Objectives: Although obesity is associated with risk for chronic kidney disease and improved survival, less is known about the associations of obesity with risk of acute kidney injury and post acute kidney injury mortality. Design: In a single-center inception cohort of almost 15,000 critically ill patients, we evaluated the association of obesity with acute kidney injury and acute kidney injury severity, as well as in-hospital and 1-year survival. Acute kidney injury was defined using the Kidney Disease Outcome Quality Initiative criteria. Measurements and Main Results: The acute kidney injury prevalence rates for normal, overweight, class I, II, and III obesity were 18.6%, 20.6%, 22.5%, 24.3%, and 24.0%, respectively, and the adjusted odds ratios of acute kidney injury were 1.18 (95% CI, 1.06–1.31), 1.35 (1.19–1.53), 1.47 (1.25–1.73), and 1.59 (1.31–1.87) when compared with normal weight, respectively. Each 5-kg/m2 increase in body mass index was associated with a 10% risk (95% CI, 1.06–1.24; p < 0.001) of more severe acute kidney injury. Within-hospital and 1-year survival rates associated with the acute kidney injury episodes were similar across body mass index categories. Conclusion: Obesity is a risk factor for acute kidney injury, which is associated with increased short- and long-term mortality.

Objective: To evaluate the prevalence and time course of systemic endotoxemia following severe multiple trauma, to define its risk factors, and to explore the correlation between post-trauma endotoxemia and organ dysfunction. Design: Prospective single-center cohort study. Setting: Emergency department and ICU of adult tertiary care level I trauma center. Patients: Forty-eight severely injured (Injury Severity Score ? 16) patients, admitted to ICU within 24 hours of injury. Interventions: None. Measurements and Main Results: Endotoxemia was not evident on initial presentation, but developed subsequently in 75% of patients, even in the absence of Gram-negative infection. Nonsurviving patients had higher endotoxin levels than survivors on day 1 (endotoxemia, 0.48 vs 0.28; p = 0.048). Shock at admission, or surgery within the first 48 hours after trauma, was associated with higher endotoxin levels and predicted subsequent maximal endotoxemia, after adjusting for other significant covariates. Maximal endotoxemia levels were higher in patients who developed organ dysfunction, reflected in a cumulative Multiple Organ Dysfunction Score greater than 25, and patients with an intermediate endotoxemia level (? 0.4) had more cardiovascular dysfunction. Conclusions: It is the first study to detect increasing levels of endotoxemia following multiple trauma. Shock and early surgery predict the development of endotoxemia; endotoxemia is particularly associated with cardiovascular dysfunction. However, Gram-negative infections are uncommon in these patients, suggesting that the gastrointestinal tract is the dominant reservoir of endotoxin. Endotoxin may be an appropriate therapeutic target in patients who have sustained severe multiple trauma.

Objectives: 1) To evaluate the ability of pulse pressure variation adjusted by respiratory changes in pleural pressure to predict fluid responsiveness compared with pulse pressure variation alone. 2) To identify factors explaining the poor performance of pulse pressure variation in acute respiratory distress syndrome. Design: Prospective study. Setting: Forty-bed university hospital general ICU. Patients: Ninety-six mechanically ventilated acute respiratory distress syndrome patients requiring fluid challenge. Interventions: Fluid challenge, 500 mL saline over 20 minutes. Measurements and Main Results: Before fluid challenge, esophageal pressure was measured at the end-inspiratory and end-expiratory occlusions. Change in pleural pressure was calculated as the difference between esophageal pressure measured at end-inspiratory and end-expiratory occlusions. Hemodynamic measurements were obtained before and after the fluid challenge. Patients were ventilated with tidal volume 7.0 ± 0.8 mL/kg predicted body weight. The fluids increased cardiac output by greater than 15% in 52 patients (responders). Adjusting pulse pressure variation for changes in pleural pressure (area under the receiver operating characteristic curve, 0.94 [0.88–0.98]) and the ratio of chest wall elastance to total respiratory system elastance (area under the receiver operating characteristic curve, 0.93 [0.88–0.98]) predicted fluid responsiveness better than pulse pressure variation (area under the receiver operating characteristic curve, 0.78 [0.69–0.86]; all p < 0.01). The gray zone approach identified a range of pulse pressure variation/changes in pleural pressure values (1.94–2.1) in 3.1% of patients for whom fluid responsiveness could not be predicted reliably. On logistic regression analysis, two independent factors affected the correct classification of fluid responsiveness at a 12% pulse pressure variation cutoff: tidal volume (adjusted odds ratio 1.57/50 mL; 95% CI, 1.05–2.34; p = 0.027) and chest wall elastance/respiratory system elastance (adjusted odds ratio, 2.035/0.1 unit; 95% CI, 1.36–3.06; p = 0.001). In patients with chest wall elastance/respiratory system elastance above the median (0.28), pulse pressure variation area under the receiver operating characteristic curve was 0.94 (95% CI, 0.84–0.99) compared with 0.76 (95% CI, 0.61–0.87) otherwise (p = 0.02). Conclusions: In acute respiratory distress syndrome patients, pulse pressure variation adjusted by changes in pleural pressure is a reliable fluid responsiveness predictor despite the low tidal volume (< 8 mL/kg). The poor predictive ability of pulse pressure variation in acute respiratory distress syndrome patients is more related to low chest wall elastance/respiratory system elastance ratios than to a low tidal volume.

Objectives: Donation after circulatory death has been responsible for 75% of the increase in the numbers of deceased organ donors in the United Kingdom. There has been concern that the success of the donation after circulatory death program has been at the expense of donation after brain death. The objective of the study was to ascertain the impact of the donation after circulatory death program on donation after brain death in the United Kingdom. Design: Retrospective cohort study. Setting: A national organ procurement organization. Patients: Patients referred and assessed as donation after circulatory death donors in the United Kingdom between October and December 2013. Interventions: None. Measurements and Main Results: A total of 257 patients were assessed for donation after circulatory death. Of these, 193 were eligible donors. Three patients were deemed medically unsuitable following surgical inspection, 56 patients did not proceed due to asystole, and 134 proceeded to donation. Four donors had insufficient data available for analysis. Therefore, 186 cases were analyzed in total. Organ donation would not have been possible in 79 of the 130 actual donors if donation after circulatory death was not available. Thirty-six donation after circulatory death donors (28% of actual donors) were judged to have the potential to progress to brain death if withdrawal of life-sustaining treatment had been delayed by up to a further 36 hours. A further 15 donation after circulatory death donors had brain death confirmed or had clinical indications of brain death with clear mitigating circumstances in all but three cases. We determined that the maximum potential donation after brain death to donation after circulatory death substitution rate observed was 8%; however due to mitigating circumstances, only three patients (2%) could have undergone brain death testing. Conclusions: The development of a national donation after circulatory death program has had minimal impact on the number of donation after brain death donors. The number of donation after brain death donors could increase with changes in end-of-life care practices to allow the evolution of brain death and increasing the availability of ancillary testing.

Objective: To determine if intravenous thiamine would reduce lactate in patients with septic shock. Design: Randomized, double-blind, placebo-controlled trial. Setting: Two US hospitals. Patients: Adult patients with septic shock and elevated (> 3 mmol/L) lactate between 2010 and 2014. Interventions: Thiamine 200 mg or matching placebo twice daily for 7 days or until hospital discharge. Measurements and Main Results: The primary outcome was lactate levels 24 hours after the first study dose. Of 715 patients meeting the inclusion criteria, 88 patients were enrolled and received study drug. There was no difference in the primary outcome of lactate levels at 24 hours after study start between the thiamine and placebo groups (median: 2.5 mmol/L [1.5, 3.4] vs. 2.6 mmol/L [1.6, 5.1], p = 0.40). There was no difference in secondary outcomes including time to shock reversal, severity of illness and mortality. 35% of the patients were thiamine deficient at baseline. In this predefined subgroup, those in the thiamine treatment group had statistically significantly lower lactate levels at 24 hours (median 2.1 mmol/L [1.4, 2.5] vs. 3.1 [1.9, 8.3], p = 0.03). There was a statistically significant decrease in mortality over time in those receiving thiamine in this subgroup (p = 0.047). Conclusion: Administration of thiamine did not improve lactate levels or other outcomes in the overall group of patients with septic shock and elevated lactate. In those with baseline thiamine deficiency, patients in the thiamine group had significantly lower lactate levels at 24 hours and a possible decrease in mortality over time.

Objective: Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database. Design: Observational cohort study. Setting: Five hospitals, from November 2008 until January 2013. Patients: Hospitalized ward patients Interventions: None Measurements And Main Results: Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score, a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC, 0.80 [95% CI, 0.80-0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC, 0.77 vs 0.74; p < 0.01), and all models were more accurate than the MEWS (AUC, 0.70 [95% CI, 0.70-0.70]). Conclusions: In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards.

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