We introduce the sample asymmetry analysis (SAA) and illustrate its utility for assessment of heart rate characteristics occurring early in the course of neonatal sepsis and systemic inflammatory response syndrome (SIRS). Conceptually, SAA describes changes in the shape of the histogram of RR intervals that are caused by reduced accelerations and/or transient decelerations of heart rate. Unlike other measures of heart rate variability, SAA allows separate quantification of the contribution of accelerations and decelerations. The application of SAA is exemplified by a study comparing 50 infants, who experienced a total of 75 episodes of sepsis and SIRS, with 50 control infants. The two groups were matched by birth weight and gestational age. RR intervals were recorded for all infants throughout their course in the Neonatal Intensive Care Unit. The sample asymmetry of the RR intervals increased in the 3-4 d preceding sepsis and SIRS, with the steepest increase in the last 24 h, from a baseline value of 3.3 (SD ϭ 1.6) to 4.2 (SD ϭ 2.3), p ϭ 0.02. After treatment and recovery, sample asymmetry returned to its baseline value of 3.3 (SD ϭ 1.3). The difference between sample asymmetry in health and before sepsis and SIRS was mainly due to fewer accelerations than to decelerations. Compared with healthy infants, infants who experienced sepsis had similar sample asymmetry in health, and elevated values before sepsis and SIRS (p ϭ 0.002). We conclude that SAA is a useful new mathematical technique for detecting the abnormal heart rate characteristics that precede neonatal sepsis and SIRS. (Pediatr Res 54: 892-898, 2003) Abbreviations SAA, sample asymmetry analysis HRC, heart rate characteristics BW, birth weight GA, gestational age HR, heart rate SIRS, systemic inflammatory response syndrome Approximately 40,000 very low birth weight infants (Ͻ1500 g) are born in the United States each year (1). Survival for this group has improved with advances in neonatal intensive care, but late-onset sepsis continues to be a major cause of morbidity and mortality (2, 3). The clinical syndrome of sepsis and SIRS is brought about by the host response to insults such as bacterial infection, and has been named the SIRS (4, 5). Neonatal sepsis occurs in as many as 25% of infants weighing Ͻ1500 g at birth (2), and the rate is about 1 per 100 patient days (6, 7). The National Institute of Child Health and Human Development Neonatal Research Network found that neonates who develop late-onset sepsis have a 17% mortality rate, more than twice the 7% mortality rate of noninfected infants, as well as increased morbidity (2).Unfortunately, early diagnosis of neonatal sepsis is difficult, as the clinical signs are neither uniform nor specific (8). Potential for conflict of interest: Medical Decision Networks of Charlottesville, VA, which supplied partial funding for this study, has a license to market technology related to heart rate characteristics (HRC) monitoring of newborn infants. As of the submission date of the final version of the article, ...

OBJECTIVE -To compare the clinical accuracy of two different continuous glucose sensors (CGS) during euglycemia and hypoglycemia using continuous glucose-error grid analysis (CG-EGA).RESEARCH DESIGN AND METHODS -FreeStyle Navigator (Abbott Laboratories, Alameda, CA) and MiniMed CGMS (Medtronic, Northridge, CA) CGSs were applied to the abdomens of 16 type 1 diabetic subjects (age 42 Ϯ 3 years) 12 h before the initiation of the study. Each system was calibrated according to the manufacturer's recommendations. Each subject underwent a hyperinsulinemic-euglycemic clamp (blood glucose goal 110 mg/dl) for 70 -210 min followed by a 1-mg ⅐ dl Ϫ1 ⅐ min Ϫ1 controlled reduction in blood glucose toward a nadir of 40 mg/dl. Arterialized blood glucose was determined every 5 min using a Beckman Glucose Analyzer (Fullerton, CA). CGS glucose recordings were matched to the reference blood glucose with 30-s precision, and rates of glucose change were calculated for 5-min intervals. CG-EGA was used to quantify the clinical accuracy of both systems by estimating combined point and rate accuracy of each system in the euglycemic (70Ϫ180 mg/dl) and hypoglycemic (Ͻ70 mg/dl) ranges.RESULTS -A total of 1,104 data pairs were recorded in the euglycemic range and 250 data pairs in the hypoglycemic range. Overall correlation between CGS and reference glucose was similar for both systems (Navigator, r ϭ 0.84; CGMS, r ϭ 0.79, NS). During euglycemia, both CGS systems had similar clinical accuracy (Navigator zones A ϩ B, 88.8%; CGMS zones A ϩ B, 89.3%, NS). However, during hypoglycemia, the Navigator was significantly more clinically accurate than the CGMS (zones A ϩ B ϭ 82.4 vs. 61.6%, Navigator and CGMS, respectively, P Ͻ 0.0005).CONCLUSIONS -CG-EGA is a helpful tool for evaluating and comparing the clinical accuracy of CGS systems in different blood glucose ranges. CG-EGA provides accuracy details beyond other methods of evaluation, including correlational analysis and the original EGA.

Patients with Insulin-Dependent Diabetes are continuously involved in a clinical optimization process: to maintain strict glycemic control without increasing their risk for hypoglycemia. This study offers quantitative tools for on-line assessment of the quality of this optimization, based on self-monitoring of blood glucose (SMBG). Ninety-six adults with Insulin Dependent Diabetes Mellitus (IDDM), age 35 ± 8 yrs., duration of diabetes 16 ± 10 yrs., HbAlc8.6 ± 1.8%, 43 of whom had a recent history of severe hypoglycemia (SH), while 53 did not, used Lifescan One Touch II meters for 135 ± 53 SMBG readings over a month. For the following six months the subjects recorded occurrence of SH. The two patient groups, with and without a history of SH, did not differ in age, duration of diabetes, HbAlc, insulin units/day, average BG or BG variability. We suggest a computational procedure based on a symmetrization of the BG measurement scale and on a superimposed BG risk function, that allows for computation of two glycemic control markers: the Low BG Index (LBGI) and the High BG Index (HBGI). The LBGI is associated with SH: the LBGI and the rate of change of the BG risk, classified correctly 77% of the subjects with vs. without a history of SH and accounted for 46% of the variance of future SH. The HBGI, in combination with age, duration of diabetes and daily insulin dose, accounted for 57% of the variance of patients' glycosylated hemoglobin. We conclude that the LBGI and the HBGI are accurate on-line SMBG measures for patients' glycemic control.

Our data support the hypothesis that under normal conditions in subjects given regular meals endogenous acyl-ghrelin acts to increase the amplitude of GH pulses.

Hormone signaling is often pulsatile, and multi-parameter deconvolution procedures have long been utilized to identify and characterize secretory events. However, the existing programs have serious limitations, including the subjective nature of initial peak selection, lack of statistical verification of presumed bursts, and user-unfriendliness of the application. Here, we describe a novel deconvolution program, AutoDecon, which addresses these concerns. We validate AutoDecon for application to serum luteinizing hormone (LH) concentration time series using synthetic data mimicking real data from normal women and then comparing the performance of AutoDecon to the performance of the widely-employed hormone pulsatility analysis program Cluster. The sensitivity of AutoDecon is higher than Cluster: ~96% vs. ~80% (p = 0.001). However, Cluster had a lower false-positive detection rate than AutoDecon: 6% vs 1%, p = 0.001. Further analysis demonstrated that the pulsatility parameters recovered by AutoDecon were indistinguishable from those characterizing the synthetic data and sampling at 5-or 10-minute intervals was optimal for maximizing the sensitivity rates for LH. Accordingly, AutoDecon presents a viable non-subjective alternative to previous pulse detection algorithms for the analysis of LH data. It is applicable to other pulsatile hormone-concentration time series and many other pulsatile phenomena. The software is free and downloadable at