Designing and piloting a generic research architecture and workflows to unlock German primary care data for secondary use

Bahls, Thomas; Pung, Johannes; Heinemann, Stephanie; Hauswaldt, Johannes; Demmer, Iris; Blumentritt, Arne; Rau, Henriette; Drepper, Johannes; Wieder, Philipp; Groh, Roland; Hummers, Eva; Schlegelmilch, Falk

doi:10.1186/s12967-020-02547-x

Cited by 16 publications

(33 citation statements)

References 19 publications

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“…For example, a previous study developed an autophagy-related gene prognostic signature with the AUC = 0.615 at 5-year ( Zhu, Wang & Hu, 2020 ). And, an immune signature for 1- and 3-year survival rate of LUAD with AUCs reaching 0.70 and 0.68 ( Guo et al, 2020 ). Both the AUCs of them were inferior to that of the nomogram.…”

Section: Discussionmentioning

confidence: 99%

Identification of a ferroptosis-related gene signature (FRGS) for predicting clinical outcome in lung adenocarcinoma

Wang

Mao

et al. 2021

PeerJ

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Background Lung adenocarcinoma (LUAD) is the most common pathological subtype of lung cancer. Ferroptosis, an oxidative, iron-dependent form of necrotic cell death, is highly associated with tumorigenesis and cancer progression. However, the prognostic value of ferroptosis progress in LUAD was still rarely be investigated. Methods Herein, we collected three mRNA expression profiles and 85 ferroptosis-related genes from public databases. The “limma” package was used to identify ferroptosis-related differentially expressed genes (DEGs). Univariate Cox regression analysis and LASSO regression analysis were applied to screen and develop a ferroptosis-related gene signature (FRGS) and a formula to calculate the risk score. Multivariate Cox regression analysis was implemented to determine independent prognostic predictors of overall survival (OS). The area under the receiver operating characteristic curve (AUC) and calibration plot were used to evaluate the predictive accuracy of the FRGS and nomogram. Results We developed a FRGS with five genes (CYBB, CISD1, FADD, SAT2, VDAC2). The AUC of the FRGS in TCGA cohort was 0.777 at 1-year, 0.721 at 3-year and 0.725 at 5-year, significantly superior to the AUC of TNM stage (1-year: 0.701, 3-year: 0.691, 5-year: 0.686). A similar phenomenon was observed in GEO cohort 1 and 2. Multivariate Cox regression analysis indicted TNM stage and risk score were independent prognostic predictors. Finally, we built a nomogram with TNM stage and FRGS, the AUCs of which markedly higher than that of FRGS or TNM stage alone. Conclusion We constructed a prognostic FRGS with five ferroptosis-related genes and a nomogram for predicting the 1-, 3- and 5-year survival rate of LUAD patients, which may provide a new understanding of the prognostic value of ferroptosis progress in LUAD and will benefit prognosis assessment of LUAD patients.

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Section: Discussionmentioning

confidence: 99%

Identification of a ferroptosis-related gene signature (FRGS) for predicting clinical outcome in lung adenocarcinoma

Wang

Mao

et al. 2021

PeerJ

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“…Due to the growing potential of largescale data analysis, the relationships between patient data and the risks associated with various diseases will be detected promptly. The data stored in EHRs, whose informational value will increase, are highly heterogeneous, and their analysis will increasingly show how groups of factors describe the combined risks of different diseases [29,44]. Therefore, it is also necessary to consider the degree of overlapping of similar patients' groups that can be described by similar characteristics and common risks.…”

Section: Discussionmentioning

confidence: 99%

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

et al. 2021

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Background The burden of chronic and societal diseases is affected by many risk factors that can change over time. The minimalisation of disease-associated risk factors may contribute to long-term health. Therefore, new data-driven health management should be used in clinical decision-making in order to minimise future individual risks of disease and adverse health effects. Methods We aimed to develop a health trajectories (HT) management methodology based on electronic health records (EHR) and analysing overlapping groups of patients who share a similar risk of developing a particular disease or experiencing specific adverse health effects. Formal concept analysis (FCA) was applied to identify and visualise overlapping patient groups, as well as for decision-making. To demonstrate its capabilities, the theoretical model presented uses genuine data from a local total knee arthroplasty (TKA) register (a total of 1885 patients) and shows the influence of step by step changes in five lifestyle factors (BMI, smoking, activity, sports and long-distance walking) on the risk of early reoperation after TKA. Results The theoretical model of HT management demonstrates the potential of using EHR data to make data-driven recommendations to support both patients’ and physicians’ decision-making. The model example developed from the TKA register acts as a clinical decision-making tool, built to show surgeons and patients the likelihood of early reoperation after TKA and how the likelihood changes when factors are modified. The presented data-driven tool suits an individualised approach to health management because it quantifies the impact of various combinations of factors on the early reoperation rate after TKA and shows alternative combinations of factors that may change the reoperation risk. Conclusion This theoretical model introduces future HT management as an understandable way of conceiving patients’ futures with a view to positively (or negatively) changing their behaviour. The model’s ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.

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“…In contrast to other tools and workflows that have been already developed to provide health data for research (e.g. (17), (18)), the main focus of our approach is on the backward propagation of research findings and data analysis results into the clinical process, illustrated as the “B” arm in Fig 1. Specifically, we investigated how patient-specific predictions based on mathematical models and corresponding computer simulations can be integrated directly into the clinical workflows to support actual clinical decision-making for treatment optimizations.…”

Section: Introductionmentioning

confidence: 99%

Data integration between clinical research and patient care: a framework for context-depending data sharing and in silico predictions

Hoffmann

Pelz

Karg

et al. 2022

Preprint

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Usually, it takes quite some time until new insights from basic or clinical research are ultimately transferred into clinical routine. On the other hand, there are still many hurdles to directly provide and use routine data in the context of basic and clinical research. Specifically, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, we emerge from a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction, and a research perspective focusing on the exploration of aggregated, but pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes.

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Designing and piloting a generic research architecture and workflows to unlock German primary care data for secondary use

Cited by 16 publications

References 19 publications

Identification of a ferroptosis-related gene signature (FRGS) for predicting clinical outcome in lung adenocarcinoma

Identification of a ferroptosis-related gene signature (FRGS) for predicting clinical outcome in lung adenocarcinoma

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

Data integration between clinical research and patient care: a framework for context-depending data sharing and in silico predictions

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