New or Progressive Multiple Organ Dysfunction Syndrome in Pediatric Severe Sepsis: A Sepsis Phenotype With Higher Morbidity and Mortality

Objectives: To describe the epidemiology, morbidity, and mortality of new or progressive multiple organ dysfunction syndrome in children with severe sepsis. Design: Secondary analysis of a prospective, cross-sectional, point prevalence study. Setting: International, multicenter PICUs. Patients: Pediatric patients with severe sepsis identified on five separate days over a 1-year period. Interventions: None. Measurements and Main Results: Of 567 patients from 128 PICUs in 26 countries enrolled, 384 (68%) developed multiple organ dysfunction syndrome within 7 days of severe sepsis recognition. Three hundred twenty-seven had multiple organ dysfunction syndrome on the day of sepsis recognition. Ninety-one of these patients developed progressive multiple organ dysfunction syndrome, whereas an additional 57 patients subsequently developed new multiple organ dysfunction syndrome, yielding a total proportion with severe sepsis-associated new or progressive multiple organ dysfunction syndrome of 26%. Hospital mortality in patients with progressive multiple organ dysfunction syndrome was 51% compared with patients with new multiple organ dysfunction syndrome (28%) and those with single-organ dysfunction without multiple organ dysfunction syndrome (10%) (p < 0.001). Survivors of new or progressive multiple organ dysfunction syndrome also had a higher frequency of moderate to severe disability defined as a Pediatric Overall Performance Category score of greater than or equal to 3 and an increase of greater than or equal to 1 from baseline: 22% versus 29% versus 11% for progressive, new, and no multiple organ dysfunction syndrome, respectively (p < 0.001). Conclusions: Development of new or progressive multiple organ dysfunction syndrome is common (26%) in severe sepsis and is associated with a higher risk of morbidity and mortality than severe sepsis without new or progressive multiple organ dysfunction syndrome. Our data support the use of new or progressive multiple organ dysfunction syndrome as an important outcome in trials of pediatric severe sepsis although efforts are needed to validate whether reducing new or progressive multiple organ dysfunction syndrome leads to improvements in more definitive morbidity and mortality endpoints

Insights into the accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models

Insights into accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data