Background. Whether or not the Australian serviceman is dentally fit for deployment is determined by the dental fitness standard based on NATO’s dental fitness standard for military personnel. Recent operations by Australian, European, and USA defence forces has found that being dentally fit to deployment is not predictive of whether or not a member is likely to have a dental problem in the twelve months between their dental assessments. In order to maintain an effective member in the field, a suitable risk management assessment pre-deployment is essential so that dental casualties are better managed either through having fewer casualties and/or having those casualties better managed in the field. Aim. The aim of this thesis is to establish a Dental Fitness Classification which more accurately predicts a member’s risk of an Unscheduled Dental Visit (UDV) than the existing Denclas system of classification. Methodology. A prospective cohort study was conducted on 875 Australian service men and women in all 3 services in 7 operational bases across Australia with ages ranging from 17-56. Participants were enrolled during their annual dental examination where clinical details were recorded and a questionnaire completed on demographics and lifestyles of participants. UDVs over the next twelve months were enumerated as any visit to the dental centre which did not form part of their planned treatment following an annual dental examination. The data were first analyzed for associations between each putative risk factor and a binary variable indicating whether or not participants had a UDV in the 12 months since enrolment into the study. Variables for the modeling were then selected that yielded moderately significant associations with the odds of a UDV. From the prospective modeling, the parameter estimates from the logistic regression model (including the intercept) were then used to create an Excel spreadsheet for use in dental clinics. The spreadsheet contained the algorithm that calculated a patient’s probability of having a UDV, based on the reduced set of clinical findings and questionnaire responses found to optimize sensitivity and specificity. Results. Prediction models were most accurate when created for the two cohorts; the dentally fit and dentally unfit. The dentally fit model has 8 variables; Denclas 2 (OR=1.50); Unsupported enamel on a an endodontically treated tooth (OR=22.87); Deep periodontal pocketing (OR=13.97); Healthy Diet (OR= 0.07); Oral Hygiene Score (OR=0.27); Years of Service (OR= 3.96 & 3.49); Prior Visits to the Dentist only for the Relief of Pain (OR=0.35) and the interaction term of Denclas and Unsupported enamel on an endodontically treated tooth (OR= 0.09). The model has a sensitivity =82.61% and a specificity =70.23%. The dentally unfit model has 3 variables; Denclas 4(OR=7.08); Large Fillings (OR=7.06) and Toothbrushing times per week (OR=1.95) with a sensitivity =62.86% and specificity = 77.71%. The dentally fit model has excellent discrimination (Area Under the Receiver Operator Characteristic (AUROC) =0.83) and the dentally unfit has a reasonable discrimination (AUROC=0.74) and both are better than the Denclas system in practice in Defence Force Dental Services around the world (OR=1.80, sensitivity= 41.67% and specificity =72.22%). These models can be used to determine a member’s risk of becoming a UDV from a simple chairside tool embedded in a Microsoft Excel® file. Conclusion. There is a set of easily obtainable variables that can more accurately predict an ADF member’s likelihood of becoming a UDV than the present Denclas system which can be adapted to determine a member’s risk at the chairside.

INTRODUCTION: In patients who receive treatment for cancer, acute kidney injury (AKI) is arguably one of the most dangerous toxicities that results from cisplatin (CP), a chemotherapeutic agent. While AKI is a common occurrence amongst people who receive cisplatin (CP-AKI), the current risk assessment, intervention methods and understanding the role of magnesium in AKI, are either limited or understudied. OBJECTIVES: We aimed to build on previous CP-AKI risk prediction models, and establish a relationship between serum magnesium levels and AKI. Additionally, we used a feasibility study to test if intravenous magnesium sulfate in patients receiving intraoperative chemotherapy with cisplatin (HIOCC) for malignant mesothelioma can attenuate CP-AKI. This feasibility study was also used to determine a proper dosing regimen to achieve serum magnesium levels of 3 - 4.8 mg/dl. METHODS: We defined acute kidney injury as a 1.5-fold increase in serum creatine, or use of renal replacement therapy (RRT). Using clinical and demographic information from Memorial Sloan Kettering’s database, we conducted multivariable and univariable regression was used to identify the most significant demographic and clinical lab values. Using the information from the statistical analysis we built on previous risk prediction models for cisplatin associated kidney injury. Using the same statistical analysis, we further explored the relationship between serum magnesium values and AKI. In the feasibility study, we recruited patients from Brigham and Woman’s hospital who were receiving HIOCC treatment for mesothelioma. They received an infusion of intravenous magnesium sulfate during surgery. Serum magnesium levels were measured pre-operatively and post-operatively along with serum creatinine values. These were used to obtain pharmacokinetic information to further adjust the infusion rate in patients, lab values were also used to identify any AKI. CONCLUSION: A score-based model created using patient’s age, serum magnesium, albumin, hemoglobin, platelets, cisplatin dose and hypertension is predictive of cisplatin associated acute kidney injury. The feasibility study allowed us to inform phase 2 of an upcoming feasibility study that will include a bolus of 6g Mg/hr and an infusion of 2 g/hr after the bolus. This will work to increase the serum magnesium levels to the therapeutic range.

Cardiovascular risk scores predict an individual’s risk of developing cardiovascular disease. Many were developed and validated in study cohorts on risk-factor lowering treatment – a cause of inaccuracy. In addition, risk scores are criticised as being biased towards the elderly due to the prominence of age as a risk predictor. Although present guidelines advocate the use of short-term (5-10 year) absolute risk scores, other approaches to redress this perceived imbalance such as lifetime risk scores are being considered. The overall objective of this thesis is to identify the most appropriate cardiovascular risk score for use in general practice, taking account of the impact of treatment and time on assessed risk. This objective was met by three different methods. First, a systematic review of cardiovascular risk scores was conducted. This explored the derivation of each score, including the extent of treatment. Next, doctors were interviewed in depth to understand their perception and use of risk scores. Finally, mathematical models were devised to determine whether a true difference in life expectancy exists at different ages but the same short-term cardiovascular risk. The models incorporated age-specific case fatality rates, competing risks and time preference to estimate the potential years of life lost due to a five-year treatment delay in different age groups with the same short-term coronary heart disease risk. The findings demonstrate that cardiovascular risk scores do not take account of treatment effects. This significantly affects their application in clinical practice. In addition, there is little difference in potential life years lost between ages at the same risk level because of higher case-fatalities in older people. When time preference is considered, any residual case for treating the same level of short-term risk differently at different ages is abolished. The overall conclusion is that the five to ten-year absolute cardiovascular risk score is the most appropriate approach to primary cardiovascular disease prevention. By overestimating risk in the young, other approaches benefit the few at the expense of the many.

Mammographic density, the amount of radiodense tissue on a mammogram, is a strong risk factor for breast cancer, with properties that could be an asset in screening and prevention programmes. Its use in risk prediction contexts is currently limited, however, mainly due to di culties in measuring and interpreting density. This research investigates rstly, the properties of density as an independent marker of breast cancer risk and secondly, how density should be measured. The rst question was addressed by analysing data from a chemoprevention trial, a trial of hormonal treatment, and a cohort study of women with a family history of breast cancer . Tamoxifen-induced density reduction was observed to be a good predictor of breast cancer risk reduction in high-risk una ected subjects. Density and its changes did not predict risk or treatment outcome in subjects with a primary invasive breast tumour. Finally absolute density predicted risk better than percent density and showed a potential to improve existing risk-prediction models, even in a population at enhanced familial risk of breast cancer. The second part of thesis focuses on density measurement and in particular evaluates two fully-automated volumetric methods, Quantra and Volpara. These two methods are highly correlated and in both cases absolute density (cm3) discriminated cases from controls better than percent density. Finally, we evaluated and compared di erent measurement methods. Our ndings suggested good reliability of the Cumulus and visual assessments. Quantra volumetric estimates appeared negligibly a ected by measurement error, but were less variable than visual bi-dimensional ones, a ecting their ability to discriminate cases from controls. Overall, visual assessments showed the strongest association with breast cancer risk in comparison to computerised methods. Our research supports the hypothesis that density should have a role in personalising screening programs and risk management. Volumetric density measuring methods, though promising, could be improved.

<p>The objective for this master thesis is to investigate the possibility to predict the risk of stocks in financial markets. The data used for model estimation has been gathered from different branches and different European countries. The four data series that are used in the estimation are price series from: Münchner Rück, Suez-Lyonnaise des Eaux, Volkswagen and OMX, a Swedish stock index. The risk prediction is done with univariate GARCH models. GARCH models are estimated and validated for these four data series. </p><p>Conclusions are drawn regarding different GARCH models, their numbers of lags and distributions. The model that performs best, out-of-sample, is the APARCH model but the standard GARCH is also a good choice. The use of non-normal distributions is not clearly supported. The result from this master thesis could be used in option pricing, hedging strategies and portfolio selection.</p>

The objective for this master thesis is to investigate the possibility to predict the risk of stocks in financial markets. The data used for model estimation has been gathered from different branches and different European countries. The four data series that are used in the estimation are price series from: Münchner Rück, Suez-Lyonnaise des Eaux, Volkswagen and OMX, a Swedish stock index. The risk prediction is done with univariate GARCH models. GARCH models are estimated and validated for these four data series. Conclusions are drawn regarding different GARCH models, their numbers of lags and distributions. The model that performs best, out-of-sample, is the APARCH model but the standard GARCH is also a good choice. The use of non-normal distributions is not clearly supported. The result from this master thesis could be used in option pricing, hedging strategies and portfolio selection.

The purpose of this thesis was to assess the prevalence and management of established cardiovascular (CV) risk factors and the prevalence and influence of self-reported markers (number of teeth and frequency of gum bleeding) of periodontal disease (PD), a less explored CV risk factor, in patients with stable chronic coronary heart disease (CHD). We studied patients from the global STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY (STABILITY) trial (n=15,828), in which patients with stable chronic CHD were randomized to either darapladib or placebo. Our studies were performed using descriptive statistics and multivariable linear, logistic and Cox regression models. The use of secondary preventive medications was generally high across the whole study population. Despite this, CV risk factors were highly prevalent, including obesity, hypertension and hypercholesterolemia. Achievement of guideline-recommended treatment targets was lacking and little improvement was seen throughout the study duration. Approximately 40% of patients reported having &lt;15 remaining teeth and 25% reported gum bleeding. More tooth loss was associated with a greater CV risk factor burden after adjustment, while the associations for gum bleeding were less evident. After multivariable adjustment for CV risk factors and socioeconomic status, more tooth loss was associated with an increased risk of major adverse CV events (a composite of CV death, myocardial infarction and stroke), CV mortality, all-cause mortality and fatal or non-fatal stroke. We found associations between a higher degree of tooth loss and elevated levels of several prognostic biomarkers known to reflect various pathophysiological mechanisms involved in CV morbidity and mortality. Most biomarkers had little attenuating effect on the relationship between tooth loss and outcomes in a multivariable model. In conclusion, we found an inadequate CV risk factor control despite a high use of evidence-based pharmacological therapies, likely to explain some of the excess risk in CHD patients. Further, we demonstrated a high prevalence of PD markers, tooth loss in particular, that were associated with a wide range of established CV risk factors, prognostic biomarkers and outcomes. Collectively, these findings indicate that tooth loss may be a significant risk factor among patients with stable chronic CHD.

Orientador: Iracema de Mattos Paranhos Calderon / Resumo: INTRODUÇÃO – Síndrome Metabólica (SM) está associada a gestações complicadas por Hiperglicemia Gestacional Leve (HGL) e Diabetes Melito Gestacional (DMG). OBJETIVO – Avaliar marcadores diagnósticos de SM, definidos por diferentes protocolos, na predição de HGL ou DMG. MÉTODO – Estudo de corte transversal, incluindo 506 mulheres, de gestação única e sem hiperglicemia, avaliadas na idade gestacional (IG) < ou ≥ 24 semanas, e submetidas a TOTG-75g e perfil glicêmico (PG) entre 24 e 28 semanas. Foram obtidos dados clínicos, antropométricos e laboratoriais – glicose de jejum (GJ), hemoglobina glicada (HbA1c), insulina basal e perfil lipídico. Os marcadores diagnósticos de SM, referenciados em três protocolos, foram relacionados a HGL ou DMG, por análises de regressão logística (OR e IC95%) e desempenho preditivo (Sensibilidade e Especificidade), p< 0,05. RESULTADOS – Dos protocolos de SM avaliados, TG ≥ 150 mg/dL, PA ≥ 130 / 85 mmHg, GJ ≥ 100 mg/dL e CC > 88 cm foram FR independentes para HGL ou DMG. Pela análise de desempenho, novos limites foram identificados – na IG < 24 sem, IMC pré ≥ 25 kg/m2 (72,7/50,6%) e CC ≥ 88 cm (78,1/43,9%); na IG ≥ 24 sem, TG ≥ 125 mg/dL (97,7/17,8%) e IMC ≥ 25 Kg/m2 (81,4/45,0%) apresentaram o melhor balanço Sens/Esp. CONCLUSÃO – Este estudo definiu marcadores diagnósticos de SM como preditores de risco independentes, mas novos limites testados tiveram melhor desempenho na predição de HGL ou DMG. Estes resultados deverão auxiliar na instituição de me... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: BACKGROUND – Metabolic Syndrome (MS) has been associated with Mild Gestational Hyperglycemia (MGH) and Gestational Diabetes Mellitus (GDM). OBJECTIVE - To assess the role of MS diagnostic markers proposed in three different sets of guidelines in the prediction of hyperglycemia (MGH or GDM) in pregnancy. METHODS – This cross-sectional cohort study undertaken between March/2014-December/2016, included women with a singleton pregnancy and no hyperglycemia at gestational age (GA) <or ≥ 24 weeks, who underwent a 75g-Oral Glucose Tolerance Test (75g-OGTT) and Glucose Profile ( GP) testing at 24-28 weeks. Clinical, anthropometric and laboratory data (fasting glucose-FG, glycated hemoglobin-HbA1c, basal insulin and lipid profile) were obtained. The relationship between MS markers and risk of MGH or GDM was evaluated by logistic regression analysis (OR, 95% CI). MS markers predictive performance (Sensitivity and Specificity) was also assessed (p <0.05). RESULTS - TG ≥ 150 mg/dL, BP ≥ 130/85 mmHg, FG ≥ 100 mg/dL and WC> 88 cm were identified as independent risk factors for MGH and GDM. Performance analysis identified new limits. Pregestational BMI≥25 kg/m2 (72.7/50.6%) and WC≥88 cm (78.1/43.9%) at GA<24 weeks; and TG≥125 mg/dL (97.7/17.8%) and BMI≥25 kg/m2 (81.4/45.0%) at GA≥24 weeks showed optimal Sensitivity/Specificity balance. CONCLUSION – MS diagnositic markers can independently predict risk, but new different thresholds showed better performance in predicting MGH and GDM. These r... (Complete abstract click electronic access below) / Doutor

Cardiovascular disease contributes significantly to global morbidity and mortality and is particularly prevalent among individuals with Type 2 diabetes, which is thought to in part be due to the association between diabetes and the metabolic syndrome. Traditional cardiovascular risk prediction scores perform well in the general population but their use in people with Type 2 diabetes is limited as they are thought to underperform in high risk groups. Indeed, the use of any risk prediction in people with Type 2 diabetes is a point of discussion among clinicians as people with diabetes are thought by some to be at immediate high risk of CVD, whereas others view them as having a degree of modifiable risk which can be addressed using risk prediction. In the general population, novel markers such as cIMT and carotid plaque, as well as other potential biomarkers of cardiovascular risk, have been explored as possible adjuncts to risk scores in the prediction of cardiovascular disease. The evidence for their use in general populations has been established, although there have been no firm conclusions with regard to recommendations for their use, which is partly due to the high degree of variability in cIMT measurement. However, the evidence for their use in people with Type 2 diabetes is sparse, despite the use of such markers as surrogate CV endpoints in clinical trials. This thesis aimed to describe the frequency, distribution and change of cIMT and carotid plaque, as well as to explore the relationship of cIMT and carotid plaque with cardiovascular risk factors, prevalent cardiovascular disease and future cardiovascular events in older people with Type 2 diabetes. The association between cIMT, carotid plaque and other novel risk markers was also explored. The analysis was performed using data from the Edinburgh Type 2 Diabetes Study (ET2DS). This study is a large, prospective cohort study of 1066 men and women with Type 2 diabetes, aged 60-75 years at recruitment, living in Edinburgh and the Lothians. cIMT and carotid plaque were measured at year 1 follow up of the study. Variables concerning cardiovascular risk factors used in this thesis were obtained from the data collection performed at baseline and year 1. A mean of 3.5 years of follow up was available for analysis and is complete for the baseline cohort as data linkage was performed. Mean values of cIMT in the ET2DS were comparable with other studies of cIMT in people with Type 2 diabetes and may indeed be higher than cIMT in the general population. Measurement of cIMT by the sonographer was comparable with computer aided measurements. Increasing cIMT was independently associated (although only modestly) with increasing age, male sex and raised systolic blood pressure. Mean cIMT was associated with prevalent vascular disease and was predictive of incident global cardiovascular events and coronary artery events (but not stroke) over and above UKPDS risk factors, although the clinical impact of this on the reclassification of vascular risk (as demonstrated by net reclassification index (NRI)) was limited. There was a high prevalence of carotid plaque, and in particular “high risk” plaque, in the ET2DS. Different measures of carotid plaque were independently associated with several cardiovascular risk factors. Carotid plaque thickness was independently associated, albeit modestly, with increasing age, male sex, duration of diabetes and hypertension, plaque score with increasing age, hypertension, smoking and low BMI, and high risk plaque with hypertension and low BMI. All measures of carotid plaque were associated with prevalent vascular disease. However, despite these associations, carotid plaque did not have any additional predictive value for incident cardiovascular events over and above UKPDS risk factors. Finally, measures of cIMT and carotid plaque in the ET2DS were associated with the biomarkers ankle brachial index (ABI) and NTproBNP. In addition these markers were significantly higher in those individuals with prevalent vascular disease, suggesting a more extensive exploration of the association of these markers in relation to cardiovascular disease in the ET2DS may be warranted. cIMT and carotid plaque are modestly associated with traditional cardiovascular risk factors and prevalent cardiovascular disease in older adults with Type 2 diabetes. cIMT has been shown to be predictive of incident events while carotid plaque was not, in people with Type 2 diabetes, over and above traditional cardiovascular risk factors, although its impact on risk reclassification may only be small. Further evidence is required from the longer follow up of the ET2DS before firm conclusions can be drawn on the usefulness of cIMT and carotid plaque as risk markers in people with Type 2 diabetes. In addition, large collaborative studies could be used to further explore the relationship of carotid plaque, and change in cIMT with incident cardiovascular events, as well as exploring the additive effect of cIMT and plaque on risk prediction.

Chronic heart failure (HF) is a condition that affects more than 900,000 people in the UK. Mortality rates associated with the condition are high, with nearly 20% of patients dying within one year of diagnosis. Continuous monitoring and risk stratification can help identify patients at risk of deterioration and may consequently improve patients' likelihood of survival. Current repeated-measure risk stratification techniques for HF patients often rely on subjective perception of symptoms, such as breathlessness, and markers of fluid retention in the body (e.g. weight). Despite the common use of such markers, studies have shown that they offer limited effectiveness in predicting HF-related events. This thesis set out to identify and evaluate new markers for repeated-measure risk stratification of HF patients. It started with an exploration of traditional HF measurements, including weight, blood pressure, heart rate and symptom scores, and aimed to improve the performance of these measurements using a data-driven approach. A multi-variate model was developed from data acquired during a randomised controlled trial of remotely-monitored HF patients. The rare occurrence of HF-related adverse events during the trial required the developement of a careful methodology. This methodology helped identify the markers with most predictive ability, which achieved moderate performance at identifying patients at risk of HF-related adverse events, clearly outperforming commonly-used thresholds. Subsequently, this thesis explored the potential value of additional, accelerometer-derived physical activity (PA) and sleep markers. For this purpose, the ability of accelerometer-derived markers to differentiate between individuals with and without HF was evaluated. It was found that markers that summarise the frequency and duration of different PA intensities performed best at differentiating between the two groups and may therefore be most suitable for future use in repeated-measure applications. As part of the analysis of accelerometer-derived HF markers, a gap in the methodology of automated accelerometer processing was identified, namely the need for self-reported sleep-onset and wake-up information. As a result, Chapter 5 of this thesis describes the development and evaluation of a data-driven solution for this problem. In summary, this thesis explored both traditional and new, accelerometer-derived markers for the early detection of HF deterioration. It utilised sound methodology to overcome limitations faced by sparse and unbalanced datasets and filled a methodological gap in the processing of signals from wrist-worn accelerometers.