Increased Intra-Abdominal Pressure During Laparoscopic Pneumoperitoneum Enhances Albuminuria via Renal Venous Congestion, Illustrating Pathophysiological Aspects of High Output Preeclampsia.

Intra-abdominal hypertension (IAH) causes severe organ dysfunction. Our aim is to evaluate the effect of increased intra-abdominal pressure (IAP) on renal function, hypothesizing that venous congestion may increase proteinuria and fluid retention without endothelial dysfunction. Three urine samples were collected from 32 non-pregnant women undergoing laparoscopic-assisted vaginal hysterectomy (LAVH) and from 10 controls placed in Trendelenburg position for 60 min. Urine sampling was done before (PRE), during or immediately after (PER), and two hours after (POST) the procedure. Urinary albumin, protein and creatinine concentrations were measured in each sample, and ratios were calculated and compared within and between groups. During LAVH, the albumin/creatinine ratio (ACR) increased and persisted POST-procedure, which was not observed in controls. A positive correlation existed between the LAVH duration and the relative change in both ACR and protein/creatinine ratio (PCR) PER- and POST-procedure. Iatrogenic IAH increases urinary ACR and PCR in non-pregnant women via a process of venous congestion. This mechanism might explain the presentation of one specific subtype of late-onset preeclampsia, where no drop of maternal cardiac output is observed.

Statistical approach of assessing the reliability of glucose sensors: the GLYCENSIT procedure.

BACKGROUND: In healthcare, patients with diabetes are instructed on how to apply intensified insulin therapy in an optimal manner. Tight blood glucose control is also performed on patients treated in the intensive care unit (ICU). Different blood glucose meters and glucose monitoring systems (GMSs) are used to achieve this goal, and some may lack reliability. METHODS: The GLYCENSIT procedure is a statistical assessment tool we are proposing for evaluating the significant difference of paired glucose measurements. The performance of the GlucoDay system in the ICU is analyzed with GLYCENSIT. RESULTS: THE GLYCENSIT ANALYSIS COMPRISES THREE PHASES: testing possible persistent measurement behavior as a function of the glycemic range, testing the number of measurement errors with respect to a standard criterion for binary assessment of glucose sensors, and computing the tolerance intervals that indicate possible test sensor deviations for new observations. The probability of the tolerance intervals directly reflects the number of samples and additionally improves current assessment techniques. The method can be tuned according to the clinician's preferences regarding significance level, tolerance level, and glycemic range cutoff values. The measurement behavior of the GlucoDay sensor is found to be persistent but inaccurate and returns wide tolerance intervals, suggesting that the GlucoDay sensor may not be sufficiently reliable for glycemia control in the ICU. CONCLUSIONS: The GLYCENSIT procedure aims to serve as statistical guide for clinicians in the assessment of glucose sensor devices.

Blood glucose measurements in arterial blood of intensive care unit patients submitted to tight glycemic control: agreement between bedside tests.

BACKGROUND: Implementing tight glycemic control (TGC) in intensive care unit (ICU) patients requires accurate blood glucose (BG) monitoring. We evaluated the performance of two commercially available bedside glucometers, Accu-Chek and HemoCue, in patients admitted to the ICU and in whom TGC was applied. METHODS: Thirty-seven adult ICU patients were prospectively included. During 48 hours, BG was determined simultaneously on the same arterial blood sample using the two point-of-care testing (POCT) glucometers as compared with the standard technique. Data of 452 paired measurements were analyzed using linear regression, Clark error grid analysis (EGA), the method of Bland-Altman, and the GLYCENSIT procedure. RESULTS: Both tested glucometers showed satisfactory results when evaluated with linear regression and EGA. Correlation coefficients were above 0.9, and 100% of all the glucose readings were within the safe zones A and B using EGA. However, when applying more appropriate tests, both sensors failed to provide sufficient accuracy in the setting of TGC in ICU patients. The Hemocue revealed a bias of >10 mg/dl with a trend to systematically overestimate the actual BG value. The bias for the Accu-Chek was 6 mg/dl with wide limits of agreement and a variable over- and underestimation of the actual BG value depending on the level of BG (hypo-, normo-, or hyperglycemia). CONCLUSIONS: When TGC is implemented in ICU practice, caution is warranted when adjusting insulin rates based only on BG readings obtained by the tested glucometers. ICU practitioners should weigh the advantages and disadvantages of such devices: a greater bias but with a more predictable error and measurement behavior versus a somewhat lower bias but with an unpredictable direction of the difference.

Glycemia prediction in critically ill patients using an adaptive modeling approach.

BACKGROUND: Strict blood glucose control by applying nurse-driven protocols is common nowadays in intensive care units (ICUs). Implementation of a predictive control system can potentially reduce the workload for medical staff but requires a model for accurately predicting the glycemia signal within a certain time horizon. METHODS: GlucoDay (A. Menarini Diagnostics, Italy) data coming from 19 critically ill patients (from a surgical ICU) are used to estimate the initial ICU "minimal" model (based on data of the first 24 hours) and to reestimate the model as new measurements are obtained. The reestimation is performed every hour or every 4 hours. For both approaches the optimal size of the data set for each reestimation is determined. RESULTS: The prediction error that is obtained when applying the 1-hour reestimation strategy is significantly smaller than when the model is reestimated only every 4 hours (p < 0.001). The optimal size of the data set to be considered in each reestimation process of the ICU minimal model is found to be 4 hours. The obtained average "mean percentage error" is 7.6% (SD 3.1%) and 14.6% (SD 7.0%) when the model is reestimated every hour and 4 hours, respectively. CONCLUSIONS: Implementation of the ICU minimal model in the appropriate reestimation process results in clinically acceptable prediction errors. Therefore, the model is able to predict glycemia trends of patients admitted to the surgical ICU and can potentially be used in a predictive control system.