التفاصيل البيبلوغرافية
العنوان:
Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients
المؤلفون:
Jonathan Montomoli , Luca Romeo , Sara Moccia , Michele Bernardini , Lucia Migliorelli , Daniele Berardini , Abele Donati , Andrea Carsetti , Maria Grazia Bocci , Pedro David Wendel Garcia , Thierry Fumeaux , Philippe Guerci , Reto Andreas Schüpbach , Can Ince , Emanuele Frontoni , Matthias Peter Hilty , Mario Alfaro-Farias, MD , Gerardo Vizmanos-Lamotte, MD, PhD , Thomas Tschoellitsch, MD , Jens Meier, MD , Hernán Aguirre-Bermeo, MD, PhD , Janina Apolo, BSc , Alberto Martínez, MD , Geoffrey Jurkolow, MD , Gauthier Delahaye, MD , Emmanuel Novy, MD , Marie-Reine Losser, MD, PhD , Tobias Wengenmayer, MD , Jonathan Rilinger, MD , Dawid L. Staudacher, MD , Sascha David, MD , Tobias Welte, MD , Klaus Stahl, MD , “Agios Pavlos” , Theodoros Aslanidis, MD, PhD , Anita Korsos, MD , Barna Babik, MD, PhD , Reza Nikandish, MD , Emanuele Rezoagli, MD, PhD , Matteo Giacomini, MD , Alice Nova, MD , Alberto Fogagnolo, MD , Savino Spadaro, MD, PhD , Roberto Ceriani, MD , Martina Murrone, MD , Maddalena A. Wu, MD , Chiara Cogliati, MD , Riccardo Colombo, MD , Emanuele Catena, MD , Fabrizio Turrini, MD, MSc , Maria Sole Simonini, MD , Silvia Fabbri, MD , Antonella Potalivo, MD , Francesca Facondini, MD , Gianfilippo Gangitano, MD , Tiziana Perin, MD , Maria Grazia Bocci, MD , Massimo Antonelli, MD , Diederik Gommers, MD, PhD , Raquel Rodríguez-García, MD , Jorge Gámez-Zapata, MD , Xiana Taboada-Fraga, MD , Pedro Castro, MD , Adrian Tellez, MD , Arantxa Lander-Azcona, MD , Jesús Escós-Orta, MD , Maria C. Martín-Delgado, MD , Angela Algaba-Calderon, MD , Diego Franch-Llasat, MD , Ferran Roche-Campo, MD, PhD , Herminia Lozano-Gómez, MD , Begoña Zalba-Etayo, MD, PhD , Marc P. Michot, MD , Alexander Klarer , Rolf Ensner, MD , Peter Schott, MD , Severin Urech, MD , Nuria Zellweger , Lukas Merki, MD , Adriana Lambert, MD , Marcus Laube, MD , Marie M. Jeitziner, RN, PhD , Beatrice Jenni-Moser, RN, MSc , Jan Wiegand, MD , Bernd Yuen, MD , Barbara Lienhardt-Nobbe , Andrea Westphalen, MD , Petra Salomon, MD , Iris Drvaric, MD , Frank Hillgaertner, MD , Marianne Sieber , Alexander Dullenkopf, MD , Lina Petersen, MD , Ivan Chau, MD , Hatem Ksouri, MD, PhD , Govind Oliver Sridharan, MD , Sara Cereghetti, MD , Filippo Boroli, MD , Jerome Pugin, MD, PhD , Serge Grazioli, MD , Peter C. Rimensberger, MD , Christian Bürkle, MD , Julien Marrel, MD , Mirko Brenni, MD , Isabelle Fleisch, MD , Jerome Lavanchy, MD , Marie-Helene Perez, MD , Anne-Sylvie Ramelet, MD , Anja Baltussen Weber, MD , Peter Gerecke, MD , Andreas Christ, MD , Samuele Ceruti, MD , Andrea Glotta, MD , Katharina Marquardt, MD , Karim Shaikh, MD , Tobias Hübner, MD , Thomas Neff, MD , Hermann Redecker, MD , Mallory Moret-Bochatay, MD , FriederikeMeyer zu Bentrup, MD, MBA , Michael Studhalter, MD , Michael Stephan, MD , Jan Brem, MD , Nadine Gehring, MD , Daniela Selz, MD , Didier Naon, MD , Gian-Reto Kleger, MD , Urs Pietsch, MD , Miodrag Filipovic, MD , Anette Ristic, MD , Michael Sepulcri, MD , Antje Heise, MD , Marilene Franchitti Laurent, MD , Jean-Christophe Laurent, MD , Pedro D. Wendel Garcia, MSc , Reto Schuepbach, MD , Dorothea Heuberger, PhD , Philipp Bühler, MD , Silvio Brugger, MD, PhD , Patricia Fodor, MD , Pascal Locher, MD , Giovanni Camen, MD , Tomislav Gaspert, MD , Marija Jovic, MD , Christoph Haberthuer, MD , Roger F. Lussman, MD , Elif Colak, MD
المصدر:
Journal of Intensive Medicine, Vol 1, Iss 2, Pp 110-116 (2021)
بيانات النشر:
Elsevier, 2021.
سنة النشر:
2021
المجموعة:
LCC:Medical emergencies. Critical care. Intensive care. First aid
مصطلحات موضوعية:
Machine learning , Extreme gradient boosting (XGBoost) , COVID-19 , Multiple organ failure , Clinical decision support system (CDSS) , Organ dysfunction score , Medical emergencies. Critical care. Intensive care. First aid , RC86-88.9
الوصف:
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
نوع الوثيقة:
article
وصف الملف:
electronic resource
اللغة:
English
تدمد:
2667-100X
العلاقة:
http://www.sciencedirect.com/science/article/pii/S2667100X21000323Test ; https://doaj.org/toc/2667-100XTest
DOI:
10.1016/j.jointm.2021.09.002
الوصول الحر:
https://doaj.org/article/7e7afa9d7e874b589c9f3eb1b5df67cbTest
رقم الانضمام:
edsdoj.7e7afa9d7e874b589c9f3eb1b5df67cb
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