OBJECTIVETight glycemic control (TGC) in critically ill patients is associated with an increased risk of hypoglycemia. Whether those short episodes of hypoglycemia are associated with adverse morbidity and mortality is a matter of discussion. Using a case-control study design, we investigated whether hypoglycemia under TGC causes permanent neurocognitive dysfunction in patients surviving critical illness.RESEARCH DESIGN AND METHODSFrom our patient data management system, we identified adult survivors treated for >72 h in our surgical intensive care unit (ICU) between 2004 and 2007 (n = 4,635) without a history of neurocognitive dysfunction or structural brain abnormalities who experienced at least one episode of hypoglycemia during treatment (hypo group) (n = 37). For each hypo group patient, one patient stringently matched for demographic- and disease-related data were identified as a control subject. We performed a battery of neuropsychological tests investigating five areas of cognitive functioning in both groups at least 1 year after ICU discharge. Test results were compared with data from healthy control subjects and between groups.RESULTSCritical illness caused neurocognitive dysfunction in all tested domains in both groups. The dysfunction was aggravated in hypo group patients in one domain, namely that of visuospatial skills (P < 0.01). Besides hypoglycemia, both hyperglycemia (r = −0.322; P = 0.005) and fluctuations of blood glucose (r = −0.309; P = 0.008) were associated with worse test results in this domain.CONCLUSIONSHypoglycemia was found to aggravate critical illness–induced neurocognitive dysfunction to a limited, but significant, extent; however, an impact of hyperglycemia and fluctuations of blood glucose on neurocognitive function cannot be excluded.

BackgroundAlthough mortality after cardiac surgery has significantly decreased in the last decade, patients still experience clinically relevant postoperative complications. Among others, atrial fibrillation (AF) is a common consequence of cardiac surgery, which is associated with prolonged hospitalization and increased mortality.MethodsWe retrospectively analyzed data from patients who underwent coronary artery bypass grafting, valve surgery or a combination of both at the University Hospital Muenster between April 2014 and July 2015. We evaluated the incidence of new onset and intermittent/permanent AF (patients with pre- and postoperative AF). Furthermore, we investigated the impact of postoperative AF on clinical outcomes and evaluated potential risk factors.ResultsIn total, 999 patients were included in the analysis. New onset AF occurred in 24.9% of the patients and the incidence of intermittent/permanent AF was 59.5%. Both types of postoperative AF were associated with prolonged ICU length of stay (median increase approx. 2 days) and duration of mechanical ventilation (median increase 1 h). Additionally, new onset AF patients had a higher rate of dialysis and hospital mortality and more positive fluid balance on the day of surgery and postoperative days 1 and 2. In a multiple logistic regression model, advanced age (odds ratio (OR) = 1.448 per decade increase, p < 0.0001), a combination of CABG and valve surgery (OR = 1.711, p = 0.047), higher C-reactive protein (OR = 1.06 per unit increase, p < 0.0001) and creatinine plasma concentration (OR = 1.287 per unit increase, p = 0.032) significantly predicted new onset AF. Higher Horowitz index values were associated with a reduced risk (OR = 0.996 per unit increase, p = 0.012). In a separate model, higher plasma creatinine concentration (OR = 2.125 per unit increase, p = 0.022) was a significant risk factor for intermittent/permanent AF whereas higher plasma phosphate concentration (OR = 0.522 per unit increase, p = 0.003) indicated reduced occurrence of this arrhythmia.ConclusionsNew onset and intermittent/permanent AF are associated with adverse clinical outcomes of elective cardiac surgery patients. Different risk factors implicated in postoperative AF suggest different mechanisms might be involved in its pathogenesis. Customized clinical management protocols seem to be warranted for a higher success rate of prevention and treatment of postoperative AF.Electronic supplementary materialThe online version of this article (10.1186/s12871-017-0455-7) contains supplementary material, which is available to authorized users.

The present data suggest that continuous infusion of HA may preserve cumulative organ function (as measured by SOFA score) with emphasis on cardiovascular function in patients following OLT.

Abstract-This paper describes a low-cost mobile communication platform as a universal rapid-prototyping system, which is based on the Quadrocopter concept. At the Integrated Hardware and Software Systems Group at the Ilmenau University of Technology these mobile platforms are used to motivate bachelor and master students to study Computer Engineering sciences. This could be done by increasing their interest in technical issues, using this platform as integral part of a new ad-hoc lab to demonstrate different aspects in the area of Mobile Communication as well as universal rapid prototyping nodes to investigate different mechanisms for self-organized mobile communication systems within the International Graduate School on Mobile Communications. Beside the three fields of application, the paper describes the current architecture concept of the mobile prototyping platform as well as the chosen control mechanism and the assigned sensor systems to fulfill all the required tasks.

Intensive care unit readmissions are associated with mortality and bad outcomes. Machine learning could help to identify patients at risk to improve discharge decisions. However, many models are black boxes, so that dangerous properties might remain unnoticed. In this study, an inherently interpretable model for 3-day ICU readmission prediction was developed. We used a retrospective cohort of 15,589 ICU stays and 169 variables collected between 2006 and 2019. A team of doctors inspected the model, checked the plausibility of each component, and removed problematic parts. Qualitative feedback revealed several challenges for interpretable machine learning in healthcare. The resulting model used 67 features and showed an area under the precision-recall curve of 0.119±0.020 and an area under the receiver operating characteristic curve of 0.680±0.025. This is on par with state-of-the-art gradient boosting machines and outperforms the Simplified Acute Physiology Score II. External validation with the Medical Information Mart for Intensive Care database version IV confirmed our findings. Hence, a machine learning model for readmission prediction with a high level of human control is feasible without sacrificing performance.