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3 results on '"Lukas Merki"'

1. Machine learning using the Extreme Gradient Boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Author

Marie M. Jeitziner, Iris Drvaric, Jan Wiegand, Abele Donati, Janina Apolo, Emanuele Rezoagli, Jesús Escós-Orta, Herminia Lozano-Gómez, Mirko Brenni, Giovanni Camen, Frank Hillgaertner, Sara Moccia, Antje Heise, Alexander Dullenkopf, Michael Stephan, Can Ince, Marcus Laube, Julien Marrel, Michele Bernardini, Barbara Lienhardt-Nobbe, Hernán Aguirre-Bermeo, Alberto Fogagnolo, Dorothea M. Heuberger, Severin Urech, Reto A. Schuepbach, Andrea Glotta, Samuele Ceruti, Isabelle Fleisch, Marc P. Michot, Alice Nova, Matthias P. Hilty, Tomislav Gaspert, Gianfilippo Gangitano, Savino Spadaro, Ivan Chau, Daniele Berardini, Tiziana Perin, Andrea Westphalen, Marie-Reine Losser, Hatem Ksouri, Marie-Hélène Perez, Theodoros Aslanidis, Christoph Haberthuer, Gerardo Vizmanos-Lamotte, Jorge Gámez-Zapata, Filippo Boroli, Adriana Lambert, Serge Grazioli, Petra Salomon, Christian Bürkle, Didier Naon, Philipp Bühler, Dawid L. Staudacher, Miodrag Filipovic, Hermann Redecker, Mario Alfaro-Farias, Massimo Antonelli, Rolf Ensner, Jerome Lavanchy, Lukas Merki, Roberto Ceriani, Anette Ristic, Chiara Cogliati, Reto Andreas Schüpbach, Daniela Selz, Begoña Zalba-Etayo, Anne-Sylvie Ramelet, Thierry Fumeaux, Andrea Carsetti, Peter Gerecke, Riccardo Colombo, Marilene Franchitti Laurent, Fabrizio Turrini, Tobias Wengenmayer, Tobias Welte, Philippe Guerci, Antonella Potalivo, Lucia Migliorelli, Barna Babik, Reza Nikandish, Pedro D. Wendel Garcia, Alberto Martínez, Maria Sole Simonini, Diederik Gommers, Xiana Taboada-Fraga, Jerome Pugin, Peter C. Rimensberger, Angela Algaba-Calderon, FriederikeMeyer zu Bentrup, Agios Pavlos, Thomas Tschoellitsch, Marianne Sieber, Karim Shaikh, Nuria Zellweger, Silvio Brugger, Geoffrey Jurkolow, Anja Baltussen Weber, Maria C. Martín-Delgado, Anita Korsós, Gian-Reto Kleger, Alexander Klarer, Emmanuel Novy, Diego Franch-Llasat, Adrian Tellez, Peter Schott, Jonathan Rilinger, Andreas Christ, Bernd Yuen, Jean-Christophe Laurent, Nadine Gehring, Pedro Castro, Sascha David, Francesca Facondini, Arantxa Lander-Azcona, Maria Grazia Bocci, Maddalena Alessandra Wu, Mallory Moret-Bochatay, Sara Cereghetti, Urs Pietsch, Martina Murrone, Gauthier Delahaye, Luca Romeo, Pascal Locher, Pedro David Wendel Garcia, Michael Sepulcri, Marija Jovic, Katharina Marquardt, Emanuele Frontoni, Patricia Fodor, Emanuele Catena, Tobias Hübner, Thomas Neff, Roger F. Lussman, Matteo Giacomini, Govind Oliver Sridharan, Beatrice Jenni-Moser, Jan Brem, Michael Studhalter, Elif Colak, Raquel Rodríguez-García, Silvia Fabbri, Jens Meier, Lina Petersen, Jonathan Montomoli, Ferran Roche-Campo, Klaus Stahl, Montomoli, J, Romeo, L, Moccia, S, Bernardini, M, Migliorelli, L, Berardini, D, Donati, A, Carsetti, A, Bocci, M, Wendel Garcia, P, Fumeaux, T, Guerci, P, Schupbach, R, Ince, C, Frontoni, E, Hilty, M, Alfaro-Farias, M, Vizmanos-Lamotte, G, Tschoellitsch, T, Meier, J, Aguirre-Bermeo, H, Apolo, J, Martinez, A, Jurkolow, G, Delahaye, G, Novy, E, Losser, M, Wengenmayer, T, Rilinger, J, Staudacher, D, David, S, Welte, T, Stahl, K, Pavlos, A, Aslanidis, T, Korsos, A, Babik, B, Nikandish, R, Rezoagli, E, Giacomini, M, Nova, A, Fogagnolo, A, Spadaro, S, Ceriani, R, Murrone, M, Wu, M, Cogliati, C, Colombo, R, Catena, E, Turrini, F, Simonini, M, Fabbri, S, Potalivo, A, Facondini, F, Gangitano, G, Perin, T, Grazia Bocci, M, Antonelli, M, Gommers, D, Rodriguez-Garcia, R, Gamez-Zapata, J, Taboada-Fraga, X, Castro, P, Tellez, A, Lander-Azcona, A, Escos-Orta, J, Martin-Delgado, M, Algaba-Calderon, A, Franch-Llasat, D, Roche-Campo, F, Lozano-Gomez, H, Zalba-Etayo, B, Michot, M, Klarer, A, Ensner, R, Schott, P, Urech, S, Zellweger, N, Merki, L, Lambert, A, Laube, M, Jeitziner, M, Jenni-Moser, B, Wiegand, J, Yuen, B, Lienhardt-Nobbe, B, Westphalen, A, Salomon, P, Drvaric, I, Hillgaertner, F, Sieber, M, Dullenkopf, A, Petersen, L, Chau, I, Ksouri, H, Sridharan, G, Cereghetti, S, Boroli, F, Pugin, J, Grazioli, S, Rimensberger, P, Burkle, C, Marrel, J, Brenni, M, Fleisch, I, Lavanchy, J, Perez, M, Ramelet, A, Weber, A, Gerecke, P, Christ, A, Ceruti, S, Glotta, A, Marquardt, K, Shaikh, K, Hubner, T, Neff, T, Redecker, H, Moret-Bochatay, M, Bentrup, F, Studhalter, M, Stephan, M, Brem, J, Gehring, N, Selz, D, Naon, D, Kleger, G, Pietsch, U, Filipovic, M, Ristic, A, Sepulcri, M, Heise, A, Franchitti Laurent, M, Laurent, J, Schuepbach, R, Heuberger, D, Buhler, P, Brugger, S, Fodor, P, Locher, P, Camen, G, Gaspert, T, Jovic, M, Haberthuer, C, Lussman, R, Colak, E, Biomedical Engineering and Physics, ACS - Microcirculation, Translational Physiology, ACS - Atherosclerosis & ischemic syndromes, Graduate School, AII - Infectious diseases, and University of Zurich

Subjects
610 Medicine & health, Organ dysfunction score, Machine learning, computer.software_genre, Logistic regression, Clinical decision support system, law.invention, law, Medicine, Clinical decision support system (CDSS), Receiver operating characteristic, RC86-88.9, business.industry, Clinical decision support systems, COVID-19, Medical emergencies. Critical care. Intensive care. First aid, Extreme Gradient Boosting (XGBoost), Intensive care unit, Multiple organ failure, Cohort, Population study, SOFA score, Original Article, Artificial intelligence, 10023 Institute of Intensive Care Medicine, business, Algorithm, computer, Predictive modelling
Abstract

Background : Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods : We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients’ Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results : The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model {0.86 vs. 0.69, P

Published
2021

2. Prognostic factors associated with mortality risk and disease progression in 639 critically ill patients with COVID-19 in Europe: Initial report of the international RISC-19-ICU prospective observational cohort

Author

Pedro David Wendel Garcia, Thierry Fumeaux, Philippe Guerci, Dorothea Monika Heuberger, Jonathan Montomoli, Ferran Roche-Campo, Reto Andreas Schuepbach, Matthias Peter Hilty, Mario Alfaro Farias, Antoni Margarit, Gerardo Vizmanos-Lamotte, Thomas Tschoellitsch, Jens Meier, Francesco S. Cardona, Josef Skola, Lenka Horakova, Hernan Aguirre-Bermeo, Janina Apolo, Emmanuel Novy, Marie-Reine Losser, Geoffrey Jurkolow, Gauthier Delahaye, Sascha David, Tobias Welte, Tobias Wengenmayer, Dawid L. Staudacher, Theodoros Aslanidis, Barna Babik, Anita Korsos, Janos Gal, Hermann Csaba, Abele Donati, Andrea Carsetti, Fabrizio Turrini, Maria Sole Simonini, Roberto Ceriani, Martina Murrone, Emanuele Rezoagli, Giovanni Vitale, Alberto Fogagnolo, Savino Spadaro, Maddalena Alessandra Wu, Chiara Cogliati, Riccardo Colombo, Emanuele Catena, Francesca Facondini, Antonella Potalivo, Gianfilippo Gangitano, Tiziana Perin, Maria Grazia Bocci, Massimo Antonelli, Diederik Gommers, Can Ince, Eric Mayor-Vázquez, Maria Cruz, Martin Delgado, Raquel Rodriguez Garcia, Jorge Gamez Zapata, Begoña Zalba-Etayo, Herminia Lozano-Gomez, Pedro Castro, Adrian Tellez, Adriana Jacas, Guido Muñoz, Rut Andrea, Jose Ortiz, Eduard Quintana, Irene Rovira, Enric Reverter, Javier Fernandez, Miquel Ferrer, Joan R. Badia, Arantxa Lander Azcona, Jesus Escos Orta, Philipp Bühler, Silvio Brugger, Daniel Hofmaenner, Simone Unseld, Frank Ruschitzka, Mallory Moret-Bochatay, Bernd Yuen, Thomas Hillermann, Hatem Ksouri, Govind Oliver Sridharan, Anette Ristic, Michael Sepulcri, Miodrag Filipovic, Urs Pietsch, Petra Salomon, Iris Drvaric, Peter Schott, Severin Urech, Adriana Lambert, Lukas Merki, Marcus Laube, Frank Hillgaertner, Marianne Sieber, Alexander Dullenkopf, Lina Petersen, Serge Grazioli, Peter C. Rimensberger, Isabelle Fleisch, Jerome Lavanchy, Katharina Marquardt, Karim Shaikh, Hermann Redecker, Michael Stephan, Jan Brem, Bjarte Rogdo, Andre Birkenmaier, Friederike Meyer zu Bentrup, Patricia Fodor, Pascal Locher, Giovanni Camen, Martin Siegemund, Nuria Zellweger, Marie-Madlen Jeitziner, Beatrice Jenni-Moser, Christian Bürkle, Gian-Reto Kleger, Marilene Franchitti Laurent, Jean-Christophe Laurent, Tomislav Gaspert, Marija Jovic, Michael Studhalter, Christoph Haberthuer, Roger F. Lussman, Daniela Selz, Didier Naon, Romano Mauri, Samuele Ceruti, Julien Marrel, Mirko Brenni, Rolf Ensner, Nadine Gehring, Antje Heise, Tobias Huebner, Thomas A. Neff, Sara Cereghetti, Filippo Boroli, Jerome Pugin, Nandor Marczin, Joyce Wong, University of Zurich, Wendel Garcia, Pedro David, RISC-19 ICU investigators, Graduate School, Translational Physiology, ACS - Atherosclerosis & ischemic syndromes, ACS - Microcirculation, Intensive Care, Grazioli, Serge, and Rimensberger, Peter

Subjects
medicine.medical_specialty, ARDS, medicine.medical_treatment, 610 Medicine & health, Disease, 2700 General Medicine, 01 natural sciences, Article, NO, law.invention, 03 medical and health sciences, 0302 clinical medicine, law, Internal medicine, Medicine, 030212 general & internal medicine, 0101 mathematics, Disease burden, Mechanical ventilation, lcsh:R5-920, Public health, ddc:618, Acute respiratory distress syndrome, Pandemic, business.industry, Incidence (epidemiology), 010102 general mathematics, COVID-19, General Medicine, medicine.disease, Intensive care unit, Coronavirus, Cohort, Absolute neutrophil count, 10209 Clinic for Cardiology, 10023 Institute of Intensive Care Medicine, lcsh:Medicine (General), business
Abstract

Background Coronavirus disease 2019 (COVID-19) is associated with a high disease burden with 10% of confirmed cases progressing towards critical illness. Nevertheless, the disease course and predictors of mortality in critically ill patients are poorly understood. Methods Following the critical developments in ICUs in regions experiencing early inception of the pandemic, the European-based, international RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry was created to provide near real-time assessment of patients developing critical illness due to COVID-19. Findings As of April 22, 2020, 639 critically ill patients with confirmed SARS-CoV-2 infection were included in the RISC-19-ICU registry. Of these, 398 had deceased or been discharged from the ICU. ICU-mortality was 24%, median length of stay 12 (IQR, 5-21) days. ARDS was diagnosed in 74%, with a minimum P/F-ratio of 110 (IQR, 80-148). Prone positioning, ECCO2R, or ECMO were applied in 57%. Off-label therapies were prescribed in 265 (67%) patients, and 89% of all bloodstream infections were observed in this subgroup (n = 66; RR=3·2, 95% CI [1·7-6·0]). While PCT and IL-6 levels remained similar in ICU survivors and non-survivors throughout the ICU stay (p = 0·35, 0·34), CRP, creatinine, troponin, d-dimer, lactate, neutrophil count, P/F-ratio diverged within the first seven days (p

Published
2020

3. Students' knowledge of symptoms and risk factors of potential life-threatening medical conditions

Author

Robert Blank, Stefan Rinderknecht, Thierry Carrel, Lukas Merki, and Adrian Businger

Subjects
Adult, Male, Medical knowledge, Health Knowledge, Attitudes, Practice, Students, Medical, Adolescent, Critical Care, MEDLINE, Health knowledge, Young Adult, Risk Factors, Surveys and Questionnaires, Medicine, Humans, Young adult, Stroke, business.industry, Basic life support, General Medicine, Middle Aged, medicine.disease, Cardiopulmonary Resuscitation, Multivariate Analysis, Minimal knowledge, Female, Medical emergency, Clinical Competence, business, Switzerland
Abstract

In order to assess medical students' knowledge of Basic Life Support (BLS) principles, we defined a minimal knowledge (MK) of three life-threatening medical conditions that should be universally known: cardiac arrest, heart attack and stroke, and compared the results with those of laypersons.Before participating in a BLS course, 406 medical students and 101 laypersons completed an MK questionnaire. Additional data were collected on participants' gender, age, education, medical education, personal experience with the condition and successful completion of a BLS course.The mean proportion of correct answers was 48.1% for medical students and 34.3% for laypersons (P0.001). No participant achieved a MK level of 100%. Multivariable analysis showed that medical background +14.8% MK (P0.001), successful completion of a BLS course +4.4% MK (P = 0.004), and personal experience of the condition +3.2% MK (P = 0.013) significantly enhanced the MK percentage. Interaction analysis suggested that there were no exponential effects of higher education and medical background, or medical background and a completed BLS course.Among medical students and laypersons there is a significant lack of knowledge regarding the typical signs and risk factors associated with serious medical conditions. Within the current study, participants with direct experience of these conditions exhibited marginally improved knowledge compared to others, indicating a wide gap in the general public's knowledge. There is an urgent need to establish learning objectives in order to encourage students to complete BLS courses during their education.

Published
2009

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