UNDERSTANDING INTERRUPTIONS IN HEALTHCARE: DEVELOPING A MODEL

Developing a Model Interruption is a known human factor that contributes to errors and catastrophic events in healthcare as well as other high-risk industries. The landmark Institute of Medicine (IOM) report, To Err is Human, brought attention to the significance of preventable errors in medicine and suggested that interruptions could be a contributing factor. Previous studies of interruptions in healthcare did not offer a conceptual model by which to study interruptions. As a result of the serious consequences of interruptions investigated in other high-risk industries, there is a need to develop a model to describe, understand, explain, and predict interruptions and their consequences in healthcare. Therefore, the purpose of this study was to develop a model grounded in the literature and to use the model to describe and explain interruptions in healthcare. Specifically, this model would be used to describe and explain interruptions occurring in a Level One Trauma Center. A trauma center was chosen because this environment is characterized as intense, unpredictable, and interrupt-driven. The first step in developing the model began with a review of the literature which revealed that the concept interruption did not have a consistent definition in either the healthcare or non-healthcare literature. Walker and Avant’s method of concept analysis was used to clarify and define the concept. The analysis led to the identification of five defining attributes which include (1) a human experience, (2) an intrusion of a secondary, unplanned, and unexpected task, (3) discontinuity, (4) externally or internally initiated, and (5) situated within a context. However, before an interruption could commence, five conditions known as antecedents must occur. For an interruption to take place (1) an intent to interrupt is formed by the initiator, (2) a physical signal must pass a threshold test of detection by the recipient, (3) the sensory system of the recipient is stimulated to respond to the initiator, (4) an interruption task is presented to recipient, and (5) the interruption task is either accepted or rejected by v the recipient. An interruption was determined to be quantifiable by (1) the frequency of occurrence of an interruption, (2) the number of times the primary task has been suspended to perform an interrupting task, (3) the length of time the primary task has been suspended, and (4) the frequency of returning to the primary task or not returning to the primary task. As a result of the concept analysis, a definition of an interruption was derived from the literature. An interruption is defined as a break in the performance of a human activity initiated internal or external to the recipient and occurring within the context of a setting or location. This break results in the suspension of the initial task by initiating the performance of an unplanned task with the assumption that the initial task will be resumed. The definition is inclusive of all the defining attributes of an interruption. This is a standard definition that can be used by the healthcare industry. From the definition, a visual model of an interruption was developed. The model was used to describe and explain the interruptions recorded for an instrumental case study of physicians and registered nurses (RNs) working in a Level One Trauma Center. Five physicians were observed for a total of 29 hours, 31 minutes. Eight registered nurses were observed for a total of 40 hours 9 minutes. Observations were made on either the 0700–1500 or the 1500-2300 shift using the shadowing technique. Observations were recorded in the field note format. The field notes were analyzed by a hybrid method of categorizing activities and interruptions. The method was developed by using both a deductive a priori classification framework and by the inductive process utilizing line-byline coding and constant comparison as stated in Grounded Theory. The following categories were identified as relative to this study: Intended Recipient - the person to be interrupted Unintended Recipient - not the intended recipient of an interruption; i.e., receiving a phone call that was incorrectly dialed Indirect Recipient – the incidental recipient of an interruption; i.e., talking with another, thereby suspending the original activity Recipient Blocked – the intended recipient does not accept the interruption Recipient Delayed – the intended recipient postpones an interruption Self-interruption – a person, independent of another person, suspends one activity to perform another; i.e., while walking, stops abruptly and talks to another person Distraction – briefly disengaging from a task Organizational Design – the physical layout of the workspace that causes a disruption in workflow Artifacts Not Available – supplies and equipment that are not available in the workspace causing a disruption in workflow Initiator – a person who initiates an interruption Interruption by Organizational Design and Artifacts Not Available were identified as two new categories of interruption. These categories had not previously been cited in the literature. Analysis of the observations indicated that physicians were found to perform slightly fewer activities per hour when compared to RNs. This variance may be attributed to differing roles and responsibilities. Physicians were found to have more activities interrupted when compared to RNs. However, RNs experienced more interruptions per hour. Other people were determined to be the most commonly used medium through which to deliver an interruption. Additional mediums used to deliver an interruption vii included the telephone, pager, and one’s self. Both physicians and RNs were observed to resume an original interrupted activity more often than not. In most interruptions, both physicians and RNs performed only one or two interrupting activities before returning to the original interrupted activity. In conclusion the model was found to explain all interruptions observed during the study. However, the model will require an even more comprehensive study in order to establish its predictive value.

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.

Identification of genes associated with quantitative traits involved in cardiovascular disease and lipoprotein metabolism

Cardiovascular disease (CVD) is a threat to public health. It has been reported to be the leading cause of death in United States. The invention of next generation sequencing (NGS) technology has revolutionized the biomedical research. To investigate NGS data of CVD related quantitative traits would contribute to address the unknown etiology and disease mechanism of CVD. NHLBI's Exome Sequencing Project (ESP) contains CVD related phenotypes and their associated NGS exomes sequence data. Initially, a subset of next generation sequencing data consisting of 13 CVD-related quantitative traits was investigated. Only 6 traits, systolic blood pressure (SBP), diastolic blood pressure (DBP), height, platelet counts, waist circumference, and weight, were analyzed by functional linear model (FLM) and 7 currently existing methods. FLM outperformed all currently existing methods by identifying the highest number of significant genes and had identified 96, 139, 756, 1162, 1106, and 298 genes associated with SBP, DBP, Height, Platelet, Waist, and Weight respectively. ^

RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION

My dissertation focuses on two aspects of RNA sequencing technology. The first is the methodology for modeling the overdispersion inherent in RNA-seq data for differential expression analysis. This aspect is addressed in three sections. The second aspect is the application of RNA-seq data to identify the CpG island methylator phenotype (CIMP) by integrating datasets of mRNA expression level and DNA methylation status. Section 1: The cost of DNA sequencing has reduced dramatically in the past decade. Consequently, genomic research increasingly depends on sequencing technology. However it remains elusive how the sequencing capacity influences the accuracy of mRNA expression measurement. We observe that accuracy improves along with the increasing sequencing depth. To model the overdispersion, we use the beta-binomial distribution with a new parameter indicating the dependency between overdispersion and sequencing depth. Our modified beta-binomial model performs better than the binomial or the pure beta-binomial model with a lower false discovery rate. Section 2: Although a number of methods have been proposed in order to accurately analyze differential RNA expression on the gene level, modeling on the base pair level is required. Here, we find that the overdispersion rate decreases as the sequencing depth increases on the base pair level. Also, we propose four models and compare them with each other. As expected, our beta binomial model with a dynamic overdispersion rate is shown to be superior. Section 3: We investigate biases in RNA-seq by exploring the measurement of the external control, spike-in RNA. This study is based on two datasets with spike-in controls obtained from a recent study. We observe an undiscovered bias in the measurement of the spike-in transcripts that arises from the influence of the sample transcripts in RNA-seq. Also, we find that this influence is related to the local sequence of the random hexamer that is used in priming. We suggest a model of the inequality between samples and to correct this type of bias. Section 4: The expression of a gene can be turned off when its promoter is highly methylated. Several studies have reported that a clear threshold effect exists in gene silencing that is mediated by DNA methylation. It is reasonable to assume the thresholds are specific for each gene. It is also intriguing to investigate genes that are largely controlled by DNA methylation. These genes are called “L-shaped” genes. We develop a method to determine the DNA methylation threshold and identify a new CIMP of BRCA. In conclusion, we provide a detailed understanding of the relationship between the overdispersion rate and sequencing depth. And we reveal a new bias in RNA-seq and provide a detailed understanding of the relationship between this new bias and the local sequence. Also we develop a powerful method to dichotomize methylation status and consequently we identify a new CIMP of breast cancer with a distinct classification of molecular characteristics and clinical features.

Identification and characterization of the vegetally localized Hermes mRNA

One way developing embryos regulate the expression of their genes is by localizing mRNAs to specific subcellular regions. In the oocyte of the frog, Xenopus laevis, many RNAs are localized specifically to the animal or the vegetal halves of the oocyte. The localization of these RNAs contributes to the primary polarity of the oocyte, the asymmetry that is the basis for patterning and lineage specification in the embryo. I have screened a cDNA library for clones containing the Xlsirt repeat, an element known to target RNAs to the vegetal cortex of the oocyte. I have identified seventeen cDNA clones that contain this element. One of these cDNAs encodes the RNA binding protein Hermes. The Hermes mRNA is localized to the vegetal cortex of the oocyte. Additionally, Hermes protein is also vegetally localized in the oocyte and is found in subcellular structures known to contain localized mRNAs. This suggests that Hermes might interact with localized RNAs. While Hermes protein is present in oocytes, it disappears at germinal vesicle breakdown during maturation. We therefore believe that the time period during which Hermes functions is during oogenesis or maturation prior to the time of Hermes degradation. To determine Hermes function, an antisense depletion strategy was used that involved injecting morpholino oligos (HE-MO) into oocytes. Injection of these morpholinos causes the level of Hennes protein to drop prematurely during maturation. Embryos produced from these oocytes exhibit cleavage defects that are most prevalent in the vegetal blastomeres. The phenotype can be partially rescued by injection of a heterologous Hermes mRNA and is therefore specific to Hermes. The Hermes expression and depletion results are consistent with a model in which Hermes interacts with one or more vegetally localized mRNAs in the oocyte and during the early stages of maturation. The interaction is required for cleavage of the vegetal blastomeres. Therefore, it is likely that at least one mRNA that interacts with Hermes is a cell cycle regulator. ^

Model results of the impact of climate change on agriculture in China

This paper assesses the impact of climate change on China's agricultural production at a cross-provincial level using the Ricardian approach, incorporating a multilevel model with farm-level group data. The farm-level group data includes 13379 farm households, across 316 villages, distributed in 31 provinces. The empirical results show that, firstly, the marginal effects and elasticities of net crop revenue per hectare with respect to climate factors indicated that the annual impact of temperature on net crop revenue per hectare was positive, and the effect of increased precipitation was negative when looking at the national totals; secondly, the total impact of simulated climate change scenarios on net crop revenues per hectare at a Chinese national total level, was an increase of between 79 USD per hectare and 207 USD per hectare for the 2050s, and an increase from 140 USD per hectare to 355 USD per hectare for the 2080s. As a result, climate change may create a potential advantage for the development of Chinese agriculture, rather than a risk, especially for agriculture in the provinces of the Northeast, Northwest and North regions. However, the increased precipitation can lead to a loss of net crop revenue per hectare, especially for the provinces of the Southwest, Northwest, North and Northeast regions.

Age model and stable oxygen isotope mean values of ice core SZ99 from Akademii Nauk ice cap on Severnaya Zemlya

Between 1999 and 2001, a 724 m long ice core was drilled on Akademii Nauk, the largest glacier on Severnaya Zemlya, Russian Arctic. The drilling site is located near the summit. The core is characterized by high melt-layer content. The melt layers are caused by melting and even by rain during the summer. We present high-resolution data of density, electrical conductivity (dielectrical profiling), stable water isotopes and melt-layer content for the upper 136 m (120 m w.e.) of the ice core. The dating by isotopic cycles and electrical conductivity peak identification suggests that this core section covers approximately the past 275 years. Singularities of volcanogenic and anthropogenic origin provide well-defined additional time markers. Long-term temperatures inferred from 12 year running mean averages of d18O reach their lowest level in the entire record around 1790. Thereafter the d18O values indicate a continuously increasing mean temperature on the Akademii Nauk ice cap until 1935, interrupted only by minor cooling episodes. The 20th century is found to be the warmest period in this record.

Gotland Deep (Baltic Sea) independent age model based on Palaeomagnetic Secular Variation (PSV) and lead (Pb) deposition profiles

Dating of sediment cores from the Baltic Sea has proven to be difficult due to uncertainties surrounding the 14C reservoir age and a scarcity of macrofossils suitable for dating. Here we present the results of multiple dating methods carried out on cores in the Gotland Deep area of the Baltic Sea. Particular emphasis is placed on the Littorina stage (8 ka ago to the present) of the Baltic Sea and possible changes in the 14C reservoir age of our dated samples. Three geochronological methods are used. Firstly, palaeomagnetic secular variations (PSV) are reconstructed, whereby ages are transferred to PSV features through comparison with varved lake sediment based PSV records. Secondly, lead (Pb) content and stable isotope analysis are used to identify past peaks in anthropogenic atmospheric Pb pollution. Lastly, 14C determinations were carried out on benthic foraminifera (Elphidium spec.) samples from the brackish Littorina stage of the Baltic Sea. Determinations carried out on smaller samples (as low as 4 µg C) employed an experimental, state-of-the-art method involving the direct measurement of CO2 from samples by a gas ion source without the need for a graphitisation step - the first time this method has been performed on foraminifera in an applied study. The PSV chronology, based on the uppermost Littorina stage sediments, produced ten age constraints between 6.29 and 1.29 cal ka BP, and the Pb depositional analysis produced two age constraints associated with the Medieval pollution peak. Analysis of PSV data shows that adequate directional data can be derived from both the present Littorina saline phase muds and Baltic Ice Lake stage varved glacial sediments. Ferrimagnetic iron sulphides, most likely authigenic greigite (Fe3S4), present in the intermediate Ancylus Lake freshwater stage sediments acquire a gyroremanent magnetisation during static alternating field (AF) demagnetisation, preventing the identification of a primary natural remanent magnetisation for these sediments. An inferred marine reservoir age offset (deltaR) is calculated by comparing the foraminifera 14C determinations to a PSV & Pb age model. This deltaR is found to trend towards younger values upwards in the core, possibly due to a gradual change in hydrographic conditions brought about by a reduction in marine water exchange from the open sea due to continued isostatic rebound.

Water level time series and sediment grain size model

The environment of ebb-tidal deltas between barrier island systems is characterized by a complex morphology with ebb- and flood-dominated channels, shoals and swash bars connecting the ebb-tidal delta platform to the adjacent island. These morphological features reveal characteristic surface sediment grain-size distributions and are subject to a continuous adaptation to the prevailing hydrodynamic forces. The mixed-energy tidal inlet Otzumer Balje between the East Frisian barrier islands of Langeoog and Spiekeroog in the southern North Sea has been chosen here as a model study area for the identification of relevant hydrodynamic drivers of morphology and sedimentology. We compare the effect of high-energy, wave-dominated storm conditions to mid-term, tide-dominated fair-weather conditions on tidal inlet morphology and sedimentology with a process-based numerical model. A multi-fractional approach with five grain-size fractions between 150 and 450 µm allows for the simulation of corresponding surface sediment grain-size distributions. Net sediment fluxes for distinct conditions are identified: during storm conditions, bed load sediment transport is generally onshore directed on the shallower ebb-tidal delta shoals, whereas fine-grained suspended sediment bypasses the tidal inlet by wave-driven currents. During fair weather the sediment transport mainly focuses on the inlet throat and the marginal flood channels. We show how the observed sediment grain-size distribution and the morphological response at mixed-energy tidal inlets are the result of both wave-dominated less frequent storm conditions and mid-term, tide-dominant fair-weather conditions.

Transcriptional response to cardiac injury in the zebrafish: systematic identification of genes with highly concordant activity across in vivo models

BACKGROUND Zebrafish is a clinically-relevant model of heart regeneration. Unlike mammals, it has a remarkable heart repair capacity after injury, and promises novel translational applications. Amputation and cryoinjury models are key research tools for understanding injury response and regeneration in vivo. An understanding of the transcriptional responses following injury is needed to identify key players of heart tissue repair, as well as potential targets for boosting this property in humans. RESULTS We investigated amputation and cryoinjury in vivo models of heart damage in the zebrafish through unbiased, integrative analyses of independent molecular datasets. To detect genes with potential biological roles, we derived computational prediction models with microarray data from heart amputation experiments. We focused on a top-ranked set of genes highly activated in the early post-injury stage, whose activity was further verified in independent microarray datasets. Next, we performed independent validations of expression responses with qPCR in a cryoinjury model. Across in vivo models, the top candidates showed highly concordant responses at 1 and 3 days post-injury, which highlights the predictive power of our analysis strategies and the possible biological relevance of these genes. Top candidates are significantly involved in cell fate specification and differentiation, and include heart failure markers such as periostin, as well as potential new targets for heart regeneration. For example, ptgis and ca2 were overexpressed, while usp2a, a regulator of the p53 pathway, was down-regulated in our in vivo models. Interestingly, a high activity of ptgis and ca2 has been previously observed in failing hearts from rats and humans. CONCLUSIONS We identified genes with potential critical roles in the response to cardiac damage in the zebrafish. Their transcriptional activities are reproducible in different in vivo models of cardiac injury.

New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use.