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, ...

We have applied principles of statistical signal processing and non-linear dynamics to analyze heart rate time series from premature newborn infants in order to assist in the early diagnosis of sepsis, a common and potentially deadly bacterial infection of the bloodstream. We began with the observation of reduced variability and transient decelerations in heart rate interval time series for hours up to days prior to clinical signs of illness. We find that measurements of standard deviation, sample asymmetry and sample entropy are highly related to imminent clinical illness. We developed multivariable statistical predictive models, and an interface to display the real-time results to clinicians. Using this approach, we have observed numerous cases in which incipient neonatal sepsis was diagnosed and treated without any clinical illness at all. This review focuses on the mathematical and statistical time series approaches used to detect these abnormal heart rate characteristics and present predictive monitoring information to the clinician.

A computerized insulin titration protocol improves glucose control by (1) increasing the percentage of glucose values in range, (2) reducing hyperglycemia, and (3) reducing severe hypoglycemia.

Intensive insulin therapy has widely and rapidly been adopted as the standard of care for the treatment of hyperglycemia in the intensive care unit (ICU). Variability in blood glucose is increasingly recognized as an important factor in outcomes in the chronic diabetic in addition to hemoglobin A1C. We tested the hypothesis that measures of blood glucose variability would be associated with mortality in the surgical ICU. A retrospective analysis of a cohort of ventilated, critically ill surgical and trauma ICU patients placed on an automated insulin protocol was performed. Blood glucose (BG) variability was measured by comparing standard deviation, percentile values, successive changes in blood glucose, and by calculating the triangular index for various glucose-related indices. Eight hundred and fifty-eight patients had 46,474 blood glucose and insulin dose data points. One hundred and twenty-one patients died for an overall mortality rate of 14 per cent. Several measures of blood glucose variability (maximum successive change in BG and the triangular index) were different between the groups despite similar mean BG between survivors (117 mg/dL) and nonsurvivors (118 mg/dL). Increased blood glucose variability is associated with mortality in the surgical ICU. Further studies should focus on the demographic, clinical, and genetic factors responsible for this observation and identify strategies to minimize BG variability.