Prognostic models for mortality after cardiac surgery in patients with infective endocarditis: a systematic review and aggregation of prediction models.

Background There are several prognostic models to estimate the risk of mortality after surgery for active infective endocarditis (IE). However, these models incorporate different predictors and their performance is uncertain. Objective We systematically reviewed and critically appraised all available prediction models of postoperative mortality in patients undergoing surgery for IE, and aggregated them into a meta-model. Data sources We searched Medline and EMBASE databases from inception to June 2020. Study eligibility criteria We included studies that developed or updated a prognostic model of postoperative mortality in patient with IE. Methods We assessed the risk of bias of the models using PROBAST (Prediction model Risk Of Bias ASsessment Tool) and we aggregated them into an aggregate meta-model based on stacked regressions and optimized it for a nationwide registry of IE patients. The meta-model performance was assessed using bootstrap validation methods and adjusted for optimism. Results We identified 11 prognostic models for postoperative mortality. Eight models had a high risk of bias. The meta-model included weighted predictors from the remaining three models (EndoSCORE, specific ES-I and specific ES-II), which were not rated as high risk of bias and provided full model equations. Additionally, two variables (age and infectious agent) that had been modelled differently across studies, were estimated based on the nationwide registry. The performance of the meta-model was better than the original three models, with the corresponding performance measures: C-statistics 0.79 (95% CI 0.76–0.82), calibration slope 0.98 (95% CI 0.86–1.13) and calibration-in-the-large –0.05 (95% CI –0.20 to 0.11). Conclusions The meta-model outperformed published models and showed a robust predictive capacity for predicting the individualized risk of postoperative mortality in patients with IE. Protocol registration PROSPERO (registration number CRD42020192602).pre-print270 K

GrassPlot - a database of multi-scale plant diversity in Palaearctic grasslands

GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG) and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). GrassPlot collects plot records (releves) from grasslands and other open habitats of the Palaearctic biogeographic realm. It focuses on precisely delimited plots of eight standard grain sizes (0.0001; 0.001;... 1,000 m(2)) and on nested-plot series with at least four different grain sizes. The usage of GrassPlot is regulated through Bylaws that intend to balance the interests of data contributors and data users. The current version (v. 1.00) contains data for approximately 170,000 plots of different sizes and 2,800 nested-plot series. The key components are richness data and metadata. However, most included datasets also encompass compositional data. About 14,000 plots have near-complete records of terricolous bryophytes and lichens in addition to vascular plants. At present, GrassPlot contains data from 36 countries throughout the Palaearctic, spread across elevational gradients and major grassland types. GrassPlot with its multi-scale and multi-taxon focus complements the larger international vegetationplot databases, such as the European Vegetation Archive (EVA) and the global database " sPlot". Its main aim is to facilitate studies on the scale-and taxon-dependency of biodiversity patterns and drivers along macroecological gradients. GrassPlot is a dynamic database and will expand through new data collection coordinated by the elected Governing Board. We invite researchers with suitable data to join GrassPlot. Researchers with project ideas addressable with GrassPlot data are welcome to submit proposals to the Governing Board

Prognostic models for mortality after cardiac surgery in patients with infective endocarditis: a systematic review and aggregation of prediction models

Background: There are several prognostic models to estimate the risk of mortality after surgery for active infective endocarditis (IE). However, these models incorporate different predictors and their performance is uncertain. Objective: We systematically reviewed and critically appraised all available prediction models of postoperative mortality in patients undergoing surgery for IE, and aggregated them into a meta-model. Data sources: We searched Medline and EMBASE databases from inception to June 2020. Study eligibility criteria: We included studies that developed or updated a prognostic model of postoperative mortality in patient with IE. Methods: We assessed the risk of bias of the models using PROBAST (Prediction model Risk Of Bias ASsessment Tool) and we aggregated them into an aggregate meta-model based on stacked regressions and optimized it for a nationwide registry of IE patients. The meta-model performance was assessed using bootstrap validation methods and adjusted for optimism. Results: We identified 11 prognostic models for postoperative mortality. Eight models had a high risk of bias. The meta-model included weighted predictors from the remaining three models (EndoSCORE, specific ES-I and specific ES-II), which were not rated as high risk of bias and provided full model equations. Additionally, two variables (age and infectious agent) that had been modelled differently across studies, were estimated based on the nationwide registry. The performance of the meta-model was better than the original three models, with the corresponding performance measures: C-statistics 0.79 (95% CI 0.76–0.82), calibration slope 0.98 (95% CI 0.86–1.13) and calibration-in-the-large –0.05 (95% CI –0.20 to 0.11). Conclusions: The meta-model outperformed published models and showed a robust predictive capacity for predicting the individualized risk of postoperative mortality in patients with IE

Prognostic assessment of valvular surgery in active infective endocarditis: multicentric nationwide validation of a new score developed from a meta-analysis

OBJECTIVES Several risk prediction models have been developed to estimate the risk of mortality after valve surgery for active infective endocarditis (IE), but few external validations have been conducted to assess their accuracy. We previously developed a systematic review and meta-analysis of the impact of IE-specific factors for the in-hospital mortality rate after IE valve surgery, whose obtained pooled estimations were the basis for the development of a new score (APORTEI). The aim of the present study was to assess its prognostic accuracy in a nationwide cohort. METHODS We analysed the prognostic utility of the APORTEI score using patient-level data from a multicentric national cohort. Patients who underwent surgery for active IE between 2008 and 2018 were included. Discrimination was evaluated using the area under the receiver operating characteristic curve, and the calibration was assessed using the calibration slope and the Hosmer-Lemeshow test. Agreement between the APORTEI and the EuroSCORE I was also analysed by Lin's concordance correlation coefficient (CCC), the Bland-Altman agreement analysis and a scatterplot graph. RESULTS The 11 variables that comprised the APORTEI score were analysed in the sample. The APORTEI score was calculated in 1338 patients. The overall observed surgical mortality rate was 25.56%. The score demonstrated adequate discrimination (area under the receiver operating characteristic curve = 0.75; 95% confidence interval 0.72-0.77) and calibration (calibration slope = 1.03; Hosmer-Lemeshow test P = 0.389). We found a lack of agreement between the APORTEI and EuroSCORE I (concordance correlation coefficient = 0.55). CONCLUSIONS The APORTEI score, developed from a systematic review and meta-analysis, showed an adequate estimation of the risk of mortality after IE valve surgery in a nationwide cohort

Prognostic assessment of valvular surgery in active infective endocarditis: multicentric nationwide validation of a new score developed from a meta-analysisis

Spanish Collaboration on Endocarditis—Grupo de Apoyo al Manejo de la Endocarditis infecciosa en ESpaña (GAMES).[Objectives] Several risk prediction models have been developed to estimate the risk of mortality after valve surgery for active infective endocarditis (IE), but few external validations have been conducted to assess their accuracy. We previously developed a systematic review and meta-analysis of the impact of IE-specific factors for the in-hospital mortality rate after IE valve surgery, whose obtained pooled estimations were the basis for the development of a new score (APORTEI). The aim of the present study was to assess its prognostic accuracy in a nationwide cohort.[Methods] We analysed the prognostic utility of the APORTEI score using patient-level data from a multicentric national cohort. Patients who underwent surgery for active IE between 2008 and 2018 were included. Discrimination was evaluated using the area under the receiver operating characteristic curve, and the calibration was assessed using the calibration slope and the Hosmer–Lemeshow test. Agreement between the APORTEI and the EuroSCORE I was also analysed by Lin’s concordance correlation coefficient (CCC), the Bland–Altman agreement analysis and a scatterplot graph.[Results] The 11 variables that comprised the APORTEI score were analysed in the sample. The APORTEI score was calculated in 1338 patients. The overall observed surgical mortality rate was 25.56%. The score demonstrated adequate discrimination (area under the receiver operating characteristic curve = 0.75; 95% confidence interval 0.72–0.77) and calibration (calibration slope = 1.03; Hosmer–Lemeshow test P = 0.389). We found a lack of agreement between the APORTEI and EuroSCORE I (concordance correlation coefficient = 0.55).[Conclusions] The APORTEI score, developed from a systematic review and meta-analysis, showed an adequate estimation of the risk of mortality after IE valve surgery in a nationwide cohort

Benchmarking plant diversity of Palaearctic grasslands and other open habitats

Aims: Understanding fine-grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine-grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location: Palaearctic biogeographic realm. Methods: We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m(2) and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results: Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi-natural) grasslands and natural grasslands are the richest vegetation type. The open-access file "GrassPlot Diversity Benchmarks" and the web tool "GrassPlot Diversity Explorer" are now available online () and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions: The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology

Benchmarking plant diversity of Palaearctic grasslands and other open habitats

Aims Understanding fine-grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine-grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location Palaearctic biogeographic realm. Methods We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m2 and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi-natural) grasslands and natural grasslands are the richest vegetation type. The open-access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology