A Time-Motion Study of Registered Nurses’ Workflow in Intensive Care Unit Remote Monitoring

Utilizing advanced information technology, Intensive Care Unit (ICU) remote monitoring allows highly trained specialists to oversee a large number of patients at multiple sites on a continuous basis. In the current research, we conducted a time-motion study of registered nurses’ work in an ICU remote monitoring facility. Data were collected on seven nurses through 40 hours of observation. The results showed that nurses’ essential tasks were centered on three themes: monitoring patients, maintaining patients’ health records, and managing technology use. In monitoring patients, nurses spent 52% of the time assimilating information embedded in a clinical information system and 15% on monitoring live vitals. System-generated alerts frequently interrupted nurses in their task performance and redirected them to manage suddenly appearing events. These findings provide insight into nurses’ workflow in a new, technology-driven critical care setting and have important implications for system design, work engineering, and personnel selection and training.

A study of factors regulating the autonomous proliferation of the steroid hormone-induced LJ6195 murine reproductive tract tumor

Neonatal estrogen treatment of BALB/c mice results in the unregulated proliferation of the cervicovaginal epithelium and eventually tumorigenesis. The conversion of the normally estrogen responsive cyclic proliferation of the vaginal epithelium to a continuous estrogen-independent pattern of growth is a complex phenomenon. The aim of this study was to gain an understanding of the mechanism(s) by which steroid hormone administration during a critical period of development alters the cyclic proliferation of vaginal epithelium, ultimately leading to carcinogenesis in the adult animal.^ The LJ6195 murine cervicovaginal tumor was induced by treating newborn female BALB/c mice with 20 $\mu$g 17$\beta$-estradiol plus 100 $\mu$g progesterone for the first 5 days after birth. In contrast to proliferation of the normal vaginal epithelium, proliferation of LJ6195 is not regulated by estradiol. Northern blot analysis of RNA from vaginal tracts of normal mice, neonatal-estrogen treated mice, and LJ6195 indicate that there is an alteration in the expression of several genes such as the estrogen receptor, c-fos, and HER2/neu. In response to neonatal estrogen treatment, the estrogen receptor is down regulated in the murine vaginal tract. Therefore, the estrogen-independent nature of this tissue is established as early as 3 months after treatment. There is strong evidence that the proliferation of LJ6195 is regulated through an autocrine growth pathway. The LJ6195 tumor expresses mRNA for the epidermal growth factor receptor. In addition, conditioned medium from the LJ6195 tumor cell line contains a growth factor(s) with epidermal growth factor-like activity. Conditioned medium from the LJ6195 cell line stimulated the proliferation of the EGF-dependent COMMA D mouse mammary gland cell line in a dose-dependent manner. The addition of an anti-mEGF-antibody to LJ6195 cell cultures significantly decreased growth. These results suggest that the EGF-receptor mediated growth pathway may play a role in regulating the estrogen-independent proliferation of the LJ6195 tumor. ^

Effect of continuous gastric enteral feeding on tonometric measurement of gastric intramucosal carbon dioxide levels in healthy volunteers

Purpose. The aim of this research was to evaluate the effect of enteral feeding on tonometric measurement of gastric regional carbon dioxide levels (PrCO2) in normal healthy volunteers. Design and methods. The sample included 12 healthy volunteers recruited by the University Clinical Research Center (UCRC). An air tonometry system monitored PrCO2 levels using a tonometer placed in the lumen of the stomach via orogastric intubation. PrCO2 was automatically measured and recorded every 10 minutes throughout the five hour study period. An oral dose of famotidine 40 mg was self-administered the evening prior to and the morning of the study. Instillation of Isocal® High Nitrogen (HN) was used for enteral feeding in hourly escalating doses of 0, 40, 60, and 80 ml/hr with no feeding during the fifth hour. Results . PrCO2 measurements at time 0 and 10 minutes (41.4 ± 6.5 and 41.8 ± 5.7, respectively) demonstrated biologic precision (Levene's Test statistic = 0.085, p-value 0.774). Biologic precision was lost between T130 and T140 40 when compared to baseline TO (Levene's Test statistic = 1.70, p-value 0.205; and 3.205, p-value 0.042, respectively) and returned to non-significant levels between T270 and T280 (Levene's Test statistic = 3.083, p-value 0.043; and 2.307, p-value 0.143, respectively). Isocal® HN significantly affected the biologic accuracy of PrCO2 measurements (repeated measures ANOVA F 4.91, p-value <0.001). After 20 minutes of enteral feeding at 40 ml/hr, PrCO2 significantly increased (41.4 ± 6.5 to 46.6 ± 4.25, F = 5.4, p-value 0.029). Maximum variance from baseline (41.4 ± 6.5 to 61.3 ± 15.2, F = 17.22, p-value <0.001) was noted after 30 minutes of Isocal® HN at 80 ml/hr or 210 minutes from baseline. The significant elevations in PrCO2 continued throughout the study. Sixty minutes after discontinuation of enteral feeding, PrCO2 remained significantly elevated from baseline (41.4 ± 6.5 to 51.8 ± 9.2, F = 10.15, p-value 0.004). Conclusion. Enteral feeding with Isocal® HN significantly affects the precision and accuracy of PrCO2 measurements in healthy volunteers. ^

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

A cross-sectional study of human symptomatology and residential formaldehyde concentrations

The cross-sectional study was performed to quantify the prevalence of symtomatology in residents of mobile homes as a function of indoor formaldehyde concentration. Formaldehyde concentrations were monitored for a seven hour period with an automated wet-chemical colorimetric analyzer. The health status of family members was ascertained by administration of questionnaires and physical exams. This is the first investigation to perform clinical assessments on residents undergoing concurrent exposure assessment in the home.^ Only 22.8% of households eligible for participation chose to cooperate. Monitoring data and health evaluations were obtained from 155 households in four Texas counties. A total of 428 residents (86.1%) were available for examination during the sampling hours. The study population included 45 infants, 126 children, and 257 adults.^ Formaldehyde concentration was not found to be significantly associated with increased risks for symptoms and signs of ocular irritation, dermal anomalies, or malaise. Three associations were identified that warrant further investigation. The relative odds associated with a doubling of formaldehyde concentration was significantly associated with parenchymal rales in adults and children. However, risk was modified by log respirable suspended particulate concentrations. Due to the presence of modification by a continuous variable, prevalence odds ratios (POR) and 95% confidence intervals (95% CI) for these associations are presented in tables. A doubling of formaldehyde concentration was also associated with an increased risk of perceived tightness in the chest in adults. Prevalence odds ratios are presented in a table due to effect modification by the average number of hours spent indoors on weekdays. Furthermore, a doubling of formaldehyde concentration was associated with an increased risk of drowsiness in children (POR = 2.60; 95% CI 1.04-6.51) and adults (POR = 1.94; 95% CI 1.20-3.14). ^

Pilot study assessment of indoor air quality in two dental settings: Particulate matter and volatile organic compounds

Objective: To assess the indoor environment of two different types of dental practices regarding VOCs, PM2.5, and ultrafine particulate concentrations and examine the relationship between specific dental activities and contaminant levels. Method: The indoor environments of two selected dental settings (private practice and community health center) will were assessed in regards to VOCs, PM 2.5, and ultrafine particulate concentrations, as well as other indoor air quality parameters (CO2, CO, temperature, and relative humidity). The sampling duration was four working days for each dental practice. Continuous monitoring and integrated sampling methods were used and number of occupants, frequency, type, and duration of dental procedures or activities recorded. Measurements were compared to indoor air quality standards and guidelines. Results: The private practice had higher CO2, CO, and most VOC concentrations than the community health center, but the community health center had higher PM2.5 and ultrafine PM concentrations. Concentrations of p-dichlorobenzene and PM2.5 exceeded some guidelines. Outdoor concentrations greatly influenced the indoor concentration. There were no significant differences in contaminant levels between the operatory and general area. Indoor concentrations during the working period were not always consistently higher than during the nonworking period. Peaks in particulate matter concentration occurred during root canal and composite procedures.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

Development of combined micro -preparation, separation, and mass spectrometry methods and application in the microdialysis study of neuropeptide metabolism

Two sets of mass spectrometry-based methods were developed specifically for the in vivo study of extracellular neuropeptide biochemistry. First, an integrated micro-concentration/desalting/matrix-addition device was constructed for matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) to achieve attomole sensitivity for microdialysis samples. Second, capillary electrophoresis (CE) was incorporated into the above micro-liquid chromatography (LC) and MALDI MS system to provide two-dimensional separation and identification (i.e. electrophoretic mobility and molecular mass) for the analysis of complex mixtures. The latter technique includes two parts of instrumentation: (1) the coupling of a preconcentration LC column to the inlet of a CE capillary, and (2) the utilization of a matrix-precoated membrane target for continuous CE effluent deposition and for automatic MALDI MS analysis (imaging) of the CE track.^ Initial in vivo data reveals a carboxypeptidase A (CPA) activity in rat brain involved in extracellular neurotensin metabolism. Benzylsuccinic acid, a CPA inhibitor, inhibited neurotensin metabolite NT1-12 formation by 70%, while inhibitors of other major extracellular peptide metabolizing enzymes increased NT1-12 formation. CPA activity has not been observed in previous in vitro experiments. Next, the validity of the methodology was demonstrated in the detection and structural elucidation of an endogenous neuropeptide, (L)VV-hemorphin-7, in rat brain upon ATP stimulation. Finally, the combined micro-LC/CE/MALDI MS was used in the in vivo metabolic study of peptide E, a mu-selective opioid peptide with 25 amino acid residues. Profiles of 88 metabolites were obtained, their identity being determined by their mass-to-charge ratio and electrophoretic mobility. The results indicate that there are several primary cleavage sites in vivo for peptide E in the release of its enkephalin-containing fragments. ^