephen Connolly, Mohammed Aboelmagd, and Usman Haroon

Subjects
Male, medicine.medical_specialty, Urologic Neoplasms, medicine.medical_treatment, Malignancy, Cohort Studies, Prostate cancer, Risk Factors, medicine, Humans, Prospective Studies, Radical surgery, Lung, Retrospective Studies, Transplantation, Carcinoma, Transitional Cell, business.industry, Incidence (epidemiology), General surgery, Incidence, Cancer, Retrospective cohort study, medicine.disease, Kidney Transplantation, Tissue Donors, Transplant Recipients, Radiation therapy, Dissection, Treatment Outcome, Urinary Bladder Neoplasms, Heart Transplantation, Female, business
Abstract

Objectives Following the first hearttransplantin Ireland in 1985, there have been almost 700 deceased donor heart and lung transplants carried out in Ireland at a single institution. In this retrospective study, our aim was to assess the incidence and management of urological malignancies arising in this national cohort. Materials and methods Our retrospective analysis included all heart and lung transplant recipients identified as having a urological malignancy. Primary outcome variables included incidence, management, and clinical outcomes following cancer diagnosis. Results A total of 28 patients (4.1%) had radiologically or histologically confirmed urological malignancies. Fourteen patientswere diagnosedwith prostate cancer, with 13 who underwent radical treatment. Eight renal cell carcinomas were diagnosed in heart transplant recipients, with 5 who underwent nephrectomies. Two bladder cancers and 1 uppertract urothelial carcinoma were diagnosed and managed with endoscopic resection, radiotherapy, and nephroureterectomy, respectively. Two patients were diagnosed with penile squamous cell carcinoma and managed with radical surgery and lymph node dissection/sampling, with 1 patient receiving adjuvant chemoradiotherapy. Conclusions Urological malignancies are not common in heart and lung transplant recipients; however, standard management options can be safely used, including radical surgery. Prospective monitoring of these patients and potential considerations for screening should be maintained.

Published
2021

11. Seroprevalence of SARS‐CoV‐2 IgG antibodies in the current COVID‐19 pandemic amongst co‐workers at a UK renal transplant centre

Author

Atul Bagul, Sameh Mayaleh, Samuel C. Barnes, John Black, and Usman Haroon

Subjects
Adult, Immunity, Herd, Male, medicine.medical_specialty, medicine.medical_treatment, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Health Personnel, 030230 surgery, Antibodies, Viral, 03 medical and health sciences, 0302 clinical medicine, Immunity, Seroepidemiologic Studies, Throat, Internal medicine, Pandemic, medicine, Seroprevalence, Humans, Nose, Transplantation, biology, business.industry, SARS-CoV-2, COVID-19, Immunosuppression, Middle Aged, Kidney Transplantation, United Kingdom, Occupational Diseases, medicine.anatomical_structure, Immunoglobulin G, biology.protein, 030211 gastroenterology & hepatology, Female, Antibody, business, Biomarkers
Abstract

INTRODUCTION: As lock-downs and social distancing measures around the world begin to ease after the global SARS-CoV-2 (COVID-19) pandemic, discussions surrounding immunity and antibody testing are on the rise. This single-centre observational study reports data from a UK renal transplant centre with regards to seroprevalence amongst staff members. Members of staff were tested for SARS-CoV-2 antibodies (IgG) with Abbott International assays. Electronic records were accessed for PCR RNA and antibody results, with data anonymised by hospital number. IgA antibodies were not tested due to test kit availability during this period. 200 members of staff (25% male, 75% female, mean age 45.3 ± 12.0 years) were tested for SARS-CoV-2 antibodies with 24/200 (12.0%) positive. Most interestingly, 2/30 (6.6%) co-workers had positive nose/throat RNA PCR but negative antibody tests. This study demonstrates that frontline healthcare workers have a relatively low seroprevalence rate of specific SARS-CoV-2 IgG antibodies. To further evaluate this, larger patient populations, multicentre studies and different antibody assays are needed to better understand whether detection of antibodies is suggestive of previous SARS-CoV-2 exposure.

Published
2021
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13. Promoting non-verbal skills in medical students

Author

Maaz Khan, Usman Haroon, Umar Mithawala, Abdulkhaliq Scerif, and Abdul Sadiq Kutty

Subjects
Medical education, Students, Medical, business.industry, MEDLINE, General Medicine, 030204 cardiovascular system & hematology, 03 medical and health sciences, Nonverbal communication, 0302 clinical medicine, Text mining, Review and Exam Preparation, 030212 general & internal medicine, Nonverbal Communication, Psychology, business, Education, Medical, Undergraduate
Published
2018

14. Urothelial carcinoma of an allograft ureter 10 years after deceased donor kidney transplantation

Author

Kevin P Gaughan, Usman Haroon, Niall F. Davis, and Ponnusamy Mohan

Subjects
Male, medicine.medical_specialty, Urologic Neoplasms, Time Factors, Population, 030232 urology & nephrology, Urology, Cystectomy, Kidney, Nephrectomy, 03 medical and health sciences, 0302 clinical medicine, Ureter, Rare Disease, Medicine, Humans, Transplantation, Homologous, 030212 general & internal medicine, Urothelium, education, Upper urinary tract, Aged, education.field_of_study, Carcinoma, Transitional Cell, business.industry, Incidence (epidemiology), General Medicine, medicine.disease, Kidney Transplantation, Transplantation, Transitional cell carcinoma, medicine.anatomical_structure, surgical procedures, operative, Treatment Outcome, Kidney Failure, Chronic, business, Rare disease
Abstract

The incidence of urothelial carcinoma (UC; formerly transitional cell carcinoma) is higher among renal transplant recipients compared with the general population. Upper urinary tract UC (UUT-UC) of allograft urothelium is a rare event with approximately 40 cases reported in the literature. Herein, we describe the clinical presentation and management of UUT-UC in a transplant ureter 10 years after deceased donor kidney transplantation.

Published
2018

15. Perioperative Management of New Oral Anticoagulants in Urological Surgery

Author

Usman Haroon, James C. Forde, Eva Browne, and Niall F. Davis

Subjects
medicine.medical_specialty, Perioperative management, business.industry, Urology, 030232 urology & nephrology, Warfarin, Novelty, Review, 030204 cardiovascular system & hematology, Urological surgery, Clinical Practice, 03 medical and health sciences, 0302 clinical medicine, Oncology, Reproductive Medicine, Clinical decision making, medicine, Time to peak, business, Intensive care medicine, medicine.drug
Abstract

New oral anticoagulants (NOACs) are increasingly replacing the use of warfarin in clinical practice. Their use has now also been extended to thromboprophylaxis in many orthopedic surgeries. This, in addition to an increasingly aging population with many complex comorbidities means that these medications will be ever more frequently encountered by urologists. Thus, a clear understanding of the mechanism of action of NOACs, their time to peak action and half-life is essential for the purpose of managing these patients perioperatively. This article demonstrates the patient and procedural variability that must be taken into account in the perioperative management of the anticoagulated patient. While the time to peak onset and half-life of NOACs can aid in determining the interval of interruption of anticoagulation, the risks of thrombosis and bleeding must be assessed before the decision to stop anticoagulation. This article takes into account the evidence available on NOACs in urological surgery in order to inform the perioperative management of these medications and to propose guidelines to aid in clinical decision making. In attempting this, we address the issue of the lack of high-level evidence surrounding NOACs in urological surgery given their relative novelty and the need for further research to better guide practice.

Published
2018

17. In silico identification of the rare-coding pathogenic mutations and structural modeling of human NNATgene associated with anorexia nervosa

Author

Azmi, Muhammad Bilal, Naeem, Unaiza, Saleem, Arisha, Jawed, Areesha, Usman, Haroon, Qureshi, Shamim Akhtar, and Azim, M. Kamran

Abstract

Purpose: Increased susceptibility towards anorexia nervosa(AN) was reported with reduced levels of neuronatin (NNAT) gene. We sought to investigate the most pathogenic rare-coding missense mutations, non-synonymous single-nucleotide polymorphisms (nsSNPs) of NNAT and their potential damaging impact on protein function through transcript level sequence and structure based in silico approaches. Methods: Gene sequence, single nucleotide polymorphisms (SNPs) of NNAT was retrieved from public databases and the putative post-translational modification (PTM) sites were analyzed. Distinctive in silico algorithms were recruited for transcript level SNPs analyses and to characterized high-risk rare-coding nsSNPs along with their impact on protein stability function. Ab initio 3D-modeling of wild-type, alternate model prediction for most deleterious nsSNP, validation and recognition of druggable binding pockets were also performed. AN 3D therapeutic compounds that followed rule of drug-likeness were docked with most pathogenic variant of NNAT to estimate the drugs’ binding free energies. Results: Conclusively, 10 transcript (201–205)-based nsSNPs from 3 rare-coding missense variants, i.e., rs539681368, rs542858994, rs560845323out of 840 exonic SNPs were identified. Transcript-based functional impact analyses predicted rs539681368(C30Y) from NNAT-204 as the high-risk rare-coding pathogenic nsSNP, deviating protein functions. The 3D-modeling analysis of AN drugs’ binding energies indicated lowest binding free energy (ΔG) and significant inhibition constant (Ki) with mutant models C30Y. Conclusions: Mutant model (C30Y) exhibiting significant drug binding affinity and the commonest interaction observed at the acetylation site K59. Thus, based on these findings, we concluded that the identified nsSNP may serve as potential targets for various studies, diagnosis and therapeutic interventions. Level of evidence: No level of evidence—open access bioinformatics research.

Published
2022
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19. Medical student’s perspective on success in OSCE by sitting after our peers

Author

Faisal Jamshaid, Ziyan Kassam, Samiullah Dost, Usman Haroon, Bilal Master, Salman Momin, and Ahmed Najjar

Subjects
Sitting Position, Educational measurement, Medical education, Students, Medical, 020205 medical informatics, Perspective (graphical), MEDLINE, Retrospective cohort study, 02 engineering and technology, General Medicine, Sitting, Education, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Clinical Competence, Educational Measurement, 030212 general & internal medicine, Clinical competence, Psychology, Retrospective Studies
Published
2018

20. INCIDENCE, MANAGEMENT AND CLINICAL OUTCOMES OF PROSTATE CANCER IN KIDNEY TRANSPLANT RECIPIENTS

Author

James C. Forde, Dilly M. Little, Gordon K. Smyth, Usman Haroon, Richard E. Power, Ponnusamy Mohan, and Niall F. Davis

Subjects
Male, medicine.medical_specialty, Time Factors, medicine.medical_treatment, Population, 030230 surgery, Androgen deprivation therapy, 03 medical and health sciences, Prostate cancer, Postoperative Complications, 0302 clinical medicine, Internal medicine, medicine, Humans, education, Survival rate, Kidney transplantation, Retrospective Studies, Transplantation, education.field_of_study, business.industry, Incidence, Incidence (epidemiology), Prostatic Neoplasms, Retrospective cohort study, Middle Aged, medicine.disease, Kidney Transplantation, Survival Rate, Treatment Outcome, business, Watchful waiting
Abstract

Objectives We reviewed the incidence, management, and survival outcomes of prostate cancer among kidney transplant recipients and compared these characteristics with a national population (nonrecipients). Materials and methods A retrospective study was performed on all kidney transplant recipients from a National Kidney Transplant Centre who were subsequently diagnosed with prostate cancer. Primary outcome variables included comparisons of incidence and 5-year overall survival in kidney transplant recipients versus nonrecipients after treatment of prostate cancer. Secondary outcome variables were prostate-specific antigen levels at diagnosis, Gleason grade, treatment strategy, and morbidity from treatment among kidney transplant recipients. Results Of 4048 kidney transplants performed, 3020 were male recipients (63.9%). In total, 34 kidney transplant recipients (1.1%) were diagnosed with prostate cancer 109 ± 83 months (range, 7-372 mo) after transplant. The mean age at prostate cancer diagnosis was 64 ± 7 years, median prostate-specific antigen level was 10 ng/dL (range, 2.6-771 ng/dL), and 76% (n = 26/34) were diagnosed with localized disease. The incidence of prostate cancer was 1126/100 000 in kidney transplant recipients compared with 160/100 000 nonrecipients in Ireland (P = .01). Treatment strategies included curative radiotherapy (n = 18), curative surgery (n = 2), androgen deprivation therapy (n = 8), and watchful waiting (n = 6). Overall survival rates at 1, 3, and 5 years were not significantly different between kidney transplant recipients with prostate cancer versus nonrecipients with prostate cancer (98% vs 98%, 80% vs 79%, and 77% vs 72%, respectively, P = .8). Conclusions The incidence of prostate cancer is significantly higher among kidney transplant recipients compared with nonrecipients in the general population, with most diagnosed with localized disease. Definitive management guidelines should be developed to increase awareness and optimize treatment options in this unique patient cohort.

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