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Indiana University-Purdue University Indianapolis (IUPUI) / Pharmacogenetic testing has the potential to improve pain management through addressing wide interindividual variations in responses to pharmacogenetically actionable opioids, ultimately decreasing costly adverse drug effects and improving responses to these medications. A recent review of pharmacogenomics in the nursing literature highlighted the need for nurses to more fully embrace the burgeoning field of pharmacogenomics in nursing research, clinical practice, and education. Despite the promise of pharmacogenetic testing, significant challenges exist for evaluating outcomes related to its implementation, including oversimplification of medication exposure, the complexity of patients' clinical profiles, and the characteristics of healthcare contexts in which medications are prescribed. A better understanding of these challenges could enhance the assessment and documentation of the benefits of pharmacogenetic testing in guiding opioid therapies. This dissertation is intended to address the challenges of evaluating outcomes of pharmacogenetic testing implementation and the need for nurses to lead pharmacogenomic-related research. The dissertation purpose was to advance the sciences of nursing, pain management, and pharmacogenomics through the development of a typology of common patterns of medication exposure to known pharmacogenetically actionable opioids (codeine & tramadol). A qualitative, person-oriented approach was used to retrospectively analyze six months of electronic health record and pharmacogenotype data in 30 underserved adult patients. An overarching typology with eight groups of patients that had one of five opioid prescription patterns (singular, episodic, switching, sustained, or multiplex) and one of three types of medical emphasis of care (pain, comorbidities, or both) were identified. This typology consisted of a description of multiple common patterns that compare and contrast salient factors of exposure and the emphasis of why individuals were seeking care. Furthermore, in an aggregate descriptive analysis evaluating key clinical profile factors, these patients had complex medical histories, extensive healthcare utilization, and experienced significant polypharmacy. These findings can aid in addressing challenges related to the implementation of pharmacogenetic testing in clinical practice and point to ways in which nurses can take the lead in pharmacogenomics research. Findings also provide a foundation for future studies aimed at developing medication exposure measures to capture its dynamic nature and identifying and tailoring interventions in this population.

Derzeitige Reichweiteunsicherheiten in der Protonentherapie verhindern das vollständige Ausschöpfen ihrer physikalischen Vorteile. Ein wesentlicher Anteil ist dabei auf die Vorhersage der Reichweite mittels Röntgen-Computertomographie (CT) zurückzuführen. Um die CT-bezogene Unsicherheit zu verringern, wird die Zwei-Spektren-Computertomographie (DECT) als vielversprechend angesehen. Innerhalb dieser Arbeit wurde die Anwendbarkeit von DECT in der Protonentherapie untersucht. Zunächst wurde ein CT-Scanprotokoll für die Strahlentherapie hinsichtlich Bildqualität und Konstanz der CT-Zahlen für verschiedene Körperregionen und -größen optimiert. Anschließend wurde die patientenindividuelle DECT- basierte Reichweitevorhersage kalibriert und ihre Genauigkeit in zwei Experimenten mit bekannter Referenz unter Verwendung eines anthropomorphen Phantoms und von homogenen biologischen Geweben verifiziert. Die klinische Relevanz von DECT wurde in einer retrospektiven Analyse von Krebspatienten mit Tumoren im Kopf, Becken oder Thorax nachgewiesen. Die systematischen Reichweiteunterschiede zwischen DECT und dem klinischen Standardverfahren konnten durch die Optimierung der Standardmethode basierend auf zusätzlichen mit DECT erworbenen Patienteninformationen reduziert werden. Somit wurde DECT erstmalig klinisch genutzt, um die Reichweiteberechnung zu verbessern. Die patientenindividuelle DECT-basierte Reichweitevorhersage kann zusätzlich Gewebevariabilitäten innerhalb eines und zwischen Patienten berücksichtigen, wie für Kopftumorpatienten gezeigt wurde. Dies legt den Grundstein für eine genauere Reichweiteberechnung und eröffnet neue Möglichkeiten für die Reduktion klinischer Sicherheitssäume, in denen die CT-bezogenen Unsicherheiten berücksichtigt sind.:1 Introduction 2 Physical Principles of Computed Tomography 2.1 Image Acquisition 2.2 Image Reconstruction 2.3 Dual-Energy Computed Tomography 3 Physical Principles of Proton Therapy 3.1 Treatment Techniques 3.2 Uncertainties in Proton Therapy 4 Principles of Stopping-Power Prediction from Computed Tomography 4.1 Single-Energy Computed Tomography 4.2 Dual-Energy Computed Tomography 5 Experimental Calibration of Stopping-Power Prediction 5.1 Scan Protocol Optimisation in Computed Tomography 5.2 Characterisation of Pseudo-Monoenergetic CT Calculation 5.3 Determination of Proton Stopping Power 5.4 Calibration of Stopping-Power Prediction Methods 6 Experimental Verification of Stopping-Power Prediction 6.1 Anthropomorphic Head Phantom 6.2 Homogeneous Biological Tissue Samples 7 Clinical Translation and Validation of Dual-Energy Computed Tomography 7.1 Feasibility of Dual-Spiral Dual-Energy CT 7.2 Range Prediction in Cerebral and Pelvic Tumour Patients 7.3 Tissue Variability in Brain-Tumour Patients 7.4 Feasibility of 4D Dual-Spiral Dual-Energy CT 7.5 DECT-Based Refinement of the Hounsfield Look-Up Table 8 Summary 9 Zusammenfassung / Range uncertainty in proton therapy currently hampers the full exploitation of its physical advantages. A substantial amount of this uncertainty arises from proton range prediction based on X-ray computed tomography (CT). Dual-energy CT (DECT) has often been suggested as a promising imaging modality to reduce this CT-related range uncertainty. Within this thesis, the translation of DECT into application in proton therapy was evaluated. First, a CT scan protocol was optimised for radiotherapy considering the image quality and CT number stability for various body regions and sizes. The patient-specific DECT-based range prediction was then calibrated and its accuracy validated in two ground-truth experiments using an anthropomorphic phantom and homogeneous biological tissues. Subsequently, the clinical relevance of DECT was demonstrated in a retrospective cohort analysis of cerebral, pelvic and thoracic tumour patients. The systematic range deviations between the DECT and state-of-the-art approach were then reduced by adapting the standard method utilizing additional patient information obtained from DECT. Hence, DECT was clinically applied for the first time to refine proton range calculation. As a further step, the use of patient-specific DECT-based range prediction also considers intra- and inter-patient tissue variabilities as quantified in brain-tumour patients. A future implementation will be an important cornerstone to improve proton range calculation and might open up the possibility to reduce clinical safety margins accounting for the CT-related range uncertainty.:1 Introduction 2 Physical Principles of Computed Tomography 2.1 Image Acquisition 2.2 Image Reconstruction 2.3 Dual-Energy Computed Tomography 3 Physical Principles of Proton Therapy 3.1 Treatment Techniques 3.2 Uncertainties in Proton Therapy 4 Principles of Stopping-Power Prediction from Computed Tomography 4.1 Single-Energy Computed Tomography 4.2 Dual-Energy Computed Tomography 5 Experimental Calibration of Stopping-Power Prediction 5.1 Scan Protocol Optimisation in Computed Tomography 5.2 Characterisation of Pseudo-Monoenergetic CT Calculation 5.3 Determination of Proton Stopping Power 5.4 Calibration of Stopping-Power Prediction Methods 6 Experimental Verification of Stopping-Power Prediction 6.1 Anthropomorphic Head Phantom 6.2 Homogeneous Biological Tissue Samples 7 Clinical Translation and Validation of Dual-Energy Computed Tomography 7.1 Feasibility of Dual-Spiral Dual-Energy CT 7.2 Range Prediction in Cerebral and Pelvic Tumour Patients 7.3 Tissue Variability in Brain-Tumour Patients 7.4 Feasibility of 4D Dual-Spiral Dual-Energy CT 7.5 DECT-Based Refinement of the Hounsfield Look-Up Table 8 Summary 9 Zusammenfassung