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1. Risk Prediction for Acute Kidney Injury in Acute Medical Admissions in the UK

Author

Blackburn, Anna, Gunda, Smita, Lopez, Berenice, Edwards, James, Spittle, Nick, Preston, Rob, Baines, Richard, Little, Jane, Falayajo, Akin, Mahmoud, Huda, Selby, Nicholas M., Shaw, Sue, Trowbridge, Stephen, Coutinho, Andrew, Samarasinghe, Yohan, Farmer, Chris, Beeson, Claire, John, Ian, Gisby, Sharon, Forni, Lui, Dachsel, Martin, Fallouh, Bassam, Ward, Emily, Sood, Bhrigu, Ostermann, Marlies, Mohanty, Manab, Robert, Stephanie, MacLaughlin, Helen, Banerjee, Anita, Wright, Kelly, Tomlinson, Laurie, Wolff, Jacob de, Laing, Chris, Prowle, John, DeFreitas, Sarah, Balasubramaniam, Gowrie, McGuinness, Daniel, Murray, John, Kanagasundaram, Suren, Harron, Camille, Magee, Brian, Harty, John, Maxwell, Peter, Morgan, Neal, Leaonard, Niall, McCarroll, Frank, Kuan, Ying, Chakarborty, Tapas, Ahmed, Aimun, Ponnusamy, Arvind, Brown, Becky, Ahmed, Shahed, Henney, Bob, Hammersley, Shirley, Obale, Begho, Nipah, Rob, Al-Sayed, Tam, Varia, Ragit, Skinner, Christopher, Young, Innes, Clark, Laura, Bassiouni, Ibrahim, Finlay, Sian, Moonie, Alasdair, Douglas, Alistair, Bell, Samira, Duthie, Fiona, Thetford, David, White, Beth, McQuarrie, Emily, McCallum, Linsay, Campbell, Iona, Millar, James, McCormick, Jenna L., Allen, Ruridh, Jamdar, Ravi, Murray, Eleanor, Hand, Malcolm, Harmouche, Ali, Fattah, Hasan, Farquhar, Fiona, Condy-Young, Helen, Morrison, Jennifer, Power, Bert, Udayaraj, Uday, Murray, Paul, Mulgrew, Chris, Boddana, Preetham, Prescott, Craig, Uniake, Mark, Bonfield, Becky, Edwards, Helena, Armstrong, Kirsty, Whitehead, Duncan, Miller, Alice, Waters, Helen, Carr, Steve, Dickenson, Steve, Subbe, Chris, Phillips, Aled, Thomas, David, Lewis, Aled, Chess, James, Price, David, Mizen, Paul, Ramasamy, Vijaya, Thomas, Mark, Hewins, Pete, Carmichael, Paul, Chand, Rob, Ramaswamy, Diwaker, Thompson, Chris, Morgan, James, Lobaz, Steven, Oh, Weng, Stoves, John, Ali, Asifa, Ali, Mansoor, Awais, Muhammed, Naudeer, Sarah, Lewington, Andy, McDonald, Suzanne, Naeem, Haroon, and Than, Nilar

Subjects
Male, medicine.medical_specialty, Kidney Function Tests, Risk Assessment, Severity of Illness Index, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Internal medicine, medicine, Humans, Prospective Studies, 030212 general & internal medicine, Risk factor, Aged, Aged, 80 and over, Framingham Risk Score, business.industry, Acute kidney injury, General Medicine, Odds ratio, Acute Kidney Injury, Middle Aged, Prognosis, medicine.disease, United Kingdom, Confidence interval, Hospitalization, Logistic Models, Creatinine, Multivariate Analysis, Female, Risk assessment, business, Cohort study, Kidney disease
Abstract

Background Acute Kidney Injury (AKI) is associated with adverse outcomes; identifying patients who are at risk of developing AKI in hospital may lead to targeted prevention. This approach is advocated in national guidelines but is not well studied in acutely unwell medical patients. We therefore aimed to undertake a UK-wide study in acute medical units (AMUs) with the following aims: to define the proportion of acutely unwell medical patients who develop hospital-acquired AKI (hAKI); to determine risk factors associated with the development of hAKI; and to assess the feasibility of using these risk factors to develop an AKI risk prediction score. Methods In September 2016, a prospective multicentre cohort study across 72 UK AMUs was undertaken. Data were collected from all patients who presented over a 24-hour period. Chronic dialysis, community-acquired AKI (cAKI) and those with fewer than two creatinine measurements were subsequently excluded. The primary outcome was the development of h-AKI. Results 2,446 individuals were admitted to the AMUs of the 72 participating centres. 384 patients (16%) sustained AKI of whom 287 (75%) were cAKI and 97 (25%) were hAKI. After exclusions, 1,235 participants remained in whom chronic kidney disease (OR 3.08, 95% CI 1.96-4.83), diuretic prescription (OR 2.33, 95% CI 1.5-3.65), a lower haemoglobin concentration and an elevated serum bilirubin were independently associated with development of hAKI. Multivariable model discrimination was moderate (c-statistic 0.75), and this did not support the development of a robust clinical risk prediction score. Mortality was higher in those with hAKI (adjusted OR 5.22; 95% CI 2.23-12.20). Conclusion AKI in AMUs is common and associated with worse outcomes, with the majority of cases community acquired. The smaller proportion of hAKI cases, only moderate discrimination of prognostic risk factor modelling and the resource implications of widespread application of an AKI clinical risk score across all AMU admissions suggests that this approach is not currently justified. More targeted risk assessment or automated methods of calculating individual risk may be more appropriate alternatives.

Published
2018

2. Kidney failure prediction models

Author

Ramspek, Chava L, Evans, Marie, Wanner, Christoph, Drechsler, Christiane, Chesnaye, Nicholas C, Szymczak, Maciej, Krajewska, Magdalena, Torino, Claudia, Porto, Gaetana, Hayward, Samantha, Caskey, Fergus, Dekker, Friedo W, Jager, Kitty J, van Diepen, Merel, EQUAL Study Investigators: Adamasco Cupisti, Adelia Sagliocca, Alberto Ferraro, Aleksandra Musiała, Alessandra Mele, Alessandro Naticchia, Alex Còsaro, Alistair Woodman, Andrea Ranghino, Andrea Stucchi, Andreas Jonsson, Andreas Schneider, Angelo Pignataro, Anita Schrander, Anke Torp, Anna McKeever, Anna Szymczak, Anna-Lena Blom, Antonella De Blasio, Antonello Pani, Aris Tsalouichos, Asad Ullah, Barbara McLaren, Bastiaan van Dam, Beate Iwig, Bellasi Antonio, Biagio Raffaele Di Iorio, Björn Rogland, Boris Perras, Butti Alessandra, Camille Harron, Carin Wallquist, Carl Siegert, Carla Barrett, Carlo Gaillard, Carlo Garofalo, Cataldo Abaterusso, Charles Beerenhout, Charlotte O'Toole, Chiara Somma, Christian Marx, Christina Summersgill, Christof Blaser, Claudia D'alessandro, Claudia Emde, Claudia Zullo, Claudio Pozzi, Colin Geddes, Cornelis Verburgh, Daniela Bergamo, Daniele Ciurlino, Daria Motta, Deborah Glowski, Deborah McGlynn, Denes Vargas, Detlef Krieter, Domenico Russo, Dunja Fuchs, Dympna Sands, Ellen Hoogeveen, Ellen Irmler, Emöke Dimény, Enrico Favaro, Eva Platen, Ewelina Olczyk, Ewout Hoorn, Federica Vigotti, Ferruccio Ansali, Ferruccio Conte, Francesca Cianciotta, Francesca Giacchino, Francesco Cappellaio, Francesco Pizzarelli, Fredrik Sundelin, Fredrik Uhlin, Gaetano Greco, Geena Roy, Gaetana Porto, Giada Bigatti, Giancarlo Marinangeli, Gianfranca Cabiddu, Gillian Hirst, Giordano Fumagalli, Giorgia Caloro, Giorgina Piccoli, Giovanbattista Capasso, Giovanni Gambaro, Giuliana Tognarelli, Giuseppe Bonforte, Giuseppe Conte, Giuseppe Toscano, Goffredo Del Rosso, Gunilla Welander, Hanna Augustyniak-Bartosik, Hans Boots, Hans Schmidt-Gürtler, Hayley King, Helen McNally, Hendrik Schlee, Henk Boom, Holger Naujoks, Houda Masri-Senghor, Hugh Murtagh, Hugh Rayner, Ilona Miśkowiec-Wiśniewska, Ines Schlee, Irene Capizzi, Isabel Bascaran Hernandez, Ivano Baragetti, Jacek Manitius, Jane Turner, Jan-Willem Eijgenraam, Jeroen Kooman, Joachim Beige, Joanna Pondel, Joanne Wilcox, Jocelyn Berdeprado, Jochen Röthele, Jonathan Wong, Joris Rotmans, Joyce Banda, Justyna Mazur, Kai Hahn, Kamila Jędrzejak, Katarzyna Nowańska, Katja Blouin, Katrin Neumeier, Kirsteen Jones, Kirsten Anding-Rost, Knut-Christian Gröntoft, Lamberto Oldrizzi, Lesley Haydock, Liffert Vogt, Lily Wilkinson, Loreto Gesualdo, Lothar Schramm, Luigi Biancone, Łukasz Nowak, Maarten Raasveld, Magdalena Durlik, Manuela Magnano, Marc Vervloet, Marco Ricardi, Margaret Carmody, Maria Di Bari, Maria Laudato, Maria Luisa Sirico, Maria Stendahl, Maria Svensson, Maria Weetman, Marjolijn van Buren, Martin Joinson, Martina Ferraresi, Mary Dutton, Merel van Diepen, Michael Matthews, Michele Provenzano, Monika Hopf, Moreno Malaguti, Nadja Wuttke, Neal Morgan, Nicola Palmieri, Nikolaus Frischmuth, Nina Bleakley, Paola Murrone, Paul Cockwell, Paul Leurs, Paul Roderick, Pauline Voskamp, Pavlos Kashioulis, Pawlos Ichtiaris, Peter Blankestijn, Petra Kirste, Petra Schulz, Phil Mason, Philip Kalra, Pietro Cirillo, Pietro Dattolo, Pina Acampora, Rincy Sajith, Rita Nigro, Roberto Boero, Roberto Scarpioni, Rosa Sicoli, Rosella Malandra, Sabine Aign, Sabine Cäsar, Sadie van Esch, Sally Chapman, Sandra Biribauer, Santee Navjee, Sarah Crosbie, Sharon Brown, Sheila Tickle, Sherin Manan, Silke Röser, Silvana Savoldi, Silvio Bertoli, Silvio Borrelli, Siska Boorsma, Stefan Heidenreich, Stefan Melander, Stefania Maxia, Stefano Maffei, Stefano Mangano, Stephanie Palm, Stijn Konings, Suresh Mathavakkannan, Susanne Schwedler, Sylke Delrieux, Sylvia Renker, Sylvia Schättel, Szyszkowska Dorota, Teresa Cicchetti, Teresa Nieszporek, Theresa Stephan, Thomas Schmiedeke, Thomas Weinreich, Til Leimbach, Tiziana Rappa, Tora Almquist, Torsten Stövesand, Udo Bahner, Ulrika Jensen, Valentina Palazzo, Walter De Simone, Wolfgang Seeger, Ying Kuan, Zbigniew Heleniak, Zeynep Aydin, Internal Medicine, Chava L, Ramspek, Marie, Evan, Christoph, Wanner, Christiane, Drechsler, Nicholas C, Chesnaye, Maciej, Szymczak, Magdalena, Krajewska, Claudia, Torino, Gaetana, Porto, Samantha, Hayward, Fergus, Caskey, Friedo W, Dekker, Kitty J, Jager, Merel, van Diepen, Study Investigators: Adamasco Cupisti, Equal, Sagliocca, Adelia, Ferraro, Alberto, Musiała, Aleksandra, Mele, Alessandra, Naticchia, Alessandro, Còsaro, Alex, Woodman, Alistair, Ranghino, Andrea, Stucchi, Andrea, Jonsson, Andrea, Schneider, Andrea, Pignataro, Angelo, Schrander, Anita, Torp, Anke, Mckeever, Anna, Szymczak, Anna, Blom, Anna-Lena, De Blasio, Antonella, Pani, Antonello, Tsalouichos, Ari, Ullah, Asad, Mclaren, Barbara, van Dam, Bastiaan, Iwig, Beate, Antonio, Bellasi, Raffaele Di Iorio, Biagio, Rogland, Björn, Perras, Bori, Alessandra, Butti, Harron, Camille, Wallquist, Carin, Siegert, Carl, Barrett, Carla, Gaillard, Carlo, Garofalo, Carlo, Abaterusso, Cataldo, Beerenhout, Charle, O'Toole, Charlotte, Somma, Chiara, Marx, Christian, Summersgill, Christina, Blaser, Christof, D'Alessandro, Claudia, Emde, Claudia, Zullo, Claudia, Pozzi, Claudio, Geddes, Colin, Verburgh, Corneli, Bergamo, Daniela, Ciurlino, Daniele, Motta, Daria, Glowski, Deborah, Mcglynn, Deborah, Vargas, Dene, Krieter, Detlef, Russo, Domenico, Fuchs, Dunja, Sands, Dympna, Hoogeveen, Ellen, Irmler, Ellen, Dimény, Emöke, Favaro, Enrico, Platen, Eva, Olczyk, Ewelina, Hoorn, Ewout, Vigotti, Federica, Ansali, Ferruccio, Conte, Ferruccio, Cianciotta, Francesca, Giacchino, Francesca, Cappellaio, Francesco, Pizzarelli, Francesco, Sundelin, Fredrik, Uhlin, Fredrik, Greco, Gaetano, Roy, Geena, Porto, Gaetana, Bigatti, Giada, Marinangeli, Giancarlo, Cabiddu, Gianfranca, Hirst, Gillian, Fumagalli, Giordano, Caloro, Giorgia, Piccoli, Giorgina, Capasso, Giovanbattista, Gambaro, Giovanni, Tognarelli, Giuliana, Bonforte, Giuseppe, Conte, Giuseppe, Toscano, Giuseppe, Del Rosso, Goffredo, Welander, Gunilla, Augustyniak-Bartosik, Hanna, Boots, Han, Schmidt-Gürtler, Han, King, Hayley, Mcnally, Helen, Schlee, Hendrik, Boom, Henk, Naujoks, Holger, Masri-Senghor, Houda, Murtagh, Hugh, Rayner, Hugh, Miśkowiec-Wiśniewska, Ilona, Schlee, Ine, Capizzi, Irene, Bascaran Hernandez, Isabel, Baragetti, Ivano, Manitius, Jacek, Turner, Jane, Eijgenraam, Jan-Willem, Kooman, Jeroen, Beige, Joachim, Pondel, Joanna, Wilcox, Joanne, Berdeprado, Jocelyn, Röthele, Jochen, Wong, Jonathan, Rotmans, Jori, Banda, Joyce, Mazur, Justyna, Hahn, Kai, Jędrzejak, Kamila, Nowańska, Katarzyna, Blouin, Katja, Neumeier, Katrin, Jones, Kirsteen, Anding-Rost, Kirsten, Gröntoft, Knut-Christian, Oldrizzi, Lamberto, Haydock, Lesley, Vogt, Liffert, Wilkinson, Lily, Gesualdo, Loreto, Schramm, Lothar, Biancone, Luigi, Nowak, Łukasz, Raasveld, Maarten, Durlik, Magdalena, Magnano, Manuela, Vervloet, Marc, Ricardi, Marco, Carmody, Margaret, Di Bari, Maria, Laudato, Maria, Luisa Sirico, Maria, Stendahl, Maria, Svensson, Maria, Weetman, Maria, van Buren, Marjolijn, Joinson, Martin, Ferraresi, Martina, Dutton, Mary, van Diepen, Merel, Matthews, Michael, Provenzano, Michele, Hopf, Monika, Malaguti, Moreno, Wuttke, Nadja, Morgan, Neal, Palmieri, Nicola, Frischmuth, Nikolau, Bleakley, Nina, Murrone, Paola, Cockwell, Paul, Leurs, Paul, Roderick, Paul, Voskamp, Pauline, Kashioulis, Pavlo, Ichtiaris, Pawlo, Blankestijn, Peter, Kirste, Petra, Schulz, Petra, Mason, Phil, Kalra, Philip, Cirillo, Pietro, Dattolo, Pietro, Acampora, Pina, Sajith, Rincy, Nigro, Rita, Boero, Roberto, Scarpioni, Roberto, Sicoli, Rosa, Malandra, Rosella, Aign, Sabine, Cäsar, Sabine, van Esch, Sadie, Chapman, Sally, Biribauer, Sandra, Navjee, Santee, Crosbie, Sarah, Brown, Sharon, Tickle, Sheila, Manan, Sherin, Röser, Silke, Savoldi, Silvana, Bertoli, Silvio, Borrelli, Silvio, Boorsma, Siska, Heidenreich, Stefan, Melander, Stefan, Maxia, Stefania, Maffei, Stefano, Mangano, Stefano, Palm, Stephanie, Konings, Stijn, Mathavakkannan, Suresh, Schwedler, Susanne, Delrieux, Sylke, Renker, Sylvia, Schättel, Sylvia, Dorota, Szyszkowska, Cicchetti, Teresa, Nieszporek, Teresa, Stephan, Theresa, Schmiedeke, Thoma, Weinreich, Thoma, Leimbach, Til, Rappa, Tiziana, Almquist, Tora, Stövesand, Torsten, Bahner, Udo, Jensen, Ulrika, Palazzo, Valentina, De Simone, Walter, Seeger, Wolfgang, Kuan, Ying, Heleniak, Zbigniew, Aydin, Zeynep, Medical Informatics, ACS - Pulmonary hypertension & thrombosis, APH - Aging & Later Life, APH - Health Behaviors & Chronic Diseases, APH - Methodology, APH - Quality of Care, and APH - Global Health

Subjects
Male, progression of chronic renal failure, medicine.medical_specialty, Time Factors, epidemiology and outcome, 030232 urology & nephrology, Risk Assessment, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Older patients, external validation, Predictive Value of Tests, medicine, Humans, Failure risk, Clinical Epidemiology, In patient, comprehensive external validation, 030212 general & internal medicine, Statistic, Aged, Aged, 80 and over, Kidney, Models, Statistical, business.industry, External validation, General Medicine, prediction, kidney failure, Europe, prediction model, medicine.anatomical_structure, chronic kidney disease, epidemiology and outcomes, prognosis, Nephrology, Emergency medicine, Disease Progression, Kidney Failure, Chronic, Female, business, prognostic, Predictive modelling, prognosi, Cohort study
Abstract

Background Various prediction models have been developed to predict the risk of kidney failure in patients with CKD. However, guideline-recommended models have yet to be compared head to head, their validation in patients with advanced CKD is lacking, and most do not account for competing risks. Methods To externally validate 11 existing models of kidney failure, taking the competing risk of death into account, we included patients with advanced CKD from two large cohorts: the European Quality Study (EQUAL), an ongoing European prospective, multicenter cohort study of older patients with advanced CKD, and the Swedish Renal Registry (SRR), an ongoing registry of nephrology-referred patients with CKD in Sweden. The outcome of the models was kidney failure (defined as RRT-treated ESKD). We assessed model performance with discrimination and calibration. Results The study included 1580 patients from EQUAL and 13,489 patients from SRR. The average c statistic over the 11 validated models was 0.74 in EQUAL and 0.80 in SRR, compared with 0.89 in previous validations. Most models with longer prediction horizons overestimated the risk of kidney failure considerably. The 5-year Kidney Failure Risk Equation (KFRE) overpredicted risk by 10%-\18%. The four- and eight-variable 2-year KFRE and the 4-year Grams model showed excellent calibration and good discrimination in both cohorts. Conclusions Some existing models can accurately predict kidney failure in patients with advanced CKD. KFRE performed well for a shorter time frame (2 years), despite not accounting for competing events. Models predicting over a longer time frame (5 years) overestimated risk because of the competing risk of death. The Grams model, which accounts for the latter, is suitable for longer-term predictions (4 years).

Published
2021
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