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Arteriovenous differences in lactate (AVLAC) across the lungs are usually small and close to zero. However, it has recently been reported that the lungs can produce increased amounts of lactate in some patients with acute respiratory distress syndrome (ARDS). The aim of this study was to evaluate lactate production in various types of acute lung injury requiring mechanical ventilation and hemodynamic monitoring. Since the differences involved are usually small, minor errors in lactate measurement could greatly influence AVLAC. Based on an analysis of these errors (see text for details), we averaged five arterial and venous samples for each measurement. We investigated 122 patients: 43 with acute lung injury (ALI), nine with cardiogenic pulmonary edema (CPE), 37 with bronchopneumonia (BPN), seven with single lung transplantation (LTX), and 26 with other causes of respiratory failure (OTHER). There was no difference in arterial lactate between the various groups. AVLAC was higher in patients with ALI than in the other groups (0.20+/-0.23 versus 0.07+/-0.11 mEq/L). In patients with ALI, AVLAC was proportional to the Murray's lung injury score (-0.032+/-0.032x; r = 0.46, p < 0.01). Lung lactate production was calculated as the product of the cardiac index times AVLAC and was significantly higher in patients with ALI than in the other groups (0.69+/-0.88 versus 0.19+/-0.30 mEq/min; p < 0.05). In patients with ALI, lung lactate production was inversely related to the PaO2/FIO2 (1.42 - 0.005x; r = 0.35, p < 0.05) but directly related to the venous admixture (-0.36 + 0.003x; r = 0.49, p < 0.01) and the lung injury score (-0.19 + 0.36x; r = 0.45, p < 0.01). Lung lactate production was not significantly related to arterial lactate levels. These data indicate that AVLAC and lung lactate production can be increased in patients with ARDS but remain within the normal range in other types of respiratory failure.

Guidelines recommend that a mean arterial pressure (MAP) value greater than 65 mm Hg should be the initial blood pressure target in septic shock, but what evidence is there to support this statement? We searched Pubmed and Google Scholar by using the key words 'arterial pressure', 'septic shock', and 'norepinephrine' and retrieved human studies published between 1 January 2000 and 31 July 2014. We identified seven comparative studies: two randomized clinical trials and five observational studies. The results of the literature review suggest that a MAP target of 65 mm Hg is usually sufficient in patients with septic shock. However, a MAP of around 75 to 85 mm Hg may reduce the development of acute kidney injury in patients with chronic arterial hypertension. Because of the high prevalence of chronic arterial hypertension in patients who develop septic shock, this finding is of considerable importance. Future studies should assess interactions between time, fluid volumes administered, and doses of vasopressors.

In septic shock, angiotensin II administration is associated with a similar level of cardiovascular resuscitation and less myocardial oxygen consumption, and inflammation compared to norepinephrine.

Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of "modifiable risk factors", measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details.

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