Elective cancer surgery in COVID-19-free surgical pathways during the SARS-CoV-2 pandemic: An international, multicenter, comparative cohort study

PURPOSE As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19–free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19–free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19–free surgical pathways. Patients who underwent surgery within COVID-19–free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19–free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score–matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19–free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION Within available resources, dedicated COVID-19–free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Elective Cancer Surgery in COVID-19-Free Surgical Pathways During the SARS-CoV-2 Pandemic: An International, Multicenter, Comparative Cohort Study.

PURPOSE: As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19-free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS: This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19-free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS: Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19-free surgical pathways. Patients who underwent surgery within COVID-19-free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19-free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score-matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19-free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION: Within available resources, dedicated COVID-19-free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Evaluation of vaccination strategies for the metropolitan area of Madrid via agent-based simulation

Objective: We analyse the impact of different vaccination strategies on the propagation of COVID-19 within the Madrid metropolitan area, starting on 27 December 2020 and ending in Summer of 2021. Materials and methods: The predictions are based on simulation using EpiGraph, an agent-based COVID-19 simulator. We first summarise the different models implemented in the simulator, then provide a comprehensive description of the vaccination model and define different vaccination strategies. The simulator-including the vaccination model-is validated by comparing its results with real data from the metropolitan area of Madrid during the third COVID-19 wave. This work considers different COVID-19 propagation scenarios for a simulated population of about 5 million. Results: The main result shows that the best strategy is to vaccinate first the elderly with the two doses spaced 56 days apart; this approach reduces the final infection rate by an additional 6% and the number of deaths by an additional 3% with respect to vaccinating first the elderly at the interval recommended by the vaccine producer. The reason is the increase in the number of vaccinated individuals at any time during the simulation. Conclusion: The existing level of detail and maturity of EpiGraph allowed us to evaluate complex scenarios and thus use it successfully to help guide the strategy for the COVID-19 vaccination campaign of the Spanish health authorities.This work has been partially funded by the agreement between the Community of Madrid and the Carlos III University of Madrid for the funding of research projects on SARS-CoV-2 and COVID-19 disease, project name 'Multi-source and multi-method prediction to support COVID-19 policy decision making', which was supported with REACT-EU funds from the European regional development fund 'a way of making Europe' (2020/00692/003) and the European High-Performance Computing Joint Undertaking (JU) under the ADMIRE project (grant agreement No 956748). We have also used the Spanish Supercomputing Network (RES) under the grant BCV-2021-1-0011. The role of all study sponsors was limited to financial support.S

Functional Identification of Proteus mirabilis eptC Gene Encoding a Core Lipopolysaccharide Phosphoethanolamine Transferase

By comparison of the Proteus mirabilis HI4320 genome with known lipopolysaccharide (LPS) phosphoethanolamine transferases, three putative candidates (PMI3040, PMI3576, and PMI3104) were identified. One of them, eptC (PMI3104) was able to modify the LPS of two defined non-polar core LPS mutants of Klebsiella pneumoniae that we use as surrogate substrates. Mass spectrometry and nuclear magnetic resonance showed that eptC directs the incorporation of phosphoethanolamine to the O-6 of l-glycero-d-mano-heptose II. The eptC gene is found in all the P. mirabilis strains analyzed in this study. Putative eptC homologues were found for only two additional genera of the Enterobacteriaceae family, Photobacterium and Providencia. The data obtained in this work supports the role of the eptC (PMI3104) product in the transfer of PEtN to the O-6 of l,d-HepII in P. mirabilis strains

Dendrimers and dendritic materials: From laboratory to medical practice in infectious diseases

Infectious diseases are one of the main global public health risks, predominantly caused by viruses, bacteria, fungi, and parasites. The control of infections is founded on three main pillars: prevention, treatment, and diagnosis. However, the appearance of microbial resistance has challenged traditional strategies and demands new approaches. Dendrimers are a type of polymeric nanoparticles whose nanometric size, multivalency, biocompatibility, and structural perfection offer boundlesspossibilities in multiple biomedical applications. This review provides the reader a general overview about the uses of dendrimers and dendritic materials in the treatment, prevention, and diagnosis of highly prevalent infectious diseases, and their advantages compared to traditional approaches. Examples of dendrimers as antimicrobial agents per se, as nanocarriers of antimicrobial drugs, as well as their uses in gene transfection, in vaccines or as contrast agents in imaging assays are presented.Despite the need to address some challenges in order to be used in the clinic, dendritic materials appear as an innovative tool with a brilliant future ahead in the clinical management of infectiousdiseases and many other health issues

Experimental Identification of Actinobacillus pleuropneumoniae Strains L20 and JL03 Heptosyltransferases, Evidence for a New Heptosyltransferase Signature Sequence.

We experimentally identified the activities of six predicted heptosyltransferases in Actinobacillus pleuropneumoniae genome serotype 5b strain L20 and serotype 3 strain JL03. The initial identification was based on a bioinformatic analysis of the amino acid similarity between these putative heptosyltrasferases with others of known function from enteric bacteria and Aeromonas. The putative functions of all the Actinobacillus pleuropneumoniae heptosyltrasferases were determined by using surrogate LPS acceptor molecules from well-defined A. hydrophyla AH-3 and A. salmonicida A450 mutants. Our results show that heptosyltransferases APL_0981 and APJL_1001 are responsible for the transfer of the terminal outer core D-glycero-D-manno-heptose (D,D-Hep) residue although they are not currently included in the CAZY glycosyltransferase 9 family. The WahF heptosyltransferase group signature sequence [S(T/S)(GA)XXH] differs from the heptosyltransferases consensus signature sequence [D(TS)(GA)XXH], because of the substitution of D(261) for S(261), being unique

European Covid-19 Forecast Hub

European Covid-19 Forecast Hub

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z)