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

Stability of SARS-CoV-2 spike antigens against mutations

Modern health care needs preventive vaccines and therapeutic treatments with stability against pathogen mutations to cope with current and future viral infections. At the beginning of the COVID-19 pandemic, our analytic and predictive tool identified a set of eight short SARS-CoV-2 S-spike protein epitopes that had the potential to persistently avoid mutation. Here a combination of genetic, Systems Biology and protein structure analyses confirm the stability of our identified epitopes against viral mutations. Remarkably, this research spans the whole period of the pandemic, during which 93.9% of the eight peptides remained invariable in the globally predominant 43 circulating variants, including Omicron. Likewise, the selected epitopes are conserved in 97% of all 1,514 known SARS-CoV-2 lineages. Finally, experimental analyses performed with these short peptides showed their specific immunoreactivity. This work opens a new perspective on the design of next-generation vaccines and antibody therapies that will remain reliable against future pathogen mutations.Dr. Lozano-Perez acknowledges the European Commission ERDF/FEDER Operational Program 'Murcia' CCI No. 2007ES161PO001 (Project No. 14-20/20). Miodrag Grbic acknowledges support from the NSERC Discovery grant (Canada). This work also has received funding from the Department of Education of the Basque Government via the Consolidated Research Group MATH MODE (IT1456-22). Besides, Ildefonso Martinez De la Fuente and Iker Malaina were supported by the UPV/EHU and Basque Center of Applied Mathematics, grant US21/27N

DianaHealth.com, an On-Line Database Containing Appraisals of the Clinical Value and Appropriateness of Healthcare Interventions : Database Development and Retrospective Analysis

To describe the development of a novel on-line database aimed to serve as a source of information concerning healthcare interventions appraised for their clinical value and appropriateness by several initiatives worldwide, and to present a retrospective analysis of the appraisals already included in the database. Database development and a retrospective analysis. The database DianaHealth.com is already on-line and it is regularly updated, independent, open access and available in English and Spanish. Initiatives are identified in medical news, in article references, and by contacting experts in the field. We include appraisals in the form of clinical recommendations, expert analyses, conclusions from systematic reviews, and original research that label any health care intervention as low-value or inappropriate. We obtain the information necessary to classify the appraisals according to type of intervention, specialties involved, publication year, authoring initiative, and key words. The database is accessible through a search engine which retrieves a list of appraisals and a link to the website where they were published. DianaHealth.com also provides a brief description of the initiatives and a section where users can report new appraisals or suggest new initiatives. From January 2014 to July 2015, the on-line database included 2940 appraisals from 22 initiatives: eleven campaigns gathering clinical recommendations from scientific societies, five sets of conclusions from literature review, three sets of recommendations from guidelines, two collections of articles on low clinical value in medical journals, and an initiative of our own. We have developed an open access on-line database of appraisals about healthcare interventions considered of low clinical value or inappropriate. DianaHealth.com could help physicians and other stakeholders make better decisions concerning patient care and healthcare systems sustainability. Future efforts should be focused on assessing the impact of these appraisals in the clinical practice

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