Prognostic Factors Associated with Ocriplasmin Efficacy for the Treatment of Symptomatic Vitreomacular Adhesion and Full-thickness Macular Hole: Analysis from Four Studies

Purpose: To assess the effect of patient baseline characteristics on the efficacy of ocriplasmin treatment for symptomatic vitreomacular adhesion (VMA) with full-thickness macular hole (FTMH) from phase 3/4 studies. Methods: Patients with symptomatic VMA and FTMH at baseline and receiving ocriplasmin treatment 12

Use of optical mapping to sort uropathogenic Escherichia coli strains into distinct subgroups

Optical maps were generated for 33 uropathogenic Escherichia coli (UPEC) isolates. For individual genomes, the NcoI restriction fragments aligned into a unique chromosome map for each individual isolate, which was then compared with the in silico restriction maps of all of the sequenced E. coli and Shigella strains. All of the UPEC isolates clustered separately from the Shigella strains as well as the laboratory and enterohaemorrhagic E. coli strains. Moreover, the individual strains appeared to cluster into distinct subgroups based on the dendrogram analyses. Phylogenetic grouping of these 33 strains showed that 32/33 were the B2 subgroup and 1/33 was subgroup A. To further characterize the similarities and differences among the 33 isolates, pathogenicity island (PAI), haemolysin and virulence gene comparisons were performed. A strong correlation was observed between individual subgroups and virulence factor genes as well as haemolysis activity. Furthermore, there was considerable conservation of sequenced-strain PAIs in the specific subgroups. Strains with different antibiotic-resistance patterns also appeared to sort into separate subgroups. Thus, the optical maps distinguished the UPEC strains from other E. coli strains and further subdivided the strains into distinct subgroups. This optical mapping procedure holds promise as an alternative way to subgroup all E. coli strains, including those involved in infections outside of the intestinal tract and epidemic strains with distinct patterns of antibiotic resistance

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Reducing a spatial database to its effective dimensionality for logistic - regression analysis of incidence of livestock disease

Large databases with multiple variables, selected because they are available and might provide an insight into establishing causal relationships, are often difficult to analyse and interpret because of multicollinearity. The objective of this study was to reduce the dimensionality of a multivariable spatial database of Zimbabwe, containing many environmental variables that were collected to predict the distribution of outbreaks of theileriosis (the tick-borne infection of cattle caused by Theileria parva and transmitted by the brown ear tick). Principal-component analysis and varimax rotation of the principal components were first used to select a reduced number of variables. The logistic-regression model was evaluated by appropriate goodness-of-fit-tests

A spatially predictive logistic regression model for occurrence of theileriosis outbreaks in Zimbabwe

Multivariate logistic regression models are mostly used to identify risk factors associated withthe occurrence of particular disease processes. Logistic regression models have also been used as tools for veterinary diagnosis by providing the probability of a particular disease in an individual animal given a set of characteristics such as diagnostic test results or other risk factors. They can also be applied to the predicition of the probability of the occurrence of future disease events. Decision making in animal disease control is constrained by cost-benefit considerations, which in turn should take into account the probability of the occurrence of particular disease events. The unit of interest in this context usually is an aggregate of spatial information such as an administrative district, province or state. With the advent of spatial databases and geographic information systems (GIS) the level of spatial aggregation can be easily controlled by the end user and is only limited by the spatial units at which the data has been collected. The relationships between various variables stored in a spatial database can be investigated and used to provide predictive tools allowing more cost-effective spatially optimised disease control. In this study a logistic regression model was developed to estimate the probability of theileriosis occurrence in Zimbabwe, and the usefulness of measures of model goodness-of-fit for decision makers was investigated. Specific attention was given to the potential of effects of spatial autocorrelation on regression coefficient estimates