Benchmarking of robotic and laparoscopic spleen-preserving distal pancreatectomy by using two different methods

Benchmarking; PancreatectomyBenchmarking; PancreatectomiaBenchmarking; PancreatectomíaBackground Benchmarking is an important tool for quality comparison and improvement. However, no benchmark values are available for minimally invasive spleen-preserving distal pancreatectomy, either laparoscopically or robotically assisted. The aim of this study was to establish benchmarks for these techniques using two different methods. Methods Data from patients undergoing laparoscopically or robotically assisted spleen-preserving distal pancreatectomy were extracted from a multicentre database (2006–2019). Benchmarks for 10 outcomes were calculated using the Achievable Benchmark of Care (ABC) and best-patient-in-best-centre methods. Results Overall, 951 laparoscopically assisted (77.3 per cent) and 279 robotically assisted (22.7 per cent) procedures were included. Using the ABC method, the benchmarks for laparoscopically assisted and robotically assisted spleen-preserving distal pancreatectomy respectively were: 150 and 207 min for duration of operation, 55 and 100 ml for blood loss, 3.5 and 1.7 per cent for conversion, 0 and 1.7 per cent for failure to preserve the spleen, 27.3 and 34.0 per cent for overall morbidity, 5.1 and 3.3 per cent for major morbidity, 3.6 and 7.1 per cent for pancreatic fistula grade B/C, 5 and 6 days for duration of hospital stay, 2.9 and 5.4 per cent for readmissions, and 0 and 0 per cent for 90-day mortality. Best-patient-in-best-centre methodology revealed milder benchmark cut-offs for laparoscopically and robotically assisted procedures, with operating times of 254 and 262.5 min, blood loss of 150 and 195 ml, conversion rates of 5.8 and 8.2 per cent, rates of failure to salvage spleen of 29.9 and 27.3 per cent, overall morbidity rates of 62.7 and 55.7 per cent, major morbidity rates of 20.4 and 14 per cent, POPF B/C rates of 23.8 and 24.2 per cent, duration of hospital stay of 8 and 8 days, readmission rates of 20 and 15.1 per cent, and 90-day mortality rates of 0 and 0 per cent respectively. Conclusion Two benchmark methods for minimally invasive distal pancreatectomy produced different values, and should be interpreted and applied differently

Knowledge Hub on the Integrated Assessment of Chemical Contaminants and their Effects on the Marine Environment

In a time of environmental awareness, spurred on by the possibility that our world is threatened by climate change, it is important to remember that there are other anthropogenic pressures, which are also essential for addressing the protection of the marine and coastal environment. Pollution is a global, complex issue that contributes to biodiversity loss and poor environmental health and comes from the production and release of many of the synthetic chemicals that we use in our daily lives. Chemical contaminants are often underrepresented as a major contributor of environmental deterioration. The Joint Programming Initiative Healthy and Productive Seas and Oceans (JPI Oceans) established in 2018 the JPI Oceans Knowledge Hub on the integrated assessment of chemical contaminants and their effects on the marine environment. The purpose of the Knowledge Hub was to provide recommendations on how to improve the methodological basis for marine chemical status assessment. The work has resulted in the following policy paper which focuses on improving the efficiency and implementation of integrated assessment methodology of effects of chemicals of emerging concern. Substantial additional knowledge of biological effects is needed to achieve Good Environmental Status (GES) of our oceans and coastal areas. The Knowledge Hub is represented by highly skilled scientists and policy makers, appointed by the JPI Oceans Management Board, to ensure that the recommendations provided are useful for policy making

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Meeting of the Ecosystem Approach Correspondence Group on on Pollution Monitoring (CorMon Pollution)

In accordance with the UNEP/MAP Programme of Work adopted by COP 21 for the biennium 2020-2021, the United Nations Environment Programme/Mediterranean Action Plan-Barcelona Convention Secretariat (UNEP/MAP) and its Programme for the Assessment and Control of Marine Pollution in the Mediterranean (MED POL) organized the Meeting of the Ecosystem Approach Correspondence Group on Pollution Monitoring (CorMon on Pollution Monitoring). The Meeting was held via videoconference on 26-27 April 2021. 2. The main objectives of the Meeting were to: a) Review the Monitoring Guidelines/Protocols for IMAP Common Indicator 18, as well as the Monitoring Guidelines/Protocols for Analytical Quality Assurance and Reporting of Monitoring Data for IMAP Common Indicators 13, 14, 17, 18 and 20; b) Take stock of the state of play of inter-laboratory testing and good laboratory practice related to IMAP Ecological Objectives 5 and 9; c) Analyze the proposal for the integration and aggregation rules for IMAP Ecological Objectives 5, 9 and 10 and assessment criteria for contaminants and nutrients; d) Recommend the ways and means to strengthen implementation of IMAP Pollution Cluster towards preparation of the 2023 MED Quality Status Report

Robot-assisted versus laparoscopic pancreatoduodenectomy:a pan-European multicenter propensity-matched study

Background: The use of robot-assisted and laparoscopic pancreatoduodenectomy is increasing, yet large adjusted analyses that can be generalized internationally are lacking. This study aimed to compare outcomes after robot-assisted pancreatoduodenectomy and laparoscopic pancreatoduodenectomy in a pan-European cohort. Methods: An international multicenter retrospective study including patients after robot-assisted pancreatoduodenectomy and laparoscopic pancreatoduodenectomy from 50 centers in 12 European countries (2009–2020). Propensity score matching was performed in a 1:1 ratio. The primary outcome was major morbidity (Clavien–Dindo ≥III). Results: Among 2,082 patients undergoing minimally invasive pancreatoduodenectomy, 1,006 underwent robot-assisted pancreatoduodenectomy and 1,076 laparoscopic pancreatoduodenectomy. After matching 812 versus 812 patients, the rates of major morbidity (31.9% vs 29.6%; P = .347) and 30-day/in-hospital mortality (4.3% vs 4.6%; P = .904) did not differ significantly between robot-assisted pancreatoduodenectomy and laparoscopic pancreatoduodenectomy, respectively. Robot-assisted pancreatoduodenectomy was associated with a lower conversion rate (6.7% vs 18.0%; P &lt; .001) and higher lymph node retrieval (16 vs 14; P = .003). Laparoscopic pancreatoduodenectomy was associated with shorter operation time (446 minutes versus 400 minutes; P &lt; .001), and lower rates of postoperative pancreatic fistula grade B/C (19.0% vs 11.7%; P &lt; .001), delayed gastric emptying grade B/C (21.4% vs 7.4%; P &lt; .001), and a higher R0-resection rate (73.2% vs 84.4%; P &lt; .001). Conclusion: This European multicenter study found no differences in overall major morbidity and 30-day/in-hospital mortality after robot-assisted pancreatoduodenectomy compared with laparoscopic pancreatoduodenectomy. Further, laparoscopic pancreatoduodenectomy was associated with a lower rate of postoperative pancreatic fistula, delayed gastric emptying, wound infection, shorter length of stay, and a higher R0 resection rate than robot-assisted pancreatoduodenectomy. In contrast, robot-assisted pancreatoduodenectomy was associated with a lower conversion rate and a higher number of retrieved lymph nodes as compared with laparoscopic pancreatoduodenectomy.</p

A MSFD complementary approach for the assessment of pressures, knowledge and data gaps in Southern European Seas : the PERSEUS experience

PERSEUS project aims to identify the most relevant pressures exerted on the ecosystems of the Southern European Seas (SES), highlighting knowledge and data gaps that endanger the achievement of SES Good Environmental Status (GES) as mandated by the Marine Strategy Framework Directive (MSFD). A complementary approach has been adopted, by a meta-analysis of existing literature on pressure/impact/knowledge gaps summarized in tables related to the MSFD descriptors, discriminating open waters from coastal areas. A comparative assessment of the Initial Assessments (IAs) for five SES countries has been also independently performed. The comparison between meta-analysis results and IAs shows similarities for coastal areas only. Major knowledge gaps have been detected for the biodiversity, marine food web, marine litter and underwater noise descriptors. The meta-analysis also allowed the identification of additional research themes targeting research topics that are requested to the achievement of GES. 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license.peer-reviewe

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Constraining the Oceanic Uptake and Fluxes of Greenhouse Gases by Building an Ocean Network of Certified Stations: The Ocean Component of the Integrated Carbon Observation System, ICOS-Oceans

The European Research Infrastructure Consortium “Integrated Carbon Observation System” (ICOS) aims at delivering high quality greenhouse gas (GHG) observations and derived data products (e.g., regional GHG-flux maps) for constraining the GHG balance on a European level, on a sustained long-term basis. The marine domain (ICOS-Oceans) currently consists of 11 Ship of Opportunity lines (SOOP – Ship of Opportunity Program) and 10 Fixed Ocean Stations (FOSs) spread across European waters, including the North Atlantic and Arctic Oceans and the Barents, North, Baltic, and Mediterranean Seas. The stations operate in a harmonized and standardized way based on community-proven protocols and methods for ocean GHG observations, improving operational conformity as well as quality control and assurance of the data. This enables the network to focus on long term research into the marine carbon cycle and the anthropogenic carbon sink, while preparing the network to include other GHG fluxes. ICOS data are processed on a near real-time basis and will be published on the ICOS Carbon Portal (CP), allowing monthly estimates of CO2 air-sea exchange to be quantified for European waters. ICOS establishes transparent operational data management routines following the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles allowing amongst others reproducibility, interoperability, and traceability. The ICOS-Oceans network is actively integrating with the atmospheric (e.g., improved atmospheric measurements onboard SOOP lines) and ecosystem (e.g., oceanic direct gas flux measurements) domains of ICOS, and utilizes techniques developed by the ICOS Central Facilities and the CP. There is a strong interaction with the international ocean carbon cycle community to enhance interoperability and harmonize data flow. The future vision of ICOS-Oceans includes ship-based ocean survey sections to obtain a three-dimensional understanding of marine carbon cycle processes and optimize the existing network design

ECMO for COVID-19 patients in Europe and Israel

Since March 15th, 2020, 177 centres from Europe and Israel have joined the study, routinely reporting on the ECMO support they provide to COVID-19 patients. The mean annual number of cases treated with ECMO in the participating centres before the pandemic (2019) was 55. The number of COVID-19 patients has increased rapidly each week reaching 1531 treated patients as of September 14th. The greatest number of cases has been reported from France (n = 385), UK (n = 193), Germany (n = 176), Spain (n = 166), and Italy (n = 136) .The mean age of treated patients was 52.6 years (range 16–80), 79% were male. The ECMO configuration used was VV in 91% of cases, VA in 5% and other in 4%. The mean PaO2 before ECMO implantation was 65 mmHg. The mean duration of ECMO support thus far has been 18 days and the mean ICU length of stay of these patients was 33 days. As of the 14th September, overall 841 patients have been weaned from ECMO support, 601 died during ECMO support, 71 died after withdrawal of ECMO, 79 are still receiving ECMO support and for 10 patients status n.a. . Our preliminary data suggest that patients placed on ECMO with severe refractory respiratory or cardiac failure secondary to COVID-19 have a reasonable (55%) chance of survival. Further extensive data analysis is expected to provide invaluable information on the demographics, severity of illness, indications and different ECMO management strategies in these patients