Prediction markets supporting technology assessment

In this thesis, we study the use of prediction markets for technology assessment. We particularly focus on their ability to assess complex issues, the design constraints required for such applications and their efficacy compared to traditional techniques. To achieve this, we followed a design science research paradigm, iteratively developing, instantiating, evaluating and refining the design of our artifacts. This allowed us to make multiple contributions, both practical and theoretical. We first showed that prediction markets are adequate for properly assessing complex issues. We also developed a typology of design factors and design propositions for using these markets in a technology assessment context. Then, we showed that they are able to solve some issues related to the R&D portfolio management process and we proposed a roadmap for their implementation. Finally, by comparing the instantiation and the results of a multi-criteria decision method and a prediction market, we showed that the latter are more efficient, while offering similar results. We also proposed a framework for comparing forecasting methods, to identify the constraints based on contingency factors. In conclusion, our research opens a new field of application of prediction markets and should help hasten their adoption by enterprises. Résumé français: Dans cette thèse, nous étudions l'utilisation de marchés de prédictions pour l'évaluation de nouvelles technologies. Nous nous intéressons plus particulièrement aux capacités des marchés de prédictions à évaluer des problématiques complexes, aux contraintes de conception pour une telle utilisation et à leur efficacité par rapport à des techniques traditionnelles. Pour ce faire, nous avons suivi une approche Design Science, développant itérativement plusieurs prototypes, les instanciant, puis les évaluant avant d'en raffiner la conception. Ceci nous a permis de faire de multiples contributions tant pratiques que théoriques. Nous avons tout d'abord montré que les marchés de prédictions étaient adaptés pour correctement apprécier des problématiques complexes. Nous avons également développé une typologie de facteurs de conception ainsi que des propositions de conception pour l'utilisation de ces marchés dans des contextes d'évaluation technologique. Ensuite, nous avons montré que ces marchés pouvaient résoudre une partie des problèmes liés à la gestion des portes-feuille de projets de recherche et développement et proposons une feuille de route pour leur mise en oeuvre. Finalement, en comparant la mise en oeuvre et les résultats d'une méthode de décision multi-critère et d'un marché de prédiction, nous avons montré que ces derniers étaient plus efficaces, tout en offrant des résultats semblables. Nous proposons également un cadre de comparaison des méthodes d'évaluation technologiques, permettant de cerner au mieux les besoins en fonction de facteurs de contingence. En conclusion, notre recherche ouvre un nouveau champ d'application des marchés de prédiction et devrait permettre d'accélérer leur adoption par les entreprises.

Polymorphisms in tumor necrosis factor-α increase susceptibility to intra-abdominal Candida infection in high-risk surgical ICU patients*.

OBJECTIVES: To evaluate the influence of genetic polymorphisms on the susceptibility to Candida colonization and intra-abdominal candidiasis, a blood culture-negative life-threatening infection in high-risk surgical ICU patients. DESIGN: Prospective observational cohort study. SETTING: Surgical ICUs from two University hospitals of the Fungal Infection Network of Switzerland. PATIENTS: Eighty-nine patients at high risk for intra-abdominal candidiasis (68 with recurrent gastrointestinal perforation and 21 with acute necrotizing pancreatitis). MEASUREMENTS AND MAIN RESULTS: Eighteen single-nucleotide polymorphisms in 16 genes previously associated with development of fungal infections were analyzed from patient's DNA by using an Illumina Veracode genotyping platform. Candida colonization was defined by recovery of Candida species from at least one nonsterile site by twice weekly monitoring of cultures from oropharynx, stools, urine, skin, and/or respiratory tract. A corrected colonization index greater than or equal to 0.4 defined "heavy" colonization. Intra-abdominal candidiasis was defined by the presence of clinical symptoms and signs of peritonitis or intra-abdominal abscess and isolation of Candida species either in pure or mixed culture from intraoperatively collected abdominal samples. Single-nucleotide polymorphisms in three innate immune genes were associated with development of a Candida corrected colonization index greater than or equal to 0.4 (Toll-like receptor rs4986790, hazard ratio = 3.39; 95% CI, 1.45-7.93; p = 0.005) or occurrence of intra-abdominal candidiasis (tumor necrosis factor-α rs1800629, hazard ratio = 4.31; 95% CI, 1.85-10.1; p= 0.0007; β-defensin 1 rs1800972, hazard ratio = 3.21; 95% CI, 1.36-7.59; p = 0.008). CONCLUSION: We report a strong association between the promoter rs1800629 single-nucleotide polymorphism in tumor necrosis factor-α and an increased susceptibility to intra-abdominal candidiasis in a homogenous prospective cohort of high-risk surgical ICU patients. This finding highlights the relevance of the tumor necrosis factor-α functional polymorphism in immune response to fungal pathogens. Immunogenetic profiling in patients at clinical high risk followed by targeted antifungal interventions may improve the prevention or preemptive management of this life-threatening infection.

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

The soil CO2 emission has high spatial variability because it depends strongly on soil properties. The purpose of this study was to (i) characterize the spatial variability of soil respiration and related properties, (ii) evaluate the accuracy of results of the ordinary kriging method and sequential Gaussian simulation, and (iii) evaluate the uncertainty in predicting the spatial variability of soil CO2 emission and other properties using sequential Gaussian simulations. The study was conducted in a sugarcane area, using a regular sampling grid with 141 points, where soil CO2 emission, soil temperature, air-filled pore space, soil organic matter and soil bulk density were evaluated. All variables showed spatial dependence structure. The soil CO2 emission was positively correlated with organic matter (r = 0.25, p < 0.05) and air-filled pore space (r = 0.27, p < 0.01) and negatively with soil bulk density (r = -0.41, p < 0.01). However, when the estimated spatial values were considered, the air-filled pore space was the variable mainly responsible for the spatial characteristics of soil respiration, with a correlation of 0.26 (p < 0.01). For all variables, individual simulations represented the cumulative distribution functions and variograms better than ordinary kriging and E-type estimates. The greatest uncertainties in predicting soil CO2 emission were associated with areas with the highest estimated values, which produced estimates from 0.18 to 1.85 t CO2 ha-1, according to the different scenarios considered. The knowledge of the uncertainties generated by the different scenarios can be used in inventories of greenhouse gases, to provide conservative estimates of the potential emission of these gases.

Fluid dynamical prediction of changed v1 flow at energies available at the CERN Large Hadron Collider

Substantial collective flow is observed in collisions between lead nuclei at Large Hadron Collider (LHC) as evidenced by the azimuthal correlations in the transverse momentum distributions of the produced particles. Our calculations indicate that the global v1-flow, which at RHIC peaked at negative rapidities (named third flow component or antiflow), now at LHC is going to turn toward forward rapidities (to the same side and direction as the projectile residue). Potentially this can provide a sensitive barometer to estimate the pressure and transport properties of the quark-gluon plasma. Our calculations also take into account the initial state center-of-mass rapidity fluctuations, and demonstrate that these are crucial for v1 simulations. In order to better study the transverse momentum flow dependence we suggest a new "symmetrized" v1S(pt) function, and we also propose a new method to disentangle global v1 flow from the contribution generated by the random fluctuations in the initial state. This will enhance the possibilities of studying the collective Global v1 flow both at the STAR Beam Energy Scan program and at LHC.

Prediction of Infant Drug Exposure Through Breastfeeding: Population PK Modeling and Simulation of Fluoxetine Exposure.

The likelihood of significant exposure to drugs in infants through breast milk is poorly defined, given the difficulties of conducting pharmacokinetics (PK) studies. Using fluoxetine (FX) as an example, we conducted a proof-of-principle study applying population PK (popPK) modeling and simulation to estimate drug exposure in infants through breast milk. We simulated data for 1,000 mother-infant pairs, assuming conservatively that the FX clearance in an infant is 20% of the allometrically adjusted value in adults. The model-generated estimate of the milk-to-plasma ratio for FX (mean: 0.59) was consistent with those reported in other studies. The median infant-to-mother ratio of FX steady-state plasma concentrations predicted by the simulation was 8.5%. Although the disposition of the active metabolite, norfluoxetine, could not be modeled, popPK-informed simulation may be valid for other drugs, particularly those without active metabolites, thereby providing a practical alternative to conventional PK studies for exposure risk assessment in this population.

A multi-center study: intra-scan and inter-scan variability of diffusion spectrum imaging.

The objective of this study was to investigate whether it is possible to pool together diffusion spectrum imaging data from four different scanners, located at three different sites. Two of the scanners had identical configuration whereas two did not. To measure the variability, we extracted three scalar maps (ADC, FA and GFA) from the DSI and utilized a region and a tract-based analysis. Additionally, a phantom study was performed to rule out some potential factors arising from the scanner performance in case some systematic bias occurred in the subject study. This work was split into three experiments: intra-scanner reproducibility, reproducibility with twin-scanner settings and reproducibility with other configurations. Overall for the intra-scanner and twin-scanner experiments, the region-based analysis coefficient of variation (CV) was in a range of 1%-4.2% and below 3% for almost every bundle for the tract-based analysis. The uncinate fasciculus showed the worst reproducibility, especially for FA and GFA values (CV 3.7-6%). For the GFA and FA maps, an ICC value of 0.7 and above is observed in almost all the regions/tracts. Looking at the last experiment, it was found that there is a very high similarity of the outcomes from the two scanners with identical setting. However, this was not the case for the two other imagers. Given the fact that the overall variation in our study is low for the imagers with identical settings, our findings support the feasibility of cross-site pooling of DSI data from identical scanners.

Validation of a clinical prediction score for ruling out coronary heart disease in primary care patients with chest pain.

Background: A patient's chest pain raises concern for the possibility of coronary heart disease (CHD). An easy to use clinical prediction rule has been derived from the TOPIC study in Lausanne. Our objective is to validate this clinical score for ruling out CHD in primary care patients with chest pain. Methods: This secondary analysis used data collected from a oneyear follow-up cohort study attending 76 GPs in Germany. Patients attending their GP with chest pain were questioned on their age, gender, duration of chest pain (1-60 min), sternal pain location, pain increases with exertion, absence of tenderness point at palpation, cardiovascular risks factors, and personal history of cardiovascular disease. Area under the curve (ROC), sensitivity and specificity of the Lausanne CHD score were calculated for patients with full data. Results: 1190 patients were included. Full data was available for 509 patients (42.8%). Missing data was not related to having CHD (p = 0.397) or having a cardiovascular risk factor (p = 0.275). 76 (14.9%) were diagnosed with a CHD. Prevalence of CHD were respectively of 68/344 (19.8%), 2/62 (3.2%), 6/103 (5.8%) in the high, intermediate and low risk category. ROC was of 72.9 (CI95% 66.8; 78.9). Ruling out patients with low risk has a sensitivity of 92.1% (CI95% 83.0; 96.7) and a specificity of 22.4% (CI95% 18.6%; 26.7%). Conclusion: The Lausanne CHD score shows reasonably good sensitivity and can be used to rule out coronary events in patients with chest pain. Patients at risk of CHD for other rarer reasons should nevertheless also be investigated.

Complicated intra-abdominal infections in Europe: preliminary data from the first three months of the CIAO Study.

The CIAO Study is a multicenter observational study currently underway in 66 European medical institutions over the course of a six-month study period (January-June 2012).This preliminary report overviews the findings of the first half of the study, which includes all data from the first three months of the six-month study period.Patients with either community-acquired or healthcare-associated complicated intra-abdominal infections (IAIs) were included in the study.912 patients with a mean age of 54.4 years (range 4-98) were enrolled in the study during the first three-month period. 47.7% of the patients were women and 52.3% were men. Among these patients, 83.3% were affected by community-acquired IAIs while the remaining 16.7% presented with healthcare-associated infections. Intraperitoneal specimens were collected from 64.2% of the enrolled patients, and from these samples, 825 microorganisms were collectively identified.The overall mortality rate was 6.4% (58/912). According to univariate statistical analysis of the data, critical clinical condition of the patient upon hospital admission (defined by severe sepsis and septic shock) as well as healthcare-associated infections, non-appendicular origin, generalized peritonitis, and serious comorbidities such as malignancy and severe cardiovascular disease were all significant risk factors for patient mortality.White Blood Cell counts (WBCs) greater than 12,000 or less than 4,000 and core body temperatures exceeding 38°C or less than 36°C by the third post-operative day were statistically significant indicators of patient mortality.

Intravitreal ranibizumab for age-related macular degeneration - optical coherence tomography guided retreatment intervals and their intra- and inter-individual variability

PURPOSE:To determine whether the need for retreatment after an initial loading phase of 3 monthly intravitreal injections of ranibizumab shows an intra-individual regular rhythm and to what degree it varies between different patients.SETTING:Prospective mono-centre cohort study.METHODS:Prospective study with 42 patients with exudative age-related macular degeneration (AMD), treatment na?ve, giving informed consent. Loading dose of 3 monthly doses of ranibizumab (0,5mg), followed by a 12 months pro re nata (PRN) regimen according to early exudative signs on spectral domain optical coherence tomography (HD-OCT Cirrus Zeiss?, cube 512x126). The follow-up visits were intensified (week 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, etc after each injection) in order to detect exudative recurrences early, and injection followed within 3 days in cases of subretinal fluid, or intraretinal cysts, or central thickness increase of >50?m. Intervals were calculated between injections and the following recurrence was calculated for the 12 month follow-up with PRN treatment. Variability was expressed as standard deviation (SD). RESULTS Visual acuity (VA) improved from a mean ETDRS letter score of 61.6 (SD 10.8) at baseline to 68.0 (SD 10.2, +6.4 letters) at month 3 and increased further to 74.7 (SD 9.0, +13.1 letters from baseline) at month 12. The 15 patients who have completed the study by October 2010 showed maintenance of the VA improvement. Retinal thickness of the central foveal subfield improved from a mean value of 366?m(baseline) to 253?m(month 3), well maintained thereafter. Mean number of injections was 8.8 (SD 3.5) per 12 months of follow-up (after 3 loading doses), ranging from 0 to 12, with mean individual treatment-recurrence intervals ranging from 28 to >365 days (mean 58 days). Intraindividual variability of treatment-recurrence intervals, measured as SD of the individual intervals, was 7.1days as a mean value(range 1.7 ? 22.6 days) for the 33 patients with more than 1 injection during follow-up. SD was higher for longer intervals of an individual patient. It ranged within 20% of the mean intra-individual interval for 30 patients(91%) and within 15% for 21 patients(64%). The first interval was within 1 week of the mean intra-individual interval in 64% of patients and within 2 weeks in 89% of patients.CONCLUSIONS:The majority of AMD patients showed a relatively stable rhythm for PRN injections of intravitreal ranibizumab after initial loading phase, associated with excellent functional and anatomical results. The initial interval between last loading dose and first recurrence may have a predictive value for further need of treatment, therefore potentially facilitating follow-up and patient care.

Ruling out coronary artery disease in primary care: development and validation of a simple prediction rule.

BACKGROUND: Chest pain can be caused by various conditions, with life-threatening cardiac disease being of greatest concern. Prediction scores to rule out coronary artery disease have been developed for use in emergency settings. We developed and validated a simple prediction rule for use in primary care. METHODS: We conducted a cross-sectional diagnostic study in 74 primary care practices in Germany. Primary care physicians recruited all consecutive patients who presented with chest pain (n = 1249) and recorded symptoms and findings for each patient (derivation cohort). An independent expert panel reviewed follow-up data obtained at six weeks and six months on symptoms, investigations, hospital admissions and medications to determine the presence or absence of coronary artery disease. Adjusted odds ratios of relevant variables were used to develop a prediction rule. We calculated measures of diagnostic accuracy for different cut-off values for the prediction scores using data derived from another prospective primary care study (validation cohort). RESULTS: The prediction rule contained five determinants (age/sex, known vascular disease, patient assumes pain is of cardiac origin, pain is worse during exercise, and pain is not reproducible by palpation), with the score ranging from 0 to 5 points. The area under the curve (receiver operating characteristic curve) was 0.87 (95% confidence interval [CI] 0.83-0.91) for the derivation cohort and 0.90 (95% CI 0.87-0.93) for the validation cohort. The best overall discrimination was with a cut-off value of 3 (positive result 3-5 points; negative result <or= 2 points), which had a sensitivity of 87.1% (95% CI 79.9%-94.2%) and a specificity of 80.8% (77.6%-83.9%). INTERPRETATION: The prediction rule for coronary artery disease in primary care proved to be robust in the validation cohort. It can help to rule out coronary artery disease in patients presenting with chest pain in primary care.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.