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Using a personalized decision support algorithm for dosing in warfarin treatment: A randomised controlled trial

Clinical Trials and Regulatory Science in Cardiology 2017年 25卷 1-6页

作者： Nielsen, Peter Brønnum Lundbye-Christensen, Søren van der Male, Meriam Larsen, Torben Bjerregaard Aalborg Thrombosis Research Unit Department of Clinical Medicine Faculty of Medicine Aalborg University Aalborg Denmark Department of Cardiology Aalborg University Hospital Aalborg Denmark Unit of Clinical Biostatistics and Bioinformatics Aalborg University Hospital Aalborg Denmark Department of Cardiology Aalborg University Hospital Denmark

Background Vitamin K-antagonists such as warfarin treatment remain the mainstay to prevent thromboembolic events in various conditions. The quality of the treatment is reflected through time in therapeutic (INR) range (TTR) with a threshold at ≥ 70% indicating ‘good quality’;achieving this quality is not trivial. We conducted a randomised controlled trial to assess the impact of decision aiding model on treatment quality in a high quality vitamin K-antagonist treatment setting. Methods We investigated if algorithm-suggested warfarin dosing was superior to standard dosing in a high-quality setting involving self-managing warfarin patients. Patients were initially allocated to either algorithm-suggested warfarin dosing or to standard care treatment, and were crossed over after three months. The trial period was a total of six months, and the primary endpoint was TTR;we also investigated a secondary endpoint of log-transformed INR variability. Results A total of 191 patients contributed to the main analysis with a mean follow-up time of 140 days;75% were males and the mean age was 65 years old. The intervention arm achieved a TTR of 81.6, while the placebo arm attained a TTR of 80.9 (difference [intervention arm minus placebo arm]: 0.67 (95% confidence interval − 2.93 to 4.27). The difference in INR variability was 0.30 (0.14 to 0.47), favouring the placebo arm in terms of lower log transformed variability. Conclusions We found no difference between the two trial-arms in a high-quality warfarin treatment setup. However in general, the model performed similarly as to routine patient self-management care. (*** number: NCT02705976) © 2016 The Authors

关键词： Anticoagulants Clinical decision support systems Randomised controlled clinical trial Therapeutic drug monitoring

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An outcome model approach to translating a randomized controlled trial results to a target population

arXiv

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arXiv 2018年

作者： Goldstein, Benjamin A. Phelan, Matthew Pagidipati, Neha J. Holman, Rury R. Pencina, Michael J. Stuart, Elizabeth A. Department of Biostatistics and Bioinformatics Duke University 2424 Erwin Road DurhamNC27705 Center for Predictive Medicine Duke Clinical Research Institute DurhamNC Department of Medicine Duke University DurhamNC Diabetes Trials Unit Oxford Centre for Diabetes Endocrinology and Metabolism University of Oxford Oxford Department of Biostatistics Johns Hopkins University BaltimoreMD

Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to translate RCT results to target populations has focused on weighting RCT data to look like the target data. Simulation work, however, has suggested that an outcome model approach may be preferable. Here we describe such an approach using source data from the 2x2 factorial NAVIGATOR trial which evaluated the impact of valsartan and nateglinide on cardiovascular outcomes and new-onset diabetes in a "pre-diabetic" population. Our target data consisted of people with "pre-diabetes" serviced at our institution. We used Random Survival Forests to develop separate outcome models for each of the 4 treatments, estimating the 5-year risk difference for progression to diabetes and estimated the treatment effect in our local patient populations, as well as sub-populations, and the results compared to the traditional weighting approach. Our models suggested that the treatment effect for valsartan in our patient population was the same as in the trial, whereas for nateglinide treatment effect was stronger than observed in the original trial. Our effect estimates were more efficient than the weighting approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Risk perception

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379P Whole exome sequencing of plasma circulating tumor DNA identifies dynamic mutational changes to guide targeted therapies in colon cancer patients

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Annals of Oncology 2022年 33卷 S709-S709页

作者： J. Martín-Arana F. Gimeno-Valiente R. Tébar-Martínez B. García-Mico V. Gambardella M. Huerta C. Martínez-Ciarpaglini J.A. Carbonell-Asins M. Cabeza Segura J. Martín-Arévalo D. Casado V. Pla L. Pérez P. Rentero-Garrido S. Rosello Keranen T.C. Fleitas J. Castillo D. Roda Perez A. Cervantes N. Tarazona Llavero Department of Medical Oncology Biomedical Research Institute INCLIVA Hospital Clínico University of Valencia Valencia Spain Cancer Evolution and Genomic Instability Laboratory University College London Cancer Institute London UK Department of Medical Oncology Biomedical Research Institute INCLIVA Hospital Clínico University of Valencia. CIBERONC Instituto de Salud Carlos III Madrid Spain Valencia Valencia Spain Department of Medical Oncology Biomedical Research Institute INCLIVA Hospital Clínico University of Valencia Valencia Valencia Spain Department of Pathology Biomedical Research Institute INCLIVA Hospital Clínico University of Valencia. CIBERONC Instituto de Salud Carlos III Madrid Spain Valencia Spain Bioinformatics and Biostatistics Unit Biomedical Research Institute INCLIVA Valencia Spain Surgical Oncology Hospital Clinico Universitario de Valencia Valencia Spain Precision Medicine Unit INCLIVA Biomedical Research Institute Valencia Spain Department of Medical Oncology Biomedical Research Institute INCLIVA Hospital Clínico University of Valencia Valencia MASSACHUSSETS Spain Department of Biochemistry and Molecular Biology Universitat de Valencia Valencia Spain

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Multinomial logistic model for coinfection diagnosis between arbovirus and malaria in Kedougou

arXiv

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arXiv 2018年

作者： Loum, Mor Absa Poursat, Marie-Anne Sow, Abdourahmane Sall, Amadou Alpha Loucoubar, Cheikh Gassiat, Elisabeth Laboratoire de Mathématiques d'Orsay Univ. Paris-Sud CNRS Université Paris-Saclay Orsay91405 France Institut Pasteur de Dakar Arboviruses and Viral Hemorrhagic Fevers Unit Institut Pasteur de Dakar Biostatistics Bioinformatics and Modeling Group

In tropical regions, populations continue to suffer morbidity and mortality from malaria and arboviral diseases. In Kedougou (Senegal), these illnesses are all endemic due to the climate and its geographical position. The cocirculation of malaria parasites and arboviruses can explain the observation of coinfected cases. Indeed there is strong resemblance in symptoms between these diseases making problematic targeted medical care of coinfected cases. This is due to the fact that the origin of illness is not obviously known. Some cases could be immunized against one or the other of the pathogens, immunity typically acquired with factors like age and exposure as usual for endemic area. Thus, coinfection needs to be better diagnosed. Using data collected from patients in Kedougou region, from 2009 to 2013, we adjusted a multinomial logistic model and selected relevant variables in explaining coinfection status. We observed specific sets of variables explaining each of the diseases exclusively and the coinfection. We tested the independence between arboviral and malaria infections and derived coinfection probabilities from the model fitting. In case of a coinfection probability greater than a threshold value to be calibrated on the data, long duration of illness and age are mostly indicative of arboviral disease while high body temperature and presence of nausea or vomiting symptoms during the rainy season are mostly indicative of malaria disease. Copyright © 2018, The Authors. All rights reserved.

关键词： Viruses

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417P New insights into second-line (2L) choices after CDK4/6 inhibitors (CDK4/6i) in hormone receptor-positive (HR+)/ human epidermal growth factor 2-negative (HER2-) metastatic breast cancer (MBC) patients (pts): Preliminary results of the HERMIONE-13 study

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Annals of Oncology 2023年 34卷 S358-S358页

作者： V. Cogliati E. Rossi A. Turla M. Ambroggi R. Berardi F. Borella L. Coltelli U.F.F. De Giorgi A. Ferro O. Garrone M. Giordano E. Landucci M. Mazzotta G. Moretti R. Palumbo P. Vici F. Zustovich F. Riva S. Capici M.E. Cazzaniga Phase 1 Research Centre Fondazione IRCCS San Gerardo dei Tintori Monza Italy Bicocca Bioinformatics Biostatistics and Bioimaging Centre (B4) University of Milan-Bicocca Monza Italy Oncology Unit Fondazione IRCCS San Gerardo dei Tintori Monza Italy Oncology AUSL di Piacenza - Ospedale Guglielmo da Saliceto Piacenza Italy Clinical Oncology Dept. of Internal Medicine AOU - Ospedali Riuniti Umberto I - G.M. Lancisi - G. Salesi Torrette Di Ancona Italy Ostetricia e ginecologia AOU Città della Salute e della scienza di Torino Turin Italy Oncology Department Ospedale Livorno Azienda USL Toscana Nord Ovest Pisa Italy Medical Oncology Department IRST - Istituto Romagnolo per lo Studio dei Tumori Dino Amadori IRCCS S.r.l. Meldola Forlì-Cesena Italy Medical Oncology Dept. Ospedale Santa Chiara - APSS Trento Trento Italy Medical Oncology Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico Milan Italy Oncology Department Asst-lariana Como CO Italy Oncologia Azienda Ospedaliero Universitaria Pisana Pisa Italy Medical Oncology Unit Sandro Pertini Hospital & Sant'Eugenio Hospital Rome Italy Medical Oncology Department IRCCS Arcispedale Santa Maria Nuova Reggio Emilia Italy Medical Oncology IRCCS ICS Maugeri Pavia Pavia Italy Phase IV Clinical Study Unit IRCCS Regina Elena National Cancer Institute Rome Italy UOC Oncologia Ospedale di Belluno AULSS 1 Dolomiti Belluno Italy

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Targeted inhibition of pathobiont virulence factor mitigates alcohol-associated liver disease

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Cell Host and Microbe 2025年第6期33卷 957-972.e6页

作者： Yang, Yongqiang Duan, Yi Lang, Sonja Fondevila, Marcos F. Schöler, David Harberts, Aenne Cabré, Noemí Chen, Sainan Shao, Yan Vervier, Kevin Miyamoto, Yukiko Zhang, Xinlian Chu, Huikuan Yang, Ling Tan, Chen Eckmann, Lars Bosques-Padilla, Francisco Verna, Elizabeth C. Abraldes, Juan G. Brown, Robert S. Vargas, Victor Altamirano, Jose Caballería, Juan Shawcross, Debbie L. Louvet, Alexandre Lucey, Michael R. Mathurin, Philippe Garcia-Tsao, Guadalupe Bataller, Ramon Stärkel, Peter Lawley, Trevor D. Schnabl, Bernd Department of Medicine University of California San Diego La Jolla CA United States National Key Laboratory of Immune Response and Immunotherapy Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei China Center for Advanced Interdisciplinary Science and Biomedicine of IHM and Department of Infectious Diseases The First Affiliated Hospital of USTC University of Science and Technology of China Anhui Hefei China University of Cologne Faculty of Medicine and University Hospital Cologne Department of Gastroenterology and Hepatology Cologne Germany Host-Microbiota Interactions Laboratory Wellcome Sanger Institute Wellcome Genome Campus Hinxton United Kingdom Division of Biostatistics and Bioinformatics Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego La Jolla CA United States Division of Gastroenterology Union Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China State Key Laboratory of Agricultural Microbiology College of Veterinary Medicine Huazhong Agricultural University Hubei Wuhan China Hospital Universitario Departamento de Gastroenterología Universidad Autonoma de Nuevo Leon Monterrey Mexico Division of Digestive and Liver Diseases Department of Medicine Columbia University College of Physicians and Surgeons New York NY United States Division of Gastroenterology (Liver Unit) University of Alberta Edmonton AB Canada Division of Gastroenterology and Hepatology Weill Cornell Medical College New York NY United States Liver Unit Hospital Universitari Vall d'Hebron Universitat Autònoma de Barcelona Barcelona Spain Centro de Investigación en Red de Enfermedades Hepáticas y Digestivas (CIBERehd) Barcelona Spain Liver Unit Hospital Clinic Barcelona Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Centro de investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBERehd) Barcelona

Alcohol-associated liver disease poses a global health burden with high mortality. Imbalances in the gut microbiota are important for disease progression. Using metagenomic sequencing of fecal samples from a multicenter, international cohort of patients with alcohol-associated hepatitis, we found that the presence of virulence factor KpsM, encoded in the genome of Escherichia coli (E. coli), correlated with patient mortality. Functional studies using gnotobiotic mouse models and genetic manipulation of bacteria demonstrated that kpsM-positive E. coli exacerbate ethanol-induced liver disease. The kpsM gene mediates the translocation of capsular polysaccharides to the cell surface. This enables kpsM-positive E. coli to evade phagocytosis by the scavenger receptor Marco on Kupffer cells in the liver, leading to bacterial spread. Importantly, inhibiting kpsM-dependent capsules with the small molecule 2-(4-phenylphenyl)benzo[g]quinoline-4-carboxylic acid (C7) attenuated ethanol-induced liver disease in mice. We show that precision targeting of the virulence factor KpsM is a promising approach to improve outcomes of patients with alcohol-associated hepatitis. © 2025

关键词： alcoholic liver disease gut-liver axis metagenomics microbiome microbiota virulence factor

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Publisher Correction: Orchestrating single-cell analysis with Bioconductor

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Nature methods 2020年第2期17卷 242页

作者： Robert A Amezquita Aaron T L Lun Etienne Becht Vince J Carey Lindsay N Carpp Ludwig Geistlinger Federico Marini Kevin Rue-Albrecht Davide Risso Charlotte Soneson Levi Waldron Hervé Pagès Mike L Smith Wolfgang Huber Martin Morgan Raphael Gottardo Stephanie C Hicks Fred Hutchinson Cancer Research Center Seattle WA USA. Cancer Research UK Cambridge Institute University of Cambridge Cambridge UK. Bioinformatics and Computational Biology Genentech Inc. San Francisco CA USA. Channing Division of Network Medicine Brigham And Women's Hospital Boston MA USA. Graduate School of Public Health and Health Policy City University of New York New York NY USA. Institute for Implementation Science in Population Health City University of New York New York NY USA. Center for Thrombosis and Hemostasis Mainz Germany. Institute of Medical Biostatistics Epidemiology and Informatics Mainz Germany. Kennedy Institute of Rheumatology University of Oxford Oxford UK. Department of Statistical Sciences University of Padua Padua Italy. Division of Biostatistics and Epidemiology Department of Healthcare Policy and Research Weill Cornell Medicine New York NY USA. Friedrich Miescher Institute for Biomedical Research Basel Switzerland. SIB Swiss Institute of Bioinformatics Basel Switzerland. European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany. Biostatistics and Bioinformatics Roswell Park Comprehensive Cancer Center Buffalo NY USA. Fred Hutchinson Cancer Research Center Seattle WA USA. rgottard@***. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. shicks19@jhu.edu.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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1773P ICECaP-2: Validation of metastasis-free survival (MFS) as a surrogate for overall survival (OS) in localized prostate cancer (LPC) in a more contemporary era

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Annals of Oncology 2023年 34卷 S958-S959页

作者： W. Xie P. Ravi M.E. Buyse S. Halabi P.W. Kantoff O. Sartor H.R. Soule N. Clarke L. Collette J. Dignam N.D. James K. Fizazi S. Gillessen N. Mottet L.R. Murphy W.R. Parulekar H. Sandler B. Tombal S. Williams C. Sweeney Department of Data Sciences Dana Farber Cancer Institute Boston MA USA Genitourinary Oncology Dana Farber Cancer Institute Boston MA USA Biostatistics International Drug Development Institute Louvain-la-Neuve Ottignies Belgium Biostatistics and Bioinformatics Duke University Medical Center Durham NC USA Medical Oncology Convergent Therapeutics Inc. Cambridge MA USA Hematology/Oncology Department Mayo Clinic - Rochester Rochester MN USA Research Department Prostate Cancer Foundation Santa Monica CA USA Urological Oncology The University of Manchester Manchester UK Statistics European Organisation for Research and Treatment of Cancer Brussels Belgium Statistics and Data Management Center NRG Oncology Philadelphia PA USA Prostate and Bladder Cancer Research Department ICR - Institute of Cancer Research London UK Cancer Medicine Department Institut Gustave Roussy Villejuif France Medical Oncology Department EOC - Ospedale Regionale Bellinzona e Valli - Istituto Oncologico della Svizzera Italiana (IOSI) Bellinzona Switzerland Uro-Oncology Department CHU Saint Etienne - Hopital Nord Saint-Étienne France MRC CTU MRC - Medical Research Council Clinical Trials Unit - University College London (UCL) London UK Cancer Research Institute Canadian Cancer Trials Group Kingston ON Canada Radiation Oncology Cedars Sinai Medical Center Los Angeles CA USA Division of Urology Cliniques Universitaires St. Luc - Université Catholique de Louvain Brussels Belgium Radiation Oncology Peter MacCallum Cancer Center Melbourne Australia South Australian Immunogenomics Cancer Institute The University of Adelaide Adelaide SA Australia

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the united States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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Correction: Impact of nutritional status on heart failure mortality: a retrospective cohort study

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Nutrition journal 2022年第1期21卷 61页

作者： Nafz Abdoul Carime Jonathan Cottenet Guillaume Clerfond Romain Eschalier Didier Quilliot JeanChristophe Eicher Bertrand Joly Catherine Quantin Biostatistics and Bioinformatics department (DIM) Dijon University Hospital Dijon France University of Bourgogne Franche-Comté Dijon France. Cardiology Department CHU Clermont-Ferrand Clermont-Ferrand France and Université Clermont Auvergne CHU Clermont-Ferrand CNRS SIGMA Clermont Institut Pascal F-63000 Clermont-Ferrand France and F-CRIN INI-CRCT Nancy France. Department of Gastroenterology and Inserm NGERE U1256 University Hospital of Nancy University of Lorraine 1 Allée du Morvan 54511 Vandoeuvre-lès-Nancy France Nutritional Assistance Department University Hospital of Nancy University of Lorraine Vandoeuvre‑lès‑Nancy France. Cardiology Department University Hospital of Dijon Dijon France. CHPCB Paray-le-Monial General Hospital Paray‑le‑Monial France. University of Bourgogne Franche-Comté Dijon France. catherine.quantin@chu-dijon.f. Inserm CIC 1432 Dijon France Dijon University Hospital Clinical Investigation Center clinical epidemiology/ clinical trials unit Dijon France. catherine.quantin@chu-dijon.f. Biostatistics Biomathematics Pharmacoepidemiology and Infectious Diseases (B2PHI) INSERM UVSQ Institut Pasteur Université Paris-Saclay Paris France. catherine.quantin@chu-dijon.f.

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