Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example

OBJECTIVES: Critical care medicine is a natural environment for machine learning approaches to improve outcomes for critically ill patients as admissions to ICUs generate vast amounts of data. However, technical, legal, ethical, and privacy concerns have so far limited the critical care medicine community from making these data readily available. The Society of Critical Care Medicine and the European Society of Intensive Care Medicine have identified ICU patient data sharing as one of the priorities under their Joint Data Science Collaboration. To encourage ICUs worldwide to share their patient data responsibly, we now describe the development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, as an example of the feasibility of sharing complex critical care data. SETTING: University hospital ICU. SUBJECTS: Data from ICU patients admitted between 2003 and 2016. INTERVENTIONS: We used a risk-based deidentification strategy to maintain data utility while preserving privacy. In addition, we implemented contractual and governance processes, and a communication strategy. Patient organizations, supporting hospitals, and experts on ethics and privacy audited these processes and the database. MEASUREMENTS AND MAIN RESULTS: AmsterdamUMCdb contains approximately 1 billion clinical data points from 23,106 admissions of 20,109 patients. The privacy audit concluded that reidentification is not reasonably likely, and AmsterdamUMCdb can therefore be considered as anonymous information, both in the context of the U.S. Health Insurance Portability and Accountability Act and the European General Data Protection Regulation. The ethics audit concluded that responsible data sharing imposes minimal burden, whereas the potential benefit is tremendous. CONCLUSIONS: Technical, legal, ethical, and privacy challenges related to responsible data sharing can be addressed using a multidisciplinary approach. A risk-based deidentification strategy, that complies with both U.S. and European privacy regulations, should be the preferred approach to releasing ICU patient data. This supports the shared Society of Critical Care Medicine and European Society of Intensive Care Medicine vision to improve critical care outcomes through scientific inquiry of vast and combined ICU datasets

Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example.

OBJECTIVES: Critical care medicine is a natural environment for machine learning approaches to improve outcomes for critically ill patients as admissions to ICUs generate vast amounts of data. However, technical, legal, ethical, and privacy concerns have so far limited the critical care medicine community from making these data readily available. The Society of Critical Care Medicine and the European Society of Intensive Care Medicine have identified ICU patient data sharing as one of the priorities under their Joint Data Science Collaboration. To encourage ICUs worldwide to share their patient data responsibly, we now describe the development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, as an example of the feasibility of sharing complex critical care data. SETTING: University hospital ICU. SUBJECTS: Data from ICU patients admitted between 2003 and 2016. INTERVENTIONS: We used a risk-based deidentification strategy to maintain data utility while preserving privacy. In addition, we implemented contractual and governance processes, and a communication strategy. Patient organizations, supporting hospitals, and experts on ethics and privacy audited these processes and the database. MEASUREMENTS AND MAIN RESULTS: AmsterdamUMCdb contains approximately 1 billion clinical data points from 23,106 admissions of 20,109 patients. The privacy audit concluded that reidentification is not reasonably likely, and AmsterdamUMCdb can therefore be considered as anonymous information, both in the context of the U.S. Health Insurance Portability and Accountability Act and the European General Data Protection Regulation. The ethics audit concluded that responsible data sharing imposes minimal burden, whereas the potential benefit is tremendous. CONCLUSIONS: Technical, legal, ethical, and privacy challenges related to responsible data sharing can be addressed using a multidisciplinary approach. A risk-based deidentification strategy, that complies with both U.S. and European privacy regulations, should be the preferred approach to releasing ICU patient data. This supports the shared Society of Critical Care Medicine and European Society of Intensive Care Medicine vision to improve critical care outcomes through scientific inquiry of vast and combined ICU datasets

INSIST Clinical Focus Group Discussions at INSIST All hands workshop Milan – a Whitepaper

In silico clinical trials hold the promise to use computational modelling of disease and its treatment to support the evaluation and introduction of new drugs and medical devices. The INSIST (In silico clinical trials for treatment of ischemic stroke) project aims to develop an in silico platform for the simulation and evaluation of novel treatments for acute ischemic stroke. INSIST comprises the generation of virtual patient populations, the in silico modeling of brain tissue death due to the lack of oxygen and nutrients following a stroke, thrombosis and thrombolysis, and thrombectomy. The combination allows in silico simulation of (pre-) clinical trials. The INSIST project organizes Focus Group Discussions involving specific stakeholders and contributors. In April 2019 Clinical Focus Group Discussions were organized to inform interested clinicians and to obtain their feedback on the concept, current state of the art, and intended further development of the INSIST project. In short, the concept of in silico stroke trials was considered promising, especially the thrombectomy modeling was regarded valuable. The modeling of tissue death and its effect on clinical outcome was considered the most complex and risky

Additional file 13 of Distinct cell type-specific protein signatures in GRN and MAPT genetic subtypes of frontotemporal dementia

Additional file 13 Overview of unique proteins detected and quantified within temporal cortical tissues for AD and unique proteins detected and quantified within temporal cortical tissues for both AD and the FTD-MAPT subtype. Proteins were selected using quality filtering on peptide level (q ≤10-3 in at least 50% of samples per group, i.e. NDC or AD). For every protein, the raw fold change and raw p-value are given for the statistical comparison between NDC and AD, and NDC and FTD-MAPT. In addition, columns are included which note whether a protein has passed statistical multiple testing comparison (q < 0.05) and whether differential protein expression is in a similar direction in AD and FTD-MAPT

Additional file 5 of Distinct cell type-specific protein signatures in GRN and MAPT genetic subtypes of frontotemporal dementia

Additional file 5 Lists of significant GO enrichment analysis results for proteins differentially expressed in frontal cortical FTD-GRN vs NDC and temporal cortical FTD-MAPT vs NDC. GO enrichment analysis was performed on proteins differentially expressed at q < 0.05, with proteins divided into lower and higher expressed proteins. For every GO term, the corresponding GO group is listed, as well as whether the GO term is considered to be a ‘Best Per Parent’ term

Additional file 3 of Distinct cell type-specific protein signatures in GRN and MAPT genetic subtypes of frontotemporal dementia

Additional file 3 Lists of unique proteins detected and quantified within frontal and temporal cortical tissues for the RiMOD-FTD genetic subtypes. Proteins were selected using quality filtering on peptide level (q ≤10-3 in at least 50% of samples per group, i.e. NDC or FTD). For every protein, the raw fold change, raw p-value, multiple comparison corrected q-value, and effect size (d) are given for the statistical comparison between NDC and FTD subtype. In addition, columns are included which note whether a protein has passed statistical testing (either p < 0.05 or q < 0.05). Furthermore, the differential expression of several well-known neurodegeneration (ND)-related proteins in frontal cortical FTD-GRN vs NDC and temporal cortical FTD-MAPT vs NDC are highlighted. -; not detected within study cohort

Additional file 8 of Distinct cell type-specific protein signatures in GRN and MAPT genetic subtypes of frontotemporal dementia

Additional file 8 Overview of MitoCarta-based analysis for proteins differentially expressed in frontal cortical FTD-GRN vs NDC. MitoCarta-based analysis was performed on proteins differentially expressed at q < 0.05. Results include MitoCarta protein annotations, results from the PANTHER overrepresentation analysis on the MitoCarta-annotated proteins, and an overview of those MitoCarta proteins involved in the respiratory chain complexes I-II

Additional file 7 of Distinct cell type-specific protein signatures in GRN and MAPT genetic subtypes of frontotemporal dementia

Additional file 7 Lists of SynGO enrichment analysis results for proteins differentially expressed in frontal cortical FTD-GRN vs NDC and temporal cortical FTD-MAPT vs NDC. SynGO enrichment analysis was performed on proteins differentially expressed at q < 0.05, with proteins divided into lower and higher expressed proteins. For every SynGO term, the proteins within that term that were measured in the different FTD subtypes are given, as well the raw and multiple testing corrected p-value for the enrichment analysis

Additional file 14 of Distinct cell type-specific protein signatures in GRN and MAPT genetic subtypes of frontotemporal dementia

Additional file 14 Lists of significant GO enrichment analysis results for proteins differentially expressed in both temporal cortical FTD-MAPT vs NDC and AD vs NDC. GO enrichment analysis was performed for proteins differentially expressed at q < 0.05 in both temporal cortical AD and FTD-MAPT vs NDC, and for proteins that are only differentially expressed (q < 0.05) in temporal cortical FTD-MAPT vs NDC. Differentially expressed proteins are divided into lower and higher expressed proteins. For every GO term, the corresponding GO group is listed, as well as whether the GO term is considered to be a ‘Best Per Parent’ term

Additional file 15 of Distinct cell type-specific protein signatures in GRN and MAPT genetic subtypes of frontotemporal dementia

Additional file 15 Lists of SynGO enrichment analysis results for proteins differentially expressed in both temporal cortical FTD-MAPT vs NDC and AD vs NDC. SynGO enrichment analysis was performed for proteins differentially expressed at q < 0.05, with proteins divided into lower and higher expressed proteins. For every SynGO term, the proteins within that term that were measured in the different FTD subtypes are given, as well the raw and multiple testing corrected p-value for the enrichment analysis