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2 results on '"Benjamin Y. Gravesteijn"'

1. Minimizing population health loss due to scarcity in OR capacity: validation of quality of life input

Author

Benjamin Y. Gravesteijn, Kira S. van Hof, Eline Krijkamp, Franck Asselman, C. René Leemans, Anouk M.I.A. van Alphen, Henriëtte van der Horst, Guy Widdershoven, Leonie Baatenburg de Jong, Hester Lingsma, Jan Busschbach, Rob Baatenburg de Jong, Public Health, Otorhinolaryngology and Head and Neck Surgery, Health Technology Assessment (HTA), Health Economics (HE), Psychiatry, Hematology, Otolaryngology / Head & Neck Surgery, AII - Cancer immunology, CCA - Cancer Treatment and quality of life, CCA - Cancer biology and immunology, APH - Aging & Later Life, APH - Mental Health, and APH - Quality of Care

Subjects
Epidemiology, Health Informatics
Abstract

Objectives A previously developed decision model to prioritize surgical procedures in times of scarce surgical capacity used quality of life (QoL) primarily derived from experts in one center. These estimates are key input of the model, and might be more context-dependent than the other input parameters (age, survival). The aim of this study was to validate our model by replicating these QoL estimates. Methods The original study estimated QoL of patients in need of commonly performed procedures in live expert-panel meetings. This study replicated this procedure using a web-based Delphi approach in a different hospital. The new QoL scores were compared with the original scores using mixed effects linear regression. The ranking of surgical procedures based on combined QoL values from the validation and original study was compared to the ranking based solely on the original QoL values. Results The overall mean difference in QoL estimates between the validation study and the original study was − 0.11 (95% CI: -0.12 - -0.10). The model output (DALY/month delay) based on QoL data from both studies was similar to the model output based on the original data only: The Spearman’s correlation coefficient between the ranking of all procedures before and after including the new QoL estimates was 0.988. Discussion Even though the new QoL estimates were systematically lower than the values from the original study, the ranking for urgency based on health loss per unit of time delay of procedures was consistent. This underscores the robustness and generalizability of the decision model for prioritization of surgical procedures.

Published
2023
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2. Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Author

Benjamin Y. Gravesteijn, Daan Nieboer, Ari Ercole, Hester F. Lingsma, David Nelson, Ben van Calster, Ewout W. Steyerberg, Cecilia Åkerlund, Krisztina Amrein, Nada Andelic, Lasse Andreassen, Audny Anke, Anna Antoni, Gérard Audibert, Philippe Azouvi, Maria Luisa Azzolini, Ronald Bartels, Pál Barzó, Romuald Beauvais, Ronny Beer, Bo-Michael Bellander, Antonio Belli, Habib Benali, Maurizio Berardino, Luigi Beretta, Morten Blaabjerg, Peter Bragge, Alexandra Brazinova, Vibeke Brinck, Joanne Brooker, Camilla Brorsson, Andras Buki, Monika Bullinger, Manuel Cabeleira, Alessio Caccioppola, Emiliana Calappi, Maria Rosa Calvi, Peter Cameron, Guillermo Carbayo Lozano, Marco Carbonara, Giorgio Chevallard, Arturo Chieregato, Giuseppe Citerio, Maryse Cnossen, Mark Coburn, Jonathan Coles, D. Jamie Cooper, Marta Correia, Amra Čović, Nicola Curry, Endre Czeiter, Marek Czosnyka, Claire Dahyot-Fizelier, Helen Dawes, Véronique De Keyser, Vincent Degos, Francesco Della Corte, Hugo den Boogert, Bart Depreitere, Đula Đilvesi, Abhishek Dixit, Emma Donoghue, Jens Dreier Guy-Loup Dulière, Patrick Esser, Erzsébet Ezer Martin Fabricius, Valery L. Feigin, Kelly Foks, Shirin Frisvold, Alex Furmanov, Pablo Gagliardo, Damien Galanaud, Dashiell Gantner, Guoyi Gao, Pradeep George, Alexandre Ghuysen, Lelde Giga, Ben Glocker, Jagoš Golubovic, Pedro A. Gomez, Johannes Gratz, Benjamin Gravesteijn, Francesca Grossi, Russell L. Gruen, Deepak Gupta, Juanita A. Haagsma, Iain Haitsma, Raimund Helbok, Eirik Helseth, Lindsay Horton, Jilske Huijben, Peter J. Hutchinson, Bram Jacobs, Stefan Jankowski, Mike Jarrett Ji-yao Jiang, Kelly Jones, Mladen Karan, Angelos G. Kolias, Erwin Kompanje, Daniel Kondziella, Evgenios Koraropoulos, Lars-Owe Koskinen, Noémi Kovács, Alfonso Lagares, Linda Lanyon, Steven Laureys, Fiona Lecky, Rolf Lefering, Valerie Legrand, Aurelie Lejeune, Leon Levi, Roger Lightfoot, Hester Lingsma, Andrew I.R. Maas, Ana M. Castaño-León, Marc Maegele, Marek Majdan, Alex Manara, Geoffrey Manley, Costanza Martino, Hugues Maréchal, Julia Mattern, Catherine McMahon, Béla Melegh, David Menon, Tomas Menovsky, Davide Mulazzi, Visakh Muraleedharan, Lynnette Murray, Nandesh Nair, Ancuta Negru, Virginia Newcombe, Quentin Noirhomme, József Nyirádi, Otesile Olubukola, Matej Oresic, Fabrizio Ortolano, Aarno Palotie, Paul M. Parizel, Jean-François Payen, Natascha Perera, Vincent Perlbarg, Paolo Persona, Wilco Peul, Anna Piippo-Karjalainen, Matti Pirinen, Horia Ples, Suzanne Polinder, Inigo Pomposo, Jussi P. Posti, Louis Puybasset, Andreea Radoi, Arminas Ragauskas, Rahul Raj, Malinka Rambadagalla, Ruben Real, Jonathan Rhodes, Sylvia Richardson, Sophie Richter, Samuli Ripatti, Saulius Rocka, Cecilie Roe, Olav Roise, Jonathan Rosand, Jeffrey V. Rosenfeld, Christina Rosenlund, Guy Rosenthal, Rolf Rossaint, Sandra Rossi, Daniel Rueckert, Martin Rusnák, Juan Sahuquillo, Oliver Sakowitz, Renan Sanchez-Porras, Janos Sandor, Nadine Schäfer, Silke Schmidt, Herbert Schoechl, Guus Schoonman, Rico Frederik Schou, Elisabeth Schwendenwein, Charlie Sewalt, Toril Skandsen, Peter Smielewski, Abayomi Sorinola, Emmanuel Stamatakis, Simon Stanworth, Ana Kowark, Robert Stevens, William Stewart, Nino Stocchetti, Nina Sundström, Anneliese Synnot, Riikka Takala, Viktória Tamás, Tomas Tamosuitis, Mark Steven Taylor, Braden Te Ao, Olli Tenovuo, Alice Theadom, Matt Thomas, Dick Tibboel, Marjolein Timmers, Christos Tolias, Tony Trapani, Cristina Maria Tudora, Peter Vajkoczy, Shirley Vallance, Egils Valeinis, Zoltán Vámos, Gregory Van der Steen, Joukje van der Naalt, Jeroen T.J.M. van Dijck, Thomas A. van Essen, Wim Van Hecke, Caroline van Heugten, Dominique Van Praag, Thijs Vande Vyvere, Audrey Vanhaudenhuyse, Roel P.J. van Wijk, Alessia Vargiolu, Emmanuel Vega, Kimberley Velt, Jan Verheyden, Paul M. Vespa, Anne Vik, Rimantas Vilcinis, Victor Volovici, Nicole von Steinbüchel, Daphne Voormolen, Petar Vulekovic, Kevin K.W. Wang, Eveline Wiegers, Guy Williams, Lindsay Wilson, Stefan Winzeck, Stefan Wolf, Zhihui Yang, Peter Ylén, Alexander Younsi, Frederik A. Zeiler, Veronika Zelinkova, Agate Ziverte, Tommaso Zoerle, Gravesteijn, B. Y., Nieboer, D., Ercole, A., Lingsma, H. F., Nelson, D., van Calster, B., Steyerberg E. W., (CENTER-TBI collaborators), Beretta, L., Ercole, Ari [0000-0001-8350-8093], Apollo - University of Cambridge Repository, Section Neuropsychology, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, RS: FPN NPPP I, Psychiatrie & Neuropsychologie, Molecular Neuroscience and Ageing Research (MOLAR), Gravesteijn, B, Nieboer, D, Ercole, A, Lingsma, H, Nelson, D, van Calster, B, Steyerberg, E, Akerlund, C, Amrein, K, Andelic, N, Andreassen, L, Anke, A, Antoni, A, Audibert, G, Azouvi, P, Azzolini, M, Bartels, R, Barzo, P, Beauvais, R, Beer, R, Bellander, B, Belli, A, Benali, H, Berardino, M, Beretta, L, Blaabjerg, M, Bragge, P, Brazinova, A, Brinck, V, Brooker, J, Brorsson, C, Buki, A, Bullinger, M, Cabeleira, M, Caccioppola, A, Calappi, E, Calvi, M, Cameron, P, Lozano, G, Carbonara, M, Chevallard, G, Chieregato, A, Citerio, G, Cnossen, M, Coburn, M, Coles, J, Cooper, D, Correia, M, Covic, A, Curry, N, Czeiter, E, Czosnyka, M, Dahyot-Fizelier, C, Dawes, Ragauskas, Arminas, Ročka, Saulius, Tamošuitis, Tomas, Vilcinis, Rimantas, Public Health, Tamosuitis, Tomas, „Elsevier Science' grupė, Academic Medical Center, Centre of Excellence in Complex Disease Genetics, Aarno Palotie / Principal Investigator, Institute for Molecular Medicine Finland, Genomics of Neurological and Neuropsychiatric Disorders, HUS Neurocenter, Statistical and population genetics, Department of Mathematics and Statistics, Biostatistics Helsinki, Clinicum, Helsinki University Hospital Area, Neurokirurgian yksikkö, Samuli Olli Ripatti / Principal Investigator, and Complex Disease Genetics

Subjects
Male, Epidemiology, Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy), Logistic regression, computer.software_genre, Data science, Machine Learning, 0302 clinical medicine, Traumatic brain injury, Brain Injuries, Traumatic, Medicine, 030212 general & internal medicine, Decision Making, Computer-Assisted, Glasgow Outcome Scale, Regression analysis, Middle Aged, Prognosis, 3142 Public health care science, environmental and occupational health, Regression, Random forest, Female, TUTORIAL, Cohort study, Algorithm, Algorithms, Adult, BIG DATA, Prognosi, cohort study, data science, machine learning, prediction, prognosis, traumatic brain injury, Machine learning, VALIDATION, 03 medical and health sciences, VDP::Teknologi: 500::Medisinsk teknologi: 620, Humans, VDP::Medisinske Fag: 700, Glasgow Coma Scale, Medicinsk bioteknologi (med inriktning mot cellbiologi (inklusive stamcellsbiologi), molekylärbiologi, mikrobiologi, biokemi eller biofarmaci), Models, Statistical, business.industry, CARE, 3141 Health care science, VDP::Medical disciplines: 700, Logistic Models, Cohort study, Data science, Machine learning, Prediction, Prognosis, Traumatic brain injury, Artificial intelligence, Gradient boosting, business, Prediction, computer, 030217 neurology & neurosurgery, Predictive modelling, VDP::Technology: 500::Medical technology: 620
Abstract

OBJECTIVE: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. STUDY DESIGN AND SETTING: We performed logistic (LR), lasso, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n=11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks, and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with GCS, Data used in preparation of this manuscript were obtained in the context of CENTER-TBI, a large collaborative project with the support of the European Union 7th Framework program (EC grant 602150). Additional funding was obtained from the Hannelore Kohl Stiftung (Germany), the OneMind (USA) and the Integra LifeSciences Corporation (USA). The funder had no role in the study design, enrollment, collection of data, writing, or publication decisions.

Published
2020
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