Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model.

The purpose of this feasibility study is to investigate if latent diffusion models (LDMs) are capable to generate contrast enhanced (CE) MRI-derived subtraction maximum intensity projections (MIPs) of the breast, which are conditioned by lesions. We trained an LDM with n = 2832 CE-MIPs of breast MRI examinations of n = 1966 patients (median age: 50 years) acquired between the years 2015 and 2020. The LDM was subsequently conditioned with n = 756 segmented lesions from n = 407 examinations, indicating their location and BI-RADS scores. By applying the LDM, synthetic images were generated from the segmentations of an independent validation dataset. Lesions, anatomical correctness, and realistic impression of synthetic and real MIP images were further assessed in a multi-rater study with five independent raters, each evaluating n = 204 MIPs (50% real/50% synthetic images). The detection of synthetic MIPs by the raters was akin to random guessing with an AUC of 0.58. Interrater reliability of the lesion assessment was high both for real (Kendall's W = 0.77) and synthetic images (W = 0.85). A higher AUC was observed for the detection of suspicious lesions (BI-RADS ≥ 4) in synthetic MIPs (0.88 vs. 0.77; p = 0.051). Our results show that LDMs can generate lesion-conditioned MRI-derived CE subtraction MIPs of the breast, however, they also indicate that the LDM tended to generate rather typical or 'textbook representations' of lesions.

Data Visualization Support for Tumor Boards and Clinical Oncology: Protocol for a Scoping Review.

BACKGROUND: Complex and expanding data sets in clinical oncology applications require flexible and interactive visualization of patient data to provide the maximum amount of information to physicians and other medical practitioners. Interdisciplinary tumor conferences in particular profit from customized tools to integrate, link, and visualize relevant data from all professions involved. OBJECTIVE: The scoping review proposed in this protocol aims to identify and present currently available data visualization tools for tumor boards and related areas. The objective of the review will be to provide not only an overview of digital tools currently used in tumor board settings, but also the data included, the respective visualization solutions, and their integration into hospital processes. METHODS: The planned scoping review process is based on the Arksey and O'Malley scoping study framework. The following electronic databases will be searched for articles published in English: PubMed, Web of Knowledge, and SCOPUS. Eligible articles will first undergo a deduplication step, followed by the screening of titles and abstracts. Second, a full-text screening will be used to reach the final decision about article selection. At least 2 reviewers will independently screen titles, abstracts, and full-text reports. Conflicting inclusion decisions will be resolved by a third reviewer. The remaining literature will be analyzed using a data extraction template proposed in this protocol. The template includes a variety of meta information as well as specific questions aiming to answer the research question: "What are the key features of data visualization solutions used in molecular and organ tumor boards, and how are these elements integrated and used within the clinical setting?" The findings will be compiled, charted, and presented as specified in the scoping study framework. Data for included tools may be supplemented with additional manual literature searches. The entire review process will be documented in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. RESULTS: The results of this scoping review will be reported per the expanded PRISMA-ScR guidelines. A preliminary search using PubMed, Web of Knowledge, and Scopus resulted in 1320 articles after deduplication that will be included in the further review process. We expect the results to be published during the second quarter of 2024. CONCLUSIONS: Visualization is a key process in leveraging a data set's potentially available information and enabling its use in an interdisciplinary setting. The scoping review described in this protocol aims to present the status quo of visualization solutions for tumor board and clinical oncology applications and their integration into hospital processes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53627.

Digital Support to Multimodal Community-Based Prehabilitation: Looking for Optimization of Health Value Generation.

Prehabilitation has shown its potential for most intra-cavity surgery patients on enhancing preoperative functional capacity and postoperative outcomes. However, its large-scale implementation is limited by several constrictions, such as: i) unsolved practicalities of the service workflow, ii) challenges associated to change management in collaborative care; iii) insufficient access to prehabilitation; iv) relevant percentage of program drop-outs; v) need for program personalization; and, vi) economical sustainability. Transferability of prehabilitation programs from the hospital setting to the community would potentially provide a new scenario with greater accessibility, as well as offer an opportunity to effectively address the aforementioned issues and, thus, optimize healthcare value generation. A core aspect to take into account for an optimal management of prehabilitation programs is to use proper technological tools enabling: i) customizable and interoperable integrated care pathways facilitating personalization of the service and effective engagement among stakeholders; ii) remote monitoring (i.e. physical activity, physiological signs and patient-reported outcomes and experience measures) to support patient adherence to the program and empowerment for self-management; and, iii) use of health risk assessment supporting decision making for personalized service selection. The current manuscript details a proposal to bring digital innovation to community-based prehabilitation programs. Moreover, this approach has the potential to be adopted by programs supporting long-term management of cancer patients, chronic patients and prevention of multimorbidity in subjects at risk.

Multiple breath washout (MBW) testing using sulfur hexafluoride: reference values and influence of anthropometric parameters.

BACKGROUND: Multiple breath washout (MBW) using sulfur hexafluoride (SF6) has the potential to reveal ventilation heterogeneity which is frequent in patients with obstructive lung disease and associated small airway dysfunction. However, reference data are scarce for this technique and mostly restricted to younger cohorts. We therefore set out to evaluate the influence of anthropometric parameters on SF6-MBW reference values in pulmonary healthy adults. METHODS: We evaluated cross-sectional data from 100 pulmonary healthy never-smokers and smokers (mean 51 (SD 20), range 20-88 years). Lung clearance index (LCI), acinar (Sacin) and conductive (Scond) ventilation heterogeneity were derived from triplicate SF6-MBW measurements. Global ventilation heterogeneity was calculated for the 2.5% (LCI2.5) and 5% (LCI5) stopping points. Upper limit of normal (ULN) was defined as the 95th percentile. RESULTS: Age was the only meaningful parameter influencing SF6-MBW parameters, explaining 47% (CI 33% to 59%) of the variance in LCI, 32% (CI 18% to 47%) in Sacin and 10% (CI 2% to 22%) in Scond. Mean LCI increases from 6.3 (ULN 7.4) to 8.8 (ULN 9.9) in subjects between 20 and 90 years. Smoking accounted for 2% (CI 0% to 8%) of the variability in LCI, 4% (CI 0% to 13%) in Sacin and 3% (CI 0% to 13%) in Scond. CONCLUSION: SF6-MBW outcome parameters showed an age-dependent increase from early adulthood to old age. The effect was most pronounced for global and acinar ventilation heterogeneity and smaller for conductive ventilation heterogeneity. No influence of height, weight and sex was seen. Reference values can now be provided for all important SF6-MBW outcome parameters over the whole age range. TRIAL REGISTRATION NUMBER: NCT04099225.

Leveraging Real-World Data for the Selection of Relevant Eligibility Criteria for the Implementation of Electronic Recruitment Support in Clinical Trials.

BACKGROUND: Even though clinical trials are indispensable for medical research, they are frequently impaired by delayed or incomplete patient recruitment, resulting in cost overruns or aborted studies. Study protocols based on real-world data with precisely expressed eligibility criteria and realistic cohort estimations are crucial for successful study execution. The increasing availability of routine clinical data in electronic health records (EHRs) provides the opportunity to also support patient recruitment during the prescreening phase. While solutions for electronic recruitment support have been published, to our knowledge, no method for the prioritization of eligibility criteria in this context has been explored. METHODS: In the context of the Electronic Health Records for Clinical Research (EHR4CR) project, we examined the eligibility criteria of the KATHERINE trial. Criteria were extracted from the study protocol, deduplicated, and decomposed. A paper chart review and data warehouse query were executed to retrieve clinical data for the resulting set of simplified criteria separately from both sources. Criteria were scored according to disease specificity, data availability, and discriminatory power based on their content and the clinical dataset. RESULTS: The study protocol contained 35 eligibility criteria, which after simplification yielded 70 atomic criteria. For a cohort of 106 patients with breast cancer and neoadjuvant treatment, 47.9% of data elements were captured through paper chart review, with the data warehouse query yielding 26.9% of data elements. Score application resulted in a prioritized subset of 17 criteria, which yielded a sensitivity of 1.00 and specificity 0.57 on EHR data (paper charts, 1.00 and 0.80) compared with actual recruitment in the trial. CONCLUSION: It is possible to prioritize clinical trial eligibility criteria based on real-world data to optimize prescreening of patients on a selected subset of relevant and available criteria and reduce implementation efforts for recruitment support. The performance could be further improved by increasing EHR data coverage.

Computer-Aided Diagnosis in Multiparametric MRI of the Prostate: An Open-Access Online Tool for Lesion Classification with High Accuracy.

Computer-aided diagnosis (CADx) approaches could help to objectify reporting on prostate mpMRI, but their use in many cases is hampered due to common-built algorithms that are not publicly available. The aim of this study was to develop an open-access CADx algorithm with high accuracy for classification of suspicious lesions in mpMRI of the prostate. This retrospective study was approved by the local ethics commission, with waiver of informed consent. A total of 124 patients with 195 reported lesions were included. All patients received mpMRI of the prostate between 2014 and 2017, and transrectal ultrasound (TRUS)-guided and targeted biopsy within a time period of 30 days. Histopathology of the biopsy cores served as a standard of reference. Acquired imaging parameters included the size of the lesion, signal intensity (T2w images), diffusion restriction, prostate volume, and several dynamic parameters along with the clinical parameters patient age and serum PSA level. Inter-reader agreement of the imaging parameters was assessed by calculating intraclass correlation coefficients. The dataset was stratified into a train set and test set (156 and 39 lesions in 100 and 24 patients, respectively). Using the above parameters, a CADx based on an Extreme Gradient Boosting algorithm was developed on the train set, and tested on the test set. Performance optimization was focused on maximizing the area under the Receiver Operating Characteristic curve (ROCAUC). The algorithm was made publicly available on the internet. The CADx reached an ROCAUC of 0.908 during training, and 0.913 during testing (p = 0.93). Additionally, established rule-in and rule-out criteria allowed classifying 35.8% of the malignant and 49.4% of the benign lesions with error rates of <2%. All imaging parameters featured excellent inter-reader agreement. This study presents an open-access CADx for classification of suspicious lesions in mpMRI of the prostate with high accuracy. Applying the provided rule-in and rule-out criteria might facilitate to further stratify the management of patients at risk.

Digital Follow-Up and the Perspective of Patient-Centered Care in Oncology: What's the PROblem?

There is accumulating evidence from randomized trials suggesting that digital patient-centered care allows a more reliable detection of tumour-related symptoms and adverse events - with a direct impact on overall survival. Consequently, a variety of unsynchronized approaches were kicked off to (electronically) measure patient-reported outcomes (PROs). Despite increasing evidence that PRO data are highly relevant for patient care, the data generated in these initial projects lack standardized processing pathways in order to impact clinical routine; therefore, potential future routine PRO assessments require adequate analysis, storage and processing to allow a robust, reproducible and reliable incorporation into routine clinical decision-making. Here, we discuss relevant challenges of digital follow-up that need to be tackled to render PRO data as relevant to physicians as laboratory or biomarker data.

Prospective trial on telemonitoring of geriatric cancer patients using handheld devices.

PURPOSE AND OBJECTIVE: Randomized trials indicate that electronic or app-based assessment of patient-reported outcomes may improve outcomes in cancer patients. To analyze if an app-based follow-up would be accepted by elderly cancer patients, we conducted a single-center prospective feasibility study (NCT03196050). MATERIALS AND METHODS: Cancer patients (≥60 years) without concurrent uncontrolled severe medical conditions and a Karnofsky performance status (KPS) ≥70 were eligible if they were able to use the smartphone app. The primary endpoint was compliance over 1 year, calculated as patient-specific and study date-specific response rate to questions sent as push notifications; in this interim analysis, we report on 4­month data. Secondary outcomes included a comparison of a subjective health status item (SPHS) with the physician-rated KPS. RESULTS: Out of 225 patients screened, 54 patients agreed to participate and 29 activated the app and participated in the study. The mean age was 66 years (61-78). The individual compliance rate averaged at 58.3% (standard deviation SD = 35%). Daily compliance was 53.3% on average (SD = 10.8%) and declined over time. The average percentage of patients who sent answers at least weekly was 75.0% (SD = 14.8%) and declined from 100% in week 1 to 53.8% in week 17 post-enrollment. Secondary outcomes indicated that questionnaires such as the EORTC-QLQ-C30 are accepted via app and that there is a significant moderate correlation between the SPHS and KPS scores (r = 0.566; p < 0.001). CONCLUSION: Our data indicate that an app-based follow-up incorporating EORTC questionnaires might be possible in highly selected elderly cancer patients with modest compliance rates. Further trials should aim at an increased participation rate.

Noncurated Data Lead to Misinterpretation of Treatment Outcomes in Patients With Prostate Cancer After Salvage or Palliative Radiotherapy.

PURPOSE: Clinical data warehouses (cDWHs) and cancer registry databases have enabled researchers to conduct clinical analytics with structured electronic health record data. However, these secondary electronic health record sources are often limited in scope because they do not capture the clinical information needed to understand complex clinical questions. Thus, we evaluated the effect of additional curation of data. MATERIALS AND METHODS: Clinical data sets of 149 patients with prostate cancer with biochemical recurrence after radical prostatectomy treated with salvage or palliative radiotherapy between 2008 and 2017 from our institutional cDWH and Gießener Tumor Documentation System (GTDS) were linked (data warehouse [DWH] population) for analyzing treatment outcomes. The linked data sets were manually curated (manual postprocessing [MPP], eg, incorporate data from established urologists). The primary outcomes were the impact on data quality of treatment outcomes and the time spent on data curation. RESULTS: We obtained significantly more information on disease progression and patient survival (nonsignificant) when using curated data; the biochemical progression-free survival rate at 5 years for the DWH and DWH plus MPP populations was 63% v 30% (P ≤ .001) and the overall survival rate was 84% v 81% (P = .479), respectively. The median deviation of completeness and the median concordance of clinical data values were 21.47% (range, 55.38%-100%) and 95.00% (range, 63.40%-100%), respectively. We spent 121 hours, 42 minutes on data curation, with most time required for laboratory values, accounting, for a total of 45 hours, 20 minutes (37.26%). CONCLUSION: Our analysis indicates that time-to-event outcomes for patients with prostate cancer cannot be extracted using secondary data sources (cDWH plus GTDS) only. Outcomes data differed between the electronic data (DWH) and the second manual extraction (DWH plus MPP) because of a lack of follow-up data. When using such unique database resources, only baseline characteristics can reliably be extracted.

MIRACUM: Medical Informatics in Research and Care in University Medicine.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Similar to other large international data sharing networks (e.g. OHDSI, PCORnet, eMerge, RD-Connect) MIRACUM is a consortium of academic and hospital partners as well as one industrial partner in eight German cities which have joined forces to create interoperable data integration centres (DIC) and make data within those DIC available for innovative new IT solutions in patient care and medical research. OBJECTIVES: Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system. This paper aims at illustrating the major building blocks and concepts which MIRACUM will apply to achieve this goal. GOVERNANCE AND POLICIES: Besides establishing an efficient governance structure within the MIRACUM consortium (based on the steering board, a central administrative office, the general MIRACUM assembly, six working groups and the international scientific advisory board), defining DIC governance rules and data sharing policies, as well as establishing (at each MIRACUM DIC site, but also for MIRACUM in total) use and access committees are major building blocks for the success of such an endeavor. ARCHITECTURAL FRAMEWORK AND METHODOLOGY: The MIRACUM DIC architecture builds on a comprehensive ecosystem of reusable open source tools (MIRACOLIX), which are linkable and interoperable amongst each other, but also with the existing software environment of the MIRACUM hospitals. Efficient data protection measures, considering patient consent, data harmonization and a MIRACUM metadata repository as well as a common data model are major pillars of this framework. The methodological approach for shared data usage relies on a federated querying and analysis concept. USE CASES: MIRACUM aims at proving the value of their DIC with three use cases: IT support for patient recruitment into clinical trials, the development and routine care implementation of a clinico-molecular predictive knowledge tool, and molecular-guided therapy recommendations in molecular tumor boards. RESULTS: Based on the MIRACUM DIC release in the nine months conceptual phase first large scale analysis for stroke and colorectal cancer cohorts have been pursued. DISCUSSION: Beyond all technological challenges successfully applying the MIRACUM tools for the enrichment of our knowledge about diagnostic and therapeutic concepts, thus supporting the concept of a Learning Health System will be crucial for the acceptance and sustainability in the medical community and the MIRACUM university hospitals.

Implementation and Feasibility of Electronic Patient-Reported Outcome (ePRO) Data Entry in the PRAEGNANT Real-Time Advanced and Metastatic Breast Cancer Registry.

PURPOSE: Patient-reported outcomes (PROs) have been incorporated into clinical trials for many symptoms and medical conditions. A transition from paper-based capture of PROs to electronic PROs (ePROs) has recently started. This study reports on the feasibility of ePRO assessment in a prospective registry including molecular data for patients with advanced breast cancer. METHODS: As part of the PRAEGNANT network, patients were invited by clinical trial staff, physicians, and nurses to complete three standardized Internet-based questionnaires (EQ 5D 5 L, CES-D and IPAQ). Feasibility was assessed by the staff members who assigned the user accounts by the patients. The completeness of the questionnaires was also assessed. RESULTS: Fifteen of 17 patients who were asked agreed to participate to complete the PRO questionnaires (EQ-5D-5L and CES-D). However, the IPAQ (physical activity) questionnaire was only validly completed by 9 patients. Feasibility was ranked better by the physicians and dedicated clinical trial staff than by the nursing staff. CONCLUSIONS: Incorporating ePRO questionnaires into an advanced breast cancer registry is feasible, and no major hurdles were reported. Involving stakeholders from the start, the application is tailored to the capacities and abilities of both patients and clinical staff. The patients' compliance was better with some questionnaires, but others may present difficulties.

Correlation of mammographic density and serum calcium levels in patients with primary breast cancer.

Percentage mammographic breast density (PMD) is one of the most important risk factors for breast cancer (BC). Calcium, vitamin D, bisphosphonates, and denosumab have been considered and partly confirmed as factors potentially influencing the risk of BC. This retrospective observational study investigated the association between serum calcium level and PMD. A total of 982 BC patients identified in the research database at the University Breast Center for Franconia with unilateral BC, calcium and albumin values, and mammogram at the time of first diagnosis were included. PMD was assessed, using a semiautomated method by two readers. Linear regression analyses were conducted to investigate the impact on PMD of the parameters of serum calcium level adjusted for albumin level, and well-known clinical predictors such as age, body mass index (BMI), menopausal status and confounder for serum calcium like season in which the BC was diagnosed. Increased calcium levels were associated with reduced PMD (P = 0.024). Furthermore, PMD was inversely associated with BMI (P < 0.001) and age (P < 0.001). There was also an association between PMD and menopausal status (P < 0.001). The goodness-of-fit of the regression model was moderate. This is the first study assessing the association between serum calcium level and PMD. An inverse association with adjusted serum calcium levels was observed. These findings add to previously published data relating to vitamin D, bisphosphonates, denosumab, and the RANK/RANKL signaling pathway in breast cancer risk and prevention.

Electronic Support for Retrospective Analysis in the Field of Radiation Oncology: Proof of Principle Using an Example of Fractionated Stereotactic Radiotherapy of 251 Meningioma Patients.

INTRODUCTION: The purpose of this study is to verify the possible benefit of a clinical data warehouse (DWH) for retrospective analysis in the field of radiation oncology. MATERIAL AND METHODS: We manually and electronically (using DWH) evaluated demographic, radiotherapy, and outcome data from 251 meningioma patients, who were irradiated from January 2002 to January 2015 at the Department of Radiation Oncology of the Erlangen University Hospital. Furthermore, we linked the Oncology Information System (OIS) MOSAIQ® to the DWH in order to gain access to irradiation data. We compared the manual and electronic data retrieval method in terms of congruence of data, corresponding time, and personal requirements (physician, physicist, scientific associate). RESULTS: The electronically supported data retrieval (DWH) showed an average of 93.9% correct data and significantly (p = 0.009) better result compared to manual data retrieval (91.2%). Utilizing a DWH enables the user to replace large amounts of manual activities (668 h), offers the ability to significantly reduce data collection time and labor demand (35 h), while simultaneously improving data quality. In our case, work time for manually data retrieval was 637 h for the scientific assistant, 26 h for the medical physicist, and 5 h for the physician (total 668 h). CONCLUSION: Our study shows that a DWH is particularly useful for retrospective analysis in the radiation oncology field. Routine clinical data for a large patient group can be provided ready for analysis to the scientist and data collection time can be significantly reduced. Furthermore, linking multiple data sources in a DWH offers the ability to improve data quality for retrospective analysis, and future research can be simplified.

Computerized patient identification for the EMBRACA clinical trial using real-time data from the PRAEGNANT network for metastatic breast cancer patients.

As breast cancer is a diverse disease, clinical trials are becoming increasingly diversified and are consequently being conducted in very small subgroups of patients, making study recruitment increasingly difficult. The aim of this study was to assess the use of data from a remote data entry system that serves a large national registry for metastatic breast cancer. The PRAEGNANT network is a real-time registry with an integrated biomaterials bank that was designed as a scientific study and as a means of identifying patients who are eligible for clinical trials, based on clinical and molecular information. Here, we report on the automated use of the clinical data documented to identify patients for a clinical trial (EMBRACA) for patients with metastatic breast cancer. The patients' charts were assessed by two independent physicians involved in the clinical trial and also by a computer program that tested patients for eligibility using a structured query language script. In all, 326 patients from two study sites in the PRAEGNANT network were included in the analysis. Using expert assessment, 120 of the 326 patients (37 %) appeared to be eligible for inclusion in the EMBRACA study; with the computer algorithm assessment, a total of 129 appeared to be eligible. The sensitivity of the computer algorithm was 0.87 and its specificity was 0.88. Using computer-based identification of patients for clinical trials appears feasible. With the instrument's high specificity, its application in a large cohort of patients appears to be feasible, and the workload for reassessing the patients is limited.

A reference architecture for semantic interoperability and its practical application.

OBJECTIVE: Reusing EPR data for secondary purposes often requires mapping to classifications and vocabularies such as ICD, LOINC or NCI thesaurus. We aimed for a common architecture which supports the use of different vocabularies and mapping tools. METHODS: We integrated the components clinical data warehouse, vocabulary resources and mapping tools with the EPR and client applications. RESULTS: In two projects we used this architecture to map laboratory parameters from the LIS to LOINC, and to map clinical data elements from the Soarian EPR to the cancer registry system using the NCI-Thesaurus®. CONCLUSION: The approach was successful in both projects. The reference architecture does not resolve the mapping task, but provides reusable integration links between the different components and thus facilitates further mapping activities.

Case-based visualization of a patient cohort using SEER epidemiologic data.

Data from cancer registries can be used to track the epidemiology of cancer and can potentially serve to guide individual diagnostic and treatment decisions. Even though some cancer registry datasets have been made publicly available for scientific and clinical use, few applications have so far provided direct access to these data from within the patient context of an electronic patient record. The goal of this project was to implement a proof-of-concept integration of the public SEER (Surveillance, Epidemiology and End Results) cancer registry dataset with a digital breast cancer tumor board at a German university hospital and to determine its utility in the clinical settings. The integration was successfully established, using data from routine documentation to provide dynamic visualizations of cohort composition and Kaplan-Meier survival plots. Evaluation feedback was favorable regarding the concept and implementation, but highlighted that important data elements, e.g. receptor status data, were missing in the SEER dataset, limiting clinical value of the system.

Employing heat maps to mine associations in structured routine care data.

OBJECTIVE: Mining the electronic medical record (EMR) has the potential to deliver new medical knowledge about causal effects, which are hidden in statistical associations between different patient attributes. It is our goal to detect such causal mechanisms within current research projects which include e.g. the detection of determinants of imminent ICU readmission. An iterative statistical approach to examine each set of considered attribute pairs delivers potential answers but is difficult to interpret. Therefore, we aimed to improve the interpretation of the resulting matrices by the use of heat maps. We propose strategies to adapt heat maps for the search for associations and causal effects within routine EMR data. METHODS: Heat maps visualize tabulated metric datasets as grid-like choropleth maps, and thus present measures of association between numerous attribute pairs clearly arranged. Basic assumptions about plausible exposures and outcomes are used to allocate distinct attribute sets to both matrix dimensions. The image then avoids certain redundant graphical elements and provides a clearer picture of the supposed associations. Specific color schemes have been chosen to incorporate preexisting information about similarities between attributes. The use of measures of association as a clustering input has been taken as a trigger to apply transformations which ensure that distance metrics always assume finite values and treat positive and negative associations in the same way. To evaluate the general capability of the approach, we conducted analyses of simulated datasets and assessed diagnostic and procedural codes in a large routine care dataset. RESULTS: Simulation results demonstrate that the proposed clustering procedure rearranges attributes similar to simulated statistical associations. Thus, heat maps are an excellent tool to indicate whether associations concern the same attributes or different ones, and whether affected attribute sets conform to any preexisting relationship between attributes. The dendrograms help in deciding if contiguous sequences of attributes effectively correspond to homogeneous attribute associations. The exemplary analysis of a routine care dataset revealed patterns of associations that follow plausible medical constellations for several diseases and the associated medical procedures and activities. Cases with breast cancer (ICD C50), for example, appeared to be associated with radiation therapy (8-52). In cross check, approximately 60 percent of the attribute pairs in this dataset showed a strong negative association, which can be explained by diseases treated in a medical specialty which routinely does not perform the respective procedures in these cases. The corresponding diagram clearly reflects these relationships in the shape of coherent subareas. CONCLUSION: We could demonstrate that heat maps of measures of association are effective for the visualization of patterns in routine care EMRs. The adjustable method for the assignment of attributes to image dimensions permits a balance between the display of ample information and a favorable level of graphical complexity. The scope of the search can be adapted by the use of pre-existing assumptions about plausible effects to select exposure and outcome attributes. Thus, the proposed method promises to simplify the detection of undiscovered causal effects within routine EMR data.

Populating the i2b2 database with heterogeneous EMR data: a semantic network approach.

In an ongoing effort to share heterogeneous electronic medical record (EMR) data in an i2b2 instance between the University Hospitals Münster and Erlangen for joint cancer research projects, an ontology based system for the mapping of EMR data to a set of common data elements has been developed. The system translates the mappings into local SQL scripts, which are then used to extract, transform and load the facts data from each EMR into the i2b2 database. By using Semantic Web standards, it is the authors' goal to reuse the laboriously compiled "mapping knowledge" in future projects, such as a comprehensive cancer ontology or even a hospital-wide clinical ontology.

IT infrastructure components to support clinical care and translational research projects in a comprehensive cancer center.

This paper presents the concept of an integrated IT infrastructure framework established at the comprehensive cancer center at the University Hospital Erlangen. The framework is based on the single source concept where data from the electronic medical record are reused for clinical and translational research projects. The applicability of the approach is illustrated by two case studies from colon cancer and prostate cancer research projects.

Semantic challenges in database Federation: lessons learned.

In this project an integrated analysis of data from disparate surgery and anaesthesiology departmental information systems was carried out. Due to the lack of shared primary keys, a multi-stage "soft" matching method was implemented. Results of the matching steps are described in detail. Inconsistencies were shown to exist for identifying data, semantic definition of documentation content and documented data itself. Minimum requirements for interdisciplinary documentation in autonomous systems should include shared semantic definitions of documentation content as well as robust and regularly validated interfaces for identifying data.