Simple scoring system to predict in-hospital mortality after surgery for infective endocarditis

BACKGROUND: Aspecific scoring systems are used to predict the risk of death postsurgery in patients with infective endocarditis (IE). The purpose of the present study was both to analyze the risk factors for in-hospital death, which complicates surgery for IE, and to create a mortality risk score based on the results of this analysis. METHODS AND RESULTS: Outcomes of 361 consecutive patients (mean age, 59.1\ub115.4 years) who had undergone surgery for IE in 8 European centers of cardiac surgery were recorded prospectively, and a risk factor analysis (multivariable logistic regression) for in-hospital death was performed. The discriminatory power of a new predictive scoring system was assessed with the receiver operating characteristic curve analysis. Score validation procedures were carried out. Fifty-six (15.5%) patients died postsurgery. BMI >27 kg/m2 (odds ratio [OR], 1.79; P=0.049), estimated glomerular filtration rate 55 mm Hg (OR, 1.78; P=0.032), and critical state (OR, 2.37; P=0.017) were independent predictors of in-hospital death. A scoring system was devised to predict in-hospital death postsurgery for IE (area under the receiver operating characteristic curve, 0.780; 95% CI, 0.734-0.822). The score performed better than 5 of 6 scoring systems for in-hospital death after cardiac surgery that were considered. CONCLUSIONS: A simple scoring system based on risk factors for in-hospital death was specifically created to predict mortality risk postsurgery in patients with IE

Conducting multicenter research in healthcare simulation: Lessons learned from the INSPIRE network

Abstract Simulation-based research has grown substantially over the past two decades; however, relatively few published simulation studies are multicenter in nature. Multicenter research confers many distinct advantages over single-center studies, including larger sample sizes for more generalizable findings, sharing resources amongst collaborative sites, and promoting networking. Well-executed multicenter studies are more likely to improve provider performance and/or have a positive impact on patient outcomes. In this manuscript, we offer a step-by-step guide to conducting multicenter, simulation-based research based upon our collective experience with the International Network for Simulation-based Pediatric Innovation, Research and Education (INSPIRE). Like multicenter clinical research, simulation-based multicenter research can be divided into four distinct phases. Each phase has specific differences when applied to simulation research: (1) Planning phase, to define the research question, systematically review the literature, identify outcome measures, and conduct pilot studies to ensure feasibility and estimate power; (2) Project Development phase, when the primary investigator identifies collaborators, develops the protocol and research operations manual, prepares grant applications, obtains ethical approval and executes subsite contracts, registers the study in a clinical trial registry, forms a manuscript oversight committee, and conducts feasibility testing and data validation at each site; (3) Study Execution phase, involving recruitment and enrollment of subjects, clear communication and decision-making, quality assurance measures and data abstraction, validation, and analysis; and (4) Dissemination phase, where the research team shares results via conference presentations, publications, traditional media, social media, and implements strategies for translating results to practice. With this manuscript, we provide a guide to conducting quantitative multicenter research with a focus on simulation-specific issues

Characterization of pediatric in-hospital cardiopulmonary resuscitation quality metrics across an international resuscitation collaborative

Copyright © 2018 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies. Objectives: Pediatric in-hospital cardiac arrest cardiopulmonary resuscitation quality metrics have been reported in few children less than 8 years. Our objective was to characterize chest compression fraction, rate, depth, and compliance with 2015 American Heart Association guidelines across multiple pediatric hospitals. Design: Retrospective observational study of data from a multicenter resuscitation quality collaborative from October 2015 to April 2017. Setting: Twelve pediatric hospitals across United States, Canada, and Europe. Patients: In-hospital cardiac arrest patients (age \u3c 18 yr) with quantitative cardiopulmonary resuscitation data recordings. Interventions: None. Measurements and Main Results: There were 112 events yielding 2,046 evaluable 60-second epochs of cardiopulmonary resuscitation (196,669 chest compression). Event cardiopulmonary resuscitation metric summaries (median [interquartile range]) by age: less than 1 year (38/112): chest compression fraction 0.88 (0.61-0.98), chest compression rate 119/min (110-129), and chest compression depth 2.3 cm (1.9-3.0 cm); for 1 to less than 8 years (42/112): chest compression fraction 0.94 (0.79-1.00), chest compression rate 117/min (110-124), and chest compression depth 3.8 cm (2.9-4.6 cm); for 8 to less than 18 years (32/112): chest compression fraction 0.94 (0.85-1.00), chest compression rate 117/min (110-123), chest compression depth 5.5 cm (4.0-6.5 cm). Compliance with guideline targets for 60-second chest compression epochs was predefined: chest compression fraction greater than 0.80, chest compression rate 100-120/min, and chest compression depth: greater than or equal to 3.4 cm in less than 1 year, greater than or equal to 4.4 cm in 1 to less than 8 years, and 4.5 to less than 6.6 cm in 8 to less than 18 years. Proportion of less than 1 year, 1 to less than 8 years, and 8 to less than 18 years events with greater than or equal to 60% of 60-second epochs meeting compliance (respectively): chest compression fraction was 53%, 81%, and 78%; chest compression rate was 32%, 50%, and 63%; chest compression depth was 13%, 19%, and 44%. For all events combined, total compliance (meeting all three guideline targets) was 10% (11/112). Conclusions: Across an international pediatric resuscitation collaborative, we characterized the landscape of pediatric in-hospital cardiac arrest chest compression quality metrics and found that they often do not meet 2015 American Heart Association guidelines. Guideline compliance for rate and depth in children less than 18 years is poor, with the greatest difficulty in achieving chest compression depth targets in younger children

Design and Deployment of a Pediatric Cardiac Arrest Surveillance System

Objective. We aimed to increase detection of pediatric cardiopulmonary resuscitation (CPR) events and collection of physiologic and performance data for use in quality improvement (QI) efforts. Materials and Methods. We developed a workflow-driven surveillance system that leveraged organizational information technology systems to trigger CPR detection and analysis processes. We characterized detection by notification source, type, location, and year, and compared it to previous methods of detection. Results. From 1/1/2013 through 12/31/2015, there were 2,986 unique notifications associated with 2,145 events, 317 requiring CPR. PICU and PEDS-ED accounted for 65% of CPR events, whereas floor care areas were responsible for only 3% of events. 100% of PEDS-OR and >70% of PICU CPR events would not have been included in QI efforts. Performance data from both defibrillator and bedside monitor increased annually. (2013: 1%; 2014: 18%; 2015: 27%). Discussion. After deployment of this system, detection has increased ∼9-fold and performance data collection increased annually. Had the system not been deployed, 100% of PEDS-OR and 50–70% of PICU, NICU, and PEDS-ED events would have been missed. Conclusion. By leveraging hospital information technology and medical device data, identification of pediatric cardiac arrest with an associated increased capture in the proportion of objective performance data is possible