Fabian Guiza Grandas

Glucose Dysregulation and Neurological Injury Biomarkers in Critically Ill Children

CONTEXT Targeting normoglycemia with intensive insulin therapy (IIT) improved short-term outcome of pediatric intensive care unit (PICU) patients but increased the incidence of hypoglycemia. Both hyperglycemia and hypoglycemia may adversely affect the developing brain. OBJECTIVE We studied the impact of targeting normoglycemia with IIT on brain injury markers. DESIGN This is a preplanned analysis of PICU patients included in a randomized controlled study. SETTING The study was conducted at a university hospital PICU. PATIENTS Seven hundred PICU patients participated. INTERVENTIONS Patients were assigned to IIT targeting normal-for-age fasting blood glucose levels or insulin infusion only to prevent excessive hyperglycemia. MAIN OUTCOME MEASURES Serum S100B and neuron-specific enolase (NSE), biomarkers of astrocytic and neuronal damage, respectively, were measured on fixed days (n = 700) and in a nested case-control design before and after hypoglycemia (n = 126). RESULTS Admission levels of S100B and NSE differed according to diagnosis and illness severity (P < 0.0001). IIT did not affect the time course of these markers. Patients experiencing hypoglycemia in PICU had higher S100B and NSE from admission onward than those without hypoglycemia. In the nested case-control study, both markers decreased after hypoglycemia (P = 0.001 and P = 0.009), unlike in the controls on matched days. CONCLUSIONS IIT in PICU did not evoke neurological damage detectable by circulating S100B and NSE, despite increased incidence of hypoglycemia. Elevated markers in patients with hypoglycemia were not caused by hypoglycemia itself but rather reflect an increased incidence of hypoglycemia in the most severely ill. This hypoglycemia risk appears difficult to capture by classical illness severity scores.

Traumatic Brain Injury in the Elderly: A Significant Phenomenon

INTRODUCTION Traumatic brain injury (TBI) in the elderly is becoming an increasingly frequent phenomenon. Studies have mainly analyzed the influence of age as a continuous variable and have not specifically looked at geriatric patients as a group. The aim of this study is to map the magnitude and characteristics of geriatric TBI and to identify factors contributing to their poorer outcome. MATERIAL AND METHODS Based on the ICD-9 register of the University Hospitals Leuven demographic and clinical variables of TBI were analyzed (2002-2008). The influence of older age on physiological variables was assessed using the Brain-IT database. RESULTS The elderly (aged ≥65 years) accounted for 38.2% of non-concussion TBI and 32.6% of ICU admissions, representing the largest age group. The elderly had a significantly lower ICP (median 10.06 mmHg versus median 14.52 mmHg; p = 0.048), but no difference in their measure of autoregulation (daily mABP/ICP correlation coefficient) compared with 20-35 year-olds. TBI was caused by a fall in 78.9% of elderly patients and 42.3% suffered a mass lesion. 72.1% had cardiovascular comorbidity. Complications did not differ from their younger counterparts. DISCUSSION Geriatric TBI is a significant phenomenon. Poorer outcomes are not yet sufficiently explained by physiological monitoring data, but reduced vascular versatility is likely to contribute. More research is needed in order to develop specific management protocols.

CHARISMA: An integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting

Histological image analysis plays a key role in understanding the effects of disease and treatment responses at the cellular level. However, evaluating histology images by hand is time-consuming and subjective. While semi-automatic and automatic approaches for image segmentation give acceptable results in some branches of histological image analysis, until now this has not been the case when applied to skeletal muscle histology images. We introduce Charisma, a new top-down cell segmentation framework for histology images which combines image processing techniques, a supervised trained classifier and a novel robust clump splitting algorithm. We evaluate our framework on real-world data from intensive care unit patients. Considering both segmentation and cell property distributions, the results obtained by our method correspond well to the ground truth, outperforming other examined methods.

Informatics in neurocritical care: new ideas for Big Data.

Purpose of reviewBig data is the new hype in business and healthcare. Data storage and processing has become cheap, fast, and easy. Business analysts and scientists are trying to design methods to mine these data for hidden knowledge. Neurocritical care is a field that typically produces large amounts of patient-related data, and these data are increasingly being digitized and stored. This review will try to look beyond the hype, and focus on possible applications in neurointensive care amenable to Big Data research that can potentially improve patient care. Recent findingsThe first challenge in Big Data research will be the development of large, multicenter, and high-quality databases. These databases could be used to further investigate recent findings from mathematical models, developed in smaller datasets. Randomized clinical trials and Big Data research are complementary. Big Data research might be used to identify subgroups of patients that could benefit most from a certain intervention, or can be an alternative in areas where randomized clinical trials are not possible. SummaryThe processing and the analysis of the large amount of patient-related information stored in clinical databases is beyond normal human cognitive ability. Big Data research applications have the potential to discover new medical knowledge, and improve care in the neurointensive care unit.