Objective: Acute kidney injury is a severe complication and one of the stronger risk factors for death in patients undergoing cardiac surgery. The relationship between postoperative brain oxygen saturation and kidney oxygen saturation with acute kidney injury in adults undergoing cardiac surgery has not been determined. We designed a single-center prospective study to determine if the continuous monitoring of postoperative brain oxygen saturation and kidney oxygen saturation could predict postoperative acute kidney injury.Methods: We conducted a prospective open cohort study from January to September 2017. The primary outcome was postoperative acute kidney injury using the Kidney Disease: Improving Global Outcomes criteria. Brain oxygen saturation and kidney oxygen saturation, the metrics of which were area measurements (%-min), were recorded during the surgery and the first 48 hours after the cardiac procedure. Receiver operating characteristic curve analysis was used to evaluate the predictive power of kidney oxygen saturation for acute kidney injury.Results: A total of 121 consecutive patients were enrolled. Thirty-five patients (28.9%) developed acute kidney injury. Brain oxygen saturation showed no statistical difference in both groups; however, kidney oxygen saturation was related to acute kidney injury (P ¼ .001). Receiver operating characteristic curve analysis showed that kidney oxygen saturation could predict the risk of acute kidney injury. Kidney oxygen saturation less than 65% (area under the curve-receiver operating characteristic, 0.679 AE 0.054, 95% confidence interval, 0.573-0.785, P ¼ .002) and 20% decrease from baseline (area under the curve-receiver operating characteristic, 0.639 AE 0.059, 95% confidence interval, 0.523-0.755, P ¼ .019) showed the better performance, respectively.Conclusions: Postoperative kidney oxygen saturation is related to the development of cardiac surgery-associated acute kidney injury. Continuous kidney saturation monitoring might be a promising, noninvasive tool for predicting acute kidney injury during the postoperative period for adult patients after cardiac surgery.

Purpose:The aims were to analyze the temporal evolution of neutrophil apoptosis, to determine the differences in neutrophil apoptosis among 28-day survivors and nonsurvivors, and to evaluate the use of neutrophil apoptosis as a predictor of mortality in patients with septic shock. Materials and Methods: Prospective multicenter observational study carried out between July 2006 and June 2009. The staining solution study included 80 patients with septic shock and 25 healthy volunteers. Neutrophil apoptosis was assessed by fluorescein isothiocyanate (FITC)-conjugated annexin V and aminoactinomycin D staining. Results: The percentage of neutrophil apoptosis was significantly decreased at 24 hours, 5 days, and 12 days after the diagnosis of septic shock (14.8% ± 13.4%, 13.4% ± 8.4%, and 15.4% ± 12.8%, respectively; P b .0001) compared with the control group (37.6% ± 12.8%). The difference in apoptosis between 28-day surviving and nonsurviving patients was nonsignificant (P N .05). The mortality rate at 28 days was 53.7%. Journal of Critical Care (2012) 27, 415.e1-415.e11 The crude hazard ratio for mortality in patients with septic shock did not differ according to the percentage of apoptosis (hazard ratio, 1.006; 95% confidence interval, 0.98-1.03; P = .60). Conclusions: During the first 12 days of septic shock development, the level of neutrophil apoptosis decreases and does not recover normal values. No differences were observed between surviving and nonsurviving patients.

a b s t r a c t a r t i c l e i n f oKeywords: Cardiac surgery Renal insufficiency Risk prediction Risk score Purpose: Acute kidney injury (AKI) is a frequent complication after cardiac surgery and is associated with increased mortality. The aim was to design a nondialytic AKI score in patients with previously normal renal function undergoing cardiac surgery. Methods: Data were collected on 909 patients who underwent cardiac surgery with cardiopulmonary bypass between 2012 and 2014. A total of 810 patients fulfilled the inclusion criteria. Patients were classified as having AKI based on the RIFLE criteria. Postoperative AKI occurred in 137 patients (16.9%). Several parameters were recorded preoperatively, intraoperatively, and at intensive care unit admission, looking for a univariate and multivariate association with AKI risk. A second data set of 741 patients, from 2 different hospitals, was recorded as a validation cohort. Results: Four independent risk factors were included in the CRATE score: creatinine (odds ratio [OR], 9.66; 95% confidence interval [CI], 4.77-19.56; P b .001), EuroSCORE (OR, 1.40; CI, 1.29-1.52; P b .001), lactate (OR, 1.03; CI, 1.01-1.04; P b .001), and cardiopulmonary bypass time (OR, 1.01; CI, 1.01-1.02; P b .001). The accuracy of the model was good, with an area under the curve of 0.89 (CI, 0.85-0.92). The CRATE score retained good discrimination in validation cohort, with an area under the curve of 0.81 (95% CI, 0.78-0.85). Conclusions: CRATE score is an accurate and easy to calculate risk score that uses affordable and widely available variables in the routine care surgical patients.

New evidence has appeared to support the fact that the over-involvement of older drivers in traffic accidents disappears when the low mileage bias is taken into account. As a group, older drivers are as safe as or safer than other age groups, and only low mileage older drivers have a high crash rate. Furthermore, the role of the medical condition of older drivers in traffic accidents, as well as the fitness to drive evaluation, are objects of controversy. We examined all this with a cohort of 4316 drivers attending Medical Driving Test Centres for a mandatory fitness to drive evaluation. Our data shows that older drivers (≥75) have a lower crash rate. Medical conditions that impair fitness to drive, as a tendency, increased with advanced age and with lower mileage group. The multivariate analysis of variance showed that there is an effect (p < 0.0001) of age-range and mileage on the annual crash rate per million kilometres driven, while a medical restriction ("fit to drive with restriction") has no effect (p > 0.05). Our data suggests that health status is not associated with increased crash risk for the low mileage group, although further studies are needed.

Purpose: Ventilator-associated pneumonia (VAP) is the main infectious complication in cardiac surgery patients and is associated with an important increase in morbidity and mortality. The aim of our study was to analyze the impact of VAP on mortality excluding other comorbidities and to study its etiology and the risk factors for its development. Materials and Methods: This prospective cohort study included 1610 postoperative cardiac surgery patients' status post cardiopulmonary bypass (CPB)

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IntroductionThe risk of mortality in cardiac surgery is generally evaluated using preoperative risk-scale models. However, intraoperative factors may change the risk factors of patients, and the organism functionality parameters determined upon ICU admittance could therefore be more relevant in deciding operative mortality. The goals of this study were to find associations between the general parameters of organism functionality upon ICU admission and the operative mortality following cardiac operations, to develop a Post Cardiac Surgery (POCAS) Scale to define operative risk categories and to validate an operative mortality risk score.MethodsWe conducted a prospective study, including 920 patients who had undergone cardiac surgery with cardiopulmonary bypass. Several parameters recorded on their ICU admission were explored, looking for a univariate and multivariate association with in-hospital mortality (90 days). In-hospital mortality was 9%. Four independent factors were included in the POCAS mortality risk model: mean arterial pressure, bicarbonate, lactate and the International Normalized Ratio (INR). The POCAS scale was compared with four other risk scores in the validation series.ResultsIn-hospital mortality (90 days) was 9%. Four independent factors were included in the POCAS mortality risk model: mean arterial pressure, bicarbonate ratio, lactate ratio and the INR. The POCAS scale was compared with four other risk scores in the validation series. Discriminatory power (accuracy) was defined with a receiver-operating characteristics (ROC) analysis. The best accuracy in predicting in-hospital mortality (90 days) was achieved by POCAS. The areas under the ROC curves of the different systems analyzed were 0.890 (POCAS), followed by 0.847 (Simplified Acute Physiology Score (SAP II)), 0.825 (Sepsis-related Organ Failure Assessment (SOFA)), 0.768 (Acute Physiology and Chronic Health Evaluation (APACHE II)), 0.754 (logistic EuroSCORE), 0.714 (standard EuroSCORE) and 0.699 (Age, Creatinine, Ejection Fraction (ACEF) score).ConclusionsOur new system to predict the operative mortality risk of patients undergoing cardiac surgery is better than others used for this purpose (SAP II, SOFA, APACHE II, logistic EuroSCORE, standard EuroSCORE, and ACEF score). Moreover, it is an easy-to-use tool since it only requires four risk factors for its calculation.

The role of illicit drugs on driving, and particularly of cannabis and driving, is the object of increasing awareness. While there is increasing evidence of their effect on psychomotor performance and increased risk of involvement in traffic accidents, limited information is available concerning factors that can predict the likelihood of driving under the influence of cannabis. The present study aims to determine the past year prevalence of driving under the influence of cannabis, and of being a passenger in a vehicle driven by a person under the influence of cannabis, as well as to examine the correlations with a broad range of potential risk factors. A total of 2500 people, aged between 14 and 70 and living in Castille and Leon (Spain), were surveyed in 2004 with regard to their consumption of alcohol and illicit drugs. Among those who reported cannabis use in the previous year, further assessment was carried out. 15.7% of those surveyed reported cannabis consumption in the previous 12 months, of whom 9.7% reported driving a vehicle under the influence of cannabis during this period, on average eight times. One out of five (19.9%) reported being a passenger in a vehicle driven by a person under the influence of cannabis, on average five times in the previous 12 months. The predictors of driving under the influence of cannabis were the population size of community, the number of drugs consumed, reference to cannabis-related problems and to being a passenger in a vehicle driven by a person under the influence of alcohol. The data show that cannabis consumption and driving is common, and requires more attention from policy makers. #