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Start Over Author "Laure W" Journal diagnostic and prognostic research
4 results on '"Laure W"'

1. Validation of prognostic models predicting mortality or ICU admission in patients with COVID-19 in low- and middle-income countries: a global individual participant data meta-analysis

Author

Johanna A. A. Damen, Banafsheh Arshi, Maarten van Smeden, Silvia Bertagnolio, Janet V. Diaz, Ronaldo Silva, Soe Soe Thwin, Laure Wynants, and Karel G. M. Moons

Subjects
Medicine (General), R5-920
Abstract

Abstract Background We evaluated the performance of prognostic models for predicting mortality or ICU admission in hospitalized patients with COVID-19 in the World Health Organization (WHO) Global Clinical Platform, a repository of individual-level clinical data of patients hospitalized with COVID-19, including in low- and middle-income countries (LMICs). Methods We identified eligible multivariable prognostic models for predicting overall mortality and ICU admission during hospital stay in patients with confirmed or suspected COVID-19 from a living review of COVID-19 prediction models. These models were evaluated using data contributed to the WHO Global Clinical Platform for COVID-19 from nine LMICs (Burkina Faso, Cameroon, Democratic Republic of Congo, Guinea, India, Niger, Nigeria, Zambia, and Zimbabwe). Model performance was assessed in terms of discrimination and calibration. Results Out of 144 eligible models, 140 were excluded due to a high risk of bias, predictors unavailable in LIMCs, or insufficient model description. Among 11,338 participants, the remaining models showed good discrimination for predicting in-hospital mortality (3 models), with areas under the curve (AUCs) ranging between 0.76 (95% CI 0.71–0.81) and 0.84 (95% CI 0.77–0.89). An AUC of 0.74 (95% CI 0.70–0.78) was found for predicting ICU admission risk (one model). All models showed signs of miscalibration and overfitting, with extensive heterogeneity between countries. Conclusions Among the available COVID-19 prognostic models, only a few could be validated on data collected from LMICs, mainly due to limited predictor availability. Despite their discriminative ability, selected models for mortality prediction or ICU admission showed varying and suboptimal calibration.

Published
2024
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2. Decision curve analysis: confidence intervals and hypothesis testing for net benefit

Author

Andrew J. Vickers, Ben Van Claster, Laure Wynants, and Ewout W. Steyerberg

Subjects
Medicine (General), R5-920
Abstract

Abstract Background A number of recent papers have proposed methods to calculate confidence intervals and p values for net benefit used in decision curve analysis. These papers are sparse on the rationale for doing so. We aim to assess the relation between sampling variability, inference, and decision-analytic concepts. Methods and results We review the underlying theory of decision analysis. When we are forced into a decision, we should choose the option with the highest expected utility, irrespective of p values or uncertainty. This is in some distinction to traditional hypothesis testing, where a decision such as whether to reject a given hypothesis can be postponed. Application of inference for net benefit would generally be harmful. In particular, insisting that differences in net benefit be statistically significant would dramatically change the criteria by which we consider a prediction model to be of value. We argue instead that uncertainty related to sampling variation for net benefit should be thought of in terms of the value of further research. Decision analysis tells us which decision to make for now, but we may also want to know how much confidence we should have in that decision. If we are insufficiently confident that we are right, further research is warranted. Conclusion Null hypothesis testing or simple consideration of confidence intervals are of questionable value for decision curve analysis, and methods such as value of information analysis or approaches to assess the probability of benefit should be considered instead.

Published
2023
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3. Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury

Author

Isabel R. A. Retel Helmrich, Ana Mikolić, David M. Kent, Hester F. Lingsma, Laure Wynants, Ewout W. Steyerberg, and David van Klaveren

Subjects
Prognostic model studies, Traumatic brain injury, PROBAST, Medicine (General), R5-920
Abstract

Abstract Background Prediction modeling studies often have methodological limitations, which may compromise model performance in new patients and settings. We aimed to examine the relation between methodological quality of model development studies and their performance at external validation. Methods We systematically searched for externally validated multivariable prediction models that predict functional outcome following moderate or severe traumatic brain injury. Risk of bias and applicability of development studies was assessed with the Prediction model Risk Of Bias Assessment Tool (PROBAST). Each model was rated for its presentation with sufficient detail to be used in practice. Model performance was described in terms of discrimination (AUC), and calibration. Delta AUC (dAUC) was calculated to quantify the percentage change in discrimination between development and validation for all models. Generalized estimation equations (GEE) were used to examine the relation between methodological quality and dAUC while controlling for clustering. Results We included 54 publications, presenting ten development studies of 18 prediction models, and 52 external validation studies, including 245 unique validations. Two development studies (four models) were found to have low risk of bias (RoB). The other eight publications (14 models) showed high or unclear RoB. The median dAUC was positive in low RoB models (dAUC 8%, [IQR − 4% to 21%]) and negative in high RoB models (dAUC − 18%, [IQR − 43% to 2%]). The GEE showed a larger average negative change in discrimination for high RoB models (− 32% (95% CI: − 48 to − 15) and unclear RoB models (− 13% (95% CI: − 16 to − 10)) compared to that seen in low RoB models. Conclusion Lower methodological quality at model development associates with poorer model performance at external validation. Our findings emphasize the importance of adherence to methodological principles and reporting guidelines in prediction modeling studies.

Published
2022
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4. Erratum to: Methods for evaluating medical tests and biomarkers

Author

Gowri Gopalakrishna, Miranda Langendam, Rob Scholten, Patrick Bossuyt, Mariska Leeflang, Anna Noel-Storr, James Thomas, Iain Marshall, Byron Wallace, Penny Whiting, Clare Davenport, Gowri GopalaKrishna, Isabel de Salis, Sue Mallett, Robert Wolff, Richard Riley, Marie Westwood, Jos Kleinen, Gary Collins, Hans Reitsma, Karel Moons, Antonia Zapf, Annika Hoyer, Katharina Kramer, Oliver Kuss, J. Ensor, J. J. Deeks, E. C. Martin, R. D. Riley, Gerta Rücker, Susanne Steinhauser, Martin Schumacher, Joie Ensor, Kym Snell, Brian Willis, Thomas Debray, Jon Deeks, Lavinia Ferrante di Ruffano, Sian Taylor-Phillips, Chris Hyde, Stuart A. Taylor, Gauraang Batnagar, STREAMLINE COLON Investigators, STREAMLINE LUNG Investigators, METRIC Investigators, Lavinia Ferrante Di Ruffano, Farah Seedat, Aileen Clarke, Sarah Byron, Frances Nixon, Rebecca Albrow, Thomas Walker, Carla Deakin, Zhivko Zhelev, Harriet Hunt, Yaling Yang, Lucy Abel, James Buchanan, Thomas Fanshawe, Bethany Shinkins, Laure Wynants, Jan Verbakel, Sabine Van Huffel, Dirk Timmerman, Ben Van Calster, Aeliko Zwinderman, Jason Oke, Jack O’Sullivan, Rafael Perera, Brian Nicholson, Hannah L. Bromley, Tracy E. Roberts, Adele Francis, Denniis Petrie, G. Bruce Mann, Kinga Malottki, Holly Smith, Lucinda Billingham, Alice Sitch, Oke Gerke, Mie Holm-Vilstrup, Eivind Antonsen Segtnan, Ulrich Halekoh, Poul Flemming Høilund-Carlsen, Bernard G. Francq, Jac Dinnes, Julie Parkes, Walter Gregory, Jenny Hewison, Doug Altman, William Rosenberg, Peter Selby, Julien Asselineau, Paul Perez, Aïssatou Paye, Emilie Bessede, Cécile Proust-Lima, Christiana Naaktgeboren, Joris de Groot, Anne Rutjes, Johannes Reitsma, Emmanuel Ogundimu, Jonathan Cook, Yannick Le Manach, Yvonne Vergouwe, Romin Pajouheshnia, Rolf Groenwold, Karen Moons, Linda Peelen, Daan Nieboer, Bavo De Cock, Micael J. Pencina, Ewout W. Steyerberg, Jennifer Cooper, Nick Parsons, Chris Stinton, Steve Smith, Andy Dickens, Rachel Jordan, Alexandra Enocson, David Fitzmaurice, Peymane Adab, Charles Boachie, Gaj Vidmar, Karoline Freeman, Martin Connock, Rachel Court, Carl Moons, Jessica Harris, Andrew Mumford, Zoe Plummer, Kurtis Lee, Barnaby Reeves, Chris Rogers, Veerle Verheyden, Gianni D. Angelini, Gavin J. Murphy, Jeremy Huddy, Melody Ni, Katherine Good, Graham Cooke, George Hanna, Jie Ma, K. G. M. (Carl) Moons, Joris A. H. de Groot, Doug G. Altman, Johannes B. Reitsma, Gary S. Collins, Karel G. M. Moons, Douglas G. Altman, Adina Najwa Kamarudin, Ruwanthi Kolamunnage-Dona, Trevor Cox, Simone Borsci, Teresa Pérez, M.Carmen Pardo, Angel Candela-Toha, Alfonso Muriel, Javier Zamora, Sabina Sanghera, Syed Mohiuddin, Richard Martin, Jenny Donovan, Joanna Coast, Mikyung Kelly Seo, John Cairns, Elizabeth Mitchell, Alison Smith, Judy Wright, Peter Hall, Michael Messenger, Nicola Calder, Nyantara Wickramasekera, Karen Vinall-Collier, Andrew Lewington, Johanna Damen, David Cairns, Michelle Hutchinson, Cathie Sturgeon, Liz Mitchel, Rebecca Kift, Sofia Christakoudi, Manohursingh Rungall, Paula Mobillo, Rosa Montero, Tjir-Li Tsui, Sui Phin Kon, Beatriz Tucker, Steven Sacks, Chris Farmer, Terry Strom, Paramit Chowdhury, Irene Rebollo-Mesa, Maria Hernandez-Fuentes, Johanna A. A. G. Damen, Thomas P. A. Debray, Pauline Heus, Lotty Hooft, Rob J. P. M. Scholten, Ewoud Schuit, Ioanna Tzoulaki, Camille M. Lassale, George C. M. Siontis, Virginia Chiocchia, Corran Roberts, Michael Maia Schlüssel, Stephen Gerry, James A. Black, Yvonne T. van der Schouw, Linda M. Peelen, Graeme Spence, David McCartney, Ann van den Bruel, Daniel Lasserson, Gail Hayward, Werner Vach, Antoinette de Jong, Coreline Burggraaff, Otto Hoekstra, Josée Zijlstra, Henrica de Vet, Sara Graziadio, Joy Allen, Louise Johnston, Rachel O’Leary, Michael Power, Louise Johnson, Ray Waters, John Simpson, Thomas R. Fanshawe, Peter Phillips, Andrew Plumb, Emma Helbren, Steve Halligan, Alastair Gale, Peggy Sekula, Willi Sauerbrei, Julia R. Forman, Susan J. Dutton, Yemisi Takwoingi, Elizabeth M. Hensor, Thomas E. Nichols, Emmanuelle Kempf, Raphael Porcher, Jennifer de Beyer, Douglas Altman, Sally Hopewell, John Dennis, Beverley Shields, Angus Jones, William Henley, Ewan Pearson, Andrew Hattersley, on behalf of the MASTERMIND consortium, Fueloep Scheibler, Anne Rummer, Sibylle Sturtz, Robert Großelfinger, Katie Banister, Craig Ramsay, Augusto Azuara-Blanco, Jennifer Burr, Manjula Kumarasamy, Rupert Bourne, Ijeoma Uchegbu, Jennifer Murphy, Alex Carter, Jen Murphy, Joachim Marti, Julie Eatock, Julie Robotham, Maria Dudareva, Mark Gilchrist, Alison Holmes, Phillip Monaghan, Sarah Lord, Andrew StJohn, Sverre Sandberg, Christa Cobbaert, Lieselotte Lennartz, Wilma Verhagen-Kamerbeek, Christoph Ebert, Andrea Horvath, for the Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Kevin Jenniskens, Jaime Peters, Bogdan Grigore, Obi Ukoumunne, Brooke Levis, Andrea Benedetti, Alexander W. Levis, John P. A. Ioannidis, Ian Shrier, Pim Cuijpers, Simon Gilbody, Lorie A. Kloda, Dean McMillan, Scott B Patten, Russell J. Steele, Roy C Ziegelstein, Charles H. Bombardier, Flavia de Lima Osório, Jesse R. Fann, Dwenda Gjerdingen, Femke Lamers, Manote Lotrakul, Sonia R Loureiro, Bernd Löwe, Juwita Shaaban, Lesley Stafford, Henk C. P. M. van Weert, Mary A. Whooley, Linda S. Williams, Karin A. Wittkampf, Albert S. Yeung, Brett D. Thombs, Chris Cooper, Tom Nieto, Claire Smith, Olga Tucker, Janine Dretzke, Andrew Beggs, Nirmala Rai, Sue Bayliss, Simon Stevens, Sue Mallet, Sudha Sundar, Emma Hall, Nuria Porta, David Lorente Estelles, Johann de Bono, and on behalf of the CTC-STOP protocol development group

Subjects
Medicine (General), R5-920
Published
2017
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