Profile of the multicenter cohort of the German Cancer Consortium's Clinical Communication Platform.

Treatment concepts in oncology are becoming increasingly personalized and diverse. Successively, changes in standards of care mandate continuous monitoring of patient pathways and clinical outcomes based on large, representative real-world data. The German Cancer Consortium's (DKTK) Clinical Communication Platform (CCP) provides such opportunity. Connecting fourteen university hospital-based cancer centers, the CCP relies on a federated IT-infrastructure sourcing data from facility-based cancer registry units and biobanks. Federated analyses resulted in a cohort of 600,915 patients, out of which 232,991 were incident since 2013 and for which a comprehensive documentation is available. Next to demographic data (i.e., age at diagnosis: 2.0% 0-20 years, 8.3% 21-40 years, 30.9% 41-60 years, 50.1% 61-80 years, 8.8% 81+ years; and gender: 45.2% female, 54.7% male, 0.1% other) and diagnoses (five most frequent tumor origins: 22,523 prostate, 18,409 breast, 15,575 lung, 13,964 skin/malignant melanoma, 9005 brain), the cohort dataset contains information about therapeutic interventions and response assessments and is connected to 287,883 liquid and tissue biosamples. Focusing on diagnoses and therapy-sequences, showcase analyses of diagnosis-specific sub-cohorts (pancreas, larynx, kidney, thyroid gland) demonstrate the analytical opportunities offered by the cohort's data. Due to its data granularity and size, the cohort is a potential catalyst for translational cancer research. It provides rapid access to comprehensive patient groups and may improve the understanding of the clinical course of various (even rare) malignancies. Therefore, the cohort may serve as a decisions-making tool for clinical trial design and contributes to the evaluation of scientific findings under real-world conditions.

Optimizing the Analytical Value of Oncology-Related Data Based on an In-Memory Analysis Layer: Development and Assessment of the Munich Online Comprehensive Cancer Analysis Platform.

BACKGROUND: Many comprehensive cancer centers incorporate tumor documentation software supplying structured information from the associated centers' oncology patients for internal and external audit purposes. However, much of the documentation data included in these systems often remain unused and unknown by most of the clinicians at the sites. OBJECTIVE: To improve access to such data for analytical purposes, a prerollout of an analysis layer based on the business intelligence software QlikView was implemented. This software allows for the real-time analysis and inspection of oncology-related data. The system is meant to increase access to the data while simultaneously providing tools for user-friendly real-time analytics. METHODS: The system combines in-memory capabilities (based on QlikView software) with innovative techniques that compress the complexity of the data, consequently improving its readability as well as its accessibility for designated end users. Aside from the technical and conceptual components, the software's implementation necessitated a complex system of permission and governance. RESULTS: A continuously running system including daily updates with a user-friendly Web interface and real-time usage was established. This paper introduces its main components and major design ideas. A commented video summarizing and presenting the work can be found within the Multimedia Appendix. CONCLUSIONS: The system has been well-received by a focus group of physicians within an initial prerollout. Aside from improving data transparency, the system's main benefits are its quality and process control capabilities, knowledge discovery, and hypothesis generation. Limitations such as run time, governance, or misinterpretation of data are considered.

Supporting the decision to perform molecular profiling for cancer patients based on routinely collected data through the use of machine learning.

BACKGROUND: Personalized medicine offers targeted therapy options for cancer treatment. However, the decision whether to include a patient into next-generation sequencing (NGS) testing is not standardized. This may result in some patients receiving unnecessary testing while others who could benefit from it are not tested. Typically, patients who have exhausted conventional treatment options are of interest for consideration in molecularly targeted therapy. To assist clinicians in decision-making, we developed a decision support tool using routine data from a precision oncology program. METHODS: We trained a machine learning model on clinical data to determine whether molecular profiling should be performed for a patient. To validate the model, the model's predictions were compared with decisions made by a molecular tumor board (MTB) using multiple patient case vignettes with their characteristics. RESULTS: The prediction model included 440 patients with molecular profiling and 13,587 patients without testing. High area under the curve (AUC) scores indicated the importance of engineered features in deciding on molecular profiling. Patient age, physical condition, tumor type, metastases, and previous therapies were the most important features. During the validation MTB experts made the same decision of recommending a patient for molecular profiling only in 10 out of 15 of their previous cases but there was agreement between the experts and the model in 9 out of 15 cases. CONCLUSION: Based on a historical cohort, our predictive model has the potential to assist clinicians in deciding whether to perform molecular profiling.

How can current oncological datasets be adjusted to support the automated patient recruitment in clinical trials?

OBJECTIVES: This study aims to identify necessary adjustments required in existing oncological datasets to effectively support automated patient recruitment. METHODS: We extracted and categorized the inclusion and exclusion criteria from 115 oncological trials registered on ClinicalTrials.gov in 2022. These criteria were then compared with the content of the oBDS (Oncological Base Dataset version 3.0), Germany's legally mandated oncological data standard. RESULTS: The analysis revealed that 42.9% of generalized inclusion and exclusion criteria are typically present as data fields in the oBDS. On average, 54.6% of all criteria per trial were covered. Notably, certain criteria such as comorbidities, pregnancy status, and laboratory values frequently appeared in trial protocols but were absent in the oBDS. CONCLUSION: The omission of criteria, notably comorbidities, within the oBDS restricts its functionality to support trial recruitment. Addressing this limitation would enhance its overall effectiveness. Furthermore, the implications of these findings extend beyond Germany, suggesting potential relevance and applicability to oncological datasets globally.

Bridging the gap: Leveraging telemedicine and IT infrastructure to connect outpatient oncology practices with specialized expert teams in the management of rare tumors.

Objectives: The treatment of rare tumors often necessitates the involvement of highly specialized teams, typically based in larger medical centers or university hospitals, which are often lacking in rural areas. The German TARGET (the Trans-sectoral Personalized Care Concept for Patients with Rare Cancers) project aims to improve the network between outpatient oncology practices and more centralized expert teams via telemedicine. Methods: The primary work involved conceptualizing the implementation of project requirements based on feedback from various TARGET project teams, and ultimately, the method of implementation using the software CentraXX. Key requirements included the utilization of an electronic health record (EHR), incorporating appropriate access mediums such as smartphones, and utilizing user-specific certificates to ensure secure and tailored access. The implementation considered technical aspects, data protection regulations, and the need for user-friendly interfaces, particularly for older patients with cancer with limited technological proficiency. Results: The results detail the successful implementation of the project requirements using CentraXX, which facilitated the implementation of an EHR, access mediums (patient app), and browser access for outpatient doctors, addressing the project's technical, security, and usability needs. Conclusion: This article presents an overview of the requirements associated with the TARGET project and outlines how they were met in terms of the IT infrastructure. By focusing on the IT implementation rather than the medical trial results, this work aims to provide valuable insights and guidance for similar projects seeking to improve telemedicine networks and digital information exchange in the context of rare cancer treatment.

Swapping data: A pragmatic approach for enabling academic-industrial partnerships.

Objectives: Academic institutions have access to comprehensive sets of real-world data. However, their potential for secondary use-for example, in medical outcomes research or health care quality management-is often limited due to data privacy concerns. External partners could help achieve this potential, yet documented frameworks for such cooperation are lacking. Therefore, this work presents a pragmatic approach for enabling academic-industrial data partnerships in a health care environment. Methods: We employ a value-swapping strategy to facilitate data sharing. Using tumor documentation and molecular pathology data, we define a data-altering process as well as rules for an organizational pipeline that includes the technical anonymization process. Results: The resulting dataset was fully anonymized while still retaining the critical properties of the original data to allow for external development and the training of analytical algorithms. Conclusion: Value swapping is a pragmatic, yet powerful method to balance data privacy and requirements for algorithm development; therefore, it is well suited to enable academic-industrial data partnerships.

Closing the cancer care gap with a patient-reported nutrition screening: A retrospective analysis of a quality improvement project on an oncology ward (CCC study).

BACKGROUND & AIMS: Early identification of patients at risk for malnutrition followed by individualized nutrition interventions is a central step to the provision of appropriate nutrition care. However, a health care professional (HCP)-based nutrition screening is not always consistently integrated into routine care. Patient-reported (PR) nutrition screening could thus potentially alleviate the burden on the HCPs and contribute to a greater number of patients who are identified and treated for malnutrition. METHODS: In 2021 a Quality Improvement Project (QIP) at our out-patient oncology clinic was undertaken to implement the change from a HCP-based nutrition screening to a PR-screening. This was followed by a retrospective analysis in which the primary outcome measure was the rate of nutrition consultations initiated for patients undergoing cancer therapy. RESULTS: In total n = 1657 patient data sets derived from comparable time periods before and after the QIP were analyzed and compared. Both groups had a comparable mean age and gender distribution. The most common diagnosis in both groups was gastrointestinal tumors. The change in routine care from a HCP-based nutrition screening to a PR-screening led to a significant increase in nutrition consultation rates (RD = 19%; p < 0.001; 95% CI 14.4%-23.5%) and screening rates (RD = 30.5%; p < 0.001; 95% CI 26.2%-34.7%). CONCLUSIONS: The change to PR-screening potentially facilitates an increase in nutrition screening rates. This in turn leads to an increased rate of patients identified at risk for malnutrition and thus referrals for nutrition consultations. Our findings indicate that a PR nutrition screening tool could play a role in closing the care gap and contribute to reducing rates of malnutrition among this population where screening is not consistently integrated into routine care.

Early mortality in German patients with lung cancer: risk factors associated with 30-and 60-day mortality.

Despite therapeutic advances, early mortality in lung cancer is still prevalent. In this study, we aimed to assess risk factors for 30- and 60-day mortality in German lung cancer patients. In this retrospective cross-sectional analysis, we used data of lung cancer patients treated at LMU Hospital Munich between 2015 and 2019. We categorized patients into 30-day mortality, 60 day-mortality, and longer survival. We used Student's t-test and ANOVA to compare means and Chi2-test to compare frequencies. We used logistic regression analysis to identify factors associated with a risk for early mortality. Of the 2454 lung cancer patients, 2.0% (n = 50) died within 30 and 1.7% (n = 41) within 30 to 60 days of diagnosis. Older age and advanced stage at diagnosis were significantly associated with early mortality in the univariate and the multivariate analysis. Patients in the 30-day mortality group significantly more often did not receive tumor-directed therapy. They were also more likely to die in an acute care setting compared to the 60-day mortality group. The group of patients who died unexpectedly (12.0%) was dominantly female, with a high proportion of patients with unintentional weight loss at the time of diagnosis. Our results suggest that in the treatment of patients with lung cancer there is a need for a greater focus on older patients. Moreover, physicians should pay special attention to females with recent weight loss and patients with a comorbidity of diabetes mellitus or renal impairment. Engaging a case manager focused on detecting patients with the above characteristics could help improve overall care.

The Mishandling of Anonymity in Terms of Medical Research Within the General Data Protection Regulation.

One of the major regulatory factors for health informatics is data privacy protection. In the European Union, a shared set of laws has been implemented - the General Data Protection Regulation. While this set of rules aims at harmonizing the European data privacy protection standards, it fails in properly detailing the handling of anonymized data. This is a problem, as, for example many current research initiatives aim at reusing patient data collected within primary care, but lack a patient consent, hence, might rely on anonymized data as being the only alternative. Within this work, we detail different aspects why the concept of anonymity is wrongly handled within the GDPR and give suggestions how the laws could be adapted.

Dynamic Survival Analysis Using In-Memory Technology.

In the field of oncology, survival analysis is one of the most important tools in terms of measuring therapy success, evaluating risk or prognostic factors. Within this work, a variety of common survival analyses were embedded into a real-time analysis platform based on a comprehensive oncological dataset. The analysis platform utilizes an in-memory database, therefore allowing spontaneous adjustments of the survival curves to selections and stratifications. In contrast to classical statistics, where individual scripts have to be formulated for every possible request, the running system allows the deduction of instantaneous results.

A Web-based survey among adults aged 40-54 years was time effective and yielded stable response patterns.

OBJECTIVE: We want to present information about response patterns obtained by Web-based survey in a large-scale epidemiological study. STUDY DESIGN AND SETTING: Within the RAPS (Risk Adapted Prevention Strategies for colorectal cancer [CRC]) study, we invited 160,000 randomly selected persons aged 40-54 years in three large German cities from 2015 to 2016 to complete a Web-based questionnaire on CRC risk factors and screening (97 items, average time for completion 15 minutes). Invitation letters and up to two reminder letters were sent to each individual. RESULTS: A total of 21.4% of women and 18.0% of men completed the questionnaire. Overall cumulative response rates were 7.5%, 14.3%, and 19.6% after the initial invitation letter, and the first and second reminder, respectively, with prevalence of and associations of key epidemiological parameters (such as family history of cancer, previous colonoscopy, etc.) being remarkably stable across waves of responses. For example, the sex and age distribution of the sample did not change with additional answers gained from additional letters. CONCLUSION: Web-based questionnaires are feasible, cost-effective, and time effective in the setting of large-scale epidemiological studies. Although response patterns were remarkably stable over several rounds of reminders with substantially increasing cumulative response rates, future research should address possibilities to further enhance response rates.

Detecting Duplicates at Hospital Admission: Comparison of Deterministic and Probabilistic Record Linkage.

Effective detection of corresponding or duplicate records in medical data sets is vital for a high quality health care system. We evaluate the efficacy of several current and novel record linkage approaches by modeling a hospital-admission scenario, wherein an incoming patient may or may not have been previously treated. Our work is to develop recommendations for how an automated system could operate in such a scenario, especially regarding comparison and classification. By using a large, anonymous, real-world data set, we can gain insight into the robustness of these methods in a way that artificial data sets cannot provide. Preliminary results show that even minor confounders have deleterious effects on our ability to classify matches. We aim to evaluate and refine a semi-supervised classification technique to cope with these influences.

Measuring Data Quality: A Review of the Literature between 2005 and 2013.

A literature review was done within a revision of a guideline concerned with data quality management in registries and cohort studies. The review focused on quality indicators, feedback, and source data verification. Thirty-nine relevant articles were selected in a stepwise selection process. The majority of the papers dealt with indicators. The papers presented concepts or data analyses. The leading indicators were related to case or data completeness, correctness, and accuracy. In the future, data pools as well as research reports from quantitative studies should be obligatory supplemented by information about their data quality, ideally picking up some indicators presented in this review.

Matching study to registry data: maintaining data privacy in a study on family based colorectal cancer.

Confidentiality of patient data in the field of medical informatics is an important task. Leaked sensitive information within this data can be adverse to and being abused against a patient. Therefore, when working with medical data, appropriate and secure models which serve as guidelines for different applications are needed. Consequently, this work presents a model for performing a privacy preserving record linkage between study and registry data. The model takes into account seven requirements related to data privacy. Furthermore, this model is exemplified with a study on family based colorectal cancer in Germany. The model is very strict and excludes possible violations towards data privacy protection to a reasonable degree. It should be applicable to similar use cases which are in need of a mapping between medical data of a study and a registry database.

Impact of variations in anonymous record linkage on weight distribution and classification.

Anonymous or privacy preserving record linkage is the term for systems allowing the linkage of data from different sources while maintaining an individual's anonymity. This work displays the impact of variations in the process of generating weights in a probabilistic record linkage system on different datasets, the resulting set of weights of candidate pairs and consequently on the final classification process. Furthermore, the results give insight into general problems of current unsupervised classification methods.