Fast Sketch-based Recovery of Correlation Outliers

Many data sources can be interpreted as time-series, and a key problem is to identify which pairs out of a large collection of signals are highly correlated. We expect that there will be few, large, interesting correlations, while most signal pairs do not have any strong correlation. We abstract this as the problem of identifying the highly correlated pairs in a collection of n mostly pairwise uncorrelated random variables, where observations of the variables arrives as a stream. Dimensionality reduction can remove dependence on the number of observations, but further techniques are required to tame the quadratic (in n) cost of a search through all possible pairs. We develop a new algorithm for rapidly finding large correlations based on sketch techniques with an added twist: we quickly generate sketches of random combinations of signals, and use these in concert with ideas from coding theory to decode the identity of correlated pairs. We prove correctness and compare performance and effectiveness with the best LSH (locality sensitive hashing) based approach

Independent Sets in Vertex-Arrival Streams

We consider the maximal and maximum independent set problems in three models of graph streams: - In the edge model we see a stream of edges which collectively define a graph; this model is well-studied for a variety of problems. We show that the space complexity for a one-pass streaming algorithm to find a maximal independent set is quadratic (i.e. we must store all edges). We further show that it is not much easier if we only require approximate maximality. This contrasts strongly with the other two vertex-based models, where one can greedily find an exact solution in only the space needed to store the independent set. - In the "explicit" vertex model, the input stream is a sequence of vertices making up the graph. Every vertex arrives along with its incident edges that connect to previously arrived vertices. Various graph problems require substantially less space to solve in this setting than in edge-arrival streams. We show that every one-pass c-approximation streaming algorithm for maximum independent set (MIS) on explicit vertex streams requires Omega({n^2}/{c^6}) bits of space, where n is the number of vertices of the input graph. It is already known that Theta~({n^2}/{c^2}) bits of space are necessary and sufficient in the edge arrival model (Halldórsson et al. 2012), thus the MIS problem is not significantly easier to solve under the explicit vertex arrival order assumption. Our result is proved via a reduction from a new multi-party communication problem closely related to pointer jumping. - In the "implicit" vertex model, the input stream consists of a sequence of objects, one per vertex. The algorithm is equipped with a function that maps pairs of objects to the presence or absence of edges, thus defining the graph. This model captures, for example, geometric intersection graphs such as unit disc graphs. Our final set of results consists of several improved upper and lower bounds for interval and square intersection graphs, in both explicit and implicit streams. In particular, we show a gap between the hardness of the explicit and implicit vertex models for interval graphs

Longitudinal immune profiling reveals key myeloid signatures associated with COVID-19.

COVID-19 pathogenesis is associated with an exaggerated immune response. However, the specific cellular mediators and inflammatory components driving diverse clinical disease outcomes remain poorly understood. We undertook longitudinal immune profiling on both whole blood and peripheral blood mononuclear cells (PBMCs) of hospitalized patients during the peak of the COVID-19 pandemic in the UK. Here, we report key immune signatures present shortly after hospital admission that were associated with the severity of COVID-19. Immune signatures were related to shifts in neutrophil to T cell ratio, elevated serum IL-6, MCP-1 and IP-10, and most strikingly, modulation of CD14+ monocyte phenotype and function. Modified features of CD14+ monocytes included poor induction of the prostaglandin-producing enzyme, COX-2, as well as enhanced expression of the cell cycle marker Ki-67. Longitudinal analysis revealed reversion of some immune features back to the healthy median level in patients with a good eventual outcome. These findings identify previously unappreciated alterations in the innate immune compartment of COVID-19 patients and lend support to the idea that therapeutic strategies targeting release of myeloid cells from bone marrow should be considered in this disease. Moreover, they demonstrate that features of an exaggerated immune response are present early after hospital admission suggesting immune-modulating therapies would be most beneficial at early timepoints

System-wide approaches to antimicrobial therapy and antimicrobial resistance in the UK: the AMR-X framework

Antimicrobial resistance (AMR) threatens human, animal, and environmental health. Acknowledging the urgency of addressing AMR, an opportunity exists to extend AMR action-focused research beyond the confines of an isolated biomedical paradigm. An AMR learning system, AMR-X, envisions a national network of health systems creating and applying optimal use of antimicrobials on the basis of their data collected from the delivery of routine clinical care. AMR-X integrates traditional AMR discovery, experimental research, and applied research with continuous analysis of pathogens, antimicrobial uses, and clinical outcomes that are routinely disseminated to practitioners, policy makers, patients, and the public to drive changes in practice and outcomes. AMR-X uses connected data-to-action systems to underpin an evaluation framework embedded in routine care, continuously driving implementation of improvements in patient and population health, targeting investment, and incentivising innovation. All stakeholders co-create AMR-X, protecting the public from AMR by adapting to continuously evolving AMR threats and generating the information needed for precision patient and population care

Weekly dose-dense chemotherapy in first-line epithelial ovarian, fallopian tube, or primary peritoneal carcinoma treatment (ICON8): primary progression free survival analysis results from a GCIG phase 3 randomised controlled trial

Background: Carboplatin and paclitaxel administered every 3 weeks is standard-of-care first-line chemotherapy for epithelial ovarian cancer. The Japanese JGOG3016 trial showed a significant improvement in progression-free and overall survival with dose-dense weekly paclitaxel and 3-weekly carboplatin. In this study, we aimed to compare efficacy and safety of two dose-dense weekly regimens to standard 3-weekly chemotherapy in a predominantly European population with epithelial ovarian cancer. Methods: In this phase 3 trial, women with newly diagnosed International Federation of Gynecology and Obstetrics stage IC–IV epithelial ovarian cancer were randomly assigned to group 1 (carboplatin area under the curve [AUC]5 or AUC6 and 175 mg/m2 paclitaxel every 3 weeks), group 2 (carboplatin AUC5 or AUC6 every 3 weeks and 80 mg/m2 paclitaxel weekly), or group 3 (carboplatin AUC2 and 80 mg/m2 paclitaxel weekly). Written informed consent was provided by all women who entered the trial. The protocol had the appropriate national research ethics committee approval for the countries where the study was conducted. Patients entered the trial after immediate primary surgery, or before neoadjuvant chemotherapy with subsequent planned delayed primary surgery. The trial coprimary outcomes were progression-free survival and overall survival. Data analyses were done on an intention-to-treat basis, and were powered to detect a hazard ratio of 0·75 in progression-free survival. The main comparisons were between the control group (group 1) and each of the weekly research groups (groups 2 and 3). Findings: Between June 6, 2011, and Nov 28, 2014, 1566 women were randomly assigned to treatment. 72% (365), completed six protocol-defined treatment cycles in group 1, 60% (305) in group 2, and 63% (322) in group 3, although 90% (454), 89% (454), and 85% (437) completed six platinum-based chemotherapy cycles, respectively. Paclitaxel dose intensification was achieved with weekly treatment (median total paclitaxel dose 1010 mg/m2 in group 1; 1233 mg/m2 in group 2; 1274 mg/m2 in group 3). By February, 2017, 1018 (65%) patients had experienced disease progression. No significant progression-free survival increase was observed with either weekly regimen (restricted mean survival time 24·4 months [97·5% CI 23·0–26·0] in group 1, 24·9 months [24·0–25·9] in group 2, 25·3 months [23·9–26·9] in group 3; median progression-free survival 17·7 months [IQR 10·6–not reached] in group 1, 20·8 months [11·9–59·0] in group 2, 21·0 months [12·0–54·0] in group 3; log-rank p=0·35 for group 2 vs group 1; group 3 vs 1 p=0·51). Although grade 3 or 4 toxic effects increased with weekly treatment, these effects were predominantly uncomplicated. Febrile neutropenia and sensory neuropathy incidences were similar across groups. Interpretation Weekly dose-dense chemotherapy can be delivered successfully as first-line treatment for epithelial ovarian cancer but does not significantly improve progression-free survival compared with standard 3-weekly chemotherapy in predominantly European populations. Funding: Cancer Research UK, Medical Research Council, Health Research Board in Ireland, Irish Cancer Society, Cancer Australia

An Association of Cancer Physicians' strategy for improving services and outcomes for cancer patients.

The Association of Cancer Physicians in the United Kingdom has developed a strategy to improve outcomes for cancer patients and identified the goals and commitments of the Association and its members.The ACP is very grateful to all of its members who have expressed views on the development of the strategy and to the sponsors of our workshops and publications, especially Cancer Research UK and Macmillan Cancer SupportThis is the final version of the article. It was first available from Cancer Intelligence via http://dx.doi.org/10.3332/ecancer.2016.60

Developing a model for decision-making around antibiotic prescribing for patients with COVID-19 pneumonia in acute NHS hospitals during the first wave of the COVID-19 pandemic: Qualitative results from the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients (PEACH Study)

Objective: To explore and model factors affecting antibiotic prescribing decision-making early in the pandemic. Design: Semistructured qualitative interview study. Setting: National Health Service (NHS) trusts/health boards in England and Wales. Participants: Clinicians from NHS trusts/health boards in England and Wales. Method: Individual semistructured interviews were conducted with clinicians in six NHS trusts/health boards in England and Wales as part of the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients study, a wider study that included statistical analysis of procalcitonin (PCT) use in hospitals during the first wave of the pandemic. Thematic analysis was used to identify key factors influencing antibiotic prescribing decisions for patients with COVID-19 pneumonia during the first wave of the pandemic (March to May 2020), including how much influence PCT test results had on these decisions. Results: During the first wave of the pandemic, recommendations to prescribe antibiotics for patients with COVID-19 pneumonia were based on concerns about secondary bacterial infections. However, as clinicians gained more experience with COVID-19, they reported increasing confidence in their ability to distinguish between symptoms and signs caused by SARS-CoV-2 viral infection alone, and secondary bacterial infections. Antibiotic prescribing decisions were influenced by factors such as clinician experience, confidence, senior support, situational factors and organisational influences. A decision-making model was developed. Conclusion: This study provides insight into the decision-making process around antibiotic prescribing for patients with COVID-19 pneumonia during the first wave of the pandemic. The importance of clinician experience and of senior review of decisions as factors in optimising antibiotic stewardship is highlighted. In addition, situational and organisational factors were identified that could be optimised. The model presented in the study can be used as a tool to aid understanding of the complexity of the decision-making process around antibiotic prescribing and planning antimicrobial stewardship support in the context of a pandemic

FebriDx point-of-care test in patients with suspected COVID-19: a pooled diagnostic accuracy study

Background: Point-of-care (POC) tests for COVID-19 could relieve pressure on isolation resource, support infection prevention and control, and help commence more timely and appropriate treatment. We aimed to undertake a systematic review and pooled diagnostic test accuracy study of available individual patient data (IPD) to evaluate the diagnostic accuracy of a commercial POC test (FebriDx) in patients with suspected COVID-19.Methods: A literature search was performed on the 1st of October 2020 to identify studies reporting diagnostic accuracy statistics of the FebriDx POC test versus real time reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2. Studies were screened for risk of bias. IPD were sought from studies meeting the inclusion and exclusion criteria. Logistic regression was performed to investigate the study effect on the outcome of the RT-PCR test result in order to determine whether it was appropriate to pool results. Diagnostic accuracy statistics were calculated with 95% confidence intervals (CIs).Results: 15 studies were screened, and we included two published studies with 527 hospitalised patients. 523 patients had valid FebriDx results for Myxovirus resistance protein A (MxA), an antiviral host response protein. The FebriDx test produced a pooled sensitivity of 0.920 (95% CI: 0.875-0.950) and specificity of 0.862 (0.819-0.896) compared with RT-PCR, where there was an estimated true COVID-19 prevalence of 0.405 (0.364-0.448) and overall FebriDx test yield was 99.2%. Patients were tested at a median of 4 days [interquartile range: 2:9] after symptom onset. No differences were found in a sub-group analysis of time tested since the onset of symptoms.Conclusions: Based on a large sample of patients from two studies during the first wave of the SARS-CoV-2 pandemic, the FebriDx POC test had reasonable diagnostic accuracy in a hospital setting with high COVID-19 prevalence, out of influenza season. More research is required to determine how FebriDx would perform in other healthcare settings with higher or lower COVID-19 prevalence, different patient populations, or when other respiratory infections are in circulation