The influence of contact pressure, moisture, and compression time on the performance of cotton anti-contamination clothing

The objectives of this research were (1) to study the effect of contact pressure, compression time, and liquid (moisture content of the fabric) on the transfer by sliding contact of non-fixed surface contamination to protective clothing constructed from uncoated, woven fabrics, (2) to study the effect of contact pressure, compression time, and liquid content on the subsequent penetration through the fabric, and (3) to determine if varying the type of contaminant changes the effect of contact pressure, compression time, and liquid content on the transfer by sliding contact and penetration of non-fixed surface contamination. ^ It was found that the combined influence of the liquid (moisture content of the fabric), load (contact pressure), compression time, and their interactions significantly influenced the penetration of all three test agents, sucrose- 14C, triolein-3H, and starch-14C through 100% cotton fabric. The combined influence of the statistically significant main effects and their interactions increased the penetration of triolein- 3H by 32,548%, sucrose-14C by 7,006%, and starch- 14C by 1,900%. ^

Contribution of activity and skin temperature to the risk of developing pressure ulcers in nursing facility residents

Purpose. The central concepts in pressure ulcer risk are exposure to external pressure caused by inactivity and tissue tolerance to pressure, a factor closely related to blood flow. Inactivity measures are effective in predicting pressure ulcer risk. The purpose of the study is to evaluate whether a physiological measure of skin blood flow improves pressure ulcer risk prediction. Skin temperature regularity and self-similarity, as proxy measures of blood flow, and not previously described, may be undefined pressure ulcer risk factors. The specific aims were to determine whether a sample of nursing facility residents at high risk of pressure ulcers classified using the Braden Scale for Pressure Sore Risk© differ from a sample of low risk residents according to (1) exposure to external pressure as measured by resident activity, (2) tissue tolerance to external pressure as measured by skin temperature, and (3) skin temperature fluctuations and recovery in response to a commonly occurring stressor, bathing and additionally whether (4) scores on the Braden Scale mobility subscale score are related to entropy and the spectral exponent. ^ Methods. A two group observational time series design was used to describe activity and skin temperature regularity and self-similarity, calculating entropy and the spectral exponent using detrended fluctuation analysis respectively. Twenty nursing facility residents wore activity and skin temperature monitors for one week. One bathing episode was observed as a commonly occurring stressor for skin temperature.^ Results. Skin temperature multiscale entropy (MSE), F(1, 17) = 5.55, p = .031, the skin temperature spectral exponent, F(1, 17) = 6.19, p = .023, and the activity mean MSE, F(1, 18) = 4.52, p = .048 differentiated the risk groups. The change in skin temperature entropy during bathing was significant, t(16) = 2.55, p = .021, (95% CI, .04-.40). Multiscale entropy for skin temperature was lowest in those who developed pressure ulcers, F(1, 18) = 35.14, p < .001.^ Conclusions. This study supports the tissue tolerance component of the Braden and Bergstrom conceptual framework and shows differences in skin temperature multiscale entropy between pressure ulcer risk categories, pressure ulcer outcome, and during a commonly occurring stressor. ^

Intensive care noise and mean arterial blood pressure in ELBW neonates

Objective. Loud noises in neonatal intensive care units (NICUs) may impede growth and development for extremely low birthweight (ELBW, < 1000 grams) newborns. The objective of this study was to measure the association between NICU sound levels and ELBW neonates' arterial blood pressure to determine whether these newborns experience noise-induced stress. ^ Methods. Noise and arterial blood pressure recordings were collected for 9 ELBW neonates during the first week of life. Sound levels were measured inside the incubator, and each subject's arterial blood pressures were simultaneously recorded for 15 minutes (at 1 sec intervals). Time series cross-correlation functions were calculated for NICU noise and mean arterial blood pressure (MABP) recordings for each subject. The grand mean noise-MABP cross-correlation was calculated for all subjects and for lower and higher birthweight groups for comparison. ^ Results. The grand mean noise-MABP cross-correlation for all subjects was mostly negative (through 300 sec lag time) and nearly reached significance at the 95% level at 111 sec lag (mean r = -0.062). Lower birthweight newborns (454-709 g) experienced significant decreases in blood pressure with increasing NICU noise after 145 sec lag (peak r = -0.074). Higher birthweight newborns had an immediate negative correlation with NICU sound levels (at 3 sec lag, r = -0.071), but arterial blood pressures increased to a positive correlation with noise levels at 197 sec lag (r = 0.075). ^ Conclusions. ELBW newborns' arterial blood pressure was influenced by NICU noise levels during the first week of life. Lower birthweight newborns may have experienced an orienting reflex to NICU sounds. Higher birthweight newborns experienced an immediate orienting reflex to increasing sound levels, but arterial blood pressure increased approximately 3 minutes after increases in noise levels. Increases in arterial blood pressure following increased NICU sound levels may result from a stress response to noise. ^

THE RELATIONSHIP BETWEEN DEGREE OF BLOOD PRESSURE REDUCTION AND MORTALITY AMONG HYPERTENSIVES IN THE HYPERTENSION DETECTION AND FOLLOW-UP PROGRAM

The relationship between degree of diastolic blood pressure (DBP) reduction and mortality was examined among hypertensives, ages 30-69, in the Hypertension Detection and Follow-up Program (HDFP). The HDFP was a multi-center community-based trial, which followed 10,940 hypertensive participants for five years. One-year survival was required for inclusion in this investigation since the one-year annual visit was the first occasion where change in blood pressure could be measured on all participants. During the subsequent four years of follow-up on 10,052 participants, 568 deaths occurred. For levels of change in DBP and for categories of variables related to mortality, the crude mortality rate was calculated. Time-dependent life tables were also calculated so as to utilize available blood pressure data over time. In addition, the Cox life table regression model, extended to take into account both time-constant and time-dependent covariates, was used to examine the relationship change in blood pressure over time and mortality.^ The results of the time-dependent life table and time-dependent Cox life table regression analyses supported the existence of a quadratic function which modeled the relationship between DBP reduction and mortality, even after adjusting for other risk factors. The minimum mortality hazard ratio, based on a particular model, occurred at a DBP reduction of 22.6 mm Hg (standard error = 10.6) in the whole population and 8.5 mm Hg (standard error = 4.6) in the baseline DBP stratum 90-104. After this reduction, there was a small increase in the risk of death. There was not evidence of the quadratic function after fitting the same model using systolic blood pressure. Methodologic issues involved in studying a particular degree of blood pressure reduction were considered. The confidence interval around the change corresponding to the minimum hazard ratio was wide and the obtained blood pressure level should not be interpreted as a goal for treatment. Blood pressure reduction was attributed, not only to pharmacologic therapy, but also to regression to the mean, and to other unknown factors unrelated to treatment. Therefore, the surprising results of this study do not provide direct implications for treatment, but strongly suggest replication in other populations. ^

BLOOD PRESSURE CHANGES IN RELATION TO DEMOGRAPHIC, ANTHROPOMETRIC, AND SEXUAL MATURATION VARIABLES IN U.S. HEALTH EXAMINATION SURVEY OF CHILDREN AND YOUTHS (UNITED STATES)

The determinants of change in blood pressure during childhood and adolescence were studied in a cohort of U.S. national probability sample of 2146 children examined on two occasions during the Health Examination Survey. Significant negative correlations between the initial level and the subsequent changes in blood pressure were observed. The multiple regression analyses showed that the major determinants of systolic blood pressure (SBP) change were change in weight, baseline SBP, and baseline upper arm girth. Race, time interval between examinations, baseline age, and height change were also significant determinants in SBP change. For the change in diastolic blood pressure (DBP), baseline DBP, baseline weight, and weight change were the major determinants. Baseline SBP, time interval and race were also significant determinants. Sexual maturation variables were also considered in the subgroup analysis for girls. Weight change was the most important predictor of the change in SBP for the group of girls who were still in the pre-menarchal or pre-breast maturation status at the time of the follow-up examination, and who had started to menstruate or to develop breast maturation at sometime between the two examinations. Baseline triceps skinfold thickness or initial SBP were more important variables than weight change for the group of girls who had already experienced menarche or breast maturation at the time of the initial survey. For the total group, pubic hair maturation was found to be a significant predictor of SBP change at the 5% significance level. The importance of weight change and baseline weight for the changes in blood pressure warrants further study. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.