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Objectives: Sepsis is a major public health concern with significant morbidity, mortality, and healthcare expenses. Early detection and antibiotic treatment of sepsis improve outcomes. However, although professional critical care societies have proposed new clinical criteria that aid sepsis recognition, the fundamental need for early detection and treatment remains unmet. In response, researchers have proposed algorithms for early sepsis detection, but directly comparing such methods has not been possible because of different patient cohorts, clinical variables and sepsis criteria, prediction tasks, evaluation metrics, and other differences. To address these issues, the PhysioNet/Computing in Cardiology Challenge 2019 facilitated the development of automated, open-source algorithms for the early detection of sepsis from clinical data., Design: Participants submitted containerized algorithms to a cloud-based testing environment, where we graded entries for their binary classification performance using a novel clinical utility-based evaluation metric. We designed this scoring function specifically for the Challenge to reward algorithms for early predictions and penalize them for late or missed predictions and for false alarms., Setting: ICUs in three separate hospital systems. We shared data from two systems publicly and sequestered data from all three systems for scoring., Patients: We sourced over 60,000 ICU patients with up to 40 clinical variables for each hour of a patient's ICU stay. We applied Sepsis-3 clinical criteria for sepsis onset., Interventions: None., Measurements and Main Results: A total of 104 groups from academia and industry participated, contributing 853 submissions. Furthermore, 90 abstracts based on Challenge entries were accepted for presentation at Computing in Cardiology., Conclusions: Diverse computational approaches predict the onset of sepsis several hours before clinical recognition, but generalizability to different hospital systems remains a challenge.

Compelling evidence indicates that exposure to urban airborne particulate matter (PM) affects health. However, how PM components interact with PM-size to cause adverse health effects needs elucidation, especially when considering soil and anthropogenic sources. We studied PM from Mexicali, Mexico, where soil particles contribute importantly to air pollution, expecting to differentiate in vitro effects related to PM-size and composition. PM samples with mean aerodynamic diameters ≤2.5μm (PM(2.5)) and ≤10μm (PM(10)) were collected in Mexicali (October 2005-March 2006) from a semi-urban (expected larger participation of soil sources) and an urban (predominately combustion sources) site. Samples were pooled by site and size, analyzed for elemental composition (particle-induced X-ray emission) and tested in vitro for: induction of human erythrocytes membrane disruption (hemolysis) (colorimetrically); inhibition of cell proliferation (ICP) (crystal violet) and TNFα/IL-6 secretion (ELISA) using J774.A1 murine monocytic cells; and DNA degradation using Balb/c3T3 cell naked DNA (electrophoretically). Results of PM elemental composition principal component analysis were used in associating cellular effects. Sixteen elements identified in PM grouped in two principal components: Component(1) (C(1)): Mg, Al, Si, P, Cl, K, Ca, Ti, V, Cr, Fe, and Component(2) (C(2)): Cu, Zn. Hemolysis was predominately induced by semi-urban-PM(10) (p<0.05) and was associated with urban-PM(10)C(1) (r=0.62, p=0.003). Major ICP resulted with semi-urban PM(2.5) (p<0.05). TNFα was mainly induced by urban samples regardless of size (p<0.05) and associated with urban-PM(2.5)C(2) (r=0.48, p=0.02). Both PM(10) samples induced highest DNA degradation (p<0.05), regardless of location. We conclude that PM-size and PM-related soil or anthropogenic elements trigger specific biological-response patterns., (Copyright © 2010 Elsevier Ltd. All rights reserved.)