检索结果-内蒙古大学图书馆

Dynamic vortical flow profile quantitatively characterizes mitral valvular-left ventricular hemodynamic coupling: In vivo analysis by dynamic 3D enstrophy mapping from 4D Flow MRI

arXiv

引用

arXiv 2018年

作者： Elbaz, Mohammed S.M. Le, Trung Bao Garg, Pankaj Roest, Arno A.W. Lelieveldt, Boudewijn P.F. Plein, Sven Westenberg, Jos J.M. Sotiropoulos, Fotis van der Geest, Rob J. Department of Radiology Northwestern University ChicagoIL United States Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Civil Engineering Stony Brook University Stony BrookNY United States University of Leeds Leeds United Kingdom Department of Pediatrics Division of Pediatric Cardiology Leiden University Medical Center Leiden Netherlands Pattern Recognition and Bioinformatics Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Computational Fluids Laboratory North Dakota State University United States

Vortical blood flow in the human left ventricular (LV) inflow initiates from the mitral valve (MV) and evolves within the LV during diastolic E-filling. Hence, vortical flow links MV and LV hemodynamics. This study sought to elucidate and quantitatively characterize the in vivo 3D dynamics of LV vortical flow over E-filling and relation to MV-LV hemodynamic coupling using 4D Flow MRI flow field. 34 healthy volunteers and 5 example patients underwent 4D Flow MRI. Vortical blood flow evolution was mapped in the LV over E-wave using enstrophy density. A new dimensionless profile PMV-LV was derived as a function of both MV vortex formation time (VFT) and LV volumetric enstrophy density. Results reveal that 3D vortical flow evolution in the healthy LV follows a bi-phasic behavior with a vortical growth phase followed by a vortical decay phase. In healthy LVs studied, the PMV-LV profile showed that the vortical growth and decay phases are characterized by a vortical growth time Tgrowth = 1.23 ± 0.25, growth rate α = 0.80 ± 0.17, decay time Tdecay = 0.96 ± 0.39 and decay rate β = -1.02 ± 0.49. Distinctly altered parameters were found in the pilot patients studied. The derived PMV-LV profile quantitatively characterizes MV-LV hemodynamic coupling by vortical flow dynamics. Results herein unravel new insights into cardiac physiology and could enable a novel standardized methodology to study MV-LV hemodynamic coupling and association to cardiac function in health and disease. Copyright © 2018, The Authors. All rights reserved.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

AB1448 DIFFERENTIATION OF IMMUNE MEDIATED VERSUS NON IMMUNE MEDIATED RHEUMATIC DISEASES BY ONLINE SYMPTOM CHECKER IN REAL-WORLD PATIENTS – MULTIPLE DIAGNOSES AND PARTICULARLY FIBROMYALGIA IS A STUMBLING BLOCK

引用

Annals of the Rheumatic Diseases 2024年 83卷 2082-2083页

作者： L. Qin F. Zegers D. Selani E.B. Van den Akker R. Bos S. Le Cessie I. Gehring H.K. Glas M. Johannesson J. Knitza L. Mathsson-Alm M.J.T. Reinders E. Steyerberg A. Van Tubergen L.M. Verhoef B. Axnäs L. Klareskog R. Knevel Leiden University Medical Center Department of Rheumatology Leiden Netherlands Leiden University Medical Center Department of Biomedical Data Sciences Leiden Netherlands Delft University of Technology Department of Intelligent Systems Pattern Recognition and Bioinformatics Delft Netherlands Leiden University Medical Center Leiden Computational Biology Department of Biomedical Data Sciences Leiden Netherlands Delft University of Technology The Delft Bioinformatics Lab Pattern Recognition and Bioinformatics Delft Netherlands Medical Center Leeuwarden Department of Rheumatology Leeuwarden Netherlands Nij Smellinghe Hospital Department of Rheumatology Drachten Netherlands Leiden University Medical Center Department of Clinical Epidemiology Leiden Netherlands Thermo Fisher Scientific Freiburg Germany Reumazorg Zuid West Nederland Goes Netherlands Karolinska Institutet and Karolinska University Hospital Department of Medicine Solna Solna Sweden University Hospital of Giessen and Marburg Philipps-University Marburg Institute for Digital Medicine Marburg Germany Uppsala University Department of Immunology Genetics and Pathology Uppsala Sweden Thermo Fisher Scientific Uppsala Sweden University Medical Center Rotterdam Department of Public Health Rotterdam Netherlands Maastricht University Medical Center Department of Internal Medicine Division of Rheumatology Maastricht Netherlands Maastricht University Care and Public Health Research Institute (CAPHRI) Maastricht Netherlands Sint Maartenskliniek Research Department Ubbergen Netherlands Elsa Science Stockholm Sweden Karolinska Institutet and Karolinska University hospital Department of Medicine Solna Solna Sweden Newcastle University Translational and Clinical Research Institute Rheumatology Newcastle upon Tyne United Kingdom

Background: Rheumatic? is an online symptom checker developed collaboratively by designers, engineers, clinical experts and patients. It is designed to identify individuals at high risk of developing rheumatic diseases and patients with immune-mediated rheumatic diseases (IRD). After completion of the questionnaire, Rheumatic? gives a weighted score based on expert assessments of the clinical significance of the answers [1] . A previous retrospective study has demonstrated the potential to distinguish IRD from osteoarthritis(OA) and gout, where clinicians filled out the questionnaire based on the medical notes [2] . In order to use a questionnaire in real life, it should be tested on real life participants, as they may describe their symptoms differently than experts. In order to evaluate the performance of the current Rheumatic? score, to differentiate between IRDs and non-IRDs we conducted a prospective real-world study in the Netherlands and assessed the differences in Rheumatic? total scores between individuals diagnosed with IRD and non-IRD. Objectives: Examine the difference in scores of Rheumatic? between IRD (Rheumatoid arthritis, Psoriatic arthritis, Myositis, Sjogren’s syndrome, SLE, Spondyloarthritis and Polymyalgia rheumatica) and non-IRD(OA, Gout, Fibromyalgia and other) diagnoses. Methods: We analyzed data submitted by Dutch participants recruited via multiple channels between July 2021 and July 2022. After completing the online questionnaire Rheumatic? participants received four follow-up surveys at 1 week, 3, 6 and 12 months asking, among other things, for previous and novel diagnoses. The theoretical total score for the participant was determined using the current, expert developed, risk assessment model post collection of all data [1,2] . We used t-test and analysis of variance (ANOVA) to compared the observed differences in mean and median of total scores among these groups. Results: 8,727 participants were included, of which 6861(79%) were female

关键词：

来源：评论

学校读者我要写书评

暂无评论

When accurate prediction models yield harmful self-fulfilling prophecies

arXiv

引用

arXiv 2023年

作者： van Amsterdam, Wouter A.C. van Geloven, Nan Krijthe, Jesse H. Ranganath, Rajesh Cinà, Giovanni Department of Data Science and Biostatistics Julius Center of Health Sciences Primary Care University Medical Center Utrecht Heidelberglaan 100 Utrecht3584 CX Netherlands University of Utrecht Heidelberglaan 100 Utrecht3584 CX Netherlands Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Pattern Recognition & Bioinformatics Delft University of Technology Delft Netherlands Courant Institute of Mathematical Science Department of Computer Science Center for Data Science New York University New York City United States Department of Medical Informatics Amsterdam University Medical Center Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands

Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisions, and are often hailed as the poster children for personalized, data-driven healthcare. We show however, that using prediction models for decision making can lead to harmful decisions, even when the predictions exhibit good discrimination after deployment. These models are harmful self-fulfilling prophecies: their deployment harms a group of patients but the worse outcome of these patients does not invalidate the predictive power of the model. Our main result is a formal characterization of a set of such prediction models. Next we show that models that are well calibrated before and after deployment are useless for decision making as they made no change in the data distribution. These results point to the need to revise standard practices for validation, deployment and evaluation of prediction models that are used in medical decisions. © 2023, CC BY.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

PO-260 Deep phenotyping of colorectal cancers by high-dimensional mass cytometry reveals tumor-specific immune landscapes

引用

ESMO Open 2018年 3卷 A328-A329页

作者： Vries, N. De Unen, V. Van Abdelaal, T. Herk, M. Van Van der Breggen, R. Sarasqueta, A. Farina Höllt, T. Lelieveldt, B. Koning, F. Miranda, N. De Leiden University Medical Center Immunohematology and Blood Transfusion Leiden The Netherlands Delft University of Technology Pattern Recognition and Bioinformatics Group Delft The Netherlands Leiden University Medical Center Pathology Leiden The Netherlands Delft University of Technology Computer Graphics and Visualization Group Delft The Netherlands Leiden University Medical Center Computational Biology Center Leiden The Netherlands Leiden University Medical Center Radiology Leiden The Netherlands

ABSTRACTIntroductionImmune checkpoint blockade has revolutionised cancer treatment. However, clinical outcomes are highly variable as only a proportion of cancer patients benefit from this treatment. To understand the mechanisms that determine responses to current immunotherapies and for the design of alternative approaches, it is crucial to characterise the immune landscape of the tumour microenvironment. An in-depth understanding of anti-tumour immunity requires a comprehensive analysis of the immune cell populations that participate in the process of tumorigenesis. The aim of this study is to unravel local and systemic profiles of lymphoid and myeloid immune subsets in colorectal cancer (CRC) using high-dimensional immunophenotyping by mass cytometry. Material and methodsThe expression of 36 immune cell markers was simultaneously assessed at the single-cell level by mass cytometry in tumour tissues (n=19), tumor-associated lymph nodes (n=17), adjacent normal mucosa (n=4), and peripheral blood samples (n=9) derived from 18 CRC patients. In total, 9 million cells were included in the analysis. Cytosplore and HSNE (Hierarchical Stochastic Neighbour Embedding) analysis tools were used to identify and visualise the composition of the lymphoid and myeloid cell populations of the innate and adaptive immune compartments in the tissues. Results and discussionsWe identified 220 immune cell subsets, including tumor-resident CD8 +CD45RO +CD103 +PD-1 +, CD4 +CD45RO +CD103 +PD-1 + and CD4 +CD45RO +ICOS +CD27 - T cell populations that were not found in tumor-associated lymph nodes, adjacent normal mucosa, and peripheral blood samples of CRC patients. In addition, we identified an overrepresentation of CD3 -CD7 +CD56 +CD45RO +CD127 - intermediate innate lymphoid cells in mismatch-repair deficient cancers and an enrichment of CD14 - myeloid populations in mismatch-repair proficient cancers. Unsupervised clustering of the samples based on the composite immune profile separated the

关键词：

来源：评论

学校读者我要写书评

暂无评论

ZygosityPredictor

引用

bioinformatics Advances 2024年第1期4卷 vbae017页

作者： Rheinnecker, Marco Fröhlich, Martina Rübsam, Marc Paramasivam, Nagarajan Heilig, Christoph E. Fröhling, Stefan Schlenk, Richard F. Hutter, Barbara Hübschmann, Daniel Computational Oncology Group Molecular Precision Oncology Program National Center for Tumor Diseases (NCT) Heidelberg German Cancer Research Center (DKFZ) Heidelberg 69120 Germany German Cancer Consortium (DKTK) Heidelberg 69120 Germany Division of Translational Medical Oncology NCT Heidelberg and DKFZ Heidelberg 69120 Germany Department of Medical Oncology NCT Heidelberg Heidelberg University Hospital Heidelberg 69120 Germany Department of Hematology Oncology and Rheumatology Heidelberg University Hospital Heidelberg 69120 Germany NCT Trial Center NCT Heidelberg Heidelberg University Hospital DKFZ Heidelberg 69120 Germany Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) Pattern Recognition and Digital Medicine Group Heidelberg 69120 Germany Innovation and Service Unit for Bioinformatics and Precision Medicine German Cancer Research Center (DKFZ) Heidelberg 69120 Germany

ZygosityPredictor provides functionality to evaluate how many copies of a gene are affected by mutations in next generation sequencing data. In cancer samples, the tool processes both somatic and germline mutations. In particular, ZygosityPredictor computes the number of affected copies for single nucleotide variants and small insertions and deletions (Indels). In addition, the tool integrates information at gene level via phasing of several variants and subsequent logic to derive how strongly a gene is affected by mutations and provides a measure of confidence. This information is of particular interest in precision oncology, e.g. when assessing whether unmutated copies of tumor-suppressor genes remain. © The Author(s) 2024.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction to: Gene expression identifies patients who develop inflammatory arthritis in a clinically suspect arthralgia cohort

引用

Arthritis research & therapy 2020年第1期22卷 289页

作者： Ellis Niemantsverdriet Erik B van den Akker Debbie M Boeters Susan J F van den Eeden Annemieke Geluk Annette H M van der Helm-van Mil Department of Rheumatology Leiden University Medical Center PO Box 9600 Leiden 2300 RC The Netherlands. e.niemantsverdriet@lumc.nl. Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands. Pattern Recognition & Bioinformatics Delft University of Technology Delft The Netherlands. Department of Rheumatology Leiden University Medical Center PO Box 9600 Leiden 2300 RC The Netherlands. Department of Infectious Diseases/Immunohematology and Blood Transfusion Leiden University Medical Center Leiden The Netherlands. Department of Rheumatology Erasmus Medical Center Rotterdam The Netherlands.

An amendment to this paper has been published and can be accessed via the original article.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The proteogenomic subtypes of acute myeloid leukemia

引用

Cancer Cell 2022年第3期40卷 301-317.e12页

作者： Jayavelu, Ashok Kumar Wolf, Sebastian Buettner, Florian Alexe, Gabriela Häupl, Björn Comoglio, Federico Schneider, Constanze Doebele, Carmen Fuhrmann, Dominik C. Wagner, Sebastian Donato, Elisa Andresen, Carolin Wilke, Anne C. Zindel, Alena Jahn, Dominique Splettstoesser, Bianca Plessmann, Uwe Münch, Silvia Abou-El-Ardat, Khali Makowka, Philipp Acker, Fabian Enssle, Julius C. Cremer, Anjali Schnütgen, Frank Kurrle, Nina Chapuy, Björn Löber, Jens Hartmann, Sylvia Wild, Peter J. Wittig, Ilka Hübschmann, Daniel Kaderali, Lars Cox, Jürgen Brüne, Bernhard Röllig, Christoph Thiede, Christian Steffen, Björn Bornhäuser, Martin Trumpp, Andreas Urlaub, Henning Stegmaier, Kimberly Serve, Hubert Mann, Matthias Oellerich, Thomas Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Department of Medicine II Hematology/Oncology University Hospital Frankfurt Goethe University Theodor-Stern-Kai 7 Frankfurt Germany German Cancer Consortium (DKTK) Partner Site Frankfurt/Mainz and German Cancer Research Center (DKFZ) Heidelberg Germany Clinical Cooperation Unit Pediatric Leukemia DKFZ and Department of Pediatric Oncology Hematology and Immunology University of Heidelberg Germany Department of Medicine University Hospital Frankfurt Goethe University Frankfurt Germany Division of Hematology/Oncology Department of Pediatric Oncology Dana-Farber Cancer Institute and Boston Children's Hospital Boston MA United States enGene Statistics GmbH Basel Switzerland Department of Biochemistry I Goethe University Frankfurt Germany Division of Stem Cells and Cancer German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance Heidelberg Germany Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH) Heidelberg Germany Bioanalytical Mass Spectrometry Group Max Planck Institute for Biophysical Chemistry Göttingen Germany Department of Medical Hematology and Oncology University Medical Center Göttingen Göttingen Germany Dr. Senckenberg Institute of Pathology University Hospital Frankfurt Frankfurt Germany Functional Proteomics Institute of Cardiovascular Physiology Goethe University Frankfurt Germany Pattern Recognition and Digital Medicine Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) Heidelberg Germany Computational Oncology Molecular Precision Oncology Program National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Bioinformatics University Medicine Greifswald Greifswald Germany Computational Systems Biochemistry Research Group Max Planck Institute of Biochemistry Martinsried Germany Department of Internal Medicine

Acute myeloid leukemia (AML) is an aggressive blood cancer with a poor prognosis. We report a comprehensive proteogenomic analysis of bone marrow biopsies from 252 uniformly treated AML patients to elucidate the molecular pathophysiology of AML in order to inform future diagnostic and therapeutic approaches. In addition to in-depth quantitative proteomics, our analysis includes cytogenetic profiling and DNA/RNA sequencing. We identify five proteomic AML subtypes, each reflecting specific biological features spanning genomic boundaries. Two of these proteomic subtypes correlate with patient outcome, but none is exclusively associated with specific genomic aberrations. Remarkably, one subtype (Mito-AML), which is captured only in the proteome, is characterized by high expression of mitochondrial proteins and confers poor outcome, with reduced remission rate and shorter overall survival on treatment with intensive induction chemotherapy. Functional analyses reveal that Mito-AML is metabolically wired toward stronger complex I-dependent respiration and is more responsive to treatment with the BCL2 inhibitor venetoclax. © 2022 Elsevier Inc.

关键词： acute myeloid leukemia BCL-2 inhibitor chemotherapy mitochondrial oxidative phosphorylation multi-omics data integration proteogenomics proteomics venetoclax

来源：评论

学校读者我要写书评

暂无评论

Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

引用

NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

来源：评论

学校读者我要写书评

暂无评论

Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

引用

Intelligence-Based Medicine 2022年 6卷 100071-100071页

作者： Smit, J.M. Krijthe, J.H. Endeman, H. Tintu, A.N. de Rijke, Y.B. Gommers, D.A.M.P.J. Cremer, O.L. Bosman, R.J. Rigter, S. Wils, E.-J. Frenzel, T. Dongelmans, D.A. De Jong, R. Peters, M.A.A. Kamps, M.J.A. Ramnarain, D. Nowitzky, R. Nooteboom, F.G.C.A. De Ruijter, W. Urlings-Strop, L.C. Smit, E.G.M. Mehagnoul-Schipper, D.J. Dormans, T. De Jager, C.P.C. Hendriks, S.H.A. Achterberg, S. Oostdijk, E. Reidinga, A.C. Festen-Spanjer, B. Brunnekreef, G.B. Cornet, A.D. Van den Tempel, W. Boelens, A.D. Koetsier, P. Lens, J.A. Faber, H.J. karakus, A. Entjes, R. De Jong, P. Rettig, T.C.D. Arbous, M.S. Lalisang, R.C.A. Tonutti, M. De Bruin, D.P. Elbers, P.W.G. Van Bommel, J. Reinders, M.J.T. Intensive Care Erasmus University Medical Center Rotterdam Netherlands Pattern Recognition & Bioinformatics Group EEMCS Delft University of Technology Delft Netherlands Clinical Chemistry Erasmus University Medical Center Rotterdam Netherlands Intensive Care UMC Utrecht Utrecht Netherlands Intensive Care OLVG Amsterdam Netherlands Anesthesiology and Intensive Care St. Antonius Hospital Nieuwegein Netherlands Intensive Care Franciscus Gasthuis Vlietland Rotterdam Netherlands Intensive Care Radboud University Medical Center Nijmegen Netherlands Intensive Care Amsterdam UMC Amsterdam Netherlands Intensive Care Bovenij Ziekenhuis Amsterdam Netherlands Intensive Care Canisius Wilhelmina Ziekenhuis Nijmegen Netherlands Intensive Care Catharina Ziekenhuis Eindhoven Eindhoven Netherlands Intensive Care ETZ Tilburg Tilburg Netherlands Intensive Care HagaZiekenhuis Den Haag Netherlands Intensive Care Laurentius Ziekenhuis Roermond Netherlands Intensive Care Northwest Clinics Alkmaar Netherlands Intensive Care Reinier de Graaf Gasthuis Delft Netherlands Intensive Care Spaarne Gasthuis Haarlem en Hoofddorp Netherlands Intensive Care VieCuri Medisch Centrum Venlo Netherlands Intensive Care Zuyderland MC Heerlen Netherlands Intensive Care Jeroen Bosch Ziekenhuis 's-Hertogenbosch Netherlands Intensive Care Albert Schweitzerziekenhuis Dordrecht Netherlands Intensive Care Haaglanden Medisch Centrum Den Haag Netherlands Intensive Care Maasstad Ziekenhuis Rotterdam Rotterdam Netherlands Intensive Care SEH BWC Martiniziekenhuis Groningen Netherlands Intensive Care Ziekenhuis Gelderse Vallei Ede Netherlands Intensive Care Ziekenhuisgroep Twente Almelo Netherlands Intensive Care Medisch Spectrum Twente Enschede Netherlands Intensive Care Ikazia Ziekenhuis Rotterdam Rotterdam Netherlands Anesthesiology Antonius Ziekenhuis Sneek Sneek Netherlands Intensive Care Medisch Centrum Leeuwarden Leeuwarden Netherlands Intensive Care IJsselland Zieke

Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85;0.88] and 0.86 [0.84;0.88], respectively. The recalibrated model predictions showed a calibration intercept of −0.04 [−0.12;0.04] and slope of 0.90 [0.85;0.95] for logistic regression model and a calibration intercept of −0.19 [−0.27;−0.10] and slope of 0.89 [0.84;0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research. © 2022 The Authors

关键词：

来源：评论

学校读者我要写书评

暂无评论

AB1480 STRATIFICATION OF PATIENTS USING ADVANCED INTEGRATIVE MODELLING OF DATA ROUTINELY ACQUIRED FOR DIAGNOSING RHEUMATIC COMPLAINTS (SPIDeRR): A MULTICENTER EUROPEAN CONSORTIUM

引用

Annals of the Rheumatic Diseases 2024年 83卷 2106-2106页

作者： J. Desai E. Van den Akker J. Askling B. Axnäs A. Banos L. Carmona L. Christiansson M. Hulsinga D. De Jong J. Knitza H. Morf F. Muehlensiepen A. Pratt G. Nagy J. Newton L. Rodriguez Rodriguez D. Schiphof S. Viatte M. Villalobos Quesada R. Knevel Leiden University Medical Center The Netherlands Department of Rheumatology Leiden Netherlands Technische Universiteit Delft Pattern Recognition & Bioinformatics Delft Netherlands Karolinska Institute Department of Rheumatology Stockholm Sweden Elsa Science AB Stockholm Sweden Idryma Iatroviologikon Ereunon Akademias Department of Rheumatology Athena Greece Instituto de Salud Musculoesqueletica Madrid Spain Phadia AB Part of Thermo Fisher Scientific Uppsala Sweden ReumaNederland Amsterdam IQVIA Solutions B.V. Amsterdam Netherlands University Hospital Giessen-Marburg Philipps University Institute for Digital Medicine Marburg Germany Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Department of Internal Medicine 3- Rheumatology & Immunology Erlangen Germany Medizinische Hochschule Brandenburg Campus Neuruppin GmbH Neuruppin Germany Translational and Clinical Research Institute Newcastle University Newcastle upon Tyne United Kingdom Semmelweis Egyetem Department of Rheumatology Budapest Hungary Academic Health Science Network North East and North Cumbria Newcastle upon Tyne United Kingdom San Carlos Research Institute (IdISSC) Hospital Clinico San Carlos Musculoskeletal Pathology Group Madrid Spain Erasmus MC University Medical Center Department of General Practice Rotterdam Netherlands University of Manchester Division of Musculoskeletal & Dermatological Sciences Manchester United Kingdom Leiden University Medical Center National eHealth Living Lab (NeLL) Leiden Netherlands Leiden University Medical Center Department of Rheumatology Leiden Netherlands Newcastle University Translational and Clinical Research Institute Newcastle upon Tyne Netherlands

Background: Musculoskeletal (MSK) symptoms are the leading contributor to various diseases and disability, affecting around 1.7 billion people globally. Early disease stratification is important to ensure appropriate and timely care (most suited healthcare provider and best treatment choice). Currently the patient journey to diagnosis and effective treatment is long and inefficient, resulting in persistent disease burden and economic loss. Suboptimal care is due to inadequate access to healthcare, insufficiently understood disease etiology, similar symptoms of different diseases, lack of discriminatory tests and a trial-and-error approach in treatment. Objectives: SPIDeRR aims to disentangle the real-life complexity of early diagnosis of musculoskeletal problems in general, and rheumatic diseases in particular, by considering the complete picture of factors influencing patients’ symptoms. It is a unique and first-in-kind consortium of 18 partners that connects all stakeholders including academic institutions, foundations/networks (o.a. patient organization) and biotech companies within Europe. Methods: SPIDeRR’s approach goes well beyond the state-of-the-art by: - developing novel high throughput methods to analyze high dimensional multimodal clinical and biologic data from various European healthcare systems to capture and predict patient trajectory towards Rheumatic disease diagnosis. - generating supervised machine learning models that facilitate patient stratification with/without rheumatic diseases underlying similar symptoms through integration of all relevant data dimensions (symptoms, comorbidities, social circumstances, electronic health records (EHR), biomarkers and genetics) from different healthcare levels (primary and secondary care as well as patients independently seeking advice online). - expediting diagnosis through integration of patients’ clinical and biological data and enabling physicians to promptly choose a personalized therapy, which will tak

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：