Laure Wynants | |
|---|---|
 Wynants in 2020 | |
| Alma mater | KU Leuven |
| Scientific career | |
| Institutions | KU Leuven Maastricht University |
| Thesis | Clinical Risk Prediction Models Based On Multicenter Data (2016) |
Laure Wynants is a Belgian epidemiologist who is a professor at Maastricht University. She studies prediction models in medicine and hospital acquired infections.
Wynants studied biostatistics at KU Leuven in Belgium. She remained there for her doctoral research, where she focused on prediction models. Her doctorate sought to predict whether ovarian tumours are benign or malignant, and how likely it is that the insertion of a catheter will cause bloodstream infection. [1]
Wynants is an assistant professor at KU Leuven. [2] She has focused on gynaecological cancers and hospital-acquired infections. She develops prediction models, models which combine a broad range of patient characteristics to understand individual probabilities of suffering from a certain disease.[ citation needed ]
In the early days of the COVID-19 pandemic, Wynants became concerned about the lack of scientific rigour applied to decision-making in COVID-19. [3] [4] She was particularly interested in translating her understanding of prediction models to better anticipate the outcomes of patients with COVID-19, [5] for example, whether prediction models could help to identify which patients should be tested, determine whether patients could recover at home, or understand who needed to be taken to critical care. [5] Despite considerable investment into artificial intelligence tools, machine-learning algorithms failed to bring support to physicians on the front-lines. [6] She argued that poor-quality, mislabeled data and data from unknown sources resulted in mediocre tools. [6] Wynants eventually established a consortium to understand COVID-19. Specifically, she created a living review to collate information on COVID-19 from published research and translate it into clinical practise. [7]
In 2020, Wynants was awarded the Edmond Hustinx Prize by Maastricht University. [5] In 2022, Significance named Wynants as among ten women statisticians who helped the world to understand COVID-19. [8]
Immunization during pregnancy is the administration of a vaccine to a pregnant individual. This may be done either to protect the individual from disease or to induce an antibody response, such that the antibodies cross the placenta and provide passive immunity to the infant after birth. In many countries, including the US, Canada, UK, Australia and New Zealand, vaccination against influenza, COVID-19 and whooping cough is routinely offered during pregnancy.
Sunetra Gupta is an Indian-born British infectious disease epidemiologist and a professor of theoretical epidemiology at the Department of Zoology, University of Oxford. She has performed research on the transmission dynamics of various infectious diseases, including malaria, influenza and COVID-19, and has received the Scientific Medal of the Zoological Society of London and the Rosalind Franklin Award of the Royal Society. She is a member of the scientific advisory board of Collateral Global, an organisation which examines the global impact of COVID-19 restrictions.
A diabetic foot disease is any condition that results directly from peripheral artery disease (PAD) or sensory neuropathy affecting the feet of people living with diabetes. Diabetic foot conditions can be acute or chronic complications of diabetes. Presence of several characteristic diabetic foot pathologies such as infection, diabetic foot ulcer and neuropathic osteoarthropathy is called diabetic foot syndrome. The resulting bone deformity is known as Charcot foot.
Sarilumab, sold under the brand name Kevzara, is a human monoclonal antibody medication against the interleukin-6 receptor. Regeneron Pharmaceuticals and Sanofi developed the drug for the treatment of rheumatoid arthritis (RA), for which it received US FDA approval on 22 May 2017 and European Medicines Agency approval on 23 June 2017.
In statistics, the one in ten rule is a rule of thumb for how many predictor parameters can be estimated from data when doing regression analysis while keeping the risk of overfitting and finding spurious correlations low. The rule states that one predictive variable can be studied for every ten events. For logistic regression the number of events is given by the size of the smallest of the outcome categories, and for survival analysis it is given by the number of uncensored events.
The International Ovarian Tumor Analysis (IOTA) group was formed in 1999 by Dirk Timmerman, Tom Bourne, and Lil Valentin. Its first aim was to develop standardised terminology, and in 2000 IOTA published a consensus statement on terms, definitions and measurements to describe the sonographic features of adnexal masses that is now widely used today. IOTA now comprises one of a portfolio of studies examining many aspects of gynaecological ultrasonography and early pregnancy within a network of contributing centres throughout the world that are coordinated from KU Leuven
Net reclassification improvement (NRI) is an index that attempts to quantify how well a new model reclassifies subjects - either appropriately or inappropriately - as compared to an old model. While c-statistics or AUC has been the standard metric for quantifying improvements over the last few decades, several studies have analyzed the limitations of this metric including lack of clinical relevance and difficulty in interpretation of small magnitude changes. This limitation can be best seen in the example of HDL and Framingham Risk Score (FRS). When models, with and without HDL, were analyzed with AUC regarding effect of HDL of modifying FRS, HDL was found not to have a statistical significant effect. However, when analyzed in terms of outcomes, HDL was found to be a significant predictor of heart disease and thus should affect FRS. To overcome this limitation the concept of reclassification, that is how well a new model correctly reclassifies cases, was introduced through the metric of NRI.
Lalita Ramakrishnan is an Indian-born American microbiologist who is known for her contributions to the understanding of the biological mechanism of tuberculosis. As of 2019 she serves as a professor of Immunology and Infectious Diseases at the University of Cambridge, where she is also a Wellcome Trust Principal Research Fellow and a practicing physician. Her research is conducted at the MRC Laboratory of Molecular Biology, where she serves as the Head of the Molecular Immunity Unit of the Department of Medicine embedded at the MRC LMB. Working with Stanley Falkow at Stanford, she developed the strategy of using Mycobacterium marinum infection as a model for tuberculosis. Her work has appeared in a number of journals, including Science, Nature, and Cell. In 2018 and 2019 Ramakrishnan coauthored two influential papers in the British Medical Journal (BMJ) arguing that the widely accepted estimates of the prevalence of latent tuberculosis—estimates used as a basis for allocation of research funds—are far too high. She is married to Mark Troll, a physical chemist.
Ewout W. Steyerberg is Professor of Clinical Biostatistics and Medical Decision Making at Leiden University Medical Center and a Professor of Medical Decision Making at Erasmus MC. He is interested in a wide range of statistical methods for medical research, but is mainly known for his seminal work on prediction modeling, which was stimulated by various research grants including a fellowship from the Royal Netherlands Academy of Arts and Sciences (KNAW). Steyerberg is one of the most cited researchers from the Netherlands. He has published over 1000 peer-reviewed articles according to PubMed, many in collaboration with clinical researchers, both in methodological and medical journals. His h-index exceeds 150 according to Google Scholar.
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by the virus SARS-CoV-2. The first known case was identified in Wuhan, China, in December 2019. The disease quickly spread worldwide, resulting in the COVID-19 pandemic.
Maria DeJoseph Van Kerkhove is an American infectious disease epidemiologist. With a background in high-threat pathogens, Van Kerkhove specializes in emerging and re-emerging infectious diseases and is based in the Health Emergencies Program at the World Health Organization (WHO). She is the technical lead of COVID-19 response and the head of emerging diseases and zoonosis unit at WHO.
Viviana Simon is a Professor of Microbiology at the Icahn School of Medicine at Mount Sinai (ISMMS). She is a member of the ISMMS Global Health and Emerging Pathogens Institute. Her research considers viral-host interactions and the mode of action of retroviral restriction factors. During the COVID-19 pandemic, Simon developed an antibody test that can determine immunity to Coronavirus disease 2019.
An aerosol-generating procedure (AGP) is a medical or health-care procedure that a public health agency such as the World Health Organization or the United States Centers for Disease Control and Prevention (CDC) has designated as creating an increased risk of transmission of an aerosol borne contagious disease, such as COVID-19. The implication is that the risk of transmission of the contagious disease from a patient having an AGP performed on them is higher than for a patient who is not having an AGP performed upon them. This then informs decisions on infection control, such as what personal protective equipment (PPE) is required by a healthcare worker performing the medical procedure.
Kimberly A. Powers is an American epidemiologist who is an associate professor of epidemiology at the UNC Gillings School of Global Public Health. She combines epidemiology, statistics and mathematical modelling to understand the transmission of infectious diseases. In 2011 her work on antiretroviral therapy for the management of human immunodeficiency virus was selected by Science as the breakthrough of the year. During the COVID-19 pandemic, Powers looked to understand the spread of SARS-CoV-2.
The Zoe Health Study, formerly the COVID Symptom Study, is a health research project of British company Zoe Limited which uses a mobile app that runs on Android and iOS.
The Randomised Evaluation of COVID-19 Therapy is a large-enrollment clinical trial of possible treatments for people in the United Kingdom admitted to hospital with severe COVID-19 infection. The trial was later expanded to Indonesia, Nepal and Vietnam. The trial has tested ten interventions on adults: eight repurposed drugs, one newly developed drug and convalescent plasma.
In an adaptive design of a clinical trial, the parameters and conduct of the trial for a candidate drug or vaccine may be changed based on an interim analysis. Adaptive design typically involves advanced statistics to interpret a clinical trial endpoint. This is in contrast to traditional single-arm clinical trials or randomized clinical trials (RCTs) that are static in their protocol and do not modify any parameters until the trial is completed. The adaptation process takes place at certain points in the trial, prescribed in the trial protocol. Importantly, this trial protocol is set before the trial begins with the adaptation schedule and processes specified. Adaptions may include modifications to: dosage, sample size, drug undergoing trial, patient selection criteria and/or "cocktail" mix. The PANDA provides not only a summary of different adaptive designs, but also comprehensive information on adaptive design planning, conduct, analysis and reporting.
Decision curve analysis evaluates a predictor for an event as a probability threshold is varied, typically by showing a graphical plot of net benefit against threshold probability. By convention, the default strategies of assuming that all or no observations are positive are also plotted.
Predictions of surgery duration (SD) are used to schedule planned/elective surgeries so that utilization rate of operating theatres be optimized. An example for a constraint is that a pre-specified tolerance for the percentage of postponed surgeries not be exceeded. The tight linkage between SD prediction and surgery scheduling is the reason that most often scientific research related to scheduling methods addresses also SD predictive methods and vice versa. Durations of surgeries are known to have large variability. Therefore, SD predictive methods attempt, on the one hand, to reduce variability, and on the other employ best available methods to produce SD predictions. The more accurate the predictions, the better the scheduling of surgeries.
Margaret Johnson is a British physician who is a consultant in thoracic medicine and chair of the St John & St Elizabeth Hospital. In the late 1980s, she was the first dedicated HIV doctor at the Royal Free Hospital.
| Authority control databases: Academics |
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