Added value of acute multimodal CT-based imaging (MCTI) : a comprehensive analysis

Introduction: MCTI is used to assess acute ischemic stroke (AIS) patients.We postulated that use of MCTI improves patient outcome regardingindependence and mortality.Methods: From the ASTRAL registry, all patients with an AIS and a non-contrast-CT (NCCT), angio-CT (CTA) or perfusion-CT (CTP) within24 h from onset were included. Demographic, clinical, biological, radio-logical, and follow-up caracteristics were collected. Significant predictorsof MCTI use were fitted in a multivariate analysis. Patients undergoingCTA or CTA&CTP were compared with NCCT patients with regards tofavourable outcome (mRS ≤ 2) at 3 months, 12 months mortality, strokemechanism, short-term renal function, use of ancillary diagnostic tests,duration of hospitalization and 12 months stroke recurrence

Data Analysis Methods for Software Systems

Using statistics, econometrics, machine learning, and functional data analysis methods, we evaluate the consequences of the lockdown during the COVID-19 pandemics for wage inequality and unemployment. We deduce that these two indicators mostly reacted to the first lockdown from March till June 2020. Also, analysing wage inequality, we conduct analysis separately for males and females and different age groups.We noticed that young females were affected mostly by the lockdown.Nevertheless, all the groups reacted to the lockdown at some level

Generating Reliable and Responsive Observational Evidence: Reducing Pre-analysis Bias

A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators. This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator. We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians’ information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service

Data Science in Healthcare

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management

Molecular Profiling of Lung Cancer

Cisplatin is a first line chemotherapeutic agent for lung cancer however, although patients may respond to therapy, resistance often develops with tumour recurrence and disease progression. Somatic alterations in the tumour may alter therapeutic responses. Consequently a model of cisplatin resistance in lung cancer derived A549 cells has been created to examine the genomic changes that occur as chemo-resistance develops. Drug resistance was induced in A549 cells through multiple rounds of cisplatin dosage and recovery over two different time courses. The concentration of cisplatin required to inhibit growth (inhibitory concentration [IC] value) was calculated at each round and cycles were continued until the IC value increased at least four-fold. Cells were harvested and total RNA extracted for whole transcriptome microarray analysis. Data was analysed using R statistics and associated packages, Affy, Limma, Mfuzz and WGCNA. A five-fold increase in IC value was generated over successive doses in both regimes, accompanied by highly significant changes in gene expression. To explore these changes, temporal expression clustering and extensive network analyses were performed across the rounds of cisplatin dosing, as well as an untreated cell culture time course that acted as a comparison to the two treated regimes. The results gathered from this robust model suggest that differences in dose and frequency of chemotherapy may affect genomic changes at specific loci that confer cisplatin resistance. Interesting and relevant pathways and genes have been discovered. In combination with analyses on a small patient cohort, these results have provided insights into the mechanism of cisplatin resistance and have highlighted new clinical biomarkers of potential use in prognosis of patients undergoing cancer treatment.Open Acces

Evidence Based Medicine

Evidence-based medicine (EBM) was introduced to the best benefit of the patient. It has transformed the pathophysiological approach to the outcome approach of today's treatments. Disease-oriented to patient-oriented medicine. And, for some, daily medical practice from patient oriented to case oriented medicine. Evidence has changed the paternalistic way of medical practice. And gave room to patients, who show a tendency towards partnership. Although EBM has introduced a different way of thinking in the day to day medical practice, there is plenty of space for implementation and improvement. This book is meant to provoke the thinker towards the unlimited borders of caring for the patient

In vivo and in vitro remodeling of a small intestinal submucosa extracellular matrix cardiac patch in an ovine model and splashing bioreactor

Previous studies have demonstrated that surgical patches comprised of small intestinal submucosa (SIS)-derived extracellular matrix (ECM) have biological remodeling potential in various tissues. In this series of experiments, the remodeling potential of a commercially available cardiac SIS-ECM patch was examined in both in vivo and in vitro models. This thesis begins by introducing the clinical need for a tissue-engineered cardiovascular scaffold that can grow with the patient and avoid the morbidity associated with currently available synthetic and biological materials. Such a patch would transform the surgical repair of congenital heart disease, in particular of patients with tetralogy of Fallot and in the repair of mitral regurgitation. The in vivo study investigated histological, mechanical, and bioelectrical properties of an SIS-ECM patch implanted in the ovine right-ventricular outflow tract (RVOT), and the histological and mechanical properties of the same patch implanted in the descending aorta and main pulmonary artery of a juvenile ovine. We found the juvenile ovine model to be a suitable model for evaluation of SIS-ECM patch remodeling, as seen by in vivo echocardiography, electrical mapping, and ex vivo optical mapping for the RVOT patch and mechanical testing, histology and immunohistochemistry for patches placed in all three positions. The in vitro study looked at an SIS-ECM patch pretreated with pepsin, seeded with mitral valve interstitial cells (MVICs), and exposed to mechanical stimulation in a splashing bioreactor for one week. Greater cell integration and proliferation and greater tissue cohesion was seen in the pepsin-treated SIS-ECM, while groups without mechanical stimulation demonstrated a stiffening effect for the bioreactor. In sheep, the SIS-ECM patch appears capable of remodeling to resemble native, functional ventricular tissue, but further validation of this patch material is required. Bioreactors can play an important role in validation of this promising scaffold material. Tissue-engineered scaffolds are unique in their complete ontological metamorphosis (from scaffold material to part of the patient's own tissue) and pose distinctive ethical challenges that must be responsibly managed. Contract studies, such as the research presented in chapters 3-5, that are funded by the medical device industry require close scrutiny and precautions to avoid conflicts of interest