DOCSLIB.ORG

Management & Practice

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Management & Practice IMANCUAGEMENT & PRACTICE IntensIve care - emergency medIcIne - anaesthesIology VOLUME 19 - ISSUE 4 - WINTER 2019/2020 The Future IcU the Future of haemodynamic monitoring: From Planet mars to resource-limited countries, F. Michard, M. Fortunato, A. Pratas, S. Rodrigues de Oliveira clinical decision support systems: Future or Present in IcU? A. Naharro-Abellán, B. Lobo-Valbuena, F. Gordo the Future of critical care Ultrasound, A. Butnar, A. Wong, S. Ho, M. Malbrain Future IcU design: return to high visibility, D. Hamilton, S. Swoboda, C. Cadenhead a Framework for addressing seasonal Influenza: a critical care Perspective, L. Busse, C. Coopersmith Will artificial Intelligence change IcU Practice? V. Herasevich, M. Keegan, M. Johnston, B. Pickering Future strategies in sedation and analgesia, B. Pastene, M. Leone critical care telemedicine: a management Fad or the Future of IcU Practice? K. Iliopoulou, A. Xyrichis the Intersection of Big data, artificial Intelligence, Precision and Predictive medicine to create the Future of critical care, G. Martin the Intelligent Intensive care Unit: Integrating care, research and education, E. Cox, I. van der Horst. Plus Introducing the Intubation credit card, A. Higgs, S. shaping the human side of medical devices in Goodhand, A. Joyce critical care: the Implication of human Factor Improving recognition of neonatal sepsis, M. Harris, studies in clinical settings, M. Micocci, A. Tase, M. Ni, A. Masino, R. Grundmeier P. Buckle, F. Rubulotta lifesaving applications of transoesophageal echocar- diagnosis, treatment and management of the diography in critical and emergency care, R. Arntfield critically Ill Patient, R. Moreno icu-management.org @ICU_Management 228 COVER STORY: THE FUTURE ICU Greg S. Martin Professor of Medicine Executive Associate Division The Intersection of Big Director Division of Pulmonary, Al- lergy, Critical Care and Sleep Medicine Data, Artificial Emory University School of Medicine Critical Care Medical Director Intelligence, Precision Grady Memorial Hospital [email protected] and Predictive Medicine to Create the Future of Critical Care Over the next 50 years, critical care will evolve from a system that reacts to patient deterioration into a system that predicts and prevents these events. The application of real-time analytics to large-scale integrated ICU patient data will facilitate creation of learning healthcare systems and delivery of personalised and even predictive critical care medicine. ver the next 50 years, critical and unpredictable acute illnesses. rollout of 5G connectivity will increase care will evolve from a system Already in the present day, patients exponentially machine-to-machine traf- Othat reacts to patient deterio- in critical care units generate extraordi- fic to more than 50% of internet traffic ration into a system that predicts and nary amounts of data, from diagnostic by 2022 (Cisco 2019; McKinsey 2017a; prevents these events. The pathway to and laboratory testing, provider notes, McKinsey 2017b). The IoT already exists in proactive critical care involves technical intermittent and continuous monitoring healthcare, being used to track equipment, and computing advances that integrate equipment, and myriad support devices patients and even providers throughout large-scale clinical data from critically ill such as mechanical ventilators. In the near the hospital. Although the evolution of IoT patients and applies complex analytics in future, the panoply of monitoring devices devices and other monitoring equipment real-time to personalise care and predict will take advantage of secure wireless complement the developments outside of untoward events. These advances will connections to facilitate contactless patient healthcare, such as in computing technol- facilitate creation of learning healthcare monitoring that functions seamlessly across ogy and the consumer markets, they have systems and delivery of personalised and healthcare environments such as the ED, unique needs in healthcare. For example, even predictive critical care medicine. In radiology, OR and the ICU. Outside of healthcare has greater demands on secure the not-so-distant future, ICU patients will healthcare, in the consumer market we communications as well as reliability and look vastly different than the patients we are already experiencing the explosion safety across patient environments such see today with multiple organ dysfunc- of internet-connected devices known as the ICU, OR, radiology, emergency tion, as we predict and prevent critical as the internet of things (IoT). Those department, pre-hospital setting and more. illness and become an environment where devices, estimated to be as many as 200 Imagine for healthcare to adopt the individualised care is delivered to patients billion by 2020 (Intel 2019), are now manufacturing production principles recovering from unforeseen traumatic using a small fraction of internet traffic of big data, where the introduction of injuries, increasingly complex surgeries but non-human, but the coming global comprehensive, real-time data collec- ICU Management & Practice 4 - 2019/2020 229 COVER STORY: THE FUTURE ICU tion and analysis results in fantastically example, the inability to consistently patients will appear as the mean (e.g. the more responsive production systems. acquire and interpret ultrasound images point estimate from a clinical study) and In healthcare, in order for the growing limits the application of one of our most are amenable to system-level interventions, constellation of monitoring, testing and available technologies. The ubiquitous whilst individual patients rarely fall exactly data to be clinically valuable, it must be nature of ultrasound in the future of criti- at the exact point estimate but are likely integrated in real-time with the entire cal care makes it necessary to solve this to fall within the range of results from the spectrum of clinical data in order to problem, and the combination of AI and group (e.g. the confidence interval), and ensure the delivery of timely, high qual- computing interfaces makes this possible. individual responses may be optimised ity patient care. An important next step or predicted by fully characterising each in handling the impending explosion of unique patient. data generated by critical care patients is in the near future, Integration of data permits the conver- data harmonisation. Our current lack of sion of the traditional ICU to a learning inter-operability between electronic health the panoply of monitoring healthcare system. A learning healthcare record (EHR) systems is confounded by devices will take advantage system (LHS) is defined by the Institute multiple instances of duplicated data. of secure wireless of Medicine as a system in which science, For example, in the data warehouse for informatics, incentives, and culture are one of our hospitals, there are multiple connections to facilitate aligned for continuous improvement and entries for haemoglobin, each recorded contactless patient innovation, with best practices seamlessly with a different label: ED-Hgb, OB-Hgb, embedded in the delivery process and STAT-Hgb and regular inpatient-Hgb, monitoring that new knowledge captured as an integral outpatient-Hgb, neonatal-Hgb and point- functions seamlessly by-product of the delivery experience of-care-Hgb! Harmonising data variables across healthcare (Institute of Medicine 2007). A LHS is a and concatenating these instances is one sociotechnical system with afferent and step towards clinically effective data report- environments efferent components where the affer- ing and utilisation. ent component assembles, analyses and Harnessing the full spectrum of clini- interprets data from various sources, and cal data needed to care for ICU patients the efferent component returns these find- requires advancing the underlying tech- The effective integration of clinical ings to the healthcare system in order to nologies that make it feasible. Computing critical care data at scale with real-time favourably change clinical practice. The power is now in the realm where basic analytics is the foundation for changes at afferent side is made possible by recent streams of real-time data can be aggre- each end of the medical care spectrum. technical innovations such as EHR data gated and reported, such as clinical lab At the level of healthcare systems, it enables and the IoT, and efferent side incorporates testing, nurse-recorded vital signs and iterative system-level improvements that elements such as behavioural psychology, intravenous infusion pump data. The next produce consistent, cutting-edge, reli- implementation science, behavioural steps require the computing and storage able, high quality care. At the level of economics, policy and organisational capabilities to handle the entire river of the individual patient, it enables care to be theory in order to effect change. The real-time data, and the associated analytic customised for each patient according to collision of big data harmonisation, EHR capacity to efficiently drive patient care. the current state of their acute and chronic interoperability and AI will make easier In the coming years, the application of conditions, while taking into account other the transition of each hospital from a artificial intelligence and machine learning relevant factors such as social support and traditional healthcare environment to a will solve some of the vexing problems other determinants of health. In essence, learning healthcare
Load more

Recommended publications
  • Genomic Biomarkers in Predictive Medicine. an Interim Analysis
    Perspective Genomic biomarkers in predictive medicine Genomic biomarkers in predictive medicine. An interim analysis Keywords: biomarkers; early detection; genomics; personalized medicine; translational research Current genomics and biotechnology markers and the limited use of such denotes the prevalence of the high-risk promise the development of biomarkers markers outside research. Failure to allele and RR denotes the relative risk of to predict individual disease risk, enable focus also results in misleading claims disease for a subject with high-risk versus early detection of disease, and improve for early detection biomarkers based on standard risk allele. Most diseases have diagnostic classification to better inform studies with inappropriate controls. Here, sufficiently low prevalences that the OR individualized treatment. I discuss these I shall discuss separately several broad and RR are essentially equal. From this objectives, commenting on progress to categories of intended use as illustrated in formula one can show that the PPV is no date and obstacles to future success. The Fig 1 (summarized in Box 1). greater than RRÃp. So if the RR is 1.22 and discussion mainly uses examples from the disease prevalence is 5% (p ¼ 0.05), oncology where the nature of the disease then the absolute risk of developing the has expedited genomic approaches for Biomarkers of Disease Risk disease for a subject with a high-risk developing biomarkers. Many of the allele is no greater than 6.1%. If the lessons being learned in oncology, how- The development of genetic biomarkers RR were 5, then the absolute risk of ever, should be applicable to other for predicting risk of disease in indivi- developing disease for a subject with a chronic diseases.
    [Show full text]
  • Xai-EWS — an Explainable AI Model Predicting Acute Critical Illness
    Health & Medicine ︱Simon Meyer Lauritsen Patient data Prediction module Output P-Creatinine Acute Kidney Injury: l Kidney eGFR e d o 78% m P-Potassium o t t .. xAI-EWS — an explainable u p n i AI model predicting P-Albumin time Patient data with explanations Explanation module Why? https://www.nature.com/articles/s41467-020-17431-x P-Creatinine n acute critical illness PhotoCredit: Kidney eGFR o i at P-Potassium n a he time it takes medical staff to choices of patients, providers, payers, .. xpl Early clinical predictions of . acute critical illness have a vital diagnose a patient’s acute critical etc. to achieve better outcomes in e influence on patient outcomes. Tillness has a pivotal influence terms of quality of care and its cost. P-Albumin For clinical medicine to benefit on the patient outcome. Acute critical With such high-stake applications, the from the higher predictive illness usually follows a deterioration simpler, more transparent systems are Low parameter value High Low parameter relevance High power of Artificial Intelligence, of the patient’s vital signs – the often chosen so that clinicians can easily however, explainable and routinely measured heart rate, body back-trace a prediction. This strategy, Overview of the xAI-EWS system. Based on patient data, the model makes a prediction, such as a 78% risk of acute kidney injury. The explanation transparent systems are temperature, respiration rate and blood however, can lead to negative outcomes module then explains the predictions in terms of input variables, e.g. p-creatinine, kidney eGFR, p-potassium and p-albumin.
    [Show full text]
  • Inflammation, Cardiovascular Disease and Cancer: Moving Toward Predictive Medicine
    Early release, published at www.cmaj.ca on November 28, 2016. Subject to revision. CMAJ Commentary Inflammation, cardiovascular disease and cancer: moving toward predictive medicine Paul M. Ridker MD MPH See also www.cmaj.ca/lookup/doi/10.1503/cmaj.160313 n this issue, Singh-Manoux and colleagues1 ity: AGP, albumin, very-low-density lipoprotein Competing interests: Paul report that measures of low-grade systemic particle size and citrate. However, because con- Ridker is listed as a co-inventor on patents held I inflammation (e.g., C-reactive protein comitant measures of CRP and IL-6 were not by the Brigham and [CRP] and interleukin-6 [IL-6]) in midlife were done in the Estonian and Finnish cohorts, clinical Women’s Hospital that strong independent predictors of all-cause, car- comparisons could not be made. These kind of relate to the use of diovascular and cancer-related mortality in the data are crucial because AGP and albumin both inflammatory biomarkers in cardiovascular disease and Whitehall II study cohort. These findings sup- correlate with systemic low-grade inflammation diabetes that have been port evidence from more than a dozen prior pro- and thus in turn with IL-6 and CRP. licensed to Seimens and spective cohort studies with regard to CRP and In this context, Singh-Manoux and colleagues AstraZeneca. He has 2 received research grants all-cause mortality and more than 50 prior report that all three inflammatory biomarkers from the US National cohort studies showing that CRP and IL-6 pre- were associated with all-cause as well as cardio- Heart, Lung, and Blood dict future myocardial infarction and stroke.3,4 vascular and cancer-related mortality, both in Institute and from Amgen, Novartis, AstraZeneca, Because CRP and IL-6 are strongly correlated univariate analyses and in analyses controlling Pfizer and Kowa.
    [Show full text]
  • Worldquant Initiative for Quantitative Prediction Established at Weill Cornell Medicine
    Contact: Jen Gundersen [email protected] 646-962-9497 WorldQuant Initiative for Quantitative Prediction Established at Weill Cornell Medicine Initiative Combines State-of-the-Art Molecular Profiling Technologies with Advanced Financial Algorithms and Computational Methods to Enhance Precision Medicine NEW YORK (April 28, 2017) – Weill Cornell Medicine today announced a gift made by WorldQuant, LLC (“WorldQuant”) and Igor Tulchinsky that will further realize the promise of precision medicine. The $5 million gift establishes a new initiative that will use predictive tools to enhance Weill Cornell Medicine’s capability to diagnose and treat a variety of illnesses, with the goal of improving outcomes for patients. The WorldQuant Initiative for Quantitative Prediction brings together financial and medical experts whose collaboration strives to enhance biomedical research. Weill Cornell Medicine’s scientists, working closely with researchers and technologists from WorldQuant, will deploy predictive tools and quantitative methods to deepen the understanding of genetic factors that drive disease in individual patients. Using sophisticated algorithms, the new initiative will enable the research team to analyze genomic data to identify patterns and trends that may predict patients’ future risk of developing disease, as well as potential outcomes. These insights may be used to improve the diagnosis and treatment of a variety of illnesses, including cancer, neurological disorders, cardiovascular diseases and infections. Weill Cornell Medicine researchers Dr. Christopher Mason, the WorldQuant Research Scholar, and Dr. Olivier Elemento, the Walter B. Wriston Research Scholar, will lead the initiative, which will involve joint work with physician-scientists at the Caryl and Israel Englander Institute for Precision Medicine and the Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine.
    [Show full text]
  • Evolution of Predictive Medicine Vladislav S.BARANOV Otts Institute of Obstetrics, Gynecology & Reproductology, Saint Petersburg, Russia
    Journal of Pharmaceutical Sciences and Drug Development Abstract Evolution of Predictive Medicine Vladislav S.BARANOV Otts Institute of obstetrics, Gynecology & Reproductology, Saint Petersburg, Russia Abstract: Achievement s in deciphering of human genome , identification of many thousands of genes responsible for genetic disorders expanded a lot of our knowledge in monogenic, olygogenic & multifactor diseases thus launching the era of “predictive medicine (PM) also to 3 PM ( predictive, preventive , personified ) ( Baranov et al 2000, Baranov,Baranova, 2018) and somewhat later in 2008 after inclusion of “participatory ’by ptof. L. of Russian academy of Sci. Chief Collaborator Depart- Hood–into 4PM . Further progressive development of ment of Genomic Medicine, Ott’s Institute of Obstetric, 4PM was in line with spectacular achievements in mo- Gynecology, Reproductology. Interested in genetic and lecular genome studies . Sophisticated identification of cytogenetic aspects of early development, gene testing of “predisposition” genes by means of whole genome testing inherited predisposition to common disorders, personal- ( GWAS , NGS) o f huge healthy and affected patient ized predictive medicine, gene therapy, The author and cohorts enriched with system genetic approach and rele- co-author of 29 books and over 400 scientific papers St vant omics technologies significantly increased the prac- Petersburg. tical value of PM thus gradually converting it into trans- Publication of speakers: lation medicine(2010) which later ( 2015) gave a birth 1. Epigenetic landscape of endometriosis to “precision, individual molecular medicine” . Since the origin of PM its “sine qua non “ (dry extract) has been 2. The evolution of ideas on the biological role of represented by so called Personal Genetic Pass (GP)-the 5-methylcytosine oxidative derivatives in the mamma- individual DNA data bank incorporating personal lian genome genetic information amenable for direct practical appli- 3.
    [Show full text]
  • Inflammation, Cardiovascular Disease and Cancer: Moving Toward Predictive Medicine
    COMMENTARY Inflammation, cardiovascular disease and cancer: moving toward predictive medicine Paul M. Ridker MD MPH n Cite as: CMAJ 2017 March 13;189:E382-3. doi: 10.1503/cmaj.161033 See related article at www.cmaj.ca/lookup/doi/10.1503/cmaj.160313 n this issue, Singh-Manoux and colleagues1 report that mea- sures of low-grade systemic inflammation (e.g., C-reactive KEY POINTS protein [CRP] and interleukin-6 [IL-6]) in midlife were strong • Metabolomic and proteomic studies are furthering the Iindependent predictors of all-cause, cardiovascular and cancer- understanding of “predictive medicine.” related mortality in the Whitehall II study cohort. These findings • Inflammation is a biologic determinant of both cardiovascular support evidence from more than a dozen prior prospective disease and cancer and can be identified by measuring high- cohort studies with regard to CRP and all-cause mortality2 and sensitivity C-reactive protein or interleukin-6. more than 50 prior cohort studies showing that CRP and IL-6 pre- • Ongoing clinical trials will determine whether reducing dict future myocardial infarction and stroke.3,4 Because CRP and inflammation reduces vascular event rates; if so, the clinical IL-6 are strongly correlated (both reflect upstream activation community will need to modify current concepts of residual risk to encompass residual cholesterol risk as well as the emerging from interleukin-1),5 there is no clinical need to measure both concept of residual inflammatory risk. factors, and of the two, CRP measured with a high-sensitivity assay (hs-CRP) is less expensive and has regulatory approval for clinical use.
    [Show full text]
  • Big Data in Healthcare
    HEALTHCARE DATA INSTITUTE INTERNATIONAL THINK TANK DEDICATED TO BIG DATA IN HEALTHCARE BIG DATA AND PREVENTION FROM PREDICTION TO DEMONSTRATION NOVEMBER 2016 1 BIG DATA AND PREVENTION FROM PREDICTION TO DEMONSTRATION Acknowledgements to the authors of this White Paper: • Isabelle Barbier-Feraud, Pagamon • Jeanne Bossi Malafosse, Lawyer • Patrice Bouexel, TeraData • Catherine Commaille-Chapus, Open Health Company • Anne Gimalac, Aston Life Sciences • Guillaume Jeannerod, Epiconcept • Magali Léo, CISS • Bruno Leroy, Sanof • Bernard Nordlinger, AP-HP, Académie de Médecine • Michel Paoli, InterMutuelles Assistance • Pablo Prados, Sanof • Jean-Yves Robin, Open Health Company 2 INTRODUCTION aking action to prevent epidemics, identifying risk factors as soon as possible in order to avoid diseases or slow their development, predicting predisposition to particular infec- Ttions from birth, and so on. These are among the goals stated time and time again by scientists and representatives of the medical community. Up to now, the response to these issues has involved the use of tradi- tional research methods, which have been and are still largely based on the notion of reproducibility: using a hypothesis that serves as the basis for the collection of data, an observation makes it possible to develop a relationship among those data and to deduce new hy- potheses or new treatments, which will then be reproduced in similar cases. For a number of years, this research has been enhanced by other very diverse sources that also take into consideration the environ- ment in which the individuals are living: environmental data, so- cio-professional data, etc. This situation is now being re-examined by the sudden emergence of new data management techniques, which must adapt to the massive, exponential production of data characterised by the phenomenon of Big Data (we will come back to a defnition of this term a bit later).
    [Show full text]
  • (P5) Medicine in Rotator Cuff Tears
    Journal of Personalized Medicine Perspective Personalized, Predictive, Participatory, Precision, and Preventive (P5) Medicine in Rotator Cuff Tears Umile Giuseppe Longo 1,* , Arianna Carnevale 1,2 , Carlo Massaroni 2 , Daniela Lo Presti 2 , Alessandra Berton 1 , Vincenzo Candela 1, Emiliano Schena 2 and Vincenzo Denaro 1 1 Department of Orthopaedic and Trauma Surgery, Campus Bio-Medico University, Via Álvaro del Portillo, 00128 Rome, Italy; [email protected] (A.C.); [email protected] (A.B.); [email protected] (V.C.); [email protected] (V.D.) 2 Unit of Measurements and Biomedical Instrumentation, Campus Bio-Medico University, Via Álvaro del Portillo, 00128 Rome, Italy; [email protected] (C.M.); [email protected] (D.L.P.); [email protected] (E.S.) * Correspondence: [email protected] Abstract: Rotator cuff (RC) disease is a common musculoskeletal disorder of the shoulder entailing pain, with reduced functionality and quality of life. The main objective of this study was to present a perspective of the current scientific evidence about personalized, predictive, participatory, precision, and preventive approaches in the management of RC diseases. The personalized, predictive, partici- patory, precision and preventive (P5) medicine model is an interdisciplinary and multidisciplinary approach that will provide researchers and clinicians with a comprehensive patrimony of knowledge in the management of RC diseases. The ability to define genetic predispositions in conjunction with the evaluation of lifestyle and environmental factors may boost the tailoring of diagnosis and therapy Citation: Longo, U.G.; Carnevale, A.; in patients suffering from RC diseases. Massaroni, C.; Lo Presti, D.; Berton, A.; Candela, V.; Schena, E.; Denaro, V.
    [Show full text]
  • How Can We Move Clinical Genomics Beyond the Hype?
    How Can We Move Clinical Genomics Beyond the Hype? Timely Analysis of Immediate Health Policy Issues October 2011 Michael L. Millenson “All of the fruits of the tremendous explosion in innovation that’s been occurring in biomedical research – which make the molecular metamorphosis possible – fulfill their purpose only when they are translated into interventions and solutions that are applied to patients.” —Dr. Andrew von Eschenbach, U.S. Food and Drug Administration Commissioner, 2006 Introduction or no value to be marketed to challenges beyond the direct doctor- providers and patients. The result of patient interaction. Health More than a decade after the this policy vacuum is a disturbing information technology systems successful mapping of the human paradox. Genomics as a field is typically possess neither the ability to genome, clinical genomics1 is beginning to demonstrate its present genomic information in a starting to permeate important parts profound potential to improve health structured way clinicians can use nor of patient care and propel ripples and health care. At the same time, the the large data storage capabilities throughout the entire U.S. health care evidence suggests that the current use such information demands. system. of clinical genomics tests and Comparative effectiveness research therapies often contributes to needs to focus on identifiable Although the field has fallen far short wasteful spending and even patient subpopulations where clinical of the transformational therapeutic harm. genomics interventions can have the impact once widely predicted, most powerful impact, but the particularly in regard to common A 2008 article in Health Affairs concept of “identifiable” can raise diseases, specific interventions have warned that the low threshold for awkward questions about race and crossed the divide between rarity and allowing “unsubstantiated [genomic] ethnicity.
    [Show full text]
  • Report on JPND Precision Medicine Workshop Amsterdam, March 22Nd 2017
    Report on JPND Precision Medicine Workshop Amsterdam, March 22nd 2017 Table of Contents 1. Workshop background and remit ............................................................................... 3 2. List of attendees .......................................................................................................... 4 3. Introduction and overview of agenda ......................................................................... 5 4. Precision medicine of neurodegenerative diseases: What needs to be done? - R.Balling, University of Luxembourg................................................................................. 5 5. Developing personalised medicine at EU level: Opportunities & challenges - L.Passante, European Commission .................................................................................. 6 6. Early ideas on stratification in neurodegenerative disease - Craig Ritchie, University of Edinburgh……………………………………………………………………………7 7. Theme 1: Approaches and data available for the stratification of neurodegenerative diseases .............................................................................................. 9 I. Omics technologies .................................................................................................... 9 II. Imaging, cognitive and fluid based markers .............................................................. 10 III. Digital technology, environmental and lifestyle measures ......................................... 10 IV. Disease taxonomy ...................................................................................................
    [Show full text]
  • Omics-Based Medicine and Systems Pathology a New Perspective for Personalized and Predictive Medicine
    H. Tanaka: Omics-based Medicine and Systems Pathology. A New Perspective for Personalized and Predictive Medicine, en 11 en11 Omics-based Medicine and Systems Pathology A New Perspective for Personalized and Predictive Medicine H. Tanaka1 1Graduate School of Biomedical Science, Tokyo Medical and Dental University, Tokyo, Japan Summary Conclusion: Omics-based medicine and Objectives systems pathology will realize a new Recent important advances in the human personalized and predictive medicine. genomics and post-genomic “omics” are now bringing about a new medical care Keywords which we call “omics-based medicine”. In Genomics, pharmacogenomics, systems this article, we investigated the pathology, Omics-based medicine, omics development and future possibilities of omics-based medicine. DOI 10.3414/ME9307 Methods We divided the development of omics- The full English version can be found in based medicine into three generations in /Methods of Information in Medicine 2010 order to clarify the main clinical goals and 49 2: 173-185/ or characteristics of informatics method of http://www.schattauer.de/de/magazine/ue each generation, together with its future bersicht/zeitschriften-a- possibilities. z/methods/issue/special/manuscript/1275 7/show.html. Results The first generation of omics-based medicine started with “genomic medicine” based on the inborn individual differences of genome. It has opened the study of genetic polymorphism of the diseases and promoted the personalized medication based on the pharmacogenetic /pharmacogenomic difference of the drug response. In the second generation of omics-based medicine, owing to the advances in the high-throughput technology, vast amount of the various post-genomic disease omics data containing comprehensive molecular information of diseased somatic cells has become available.
    [Show full text]
  • Predictive and Precision Medicine with Genomic Data
    Clinical Chemistry 66:1 Q&A 33–41 (2020) Predictive and Precision Medicine with Genomic Data 1* Moderator: Linnea M. Baudhuin Downloaded from https://academic.oup.com/clinchem/article-abstract/66/1/33/5688834 by Brigham & Women's Hospital user on 21 January 2020 Experts: Leslie G. Biesecker,2 Wylie Burke,3 Eric D. Green,4 and Robert C. Green5,6,7 Genome and exome analyses have become instrumental in Biesecker). Each of us cur- establishing genetic diagnoses for critically ill newborns and rently has a major leader- in cases of previous diagnostic dilemmas. Genome and ex- ship role: one as the Direc- ome sequencing of presumably healthy individuals is now tor of NHGRI (E.D. gaining traction. The roll-out of genomic medicine to the Green) and one as the cur- generally healthy population offers a new opportunity to rent President of the Amer- provide insights into the current and future healthcare of ican Society of Human individuals and their families. Indeed, numerous projects Genetics (L.G. Biesecker). such as Geisinger’s MyCode Initiative, the National Hu- From these collective van- man Genome Research Institute (NHGRI)’s8 ClinSeq®, tage points, we have a and the various Genomes2People research projects at broad view of genomics Brigham Health and Harvard Medical School are investi- research and the imple- mentation of genomic medicine—a landscape that in- gating the use of genome sequencing for apparently healthy cludes developing technologies, understanding genome individuals. However, there are many scientific and ethical structure and function, identifying disease genes, imple- questions about the benefits, harms, and costs of this ap- menting genomic-based clinical tests, and designing pre- proach.
    [Show full text]