Author
C. Alba
Bio: C. Alba is an academic researcher from University Health Network. The author has contributed to research in topics: Population & Viral load. The author has an hindex of 3, co-authored 3 publications receiving 343 citations.
Papers
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McMaster University1, Universidade Federal do Rio Grande do Sul2, University Health Network3, Alberta Health Services4, University of Lausanne5, North Shore-LIJ Health System6, Dalhousie University7, Royal Children's Hospital8, Ottawa Hospital Research Institute9, York University10, University of London11, Innlandet Hospital Trust12, Bond University13
TL;DR: This article covers studies answering questions about the prognosis of a typical patient from a broadly defined population and considers how to establish degree of confidence in estimates from such bodies of evidence.
Abstract: Introduction The term prognosis refers to the likelihood of future health outcomes in people with a given disease or health condition or with particular characteristics such as age, sex, or genetic profile. Patients and healthcare providers may be interested in prognosis for several reasons, so prognostic studies may have a variety of purposes,1–4 including establishing typical prognosis in a broad population, establishing the effect of patients’ characteristics on prognosis, and developing a prognostic model (often referred to as a clinical prediction rule) (Table 1). Considerations in determining the trustworthiness of estimates of prognosis arising from these types of studies differ. This article covers studies answering questions about the prognosis of a typical patient from a broadly defined population; we will consider prognostic studies assessing risk factors and clinical prediction guides in subsequent papers. Knowing the likely course of their disease may help patients to come to terms with, and plan for, the future. Knowledge of the risk of adverse outcomes or the likelihood of spontaneous resolution of symptoms is critical in predicting the likely effect of treatment and planning diagnostic investigations.5 If the probability of facing an adverse outcome is very low or the spontaneous remission of the disease is high (“good prognosis”), the possible absolute benefits of treatment will inevitably be low and serious adverse effects related to treatment or invasive diagnostic tests, even if rare, will loom large in any decision. If instead the probability of an adverse outcome is high (“bad prognosis”), the impact of new diagnostic information or of effective treatment may be large and patients may be ready to accept higher risks of diagnostic investigation and treatment related adverse effects. Inquiry into the credibility or trustworthiness of prognostic estimates has, to date, largely focused on individual studies of prognosis. Systematic reviews of the highest quality evidence including all the prognostic studies assessing a particular clinical situation are, however, gaining increasing attention, including the Cochrane Collaboration’s work (in progress) to define a template for reviews of prognostic studies (http://prognosismethods.cochrane.org/scope-ourwork). Trustworthy systematic reviews will not only ensure comprehensive collection, summarization, and critique of the primary studies but will also conduct optimal analyses. Matters that warrant consideration in such analyses include the method used to pool rates and whether analyses account for all the relevant covariates; the literature provides guidance on both questions.6 7 In this article, we consider how to establish degree of confidence in estimates from such bodies of evidence. The guidance in this article is directed primarily at researchers conducting systematic reviews of prognostic studies. It will also be useful to anyone interested in prognostic estimates and their associated confidence (including guideline developers) when evaluating a body of evidence (for example, a guideline panel using baseline risk estimates to estimate the absolute effect of Summary poIntS
472 citations
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TL;DR: This study shows that an ultra-short course of direct-acting antivirals and ezetimibe can prevent the establishment of chronic HCV infection in the recipient, alleviating many of the concerns with transplanting organs from HCV-infected donors.
71 citations
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TL;DR: Significant differences in reasons for readmissions are described between the general LVAD population and those supported for more than 1 year, which resulted in decreased susceptibility to major bleeds and increased susceptibility to infection.
Abstract: Background: The literature examining clinical outcomes and readmissions during extended (> 1 yr) left ventricular assist device (LVAD) support is scarce, particularly in the era of continuous-flow LVADs. Methods: We completed a retrospective cohort study on consecutive LVAD patients from June 2006 to March 2015, focusing on those who received more than 1 year of total LVAD support time. Demographic characteristics, clinical outcomes and readmissions were analyzed using standard statistical methods. All readmissions were categorized as per the Interagency Registry for Mechanically Assisted Circulatory Support 2015 guidelines. Results: Of the 103 patients who received LVADs during the study period, 37 received LVAD support for more than 1 year, with 18 receiving support for more than 2 years. Average support time was 786 ± 381 days, with total support time reaching 80 patient-years. During a median follow-up of 2 years, 27 patients died, with 1-year conditional survival of 74%. Median freedom from first readmission was 106 days (range 1–603 d), with an average length of stay of 6 days. Readmissions resulted in an average of 41 ± 76 days in hospital per patient. Reasons for readmission were major infection (24%), major bleeding (19%) and device malfunction/thrombus (13%). There were a total of 112 procedures completed during the readmissions, with 60% of procedures being done in 13% (n = 5) of patients. Conclusion: Continuous-flow LVADs provide excellent long-term survival. The present study describes marked differences in reasons for readmissions between the general LVAD population and those supported for more than 1 year. Prolonged LVAD support resulted in decreased susceptibility to major bleeds and increased susceptibility to infection.
13 citations
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TL;DR: The optimum distance for avoiding person-to-person virus transmission is investigated and the use of face masks and eye protection to prevent transmission of viruses is assessed to investigate the effects of physical distance, face masks, and eye Protection on virus transmission in health-care and non-health-care settings.
2,900 citations
12 Aug 2020
892 citations
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TL;DR: These European Association for the Study of the Liver recommendations on treatment of hepatitis C describe the optimal management of patients with recently acquired and chronic HCV infections in 2020 and onwards.
582 citations
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University of Glasgow1, University of Birmingham2, Karolinska University Hospital3, Linköping University4, University of Mainz5, Nagoya University6, The Chinese University of Hong Kong7, Rabin Medical Center8, Tel Aviv University9, McGill University Health Centre10, Kyoto Prefectural University of Medicine11, University Hospitals Birmingham NHS Foundation Trust12, Inova Fairfax Hospital13, Newcastle upon Tyne Hospitals NHS Foundation Trust14, Newcastle University15
TL;DR: Biopsy-confirmed fibrosis is found to be associated with risk of mortality and liver-related morbidity in patients with NAFLD, with and without adjustment for confounding factors and in Patients with reported NASH.
498 citations
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TL;DR: In this article, a systematic review is conducted to identify prognostic factors that may be used in decision-making related to the care of patients infected with SARS-CoV-2.
Abstract: Background and purpose The objective of our systematic review is to identify prognostic factors that may be used in decision-making related to the care of patients infected with COVID-19. Data sources We conducted highly sensitive searches in PubMed/MEDLINE, the Cochrane Central Register of Controlled Trials (CENTRAL) and Embase. The searches covered the period from the inception date of each database until April 28, 2020. No study design, publication status or language restriction were applied. Study selection and data extraction We included studies that assessed patients with confirmed or suspected SARS-CoV-2 infectious disease and examined one or more prognostic factors for mortality or disease severity. Reviewers working in pairs independently screened studies for eligibility, extracted data and assessed the risk of bias. We performed meta-analyses and used GRADE to assess the certainty of the evidence for each prognostic factor and outcome. Results We included 207 studies and found high or moderate certainty that the following 49 variables provide valuable prognostic information on mortality and/or severe disease in patients with COVID-19 infectious disease: Demographic factors (age, male sex, smoking), patient history factors (comorbidities, cerebrovascular disease, chronic obstructive pulmonary disease, chronic kidney disease, cardiovascular disease, cardiac arrhythmia, arterial hypertension, diabetes, dementia, cancer and dyslipidemia), physical examination factors (respiratory failure, low blood pressure, hypoxemia, tachycardia, dyspnea, anorexia, tachypnea, haemoptysis, abdominal pain, fatigue, fever and myalgia or arthralgia), laboratory factors (high blood procalcitonin, myocardial injury markers, high blood White Blood Cell count (WBC), high blood lactate, low blood platelet count, plasma creatinine increase, high blood D-dimer, high blood lactate dehydrogenase (LDH), high blood C-reactive protein (CRP), decrease in lymphocyte count, high blood aspartate aminotransferase (AST), decrease in blood albumin, high blood interleukin-6 (IL-6), high blood neutrophil count, high blood B-type natriuretic peptide (BNP), high blood urea nitrogen (BUN), high blood creatine kinase (CK), high blood bilirubin and high erythrocyte sedimentation rate (ESR)), radiological factors (consolidative infiltrate and pleural effusion) and high SOFA score (sequential organ failure assessment score). Conclusion Identified prognostic factors can help clinicians and policy makers in tailoring management strategies for patients with COVID-19 infectious disease while researchers can utilise our findings to develop multivariable prognostic models that could eventually facilitate decision-making and improve patient important outcomes. Systematic review registration Prospero registration number: CRD42020178802. Protocol available at: https://www.medrxiv.org/content/10.1101/2020.04.08.20056598v1.
428 citations