University of Oxford

About: University of Oxford is a education organization based out in Oxford, Oxfordshire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 99713 authors who have published 258108 publications receiving 12972806 citations. The organization is also known as: Oxford University & Oxon..

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Questionnaire on the perceptions of patients about total hip replacement

Abstract: We have developed a 12-item questionnaire for patients having a total knee replacement (TKR). We made a prospective study of 117 patients before operation and at follow-up six months later, asking them to complete the new questionnaire and the form SF36. Some also filled in the Stanford Health Assessment Questionnaire (HAQ). An orthopaedic surgeon completed the American Knee Society (AKS) clinical score. The single score derived from the new questionnaire had high internal consistency, and its reproducibility, examined by test-retest reliability, was found to be satisfactory. Its validity was established by obtaining significant correlations in the expected direction with the AKS scores and the relevant parts of the SF36 and HAQ. Sensitivity to change was assessed by analysing the differences between the preoperative scores and those at follow-up. We also compared change in scores with the patients' retrospective judgement of change in their condition. The effect size for the new questionnaire compared favourably with those for the relevant parts of the SF36. The change scores for the new knee questionnaire were significantly greater (p < 0.0001) for patients who reported the most improvement in their condition. The new questionnaire provides a measure of outcome for TKR that is short, practical, reliable, valid and sensitive to clinically important changes over time.

Procedures for the Analysis of Comparative Data Using Phylogenetically Independent Contrasts

Abstract: We discuss and clarify several aspects of applying Felsenstein's (1985, Am. Nat. 125: 1n15) procedures to test for correlated evolution of continuous traits. This is one of several available comparative methods that maps data for phenotypic traits onto an existing phylogenetic tree (derived from independent information). Application of Felsenstein's method does not require an entirely dichotomous topology. It also does not require an assumption of gradual, clocklike character evolution, as might be modeled by Brownian motion. Almost any available information can be used to estimate branch lengths (e.g., genetic distances, divergence times estimated from the fossil record or from molecular clocks, numbers of character changes from a cladistic analysis). However, the adequacy for statistical purposes of any proposed branch lengths must be verified empirically for each phytogeny and for each character. We suggest a simple way of doing this, based on graphical analysis of plots of standardized independent contrasts versus their standard deviations (i.e., the square roots of the sums of their branch lengths). In some cases, the branch lengths and/or the values of traits being studied will require transformation. An example involving the scaling of mammalian home range area is presented. Once adequately standardized, sets of independent contrasts can be analyzed using either linear or nonlinear (multiple) regression. In all cases, however, regressions (or correlations) must be computed through the origin. We also discuss ways of correcting for body size effects and how this relates to making graphical representations of relationships of standardized independent contrasts. We close with a consideration of the types of traits that can be analyzed with independent contrasts procedures and conclude that any (continuous) trait that is inherited from ancestors is appropriate for analysis, regardless of the mechanism of inheritance (e.g., genetic or cultural).

Nicotine replacement therapy for smoking cessation.

Abstract: BACKGROUND: The aim of nicotine replacement therapy (NRT) is to replace nicotine from cigarettes. This reduces withdrawal symptoms associated with smoking cessation thus helping resist the urge to smoke cigarettes. OBJECTIVES: The aims of this review were to determine the effectiveness of the different forms of nicotine replacement therapy (chewing gum, transdermal patches, nasal spray, inhalers and tablets) in achieving abstinence from cigarettes, or a sustained reduction in amount smoked; to determine whether the effect is influenced by the clinical setting in which the smoker is recruited and treated, the dosage and form of the NRT used, or the intensity of additional advice and support offered to the smoker; to determine whether combinations of NRT are more effective than one type alone; and to determine its effectiveness compared to other pharmacotherapies. SEARCH STRATEGY: We searched the Cochrane Tobacco Addiction Group trials register in July 2002. SELECTION CRITERIA: Randomized trials in which NRT was compared to placebo or no treatment, or where different doses of NRT were compared. We excluded trials which did not report cessation rates, and those with follow-up of less than six months. DATA COLLECTION AND ANALYSIS: We extracted data in duplicate on the type of subjects, the dose and duration and form of nicotine therapy, the outcome measures, method of randomization, and completeness of follow-up. The main outcome measure was abstinence from smoking after at least six months of follow-up. We used the most rigorous definition of abstinence for each trial, and biochemically validated rates if available. Where appropriate, we performed meta-analysis using a fixed effects model (Peto). MAIN RESULTS: We identified 110 trials; 96 with a non NRT control group. The odds ratio for abstinence with NRT compared to control was 1.74 (95% confidence interval 1.64 - 1.86), The odds ratios for the different forms of NRT were 1.66 for gum, 1.74 for patches, 2.27 for nasal spray, 2.08 for inhaled nicotine and 2.08 for nicotine sublingual tablet/lozenge. These odds were largely independent of the duration of therapy, the intensity of additional support provided or the setting in which the NRT was offered. In highly dependent smokers there was a significant benefit of 4 mg gum compared with 2mg gum (odds ratio 2.67, 95% confidence interval 1.69 - 4.22). There was weak evidence that combinations of forms of NRT are more effective. Higher doses of nicotine patch may produce small increases in quit rates. Only one study directly compared NRT to another pharmacotherapy, in which bupropion was significantly more effective than nicotine patch or placebo. REVIEWER'S CONCLUSIONS: All of the commercially available forms of NRT (nicotine gum, transdermal patch, the nicotine nasal spray, nicotine inhaler and nicotine sublingual tablets/lozenges) are effective as part of a strategy to promote smoking cessation. They increase quit rates approximately 1.5 to 2 fold regardless of setting. The effectiveness of NRT appears to be largely independent of the intensity of additional support provided to the smoker. Provision of more intense levels of support, although beneficial in facilitating the likelihood of quitting, is not essential to the success of NRT. There is promising evidence that bupropion may be more effective than NRT (either alone or in combination). However, its most appropriate place in the therapeutic armamentarium requires further study and consideration.