SUMMARY Background The ability to predict the development of venous thromboembolism is highly desirable. Objective We aim to determine the association between hyperglycemia and venous thromboembolism in non-diabetic critically ill children. Patients/Methods We conducted a retrospective cohort study that included children in the pediatric intensive care unit on vasopressor or on mechanical ventilator and without history of diabetes mellitus or prior diagnosis of thrombosis. Based on maximum blood glucose >150 mg/dl while admitted to the unit, children were categorized as hyperglycemic or non-hyperglycemic. Primary outcome was development of venous thromboembolism while admitted to the unit. We determined the association between hyperglycemia and venous thromboembolism using logistic regression models adjusting for selected subject characteristics. Results Of the 789 subjects analyzed, 34 subjects developed venous thromboembolism (incidence: 4.3%; 95% confidence interval: 3.0%–6.0%). Venous thromboembolism was more likely to develop in hyperglycemic subjects compared with non-hyperglycemic subjects. A total of 31 subjects (6.2%; 95% confidence interval: 4.2%–8.7%) developed venous thromboembolism after becoming hyperglycemic compared with 3 non-hyperglycemic subjects with venous thromboembolism (1.0%, 95% confidence interval: 0.2%–3.0%). When adjusted for age, diagnosis, presence of central venous catheter, prophylactic antithrombotic use and severity of illness, the odds ratio of venous thromboembolism with hyperglycemia was 4.1 (95% confidence interval: 1.2–14.1). For every 10 mg/dl increase in maximum blood glucose, adjusted odds ratio of venous thromboembolism was 1.04 (95% confidence interval: 1.01–1.06). Conclusion Hyperglycemia is associated with venous thromboembolism in critically ill non-diabetic children. Maximum blood glucose is a potential predictor of venous thromboembolism in this population.

Image population analysis is the class of statistical methods that plays a central role in understanding the development, evolution, and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP, we introduce specially designed streaming algorithms and data structures that provide an optimal solution for out-of-core multiimage processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets.