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An accurate estimation of survival is essential in selecting the appropriate treatment for patients suffering from symptomatic spinal bone metastases. This thesis describes the development and validation of a predictive model, meant to assist clinicians in their daily practice.

Background: Intra-luminal thrombosis is a key factor in Abdominal Aortic Aneurysms (AAA) growth. Patients with AAA form dense clots that are resistant to fibrinolysis. Thrombin-activatable fibrinolysis inhibitor (TAFI) has been shown to influence AAA development in murine models. Objective: The aim of this study is to characterise the role of TAFI in human AAA. Methods: Plasma levels of TAFI, TAFI activation peptide (TAFI-AP), activated/inactivated TAFI (TAFIa/ai) and plasmin-α2-antiplasmin complex were measured by ELISAs in patients with AAA (n=202) and controls (n=188). Results: TAFIa/ai and TAFI-AP levels were higher in patients than controls (median (IQR): 20.3 (14.6-32.8) vs. 14.2 (11.2-19.3) and 355.0 (232.4-528.1) vs. 248.6 (197.1-328.1) ng/ml). TAFIa/ai was positively correlated with TAFI-AP (r=0.164). Intact TAFI levels were not different between patients and controls (13.4 (11.2-16.1) vs. 12.8 (10.6-15.4) μg/ml). Plasmin-α2-antiplasmin was higher in AAA patients than controls (690.0 (489.1-924.3) vs. 480.7 (392.6-555.3) ng/ml). Conclusions: The increase in TAFIa/ai and TAFI-AP suggest an increased TAFI activation in patients with AAA. Prospective studies are required to further elucidate the role of TAFI and fibrinolysis in AAA pathogenesis.

Study Design.A retrospective cohort study.Objective.The aim of this study was to assess and compare the predictive accuracy of six models designed to estimate survival of patients suffering from spinal bone metastases Just (SBMs).Summary of Background Data.On the basis of the estimated survival of patients with SBM, extent of treatment can be adjusted. To aid clinicians in the difficult task of assessing probability of survival, prognostic scoring systems have been developed by Tomita, Tokuhashi, Van der Linden, Bauer, Rades, and Bollen.Methods.All patients who were treated for SBM between 2000 and 2010 were included in this international, multicenter, retrospective study (n=1379). Medical records were reviewed for all items needed to use the scoring systems. Survival time was calculated as the difference between start of treatment for SBM and date of death. Survival curves were estimated using the Kaplan-Meier method and accuracy was assessed with the c-statistic. Survival rates of the worst prognostic groups were evaluated at 4 months.Results.Median follow-up was 6.7 years [95% confidence interval (95% CI) 5.6-7.7] with a minimum of 2.3 years and a maximum of 12.3 years. The overall median survival was 5.1 months (95% CI 4.6-5.6). The most common primary tumors were breast (n=388, 28%), lung (n=318, 23%), and prostate cancer (n=259, 19%). The Tokuhashi, Bauer, Tomita, and Van der Linden models performed similar with a c-statistic of 0.64 to 0.66 and a 4-month accuracy of 62% to 65%. The Rades model (c-statistic 0.44) and Bollen model (c-statistic 0.70) had a 4-month accuracy of 69% and 75%, respectively.Conclusion.The Bollen model performs better than the other models. However, improvements are still warranted to increase the accuracy.Level of Evidence: 3

Item does not contain fulltext PURPOSE: Patients with bone metastases have a widely varying survival. A reliable estimation of survival is needed for appropriate treatment strategies. Our goal was to assess the value of simple prognostic factors, namely, patient and tumor characteristics, Karnofsky performance status (KPS), and patient-reported scores of pain and quality of life, to predict survival in patients with painful bone metastases. METHODS AND MATERIALS: In the Dutch Bone Metastasis Study, 1157 patients were treated with radiation therapy for painful bone metastases. At randomization, physicians determined the KPS; patients rated general health on a visual analogue scale (VAS-gh), valuation of life on a verbal rating scale (VRS-vl) and pain intensity. To assess the predictive value of the variables, we used multivariate Cox proportional hazard analyses and C-statistics for discriminative value. Of the final model, calibration was assessed. External validation was performed on a dataset of 934 patients who were treated with radiation therapy for vertebral metastases. RESULTS: Patients had mainly breast (39%), prostate (23%), or lung cancer (25%). After a maximum of 142 weeks' follow-up, 74% of patients had died. The best predictive model included sex, primary tumor, visceral metastases, KPS, VAS-gh, and VRS-vl (C-statistic = 0.72, 95% CI = 0.70-0.74). A reduced model, with only KPS and primary tumor, showed comparable discriminative capacity (C-statistic = 0.71, 95% CI = 0.69-0.72). External validation showed a C-statistic of 0.72 (95% CI = 0.70-0.73). Calibration of the derivation and the validation dataset showed underestimation of survival. CONCLUSION: In predicting survival in patients with painful bone metastases, KPS combined with primary tumor was comparable to a more complex model. Considering the amount of variables in complex models and the additional burden on patients, the simple model is preferred for daily use. In addition, a risk table for survival is provided.

The analysis of problem-solving activities carried out by students in learning settings involves studying the students' actions and assessing the solutions they have created. This analysis constitutes an ideal starting point to support an automatic intervention in the student activity by means of feedback or other means to help students build their own knowledge. In this paper, we present a model-driven framework to facilitate the automation of this problem-solving analysis and of providing feedback. This framework includes a set of authoring tools that enable software developers to specify the analysis process and its intervention mechanisms by means of visual languages. The models specified in this way are computed by the framework in order to create technological support to automate the problem-solving analysis. The use of the framework is illustrated thanks to a case study in the field of System Dynamics where problem-solving practices are analysed.

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