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Simple Summary: A 90-day mortality predictive model for curative gastric cancer resection based on the Spanish EURECCA Esophagogastric Cancer database was externally validated using the GASTRODATA registry. The externally validated model showed a modestly worse performance compared to the original model, nevertheless maintaining its discriminating ability in clinical practice. Background: Radical gastrectomy remains the main treatment for gastric cancer, despite its high mortality. A clinical predictive model of 90-day mortality (90DM) risk after gastric cancer surgery based on the Spanish EURECCA registry database was developed using a matching learning algorithm. We performed an external validation of this model based on data from an international multicenter cohort of patients. Methods: A cohort of patients from the European GASTRODATA database was selected. Demographic, clinical, and treatment variables in the original and validation cohorts were compared. The performance of the model was evaluated using the area under the curve (AUC) for a random forest model. Results: The validation cohort included 2546 patients from 24 European hospitals. The advanced clinical T- and N-category, neoadjuvant therapy, open procedures, total gastrectomy rates, and mean volume of the centers were significantly higher in the validation cohort. The 90DM rate was also higher in the validation cohort (5.6%) vs. the original cohort (3.7%). The AUC in the validation model was 0.716. Conclusion: The externally validated model for predicting the 90DM risk in gastric cancer patients undergoing gastrectomy with curative intent continues to be as useful as the original model in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Stroke is a highly prevalent disease that can provoke severe disability. We evaluate a predictive model based on the Minimum Basic Data Set (MBDS) compiled by the Spain Health Ministry, obtained for the period 2008–2012 for patients with ischaemic stroke in Spain, to establish the model's validity and to optimise its calibration. The MBDS is the main clinical-administrative database for hospitalisations recorded in Spain, and to our knowledge, no predictive models for stroke mortality have previously been developed using this resource. The main study aim is to perform an external validation and recalibration of the coefficients of this predictive model with respect to a chronologically later cohort. Material and Methods: External validation (testing the model on a different cohort to assess its performance) and recalibration (validation with optimisation of model coefficients) were performed using the MBDS for patients admitted for ischaemic stroke in the period 2016–2018. A cohort study was designed, in which a recalibrated model was obtained by applying the variables of the original model without their coefficients. The variables from the original model were then applied to the subsequent cohort, together with the coefficients from the initial model. The areas under the curve (AUC) of the recalibration and the external validation procedure were compared. Results: The recalibrated model produced an AUC of 0.743 and was composed of the following variables: age (odds ratio, OR:1.073), female sex (OR:1.143), ischaemic heart disease (OR:1.192), hypertension (OR:0.719), atrial fibrillation (OR:1.414), hyperlipidaemia (OR:0.652), heart failure (OR:2.133) and posterior circulation stroke (OR: 0.755). External validation produced an AUC of 0.726. Conclusions: The recalibrated clinical model thus obtained presented moderate-high discriminant ability and was generalisable to predict death for patients with ischaemic stroke. Rigorous external validation slightly decreased the AUC but confirmed the validity of the baseline model for the chronologically later cohort. [ABSTRACT FROM AUTHOR]

Background: The Kidney Failure Risk Equation (KFRE) is a 2- and 5-year kidney failure prediction model that is applied in chronic kidney disease (CKD) G3 + . The Grams model predicts kidney failure and death at 2 and 4 years in CKD G4 + . There are limited external validations of the Grams model, especially for predicting mortality before kidney failure., Methods: We performed an external validation of the Grams and Kidney Failure Risk Equation prediction models in incident patients with CKD G4 + at Hospital Universitario Fundación Alcorcón, Spain, between 1/1/2014 and 31/12/2018, ending follow-up on 30/09/2023. Discrimination was performed calculating the area under the receiver-operating characteristic curve. Calibration was assessed using the Hosmer-Lemeshow test and the Brier score., Results: The study included 339 patients (mean age 72.2 ± 12.7 years and baseline estimated glomerular filtration rate 20.6 ± 5.0 ml/min). Both models showed excellent discrimination. The area under the curve (AUC) for Kidney Failure Risk Equation-2 and Grams-2 were 0.894 (95% CI 0.857-0.931) and 0.897 (95%CI 0.859-0.935), respectively. For Grams-4 the AUC was 0.841 (95%CI 0.798-0.883), and for Kidney Failure Risk Equation-5 it was 0.823 (95% CI 0.779-0.867). For death before kidney failure, the Grams model showed acceptable discrimination (AUC 0.708 (95% CI 0.626-0.790) and 0.744 (95% CI 0.683-0.804) for Grams-2 and Grams-4, respectively). Both models presented excellent calibration for predicting kidney failure. Grams model calibration to estimate mortality before kidney failure was also excellent. In all cases, Hosmer-Lemeshow test resulted in a p-value greater than 0.05, and the Brier score was less than 0.20., Conclusions: In a cohort of patients with CKD G4 + from southern Europe, both the Grams and Kidney Failure Risk Equation models are accurate in estimating the risk of kidney failure. Additionally, the Grams model provides a reliable estimate of the risk of mortality before kidney failure., (© 2023. The Author(s) under exclusive licence to Italian Society of Nephrology.)

Document Reviewers: Luis Alcocer (Mexico), Christina Antza (Greece), Mustafa Arici (Turkey), Eduardo Barbosa (Brazil), Adel Berbari (Lebanon), Luís Bronze (Portugal), John Chalmers (Australia), Tine De Backer (Belgium), Alejandro de la Sierra (Spain), Kyriakos Dimitriadis (Greece), Dorota Drozdz (Poland), Béatrice Duly-Bouhanick (France), Brent M. Egan (USA), Serap Erdine (Turkey), Claudio Ferri (Italy), Slavomira Filipova (Slovak Republic), Anthony Heagerty (UK), Michael Hecht Olsen (Denmark), Dagmara Hering (Poland), Sang Hyun Ihm (South Korea), Uday Jadhav (India), Manolis Kallistratos (Greece), Kazuomi Kario (Japan), Vasilios Kotsis (Greece), Adi Leiba (Israel), Patricio López-Jaramillo (Colombia), Hans-Peter Marti (Norway), Terry McCormack (UK), Paolo Mulatero (Italy), Dike B. Ojji (Nigeria), Sungha Park (South Korea), Priit Pauklin (Estonia), Sabine Perl (Austria), Arman Postadzhian (Bulgaria), Aleksander Prejbisz (Poland), Venkata Ram (India), Ramiro Sanchez (Argentina), Markus Schlaich (Australia), Alta Schutte (Australia), Cristina Sierra (Spain), Sekib Sokolovic (Bosnia and Herzegovina), Jonas Spaak (Sweden), Dimitrios Terentes-Printzios (Greece), Bruno Trimarco (Italy), Thomas Unger (The Netherlands), Bert-Jan van den Born (The Netherlands), Anna Vachulova (Slovak Republic), Agostino Virdis (Italy), Jiguang Wang (China), Ulrich Wenzel (Germany), Paul Whelton (USA), Jiri Widimsky (Czech Republic), Jacek Wolf (Poland), Grégoire Wuerzner (Switzerland), Eugene Yang (USA), Yuqing Zhang (China)., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Ecological behaviour and productive capacity of chestnut ( Castanea sativa Mill.) coppice stands are key factors in predicting forest growth and subsequent management decision, especially in areas where timber production is the primary objective. The effects of soil nutrients and environmental factors on site productivity in chestnut coppice stands in North-West Spain were studied. Site productivity described by site index was related to environmental characteristics, including edaphic, physiographic and climatic variables. The key factors affecting site productivity were evaluated according to two different statistical analyses: the CHAID procedure and parametric regression techniques. The CHAID algorithm applied separately to each type of variable revealed that the most important to explain SI were edaphic (sand and clay percentage, pH, stoniness) and climatic variables (summer and spring precipitation and mean annual temperature) (24 and 47 %, respectively). According to the regression tree and the parametric regression model for all variables, summer precipitation was the most significant variable (51 and 53 %, respectively). The results show the importance of climatic variables for chestnut coppice stands growth and provide further information about the ecology of the species in North-West Spain. The use of specimens from sites representing a wide range of habitats/growing conditions of this species means that both the results and methodology described here are of great relevance for improving the management of this species throughout its European range. [ABSTRACT FROM AUTHOR]

The relationships between climatic variables, soil parameters, tree nutrition and site index (SI) were examined in 30 chestnut coppice stands in Asturias, the region with the largest area of chestnut coppice in north-west Spain. SI was estimated from a top height projection equation for this species. A soil sample was obtained at 0- to 20-cm depth and several leaves were collected from the upper zone of the dominant trees in each plot for further analysis. Chi-square automatic interaction detection and parametric regression techniques were used to determine the key factors affecting SI. According to the regression tree and the parametric regression model for all variables, the extractable Mg in the soil and mean temperature of the warmest month were the most important variables explaining SI in the region. Elevation and foliar concentrations of phosphorus were not significant factors in the parametric regression analysis (although considered key factors when foliar nutrients and physiographical variables were analysed separately). The results show the importance, for chestnut growth, of site selection and balanced fertilizer treatment consistent with the soil requirements of the species. [ABSTRACT FROM AUTHOR]

• Background: The effects of foliar nutrients and environmental variables on site productivity in Maritime pine ( Pinus pinaster Ait.) stands in Asturias (NW Spain) were studied. • Methods: Site productivity was evaluated by means of site index. The environmental characteristics considered included physiographic, edaphic and climatic variables. Two different statistical analyses were carried out to determine the key factors affecting site productivity: the CHAID procedure and parametric regression techniques. • Results: The regression trees obtained by applying the CHAID algorithm separately to each type of variable revealed the order of importance of the environmental factors. In addition, the variables corresponding to the first level of branches of the CHAID regression trees were consistent with those identified by parametric regression. According to the parametric regression model, a physical soil characteristic (soil depth) and a climatic characteristic (mean summer temperature) were the most important variables for explaining site productivity in the region. Foliar concentrations of phosphorus and elevation were not significant factors in the parametric regression analysis (although considered key factors when foliar nutrients and physiographic variables were analyzed separately). • Conclusion: Although the percentage variability in SI explained by the best parametric model was not very high (45%), the model is parsimonious and provides robust estimates of the spatial distribution of this variable. The results obtained provide further information about the ecology of this species in Asturias, and represent an important tool in forest management, especially for afforested areas. [ABSTRACT FROM AUTHOR]