Um Patienten mit Palliativbedarf proaktiv zu identifizieren wurde am Universitätsklinikum Würzburg am 01.03.2019 ein Palliativscreening auf Basis der Pflegeanamnese etabliert. Dessen Prädiktivität auf das 6-Monats Überleben wurde in der vorliegenden Arbeit in einer uro-onkologischen Patientenkohorte untersucht. Für die Patientenkohorte wurden aus dem klinischen Informationssystem aufenthalts-, personen- und tumorspezifische Daten sowie das Palliativscreening aus der Pflegeanamnese ausgelesen. Ergänzend zur Auswertung des automatisiert generierten Palliativscreenings wurden die Einzelitems rechnerisch in einem berechneten Palliativscreening zusammengeführt um eine Zuverlässigkeitsprüfung des automatisiert generierten Palliativscreenings zu ermöglichen. In einer zweiten Auswertung wurde geprüft, ob der Patient im 6-Monats Nachbeobachtungszeitraum nach Aufnahme verstorben ist. Unsere Studie belegt die Prädiktivität des Palliativscreenings in einer uro-onkologischen Kohorte für das 6-Monats Überleben. Ein automatisiert generiertes Screening, ist in unserer Studie vergleichbar prädiktiv auf das 6-Monats Überleben als eine manuelle rechnerische Rekonstruktion. Bei Patienten mit Prostatakarzinom weist das Palliativscreening eine niedrigere Korrelation mit dem 6-Monats Überleben auf als bei Patienten mit anderen urologischen Entitäten. / To proactively identify patients with palliative care needs, a palliative care screening based on nursing history was established at Würzburg University Hospital on March 1, 2019. Its predictivity on 6-month survival was investigated in a uro-oncology patient cohort in the present study. For the patient cohort, stay-, person-, and tumor-specific data as well as the palliative screening from the nursing history were collected from the clinical information system. In addition to the evaluation of the automatically generated palliative screening, the individual items were combined in a calculated palliative screening to enable a reliability test of the automatically generated palliative screening. In a second evaluation, we tested whether the patient died in the 6-month follow-up period after admission. Our study demonstrates the predictive power of palliative screening in a uro-oncology cohort for 6-month survival. Automated generated screening, in our study, is comparably predictive on 6-month survival than manual computational reconstruction. In patients with prostate cancer, palliative screening has a lower correlation with 6-month survival than in patients with other urologic entities.

Estimation of the sensitivity and specificity of cancer screening tests using data from population-level databases is complicated by the lack of independent confirmation of test results using a "gold standard''. The true sensitivity and specificity are unknown and errors in measurement can occur due to subjective clinical judgment, technical imperfections or interpretational differences. A further complication is clustered data (such as patients nested within examiners within screening centre), which are common in population-based screening. We propose a cancer screening model that accommodates the partially unobserved disease status, clustered data structures, general covariate effects, and the dependence between exams. The model is applied to the estimation of the diagnostic accuracy of mammography and clinical breast examination using a cohort consisting of women 50 to 69 years of age screened at the OBSP between January 1, 2002 and December 31, 2003. When offered in addition to mammography, we found CBE may benefit women using hormone therapy but not likely benefit women with dense breast tissues. The thesis also discusses two measures of interest, the length of the pre-clinical state and the false negative rate. Two estimation procedures are proposed to model the pre-clinical state duration, the false negative rate of screening exam, and the underlying incidence rate in the screened population. Both methods assume the sojourn time follows a negative exponential distribution, but we consider two different forms for the false negative rate: 1) constant with time and 2) an exponential function to compensate for the fact that lesions should become easier to detect the closer they are to become clinically evident. The proposed methods are illustrated with another cohort of women who were first screened through the OBSP between January 1, 2003, and December 31, 2004 and were followed up until December 31, 2009. / Thesis / Doctor of Philosophy (PhD)

No description available.

No description available.

Cell based High Throughput Screening (HTS) has become a very important method in pharmaceutical drug discovery and presently carried out using robots and well plates. A microfluidics based device for cell based HTS using traditional cell culture protocol would be a significant addition to the field. In this thesis novel microfluidic HTS devices targeted for cell based assays having traditional non-compartmentalized agar gel as cell culture medium and electric control over drug dose is being reported. The basic design of device consists of a gel layer supported by a nanoporous membrane that is bonded to microchannels underneath it. The pores of membrane are blocked everywhere except in selected regions that serve as fluidic interfaces between the microchannel below and the gel above. Upon application of electric field nanopores start to act as electrokinetic pumps. By selectively switching an array of such micropumps, a number of spots -containing drug molecules- are created simultaneously in the gel layer. By diffusion drugs reach to the top surface of gel where cells are to be grown. Based on this principle, a number of different devices are fabricated using microfabrication technology. The fabricated devices include, single drug spot forming device, multiple drug spot forming device and microarray of drug spots forming device. By controlling pumping potential and duration spots sizes ranging from 200μm to 6mm diameter and having inter-spot distances of 0.4mm-10mm have been created. Absence of diffusional transport through the nanoporous interfaces without electric field is demonstrated. A number of representative molecules, including surrogate drug molecules (trypan blue, and methylene blue) and biomolecules (DNA and protein) were selected for demonstration purpose. Dosing range of 50-3000 μg and spot density of 156 spots/cm² were achieved. The drug spot density was found to be limited by molecular diffusion in gel and hence numerical study was carried to find out ways for density increase. Based on this simulation, a method for diffusion reduction called diffusion barrier was proposed. Diffusion barrier used specially dimensioned (having shallow grooves) gel sheet to reduce the diffusion. / Thesis / Master of Applied Science (MASc)

High-dimensional data, data in which the number of dimensions exceeds the number of observations, is increasingly common in statistics. The term "ultra-high dimensional" is defined by Fan and Lv (2008) as describing the situation where log(p) is of order O(na) for some a in the interval (0, ½). It arises in many contexts such as gene expression data, proteomic data, imaging data, tomography, and finance, as well as others. High-dimensional data present a challenge to traditional statistical techniques. In traditional statistical settings, models have a small number of features, chosen based on an assumption of what features may be relevant to the response of interest. In the high-dimensional setting, many of the techniques of traditional feature selection become computationally intractable, or does not yield unique solutions. Current research in modeling high-dimensional data is heavily focused on methods that screen the features before modeling; that is, methods that eliminate noise-features as a pre-modeling dimension reduction. Typically noise feature are identified by exploiting properties of independent random variables, thus the term "independence screening." There are methods for modeling high-dimensional data without feature screening first (e.g. LASSO or SCAD), but simulation studies show screen-first methods perform better as dimensionality increases. Many proposals for independence screening exist, but in my literature review certain themes recurred: A) The assumption of sparsity: that all the useful information in the data is actually contained in a small fraction of the features (the "active features"), the rest being essentially random noise (the "inactive" features). B) In many newer methods, initial dimension reduction by feature screening reduces the problem from the high-dimensional case to a classical case; feature selection then proceeds by a classical method. C) In the initial screening, removal of features independent of the response is highly desirable, as such features literally give no information about the response. D) For the initial screening, some statistic is applied pairwise to each feature in combination with the response; the specific statistic chosen so that in the case that the two random variables are independent, a specific known value is expected for the statistic. E) Features are ranked by the absolute difference between the calculated statistic and the expected value of that statistic in the independent case, i.e. features that are most different from the independent case are most preferred. F) Proof is typically offered that, asymptotically, the method retains the true active features with probability approaching one. G) Where possible, an iterative version of the process is explored, as iterative versions do much better at identifying features that are active in their interactions, but not active individually.

Thesis (MPH.) -- University of Limpopo, 2017 / Refer to document

Data for which the number of predictors exponentially exceeds the number of observations is becoming increasingly prevalent in fields such as bioinformatics, medical imaging, computer vision, And social network analysis. One of the leading questions statisticians must answer when confronted with such “big data” is how to reduce a set of exponentially many predictors down to a set of a mere few predictors which have a truly causative effect on the response being modelled. This process is often referred to as feature screening. In this work we propose three new methods for feature screening. The first method we propose (TC-SIS) is specifically intended for use with data having both categorical response and predictors. The second method we propose (JCIS) is meant for feature screening for interactions between predictors. JCIS is rare among interaction screening methods in that it does not require first finding a set of causative main effects before screening for interactive effects. Our final method (GenCorr) is intended for use with data having a multivariate response. GenCorr is the only method for multivariate screening which can screen for both causative main effects and causative interactions. Each of these aforementioned methods will be shown to possess both theoretical robustness as well as empirical agility.

Statement of the problem: Methicillin-resistant Staphyloccocus aureus (MRSA) is a pathogen of increasing concern and is associated with higher hospital readmission rates, poorer prognosis, and increased mortality resulting in increasing costs to the Canadian healthcare system.1-13 Institutions have been challenged with developing effective infection control programs to prevent the spread of MRSA. The purpose of this thesis was to examine the clinical and cost-effectiveness of a universal MRSA screening intervention within a large tertiary care facility. Methods of investigation: The retrospective population-based observational study consisted of two periods. In the first period (24 months), patients admitted to the Ottawa Hospital underwent risk factor-based screening. In the second period (20 months), universal MRSA screening was implemented in which all patients were screened for MRSA upon admission. Results: The regression analysis demonstrated that the universal MRSA screening intervention was not effective in reducing the number of nosocomial MRSA cases. The economic analysis estimated that the universal MRSA screening intervention incurred an additional cost of $1.16 million/year with an estimated additional cost per patient screened of $17.76. Conclusions: The universal MRSA screening intervention was not clinically or economically effective. Further research is required to verify/dispute these findings in other settings.

Purpose: The objective of this study was to investigate both depression and psychological distress as determinants of breast and cervical cancer screening. Methods: Ontario female respondents to the Canadian Community Health Survey version 1.2 (2002) were assessed for both Major Depressive Disorder (World Mental Health-Composite International Diagnostic Interview for depression) and psychological distress (Kessler 6-item Distress Scale (K6)>/= 8). Respondents eligible for screening (N=4042 for cervical cancer; N=1403 for breast cancer) were linked to Ontario administrative health service data to prospectively ascertain screening outcomes. Results: Women with K6 >/= 8 had reduced breast cancer screening compliance in adjusted analyses (AOR 0.63, 95% CI 0.40-0.97). The association between K6 >/= 8 and cervical cancer screening approached significance in women over age 40 (AOR=0.65, 95%CI 0.41-1.04). Conclusion: Decreased likelihood of screening in women with clinically significant psychological distress suggests that attention to adequacy of preventive services is a potential target for intervention.