Between 75% and 80% of patients with sepsis arrive in the hospital through the emergency department. Early diagnosis is important to alter patient prognosis, but currently, there is no reliable biomarker. The innate immune response links inflammation and coagulation. Several coagulation -related biomarkers are associated with poor prognosis in the ICU. The role of coagulation biomarkers to aid in early sepsis diagnosis has not previously been investigated. The objective of our study is to determine the individual or combined accuracy of coagulation and inflammation biomarkers with standard biochemical tests to diagnose adult septic patients presenting to the emergency department. in the Emergency Department is a prospective, observational cohort study with a target enrolment of 250 suspected septic patients from two Canadian emergency departments. The emergency physicians will enroll patients with suspected sepsis. Blood samples will be collected at two time points (initial presentation and 4 hr followingortance of early coagulation abnormalities to identify additional tools for sepsis diagnosis. To determine how several existing crisis standards of care triage protocols would have distinguished between patients with coronavirus disease 2019 requiring intensive care. Retrospective cohort study. Single urban academic medical center. One-hundred twenty patients with coronavirus disease 2019 who required intensive care and mechanical ventilation. None. The characteristics of each patient at the time of ICU triage were used to determine how patients would have been prioritized using four crisis standards of care protocols. The vast majority of patients in the cohort would have been in the highest priority group using a triage protocol focusing on Sequential Organ Failure Assessment alone. Prioritization based on Sequential Organ Failure Assessment and 1-year life expectancy would have resulted in only slightly more differentiation between patients. Prioritization based on Sequential Organ Failure Assessment and 5-year life expectancy would have added significant additional differentiation depe scarcity. Several inflammation markers have been reported to be associated with unfavorable clinical outcomes in critically ill patients. We aimed to elucidate whether serum interleukin-6 concentration considered with Sequential Organ Failure Assessment score can better predict mortality in critically ill patients. A prospective observational study. Five university hospitals in 2016-2018. Critically ill adult patients who met greater than or equal to two systemic inflammatory response syndrome criteria at admission were included, and those who died or were discharged within 48 hours were excluded. Inflammatory biomarkers including interleukin (interleukin)-6, -8, and -10; tumor necrosis factor-α; C-reactive protein; and procalcitonin were blindly measured daily for 3 days. Area under the receiver operating characteristic curve for Sequential Organ Failure Assessment score at day 2 according to 28-day mortality was calculated as baseline. Combination models of Sequential Organ Failure Assessment score and addiine (area under the receiver operating characteristic curve = 0.844, area under the receiver operating characteristic curve improvement = 0.068 [0.002-0.133]), whereas other biomarkers did not improve accuracy in predicting 28-day mortality. Accuracy for 28-day mortality prediction was improved by adding serum interleukin-6 concentration to Sequential Organ Failure Assessment score.Accuracy for 28-day mortality prediction was improved by adding serum interleukin-6 concentration to Sequential Organ Failure Assessment score. To train a deep learning classification algorithm to predict chest radiograph severity scores and clinical outcomes in patients with coronavirus disease 2019 (COVID-19). In this retrospective cohort study, patients aged 21-50 years who presented to the emergency department (ED) of a multicenter urban health system from March 10 to 26, 2020, with COVID-19 confirmation at real-time reverse-transcription polymerase chain reaction screening were identified. The initial chest radiographs, clinical variables, and outcomes, including admission, intubation, and survival, were collected within 30 days ( = 338; median age, 39 years; 210 men). Two fellowship-trained cardiothoracic radiologists examined chest radiographs for opacities and assigned a clinically validated severity score. A deep learning algorithm was trained to predict outcomes on a holdout test set composed of patients with confirmed COVID-19 who presented between March 27 and 29, 2020 ( = 161; median age, 60 years; 98 men) for both younger (age range, 21-50 years; = 51) and older (age >50 years, = 110) populations. Bootstrapping was used to compute CIs. The model trained on the chest radiograph severity score produced the following areas under the receiver operating characteristic curves (AUCs) 0.80 (95% CI 0.73, 0.88) for the chest radiograph severity score, 0.76 (95% CI 0.68, 0.84) for admission, 0.66 (95% CI 0.56, 0.75) for intubation, and 0.59 (95% CI 0.49, 0.69) for death. The model trained on clinical variables produced an AUC of 0.64 (95% CI 0.55, 0.73) for intubation and an AUC of 0.59 (95% CI 0.50, 0.68) for death. Combining chest radiography and clinical variables increased the AUC of intubation and death to 0.88 (95% CI 0.79, 0.96) and 0.82 (95% CI 0.72, 0.91), respectively. The combination of imaging and clinical information improves outcome predictions. © RSNA, 2020.The combination of imaging and clinical information improves outcome predictions.Supplemental material is available for this article.© RSNA, 2020. To develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease tracking and outcome prediction. A convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on ∼160,000 anterior-posterior images from CheXpert and transfer learning on 314 frontal CXRs from COVID-19 patients. The algorithm was evaluated on internal and external test sets from different hospitals (154 and 113 CXRs respectively). IPI-145 PXS scores were correlated with radiographic severity scores independently assigned by two thoracic radiologists and one in-training radiologist (Pearson r). For 92 internal test set patients with follow-up CXRs, PXS score change was compared to radiologist assessments of change (Spearman ρ). The association between PXS score and subsequent intubation or death was assessed. Bootstrap 95% confidence intervals (CI) were calculated.