Explorative cost-effectiveness analysis of colorectal cancer recurrence detection with next-generation sequencing liquid biopsy in Spain, France, and Germany.

Background: Next-generation sequencing liquid biopsy (NGS-LB) for colorectal cancer (CRC) detection and surveillance remains an expensive technology as economies of scale have not yet been realized. Nevertheless, the cost of sequencing has decreased while sensitivity has increased, raising the question of whether cost-effectiveness (CE) has already been achieved from the perspective of European healthcare systems. Objectives: This health economic (HE) modeling study explores the CE of NGS-LB for CRC based on direct treatment costs compared to standard care without liquid biopsy in Spain, France, and Germany. Methods: A structured literature search was used to collect evidence from 2009 to 2020 on the stage-dependent quality of life (quality-adjusted life-years, QALY), efficacy, and total direct treatment costs (TDC) of NGS-LB. A decision-analytic Markov model was developed. Over the remaining lifetime, cumulative life expectancy (LE), TDC, and QALYs were calculated for 60-year-old men and women in CRC stage III with different assumed effects of NGS-LB of 1% or 3% on improved survival and reduced stage progression, respectively. Results: The use of NGS-LB increases LE by 0.19 years in Spanish men (France: 0.19 years, Germany: 0.13 years) and by 0.21 years in Spanish women (France: 0.21 years, Germany: 0.14 years), respectively. The 3% discounted cost per QALY gained was 35,571.95 € for Spanish men (France: 31,705.15 €, Germany: 37,537.68 €) and 35,435.71 € for Spanish women (France: 31,295.57 €, Germany: 38,137.08 €) in the scenario with 3% improved survival and reduced disease progression. Compared to the other two countries, Germany has by far the highest TDC, which can amount to >80k euros in the last treatment year. Conclusion: In this explorative HE modeling study, NGS-LB achieves generally accepted CE levels in CRC treatment from the health system perspective in three major European economies under assumptions of small improvements in cancer recurrence and survival. Confirmation of these findings through clinical trials is encouraged.

Is it worthwhile to use next generation liquid biopsy for cancer recurrence detection on patients with colorectal cancer? Colon cancer is common. Worldwide, almost one million people die from it every year. Next Generation Sequencing Liquid Biopsy is a very sensitive technology for detecting cancer cells and their genetic information in the blood. Therefore, it is a good way to detect cancer and to detect early recurrence of a previously treated tumor. This test procedure is not yet used very often. Therefore, it is still expensive. Furthermore, there are still no studies that have demonstrated that and how liquid biopsy can aid doctors and patients after initial treatment. The research team of this study has developed an analytical model to investigate what performance liquid biopsy should have to demonstrate an affordable patient benefit in terms of quality of life, survival and cost per additional quality-adjusted life year gained. To do this, they studied the existing medical literature and many cost studies on colorectal cancer for the countries of Spain, France and Germany to feed their model. Then, they made different assumptions about the performance of liquid biopsy and did calculations. In the process, they also particularly examined the significance of specific influencing factors such as costs or disease progression in so-called sensitivity analyses. As a result, the authors found that there are large differences in treatment costs for colorectal cancer between the three countries Spain, France and Germany. Furthermore, even small improvements in the progression of cancer and the survival of cancer patients lead to the economic efficiency of liquid biopsy for the health care system. However, these are still thought experiments, so the research team of this study says that there should be further clinical trials to assess the impact of liquid biopsy on cancer progression and patient survival by using this technology. By this, one could confirm or contradict the authors' educated assumptions and possibly pave a new way towards medical progress for people with colorectal cancer.

Is there a place for sigmoidoscopy in colorectal cancer screening? A systematic review and critical appraisal of cost-effectiveness models.

INTRODUCTION: Screening for colorectal cancer (CRC) is effective in reducing both incidence and mortality. Colonoscopy and stool tests are most frequently used for this purpose. Sigmoidoscopy is an alternative screening measure with a strong evidence base. Due to its distinct characteristics, it might be preferred by subgroups. The aim of this systematic review is to analyze the cost-effectiveness of sigmoidoscopy for CRC screening compared to other screening methods and to identify influencing parameters. METHODS: A systematic literature search for the time frame 01/2010-01/2023 was conducted using the databases MEDLINE, Embase, EconLit, Web of Science, NHS EED, as well as the Cost-Effectiveness Registry. Full economic analyses examining sigmoidoscopy as a screening measure for the general population at average risk for CRC were included. Incremental cost-effectiveness ratios were calculated. All included studies were critically assessed based on a questionnaire for modelling studies. RESULTS: Twenty-five studies are included in the review. Compared to no screening, sigmoidoscopy is a cost-effective screening strategy for CRC. When modelled as a single measure strategy, sigmoidoscopy is mostly dominated by colonoscopy or modern stool tests. When combined with annual stool testing, sigmoidoscopy in 5-year intervals is more effective and less costly than the respective strategies alone. The results of the studies are influenced by varying assumptions on adherence, costs, and test characteristics. CONCLUSION: The combination of sigmoidoscopy and stool testing represents a cost-effective screening strategy that has not received much attention in current guidelines. Further research is needed that goes beyond a narrow focus on screening technology and models different, preference-based participation behavior in subgroups.

A German Smartphone-Based Self-management Tool for Psoriasis: Community-Driven Development and Evaluation of Quality-of-Life Effects.

BACKGROUND: Psoriasis is a chronic disease characterized by inflammation, increased scaling, itching, and other symptoms. Psoriasis is not contagious, but patients have often felt shunned. Therefore, in addition to psoriasis symptoms, stress, anxiety, and depression can also affect quality of life (QoL). Surveys show that only a quarter of patients are satisfied with the success of their therapy. However, in addition to medical therapy, self-management can also make it easier to deal with chronic diseases like psoriasis. OBJECTIVE: The aim of this project was to develop a smartphone-based self-management tool (SMT) specifically for patients with psoriasis using a community-driven process. The impact of the SMT on QoL as well as its acceptance and usability were evaluated. METHODS: In collaboration with an internet-based self-help community, 2 user surveys were conducted to determine the requirements for a smartphone-based SMT. The surveys consisted of semistructured questionnaires asking for desired features in an SMT for psoriasis. A pilot study was conducted to evaluate QoL, acceptance, and usability. Community users were recruited to use the app for 21 days and complete the Dermatology Life Quality Index (DLQI) questionnaire at the beginning (T0) and end (T1). Afterward, participants were asked to complete another questionnaire on usability and ease of use. RESULTS: SMT requirements were collected from 97 members of an internet-based community. The SMT was built as a progressive web app that communicates with a server back end and an Angular web app for content management. The app was used by 15 participants who also provided qualitative feedback, and 10 participants answered all questionnaires. The average DLQI score was 7.1 (SD 6.2) at T0 and 6.9 (SD 6.6) at T1. The minimal required sample size of 27 was not reached. CONCLUSIONS: The high degree of community participation in the development process and the responses during the requirement engineering process indicated that there is a general need for an independently developed SMT for patients with psoriasis. However, the feedback received after app use shows that the SMT does not meet the needs of the community. It can be concluded that a more customizable app is needed. The focus and needs of the users were very heterogeneous. Similar developments and research could benefit from the findings of this project.

Quality of Life Assessment for Tonsillar Infections and Their Treatment.

Background and Objectives: Tonsillar infections are a common reason to see a physician and lead to a reduction in the patients' health-related quality of life (HRQoL). HRQoL may be an important criterion in decision science and should be taken into account when deciding when to perform tonsillectomy, especially for chronic tonsillitis. The aim of this study was to determine the health utility for different states of tonsillar infections. Materials and Methods: Hospitalized patients with acute tonsillitis or a peritonsillar abscess were asked about their HRQoL with the 15D questionnaire. Patients who had undergone tonsillectomy were reassessed six months postoperatively. Results: In total, 65 patients participated in the study. The health states of acute tonsillitis and peritonsillar abscess had both a utility of 0.72. Six months after tonsillectomy, the mean health utility was 0.95. Conclusions: Our study confirms a substantial reduction in utility due to tonsillar infections. Tonsillectomy significantly improves the utility and therefore HRQoL six months after surgery.

Additional offer of sigmoidoscopy in colorectal cancer screening in Germany: rationale and protocol of the decision-analytic modelling approach in the SIGMO study.

INTRODUCTION: In Germany, statutory insured persons are entitled to a stool test (faecal immunochemical test (FIT)) or colonoscopy for colorectal cancer (CRC) screening, depending on age and sex, yet participation rates are rather low. Sigmoidoscopy is a currently not available screening measure that has a strong evidence base for incidence and mortality reduction. Due to its distinct characteristics, it might be preferred by some, who now reject colonoscopy. The objective of this study is to estimate the economic consequences of the additional offer of sigmoidoscopy for CRC screening in Germany compared with the present screening practice while considering the preferences of the general population. METHODS AND ANALYSIS: A decision-analytic modelling approach will be developed that compares the present CRC screening programme in Germany (FIT, colonoscopy) with a programme extended by sigmoidoscopy from a societal perspective. A decision tree and Markov model will be combined to assess both short-term and long-term effects, such as CRC and adenoma detection rates, the number of CRC cases, CRC mortality as well as complications. The incremental cost per quality-adjusted life year gained for each alternative will be calculated. The model will incorporate the general population's preferences based on a discrete choice experiment. Further, input parameters will be taken from the literature, the German cancer registry and health insurance claims data. ETHICS AND DISSEMINATION: Ethical approval for the study was obtained from the Ethics Committee of Hannover Medical School (ID: 8671_BO_K_2019). The findings of the study will be published in peer-reviewed journals and presented at national and/or international conferences. TRIAL REGISTRATION NUMBER: DRKS00019010.

Exploring Structural Uncertainty and Impact of Health State Utility Values on Lifetime Outcomes in Diabetes Economic Simulation Models: Findings from the Ninth Mount Hood Diabetes Quality-of-Life Challenge.

BACKGROUND: Structural uncertainty can affect model-based economic simulation estimates and study conclusions. Unfortunately, unlike parameter uncertainty, relatively little is known about its magnitude of impact on life-years (LYs) and quality-adjusted life-years (QALYs) in modeling of diabetes. We leveraged the Mount Hood Diabetes Challenge Network, a biennial conference attended by international diabetes modeling groups, to assess structural uncertainty in simulating QALYs in type 2 diabetes simulation models. METHODS: Eleven type 2 diabetes simulation modeling groups participated in the 9th Mount Hood Diabetes Challenge. Modeling groups simulated 5 diabetes-related intervention profiles using predefined baseline characteristics and a standard utility value set for diabetes-related complications. LYs and QALYs were reported. Simulations were repeated using lower and upper limits of the 95% confidence intervals of utility inputs. Changes in LYs and QALYs from tested interventions were compared across models. Additional analyses were conducted postchallenge to investigate drivers of cross-model differences. RESULTS: Substantial cross-model variability in incremental LYs and QALYs was observed, particularly for HbA1c and body mass index (BMI) intervention profiles. For a 0.5%-point permanent HbA1c reduction, LY gains ranged from 0.050 to 0.750. For a 1-unit permanent BMI reduction, incremental QALYs varied from a small decrease in QALYs (-0.024) to an increase of 0.203. Changes in utility values of health states had a much smaller impact (to the hundredth of a decimal place) on incremental QALYs. Microsimulation models were found to generate a mean of 3.41 more LYs than cohort simulation models (P = 0.049). CONCLUSIONS: Variations in utility values contribute to a lesser extent than uncertainty captured as structural uncertainty. These findings reinforce the importance of assessing structural uncertainty thoroughly because the choice of model (or models) can influence study results, which can serve as evidence for resource allocation decisions.HighlightsThe findings indicate substantial cross-model variability in QALY predictions for a standardized set of simulation scenarios and is considerably larger than within model variability to alternative health state utility values (e.g., lower and upper limits of the 95% confidence intervals of utility inputs).There is a need to understand and assess structural uncertainty, as the choice of model to inform resource allocation decisions can matter more than the choice of health state utility values.

Assessment of SARS-CoV-2 Infection among Healthcare Workers of a German COVID-19 Treatment Center.

To date, more than 160 million people have been infected with COVID-19 worldwide. In the present study, we investigated the history of SARS-CoV-2 infection among 3067 healthcare workers (HCW) in a German COVID-19 treatment center during the early phase of the pandemic (July 2020) based on the seroprevalence of SARS-CoV-2 antibodies and self-reported previous PCR results. The results demonstrate a low prevalence of SARS-CoV-2 infection (n = 107 [3.5%]) with no increased risk for employees with a high level of patient exposure in general or working in COVID-19-confined areas in particular. This suggests that the local hygiene standards implemented in our hospital during the first wave of COVID-19 pandemic were effective in preventing patient-to-HCW transmission. No evidence for highly mobile staff serving as a vector for SARS-CoV-2 transmission could be found. In addition, impairment of smell and/or taste was strongly associated with SARS-CoV-2 history.

Evaluating the Ability of Economic Models of Diabetes to Simulate New Cardiovascular Outcomes Trials: A Report on the Ninth Mount Hood Diabetes Challenge.

OBJECTIVES: The cardiovascular outcomes challenge examined the predictive accuracy of 10 diabetes models in estimating hard outcomes in 2 recent cardiovascular outcomes trials (CVOTs) and whether recalibration can be used to improve replication. METHODS: Participating groups were asked to reproduce the results of the Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME) and the Canagliflozin Cardiovascular Assessment Study (CANVAS) Program. Calibration was performed and additional analyses assessed model ability to replicate absolute event rates, hazard ratios (HRs), and the generalizability of calibration across CVOTs within a drug class. RESULTS: Ten groups submitted results. Models underestimated treatment effects (ie, HRs) using uncalibrated models for both trials. Calibration to the placebo arm of EMPA-REG OUTCOME greatly improved the prediction of event rates in the placebo, but less so in the active comparator arm. Calibrating to both arms of EMPA-REG OUTCOME individually enabled replication of the observed outcomes. Using EMPA-REG OUTCOME-calibrated models to predict CANVAS Program outcomes was an improvement over uncalibrated models but failed to capture treatment effects adequately. Applying canagliflozin HRs directly provided the best fit. CONCLUSIONS: The Ninth Mount Hood Diabetes Challenge demonstrated that commonly used risk equations were generally unable to capture recent CVOT treatment effects but that calibration of the risk equations can improve predictive accuracy. Although calibration serves as a practical approach to improve predictive accuracy for CVOT outcomes, it does not extrapolate generally to other settings, time horizons, and comparators. New methods and/or new risk equations for capturing these CV benefits are needed.

PrositNG - A Machine Learning Supported Disease Model Generation Software.

Decision models (DM), especially Markov Models, play an essential role in the economic evaluation of new medical interventions. The process of DM generation requires expert knowledge of the medical domain and is a time-consuming task. Therefore, the authors propose a new model generation software PrositNG that is connectable to database systems of real-world routine care data. The structure of the model is derived from the entries in a database system by the help of Machine Learning algorithms. The software was implemented with the programming language Java. Two data sources were successfully utilized to demonstrate the value of PrositNG. However, a good understanding of the local documentation routine and software is paramount to use real-world data for model generation.

Benefit of Digital Tools Used for Integrated Personalized Diabetes Management: Results From the PDM-ProValue Study Program.

BACKGROUND: Integrated personalized diabetes management (iPDM) is a digitally supported therapeutic concept to improve patient-physician interaction to overcome the aspects of clinical inertia. Integrated personalized diabetes management can support decision making and improve therapeutic outcomes of suboptimally controlled persons with insulin-treated type 2 diabetes (T2D). In this paper, we report the results of an analysis of the PDM-ProValue study program on the effectiveness and perceived benefit of this approach, with a focus on how physicians used and assessed the digital tools provided for the iPDM process. MATERIALS AND METHODS: The study program included two 12-month, prospective, controlled, cluster-randomized multicenter trials. A total of 101 practices participated with 907 patients. Practices were cluster-randomized to an intervention group and a control group. Digital tools for data visualization and analysis applied were used. HCP were asked to assess the use, relevance, and usefulness of the tools. RESULTS: A clear preference was stated for the visual overview over more statistically complex analyses. A total of 83% of the participants rated a high relevance of the "daily profile," 81% of the "total profile," and 68% the "risk illustrated by traffic light symbols" for the therapy decision. The overall iPDM process was very favorably rated with respect to structuredness and potential for personalized treatment and well accepted among health care professionals (HCP). CONCLUSIONS: Embedding digital tools in a structured process (iPDM) were proved to provide a benefit for insulin-treated T2D patients and their physicians. These results offer insight for further development and improvement of the tools and add information on how to overcome clinical inertia.

The Challenge of Transparency and Validation in Health Economic Decision Modelling: A View from Mount Hood.

Transparency in health economic decision modelling is important for engendering confidence in the models and in the reliability of model-based cost-effectiveness analyses. The Mount Hood Diabetes Challenge Network has taken a lead in promoting transparency through validation with biennial conferences in which diabetes modelling groups meet to compare simulated outcomes of pre-specified scenarios often based on the results of pivotal clinical trials. Model registration is a potential method for promoting transparency, while also reducing the duplication of effort. An important network initiative is the ongoing construction of a diabetes model registry (https://www.mthooddiabeteschallenge.com). Following the 2012 International Society for Pharmacoeconomics and Outcomes Research and the Society of Medical Decision Making (ISPOR-SMDM) guidelines, we recommend that modelling groups provide technical and non-technical documentation sufficient to enable model reproduction, but not necessarily provide the model code. We also request that modelling groups upload documentation on the methods and outcomes of validation efforts, and run reference case simulations so that model outcomes can be compared. In this paper, we discuss conflicting definitions of transparency in health economic modelling, and describe the ongoing development of a registry of economic models for diabetes through the Mount Hood Diabetes Challenge Network, its objectives and potential further developments, and highlight the challenges in its construction and maintenance. The support of key stakeholders such as decision-making bodies and journals is key to ensuring the success of this and other registries. In the absence of public funding, the development of a network of modellers is of huge value in enhancing transparency, whether through registries or other means.

Integrated personalized diabetes management improves glycemic control in patients with insulin-treated type 2 diabetes: Results of the PDM-ProValue study program.

AIMS: Globally, many patients with insulin-treated type-2 diabetes are suboptimally controlled. The PDM-ProValue study program evaluated whether integrated personalized diabetes management (iPDM) has the potential to improve clinical outcomes. METHODS: 101 practices with 907 patients participated in the 12-month, prospective, controlled, cluster-randomized study program. HbA1c levels, therapy changes, frequency of hypoglycemic episodes, patient reported outcomes, and physician satisfaction were assessed. RESULTS: iPDM led to a greater reduction in HbA1c after 12 months vs. usual care (-0.5%, p < 0.0001 vs. -0.3%, p < 0.0001), (Diff. 0.2%, p = 0.0324). Most of the HbA1c reduction occurred after 3 months and remained stable thereafter. The percentage of patients with therapy adjustments was higher in the iPDM group at all visits (p < 0.01 at week 3, month 3, month 6). Patient adherence at month 12 was higher in the iPDM group compared to baseline (Odds ratio = 2.39; p = 0.0003); also, patient treatment satisfaction (DTSQc: 12.2 vs. 10.4, δ = 1.78, p = 0.004; DTSQs: 31.0 vs. 30.0, δ = 0.924, p = 0.02), and physician satisfaction was higher in the intervention group. CONCLUSIONS: iPDM improved the use of diagnostic data leading to better glycemic control, more timely treatment adjustments (indicating reduced clinical inertia), and increased patient adherence and treatment satisfaction among patients and physicians.

Digital Diabetes Self-Management: A Trilateral Serial.

The way diabetes patients cope with their disease in day-to-day routines is decisive for the development or the prevention of medical complications. Smartphones have created the ubiquitous environment to support health care with mobile applications (mHealth). This article comments on the publication by Offringa et al in JDST, which is one of few studies that tries to isolate the effects of a diabetes app. At the same time, it is a good example to discuss general aspects of mHealth in diabetes care. Treatment context, eHealth literacy, interoperability, and efficiency will determine the success of diabetes apps. The development has not yet reached its end. A triple quality feedback loop linking persons with diabetes, health care providers, and mHealth providers is suggested.

Healthy, Sick, Dead - An Educational Blueprint to State Transition Disease Modelling.

Disease Modelling of chronic diseases such as diabetes or asthma plays an important role in medical decision making. State transition models are the most frequently used method. The objective is to illustrate the elements and the most important underlying procedures for designing a decision analytic Markov model with only three-states. METHOD: Being "healthy" can be interpreted as a norm state, being "sick" as a temporary state and "dead" as an absorbing state. Transitions with accompanying transition probabilities that allow a cohort of model objects "to flow" between the cumulative exhaustive and mutually exclusive states complete the model structure. Half-cycle correction helps in overcoming the fitting problem of the discrete time valuation of Markov models. A model with the three states healthy, sick and dead is the easiest way to define a reasonable model that covers almost all aspects of a Markov disease model. The absorbing state dead helps in terminating a model. The temporary state sick acts as an event counter and the state healthy serves as a reservoir of modelling objects. The definition of the number and length of cycles completes the definition of a simple state transition model. Additional supplementary material with a functional sample model is provided.

Clinical Cancer Registries - Are They Up for Health Services Research?

Clinical cancer registries are a valuable data source for health services research (HSR). HSR is in need of high quality routine care data for its evaluations. However, the secondary use of routine data - such as documented cancer cases in a disease registry - poses new challenges in terms of data quality, IT-management, documentation processes and data privacy. In the clinical cancer registry Heilbronn-Franken, real-world data from the Giessen Tumor Documentation System (GTDS) was utilized for analyses of patients' disease processes and guideline adherence in follow-up care. A process was developed to map disease state definitions to fields of the GTDS database and extract patients' disease progress information. Thus, the disease process of sub-cohorts could be compared to each other, e.g., comparison of disease free survival of HER2 (human epidermal growth factor receptor 2)-positive and -negative women who were treated with Trastuzumab, a targeted therapy applied in breast cancer. In principle, such comparisons are feasible and of great value for HSR as they depict a routine care setting of a diverse patient cohort. Yet, local documentation practice, missing flow of information from external health care providers or small sub-cohorts impede the analyses of clinical cancer registries data bases and usage for HSR.

PROSIT Open Source Disease Models for Diabetes Mellitus.

UNLABELLED: There has been legitimate criticism with regard to the quality and the transparency of health economic modelling studies. For that reason, the aim of the PROSIT Disease Modelling Community is to develop transparent open source health economic disease models for diabetes mellitus. RESULTS: Markov type models were developed in the open source spread sheet software OpenOffice Calc for myocardial infarction, stroke, retinopathy, nephropathy, diabetic foot syndrome, and hypoglycemia. The basic concept is to describe a disease as a cascade of disease states with transitions between them. The transition probability is based on time, gender, age, disease related risks and medical interventions. An internet platform hosts the models and the documentation for public download. Incidence rates of complications were derived from population data and clinical studies. The models have to be adapted according to the specific needs and type of health economic analysis. The software is prepared to allow validation and model testing. The PROSIT Disease Modelling Community with its Markov models for diabetes mellitus suggests a new approach and methodology for developing health economic disease models in a transparent and sustainable manner. Going open source with disease models could overcome the lack in credibility that hampers modelling based health economic studies.

Transition probabilities of HER2-positive and HER2-negative breast cancer patients treated with Trastuzumab obtained from a clinical cancer registry dataset.

Records of female breast cancer patients were selected from a clinical cancer registry and separated into three cohorts according to HER2-status (human epidermal growth factor receptor 2) and treatment with or without Trastuzumab (a humanized monoclonal antibody). Propensity score matching was used to balance the cohorts. Afterwards, documented information about disease events (recurrence of cancer, metastases, remission of local/regional recurrences, remission of metastases and death) found in the dataset was leveraged to calculate the annual transition probabilities for every cohort.

A method for using real world data in breast cancer modeling.

OBJECTIVES: Today, hospitals and other health care-related institutions are accumulating a growing bulk of real world clinical data. Such data offer new possibilities for the generation of disease models for the health economic evaluation. In this article, we propose a new approach to leverage cancer registry data for the development of Markov models. Records of breast cancer patients from a clinical cancer registry were used to construct a real world data driven disease model. METHODS: We describe a model generation process which maps database structures to disease state definitions based on medical expert knowledge. Software was programmed in Java to automatically derive a model structure and transition probabilities. We illustrate our method with the reconstruction of a published breast cancer reference model derived primarily from clinical study data. In doing so, we exported longitudinal patient data from a clinical cancer registry covering eight years. The patient cohort (n=892) comprised HER2-positive and HER2-negative women treated with or without Trastuzumab. RESULTS: The models generated with this method for the respective patient cohorts were comparable to the reference model in their structure and treatment effects. However, our computed disease models reflect a more detailed picture of the transition probabilities, especially for disease free survival and recurrence. CONCLUSIONS: Our work presents an approach to extract Markov models semi-automatically using real world data from a clinical cancer registry. Health care decision makers may benefit from more realistic disease models to improve health care-related planning and actions based on their own data.

Integrated Personalized Diabetes Management (PDM): Design of the ProValue Studies: Prospective, Cluster-Randomized, Controlled, Intervention Trials for Evaluation of the Effectiveness and Benefit of PDM in Patients With Insulin-Treated Type 2 Diabetes.

BACKGROUND: Collaborative use of structured self-monitoring of blood glucose (SMBG) data and data management software, utilized within a 6-step cycle enables integrated Personalized Diabetes Management (PDM). The 2 PDM-ProValue studies shall assess the effectiveness of this approach in improving patient outcomes and practice efficiencies in outpatient settings. METHODS: The PDM-ProValue studies are 12-month, prospective, cluster-randomized, multicenter, trials to determine if use of integrated PDM in daily life improves glycemic control in insulin-treated type 2 diabetes patients. Fifty-four general medical practices (GPs) and 36 diabetes-specialized practices (DSPs) across Germany will be recruited. The practices will be randomly assigned to the control groups (CNL) or the intervention groups (INT) via cluster-randomization. CNL practices will continue with their usual care; INT practices will utilize integrated PDM. The sample size is 1,014 patients (n = 540 DSP patients, n = 474 GP patients). Each study is designed to detect a between-group difference in HbA1c change of at least 0.4% at 12 months with a power of 90% and 2-sided significance level of .05. Differences in timing and degree of treatment adaptions, treatment decisions, blood glucose target ranges, hypoglycemia, self-management behaviors, quality of life, patients attitudes, clinician satisfaction, practice processes, and resource consumption will be assessed. Study endpoints will be analyzed for the modified intent-to-treat and per protocol populations. Trial results are expected to be available in late 2016. DISCUSSION: Effective and efficient strategies to optimize diabetes management are needed. These randomized studies will help determine if PDM is beneficial.

Prediction of 5-Year Survival with Data Mining Algorithms.

Survival time prediction at the time of diagnosis is of great importance to make decisions about treatment and long-term follow-up care. However, predicting the outcome of cancer on the basis of clinical information is a challenging task. We now examined the ability of ten different data mining algorithms (Perceptron, Rule Induction, Support Vector Machine, Linear Regression, Naïve Bayes, Decision Tree, k-nearest Neighbor, Logistic Regression, Neural Network, Random Forest) to predict the dichotomous attribute "5-year-survival" based on seven attributes (sex, UICC-stage, etc.) which are available at the time of diagnosis. For this study we made use of the nationwide German research data set on colon cancer provided by the Robert Koch Institute. To assess the results a comparison between data mining algorithms and physicians' opinions was performed. Therefore, physicians guessed the survival time by leveraging the same seven attributes. The average accuracy of the physicians' opinion was 59%, the average accuracy of the machine learning algorithms was 67.7%.