https://www.selleckchem.com/products/ITF2357(Givinostat).html RESULTS In modified-ITT (mITT) analysis including 83 Intervention and 41 Usual Care eligible patients, fluid balance at 72 hours or ICU discharge was significantly lower (-1.37L favoring Intervention arm, 0.65 ± 2.85L Intervention arm vs. 2.02 ± 3.44L Usual Care arm, p=0.021. Fewer patients required renal replacement therapy (5.1% vs 17.5%, p=0.04) or mechanical ventilation (17.7% vs 34.1%, p=0.04) in the Intervention arm compared to Usual Care. In the all-randomized Intent to Treat (ITT) population (102 Intervention, 48 Usual Care) there were no significant differences in safety signals. INTERPRETATION Physiologically informed fluid and vasopressor resuscitation using passive leg raise-induced stroke volume change to guide management of septic shock is safe and demonstrated lower net fluid balance and reductions in the risk of renal and respiratory failure. Dynamic assessments to guide fluid administration may improve outcomes for septic shock patients compared with Usual Care. BACKGROUND Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality. We hypothesized that applying machine learning to clinical and quantitative CT imaging features would improve mortality prediction in COPD. METHODS We selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest (RSF). We used top features in a Cox regression to create a machine learning mortality prediction (MLMP-COPD) model, and also assessed the performance of other statistical and machine learning models. We trained the models in moderate-to-severe COPD subjects from a subset of COPDGene, and tested prediction performance in the remainder of individuals with moderate-to-severe COPD in COPDGene and ECLIPSE. We compared our model to BODE; BODE modifications; and the age, dyspnea, obstruction (ADO) index. RESULTS We included 2,632 COPDGene and 1,268 ECLIPSE participants. The top predictors of mortality were 6