Software Application Profile: Opal and Mica: open-source software solutions for epidemiological data management, harmonization and dissemination

Towards an Interoperability Landscape for a National Research Data Infrastructure for Personal Health Data

The German initiative "National Research Data Infrastructure for Personal Health Data" (NFDI4Health) focuses on research data management in health research. It aims to foster and develop harmonized informatics standards for public health, epidemiological studies, and clinical trials, facilitating access to relevant data and metadata standards. This publication lists syntactic and semantic data standards of potential use for NFDI4Health and beyond, based on interdisciplinary meetings and workshops, mappings of study questionnaires and the NFDI4Health metadata schema, and literature search. Included are 7 syntactic, 32 semantic and 9 combined syntactic and semantic standards. In addition, 101 ISO Standards from ISO/TC 215 Health Informatics and ISO/TC 276 Biotechnology could be identified as being potentially relevant. The work emphasizes the utilization of standards for epidemiological and health research data ensuring interoperability as well as the compatibility to NFDI4Health, its use cases, and to (inter-)national efforts within these sectors. The goal is to foster collaborative and inter-sectoral work in health research and initiate a debate around the potential of using common standards.

Haemoglobin values, transfusion practices, and long-term outcomes in critically ill patients with traumatic brain injury: a secondary analysis of CENTER-TBI

Haemoglobin (Hb) thresholds and red blood cells (RBC) transfusion strategies in traumatic brain injury (TBI) are controversial. Our objective was to assess the association of Hb values with long-term outcomes in critically ill TBI patients. We conducted a secondary analysis of CENTER-TBI, a large multicentre, prospective, observational study of European TBI patients. All patients admitted to the Intensive Care Unit (ICU) with available haemoglobin data on admission and during the first week were included. During the first seven days, daily lowest haemoglobin values were considered either a continous variable or categorised as < 7.5 g/dL, between 7.5-9.5 and > 9.5 g/dL. Anaemia was defined as haemoglobin value < 9.5 g/dL. Transfusion practices were described as "restrictive" or "liberal" based on haemoglobin values before transfusion (e.g. < 7.5 g/dL or 7.5-9.5 g/dL). Our primary outcome was the Glasgow outcome scale extended (GOSE) at six months, defined as being unfavourable when < 5. Of 1590 included, 1231 had haemoglobin values available on admission. A mean Injury Severity Score (ISS) of 33 (SD 16), isolated TBI in 502 (40.7%) and a mean Hb value at ICU admission of 12.6 (SD 2.2) g/dL was observed. 121 (9.8%) patients had Hb < 9.5 g/dL, of whom 15 (1.2%) had Hb < 7.5 g/dL. 292 (18.4%) received at least one RBC transfusion with a median haemoglobin value before transfusion of 8.4 (IQR 7.7-8.5) g/dL. Considerable heterogeneity regarding threshold transfusion was observed among centres. In the multivariable logistic regression analysis, the increase of haemoglobin value was independently associated with the decrease in the occurrence of unfavourable neurological outcomes (OR 0.78; 95% CI 0.70-0.87). Congruous results were observed in patients with the lowest haemoglobin values within the first 7 days < 7.5 g/dL (OR 2.09; 95% CI 1.15-3.81) and those between 7.5 and 9.5 g/dL (OR 1.61; 95% CI 1.07-2.42) compared to haemoglobin values > 9.5 g/dL. Results were consistent when considering mortality at 6 months as an outcome. The increase of hemoglobin value was associated with the decrease of mortality (OR 0.88; 95% CI 0.76-1.00); haemoglobin values less than 7.5 g/dL was associated with an increase of mortality (OR 3.21; 95% CI 1.59-6.49). Anaemia was independently associated with long-term unfavourable neurological outcomes and mortality in critically ill TBI patients.Trial registration: CENTER-TBI is registered at ClinicalTrials.gov, NCT02210221, last update 2022-11-07.

Therapy Intensity Level Scale for Traumatic Brain Injury: Clinimetric Assessment on Neuro-Monitored Patients Across 52 European Intensive Care Units

Intracranial pressure (ICP) data from traumatic brain injury (TBI) patients in the intensive care unit (ICU) cannot be interpreted appropriately without accounting for the effect of administered therapy intensity level (TIL) on ICP. A 15-point scale was originally proposed in 1987 to quantify the hourly intensity of ICP-targeted treatment. This scale was subsequently modified-through expert consensus-during the development of TBI Common Data Elements to address statistical limitations and improve usability. The latest 38-point scale (hereafter referred to as TIL) permits integrated scoring for a 24-h period and has a five-category, condensed version (TIL(Basic)) based on qualitative assessment. Here, we perform a total- and component-score analysis of TIL and TIL(Basic) to: 1) validate the scales across the wide variation in contemporary ICP management; 2) compare their performance against that of predecessors; and 3) derive guidelines for proper scale use. From the observational Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study, we extract clinical data from a prospective cohort of ICP-monitored TBI patients (n = 873) from 52 ICUs across 19 countries. We calculate daily TIL and TIL(Basic) scores (TIL24 and TIL(Basic)24, respectively) from each patient's first week of ICU stay. We also calculate summary TIL and TIL(Basic) scores by taking the first-week maximum (TILmax and TIL(Basic)max) and first-week median (TILmedian and TIL(Basic)median) of TIL24 and TIL(Basic)24 scores for each patient. We find that, across all measures of construct and criterion validity, the latest TIL scale performs significantly greater than or similarly to all alternative scales (including TIL(Basic)) and integrates the widest range of modern ICP treatments. TILmedian outperforms both TILmax and summarized ICP values in detecting refractory intracranial hypertension (RICH) during ICU stay. The RICH detection thresholds which maximize the sum of sensitivity and specificity are TILmedian ≥ 7.5 and TILmax ≥ 14. The TIL24 threshold which maximizes the sum of sensitivity and specificity in the detection of surgical ICP control is TIL24 ≥ 9. The median scores of each TIL component therapy over increasing TIL24 reflect a credible staircase approach to treatment intensity escalation, from head positioning to surgical ICP control, as well as considerable variability in the use of cerebrospinal fluid drainage and decompressive craniectomy. Since TIL(Basic)max suffers from a strong statistical ceiling effect and only covers 17% (95% confidence interval [CI]: 16-18%) of the information in TILmax, TIL(Basic) should not be used instead of TIL for rating maximum treatment intensity. TIL(Basic)24 and TIL(Basic)median can be suitable replacements for TIL24 and TILmedian, respectively (with up to 33% [95% CI: 31-35%] information coverage) when full TIL assessment is infeasible. Accordingly, we derive numerical ranges for categorising TIL24 scores into TIL(Basic)24 scores. In conclusion, our results validate TIL across a spectrum of ICP management and monitoring approaches. TIL is a more sensitive surrogate for pathophysiology than ICP and thus can be considered an intermediate outcome after TBI.

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study

Introduction

Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma.

Methods

Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics.

Results

From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor.

Conclusions

Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.

Early systemic insults following traumatic brain injury: association with biomarker profiles, therapy for intracranial hypertension, and neurological outcomes-an analysis of CENTER-TBI data

PURPOSE

We analysed the impact of early systemic insults (hypoxemia and hypotension, SIs) on brain injury biomarker profiles, acute care requirements during intensive care unit (ICU) stay, and 6-month outcomes in patients with traumatic brain injury (TBI).

METHODS

From patients recruited to the Collaborative European neurotrauma effectiveness research in TBI (CENTER-TBI) study, we documented the prevalence and risk factors for SIs and analysed their effect on the levels of brain injury biomarkers [S100 calcium-binding protein B (S100B), neuron-specific enolase (NSE), neurofilament light (NfL), glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and protein Tau], critical care needs, and 6-month outcomes [Glasgow Outcome Scale Extended (GOSE)].

RESULTS

Among 1695 TBI patients, 24.5% had SIs: 16.1% had hypoxemia, 15.2% had hypotension, and 6.8% had both. Biomarkers differed by SI category, with higher S100B, Tau, UCH-L1, NSE and NfL values in patients with hypotension or both SIs. The ratio of neural to glial injury (quantified as UCH-L1/GFAP and Tau/GFAP ratios) was higher in patients with hypotension than in those with no SIs or hypoxia alone. At 6 months, 380 patients died (22%), and 759 (45%) had GOSE ≤ 4. Patients who experienced at least one SI had higher mortality than those who did not (31.8% vs. 19%, p < 0.001).

CONCLUSION

Though less frequent than previously described, SIs in TBI patients are associated with higher release of neuronal than glial injury biomarkers and with increased requirements for ICU therapies aimed at reducing intracranial hypertension. Hypotension or combined SIs are significantly associated with adverse 6-month outcomes. Current criteria for hypotension may lead to higher biomarker levels and more negative outcomes than those for hypoxemia suggesting a need to revisit pressure targets in the prehospital settings.

A Federated Database for Obesity Research: An IMI-SOPHIA Study

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

INSPIRE datahub: a pan-African integrated suite of services for harmonising longitudinal population health data using OHDSI tools

Introduction

Population health data integration remains a critical challenge in low- and middle-income countries (LMIC), hindering the generation of actionable insights to inform policy and decision-making. This paper proposes a pan-African, Findable, Accessible, Interoperable, and Reusable (FAIR) research architecture and infrastructure named the INSPIRE datahub. This cloud-based Platform-as-a-Service (PaaS) and on-premises setup aims to enhance the discovery, integration, and analysis of clinical, population-based surveys, and other health data sources.

Methods

The INSPIRE datahub, part of the Implementation Network for Sharing Population Information from Research Entities (INSPIRE), employs the Observational Health Data Sciences and Informatics (OHDSI) open-source stack of tools and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to harmonise data from African longitudinal population studies. Operating on Microsoft Azure and Amazon Web Services cloud platforms, and on on-premises servers, the architecture offers adaptability and scalability for other cloud providers and technology infrastructure. The OHDSI-based tools enable a comprehensive suite of services for data pipeline development, profiling, mapping, extraction, transformation, loading, documentation, anonymization, and analysis.

Results

The INSPIRE datahub's "On-ramp" services facilitate the integration of data and metadata from diverse sources into the OMOP CDM. The datahub supports the implementation of OMOP CDM across data producers, harmonizing source data semantically with standard vocabularies and structurally conforming to OMOP table structures. Leveraging OHDSI tools, the datahub performs quality assessment and analysis of the transformed data. It ensures FAIR data by establishing metadata flows, capturing provenance throughout the ETL processes, and providing accessible metadata for potential users. The ETL provenance is documented in a machine- and human-readable Implementation Guide (IG), enhancing transparency and usability.

Conclusion

The pan-African INSPIRE datahub presents a scalable and systematic solution for integrating health data in LMICs. By adhering to FAIR principles and leveraging established standards like OMOP CDM, this architecture addresses the current gap in generating evidence to support policy and decision-making for improving the well-being of LMIC populations. The federated research network provisions allow data producers to maintain control over their data, fostering collaboration while respecting data privacy and security concerns. A use-case demonstrated the pipeline using OHDSI and other open-source tools.

Strengths and limitations of non-disclosive data analysis: a comparison of breast cancer survival classifiers using VisualSHIELD

Preserving data privacy is an important concern in the research use of patient data. The DataSHIELD suite enables privacy-aware advanced statistical analysis in a federated setting. Despite its many applications, it has a few open practical issues: the complexity of hosting a federated infrastructure, the performance penalty imposed by the privacy-preserving constraints, and the ease of use by non-technical users. In this work, we describe a case study in which we review different breast cancer classifiers and report our findings about the limits and advantages of such non-disclosive suite of tools in a realistic setting. Five independent gene expression datasets of breast cancer survival were downloaded from Gene Expression Omnibus (GEO) and pooled together through the federated infrastructure. Three previously published and two newly proposed 5-year cancer-free survival risk score classifiers were trained in a federated environment, and an additional reference classifier was trained with unconstrained data access. The performance of these six classifiers was systematically evaluated, and the results show that i) the published classifiers do not generalize well when applied to patient cohorts that differ from those used to develop them; ii) among the methods we tried, the classification using logistic regression worked better on average, closely followed by random forest; iii) the unconstrained version of the logistic regression classifier outperformed the federated version by 4% on average. Reproducibility of our experiments is ensured through the use of VisualSHIELD, an open-source tool that augments DataSHIELD with new functions, a standardized deployment procedure, and a simple graphical user interface.

An innovative technological infrastructure for managing SARS-CoV-2 data across different cohorts in compliance with General Data Protection Regulation

Background

The ORCHESTRA project, funded by the European Commission, aims to create a pan-European cohort built on existing and new large-scale population cohorts to help rapidly advance the knowledge related to the prevention of the SARS-CoV-2 infection and the management of COVID-19 and its long-term sequelae. The integration and analysis of the very heterogeneous health data pose the challenge of building an innovative technological infrastructure as the foundation of a dedicated framework for data management that should address the regulatory requirements such as the General Data Protection Regulation (GDPR).

Methods

The three participating Supercomputing European Centres (CINECA - Italy, CINES - France and HLRS - Germany) designed and deployed a dedicated infrastructure to fulfil the functional requirements for data management to ensure sensitive biomedical data confidentiality/privacy, integrity, and security. Besides the technological issues, many methodological aspects have been considered: Berlin Institute of Health (BIH), Charité provided its expertise both for data protection, information security, and data harmonisation/standardisation.

Results

The resulting infrastructure is based on a multi-layer approach that integrates several security measures to ensure data protection. A centralised Data Collection Platform has been established in the Italian National Hub while, for the use cases in which data sharing is not possible due to privacy restrictions, a distributed approach for Federated Analysis has been considered. A Data Portal is available as a centralised point of access for non-sensitive data and results, according to findability, accessibility, interoperability, and reusability (FAIR) data principles. This technological infrastructure has been used to support significative data exchange between population cohorts and to publish important scientific results related to SARS-CoV-2.

Conclusions

Considering the increasing demand for data usage in accordance with the requirements of the GDPR regulations, the experience gained in the project and the infrastructure released for the ORCHESTRA project can act as a model to manage future public health threats. Other projects could benefit from the results achieved by ORCHESTRA by building upon the available standardisation of variables, design of the architecture, and process used for GDPR compliance.

Mining the contribution of intensive care clinical course to outcome after traumatic brain injury

Existing methods to characterise the evolving condition of traumatic brain injury (TBI) patients in the intensive care unit (ICU) do not capture the context necessary for individualising treatment. Here, we integrate all heterogenous data stored in medical records (1166 pre-ICU and ICU variables) to model the individualised contribution of clinical course to 6-month functional outcome on the Glasgow Outcome Scale -Extended (GOSE). On a prospective cohort (n = 1550, 65 centres) of TBI patients, we train recurrent neural network models to map a token-embedded time series representation of all variables (including missing values) to an ordinal GOSE prognosis every 2 h. The full range of variables explains up to 52% (95% CI: 50-54%) of the ordinal variance in functional outcome. Up to 91% (95% CI: 90-91%) of this explanation is derived from pre-ICU and admission information (i.e., static variables). Information collected in the ICU (i.e., dynamic variables) increases explanation (by up to 5% [95% CI: 4-6%]), though not enough to counter poorer overall performance in longer-stay (>5.75 days) patients. Highest-contributing variables include physician-based prognoses, CT features, and markers of neurological function. Whilst static information currently accounts for the majority of functional outcome explanation after TBI, data-driven analysis highlights investigative avenues to improve the dynamic characterisation of longer-stay patients. Moreover, our modelling strategy proves useful for converting large patient records into interpretable time series with missing data integration and minimal processing.

Comparing Decentralized Learning Methods for Health Data Models to Nondecentralized Alternatives: Protocol for a Systematic Review

BACKGROUND

Considering the soaring health-related costs directed toward a growing, aging, and comorbid population, the health sector needs effective data-driven interventions while managing rising care costs. While health interventions using data mining have become more robust and adopted, they often demand high-quality big data. However, growing privacy concerns have hindered large-scale data sharing. In parallel, recently introduced legal instruments require complex implementations, especially when it comes to biomedical data. New privacy-preserving technologies, such as decentralized learning, make it possible to create health models without mobilizing data sets by using distributed computation principles. Several multinational partnerships, including a recent agreement between the United States and the European Union, are adopting these techniques for next-generation data science. While these approaches are promising, there is no clear and robust evidence synthesis of health care applications.

OBJECTIVE

The main aim is to compare the performance among health data models (eg, automated diagnosis and mortality prediction) developed using decentralized learning approaches (eg, federated and blockchain) to those using centralized or local methods. Secondary aims are comparing the privacy compromise and resource use among model architectures.

METHODS

We will conduct a systematic review using the first-ever registered research protocol for this topic following a robust search methodology, including several biomedical and computational databases. This work will compare health data models differing in development architecture, grouping them according to their clinical applications. For reporting purposes, a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram will be presented. CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies)-based forms will be used for data extraction and to assess the risk of bias, alongside PROBAST (Prediction Model Risk of Bias Assessment Tool). All effect measures in the original studies will be reported.

RESULTS

The queries and data extractions are expected to start on February 28, 2023, and end by July 31, 2023. The research protocol was registered with PROSPERO, under the number 393126, on February 3, 2023. With this protocol, we detail how we will conduct the systematic review. With that study, we aim to summarize the progress and findings from state-of-the-art decentralized learning models in health care in comparison to their local and centralized counterparts. Results are expected to clarify the consensuses and heterogeneities reported and help guide the research and development of new robust and sustainable applications to address the health data privacy problem, with applicability in real-world settings.

CONCLUSIONS

We expect to clearly present the status quo of these privacy-preserving technologies in health care. With this robust synthesis of the currently available scientific evidence, the review will inform health technology assessment and evidence-based decisions, from health professionals, data scientists, and policy makers alike. Importantly, it should also guide the development and application of new tools in service of patients' privacy and future research.

TRIAL REGISTRATION

PROSPERO 393126; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=393126.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/45823.

The lower limit of reactivity as a potential individualised cerebral perfusion pressure target in traumatic brain injury: a CENTER-TBI high-resolution sub-study analysis

BACKGROUND

A previous retrospective single-centre study suggested that the percentage of time spent with cerebral perfusion pressure (CPP) below the individual lower limit of reactivity (LLR) is associated with mortality in traumatic brain injury (TBI) patients. We aim to validate this in a large multicentre cohort.

METHODS

Recordings from 171 TBI patients from the high-resolution cohort of the CENTER-TBI study were processed with ICM+ software. We derived LLR as a time trend of CPP at a level for which the pressure reactivity index (PRx) indicates impaired cerebrovascular reactivity with low CPP. The relationship with mortality was assessed with Mann-U test (first 7-day period), Kruskal-Wallis (daily analysis for 7 days), univariate and multivariate logistic regression models. AUCs (CI 95%) were calculated and compared using DeLong's test.

RESULTS

Average LLR over the first 7 days was above 60 mmHg in 48% of patients. %time with CPP < LLR could predict mortality (AUC 0.73, p =  < 0.001). This association becomes significant starting from the third day post injury. The relationship was maintained when correcting for IMPACT covariates or for high ICP.

CONCLUSIONS

Using a multicentre cohort, we confirmed that CPP below LLR was associated with mortality during the first seven days post injury.

Towards autoregulation-oriented management after traumatic brain injury: increasing the reliability and stability of the CPPopt algorithm

PURPOSE

CPPopt denotes a Cerebral Perfusion Pressure (CPP) value at which the Pressure-Reactivity index, reflecting the global state of Cerebral Autoregulation, is best preserved. CPPopt has been investigated as a potential dynamically individualised CPP target in traumatic brain injury patients admitted in intensive care unit. The prospective bedside use of the concept requires ensured safety and reliability of the CPP recommended targets based on the automatically-generated CPPopt. We aimed to: Increase stability and reliability of the CPPopt automated algorithm by fine-tuning; perform outcome validation of the adjusted algorithm in a multi-centre TBI cohort.

METHODS

ICM + software was used to derive CPPopt and fine-tune the algorithm. Parameters for improvement of the algorithm were selected based on qualitative and quantitative assessment of stability and reliability metrics. Patients enrolled in the Collaborative European Neuro Trauma Effectiveness Research in TBI (CENTER-TBI) high-resolution cohort were included for retrospective validation. Yield and stability of the new algorithm were compared to the previous algorithm using Mann-U test. Area under the curves for mortality prediction at 6 months were compared with the DeLong Test.

RESULTS

CPPopt showed higher stability (p < 0.0001), but lower yield compared to the previous algorithm [80.5% (70-87.5) vs 85% (75.7-91.2), p < 0.001]. Deviation of CPPopt could predict mortality with an AUC of [AUC = 0.69 (95% CI 0.59-0.78), p < 0.001] and was comparable with the previous algorithm.

CONCLUSION

The CPPopt calculation algorithm was fine-tuned and adapted for prospective use with acceptable lower yield, improved stability and maintained prognostic power.

A. Sørensen T, Elhakeem A, Santos AC, Pinot de Moira A, Heude B, Iñiguez C, Pizzi C, Simons E, Voerman E, Corpeleijn E, Zariouh F, Santorelli G, Inskip HM, Barros H, Carson J, Harris JR, Nader JL, Ronkainen J, Strandberg-Larsen K, Santa-Marina L, Calas L, Cederkvist L, Popovic M, Charles MA, Welten M, Vrijheid M, Azad M, Subbarao P, Burton P, Mandhane PJ, Huang RC, Wilson RC, Haakma S, Fernández-Barrés S, Turvey S, Santos S, Tough SC, Sebert S, Moraes TJ, Salika T, Jaddoe VWV, Lawlor DA, Nybo Andersen AM. Gestational age at birth and body size from infancy through adolescence: An individual participant data meta-analysis on 253,810 singletons in 16 birth cohort studies

BACKGROUND

Preterm birth is the leading cause of perinatal morbidity and mortality and is associated with adverse developmental and long-term health outcomes, including several cardiometabolic risk factors and outcomes. However, evidence about the association of preterm birth with later body size derives mainly from studies using birth weight as a proxy of prematurity rather than an actual length of gestation. We investigated the association of gestational age (GA) at birth with body size from infancy through adolescence.

METHODS AND FINDINGS

We conducted a two-stage individual participant data (IPD) meta-analysis using data from 253,810 mother-child dyads from 16 general population-based cohort studies in Europe (Denmark, Finland, France, Italy, Norway, Portugal, Spain, the Netherlands, United Kingdom), North America (Canada), and Australasia (Australia) to estimate the association of GA with body mass index (BMI) and overweight (including obesity) adjusted for the following maternal characteristics as potential confounders: education, height, prepregnancy BMI, ethnic background, parity, smoking during pregnancy, age at child's birth, gestational diabetes and hypertension, and preeclampsia. Pregnancy and birth cohort studies from the LifeCycle and the EUCAN-Connect projects were invited and were eligible for inclusion if they had information on GA and minimum one measurement of BMI between infancy and adolescence. Using a federated analytical tool (DataSHIELD), we fitted linear and logistic regression models in each cohort separately with a complete-case approach and combined the regression estimates and standard errors through random-effects study-level meta-analysis providing an overall effect estimate at early infancy (>0.0 to 0.5 years), late infancy (>0.5 to 2.0 years), early childhood (>2.0 to 5.0 years), mid-childhood (>5.0 to 9.0 years), late childhood (>9.0 to 14.0 years), and adolescence (>14.0 to 19.0 years). GA was positively associated with BMI in the first decade of life, with the greatest increase in mean BMI z-score during early infancy (0.02, 95% confidence interval (CI): 0.00; 0.05, p < 0.05) per week of increase in GA, while in adolescence, preterm individuals reached similar levels of BMI (0.00, 95% CI: -0.01; 0.01, p 0.9) as term counterparts. The association between GA and overweight revealed a similar pattern of association with an increase in odds ratio (OR) of overweight from late infancy through mid-childhood (OR 1.01 to 1.02) per week increase in GA. By adolescence, however, GA was slightly negatively associated with the risk of overweight (OR 0.98 [95% CI: 0.97; 1.00], p 0.1) per week of increase in GA. Although based on only four cohorts (n = 32,089) that reached the age of adolescence, data suggest that individuals born very preterm may be at increased odds of overweight (OR 1.46 [95% CI: 1.03; 2.08], p < 0.05) compared with term counterparts. Findings were consistent across cohorts and sensitivity analyses despite considerable heterogeneity in cohort characteristics. However, residual confounding may be a limitation in this study, while findings may be less generalisable to settings in low- and middle-income countries.

CONCLUSIONS

This study based on data from infancy through adolescence from 16 cohort studies found that GA may be important for body size in infancy, but the strength of association attenuates consistently with age. By adolescence, preterm individuals have on average a similar mean BMI to peers born at term.

Towards an Interoperable Ecosystem of Research Cohort and Real-world Data Catalogues Enabling Multi-center Studies

OBJECTIVES

Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness.

METHODS

In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records.

RESULTS

We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact.

CONCLUSIONS

We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata 'common data elements' using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR.

Software Application Profile: ShinyDataSHIELD—an R Shiny application to perform federated non-disclosive data analysis in multicohort studies

Motivation

DataSHIELD is an open-source software infrastructure enabling the analysis of data distributed across multiple databases (federated data) without leaking individuals’ information (non-disclosive). It has applications in many scientific domains, ranging from biosciences to social sciences and including high-throughput genomic studies. R is the language used to interact with (and build) DataSHIELD. This creates difficulties for researchers who do not have experience writing R code or lack the time to learn how to use the DataSHIELD functions. To help new researchers use the DataSHIELD infrastructure and to improve the user-friendliness for experienced researchers, we present ShinyDataSHIELD.

Implementation

ShinyDataSHIELD is a web application with an R backend that serves as a graphical user interface (GUI) to the DataSHIELD infrastructure.

General features

The version of the application presented here includes modules to perform: (i) exploratory analysis through descriptive summary statistics and graphical representations (scatter plots, histograms, heatmaps and boxplots); (ii) statistical modelling (generalized linear fixed and mixed-effects models, survival analysis through Cox regression); (iii) genome-wide association studies (GWAS); and (iv) omic analysis (transcriptomics, epigenomics and multi-omic integration).

Availability

ShinyDataSHIELD is publicly hosted online [https://datashield-demo.obiba.org/ ], the source code and user guide are deposited on Zenodo DOI 10.5281/zenodo.6500323, freely available to non-commercial users under ‘Commons Clause’ License Condition v1.0. Docker images are also available [https://hub.docker.com/r/brgelab/shiny-data-shield ].

The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach

When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale–Extended (GOSE) into eight, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE > 1]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n = 1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of two design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of ten validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of eight high-impact predictors to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74–0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54%– 60%) explanation of ordinal variation in 6-month GOSE (Somers’ D_xy). Model performance and the effect of expanding the predictor set decreased at higher GOSE thresholds, indicating the difficulty of predicting better functional outcomes shortly after ICU admission. Our results motivate the search for informative predictors that improve confidence in prognosis of higher GOSE and the development of ordinal dynamic prediction models.

dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system

OBJECTIVE

Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data.

RESULTS

We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures.

Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic

BACKGROUND

The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data 'silos' that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR.

RESULTS

In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors' research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital.

CONCLUSIONS

Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.

Opportunities and Challenges in High-Quality Contemporary Data Collection in Traumatic Brain Injury: The CENTER-TBI Experience

Strong evidence in support of guidelines for traumatic brain injury (TBI) is lacking. Large-scale observational studies may offer a complementary source of evidence to clinical trials to improve the care and outcome for patients with TBI. They are, however, challenging to execute. In this review, we aim to characterize opportunities and challenges of large-scale collaborative research in neurotrauma. We use the setup and conduct of Collaborative European Neurotrauma Effectiveness Research in TBI (CENTER-TBI) as an illustrative example. We highlight the importance of building a team and of developing a network for younger researchers, thus investing toward the future. We involved investigators early in the design phase and recognized their efforts in a group contributor list on all publications. We found, however, that translation to academic credits often failed, and we suggest that the current system of academic credits be critically appraised. We found substantial variability in consent procedures for participant enrollment within and between countries. Overall, obtaining approvals typically required 4-6 months, with outliers up to 18 months. Research costs varied considerably across Europe and should be defined by center. We substantially underestimated costs of data curation, and we suggest that 15-20% of the budget be reserved for this purpose. Streamlining analyses and accommodating external research proposals demanded a structured approach. We implemented a systematic inventory of study plans and found this effective in maintaining oversight and in promoting collaboration between research groups. Ensuring good use of the data was a prominent feature in the review of external proposals. Multiple interactions occurred with industrial partners, mainly related to biomarkers and neuroimaging, and resulted in various formal collaborations, substantially extending the scope of CENTER-TBI. Overall, CENTER-TBI has been productive, with over 250 international peer-reviewed publications. We have ensured mechanisms to maintain the infrastructure and continued analyses. We see potential for individual patient data meta-analyses in connection to other large-scale projects. Our collaboration with Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) has taught us that although standardized data collection and coding according to common data elements can facilitate such meta-analyses, further data harmonization is required for meaningful results. Both CENTER-TBI and TRACK-TBI have demonstrated the complexity of the conduct of large-scale collaborative studies that produce high-quality science and new insights.

Semantic Metadata Annotation Services in the Biomedical Domain—A Literature Review

For all research data collected, data descriptions and information about the corresponding variables are essential for data analysis and reuse. To enable cross-study comparisons and analyses, semantic interoperability of metadata is one of the most important requirements. In the area of clinical and epidemiological studies, data collection instruments such as case report forms (CRFs), data dictionaries and questionnaires are critical for metadata collection. Even though data collection instruments are often created in a digital form, they are mostly not machine readable; i.e., they are not semantically coded. As a result, the comparison between data collection instruments is complex. The German project NFDI4Health is dedicated to the development of national research data infrastructure for personal health data, and as such searches for ways to enhance semantic interoperability. Retrospective integration of semantic codes into study metadata is important, as ongoing or completed studies contain valuable information. However, this is labor intensive and should be eased by software. To understand the market and find out what techniques and technologies support retrospective semantic annotation/enrichment of metadata, we conducted a literature review. In NFDI4Health, we identified basic requirements for semantic metadata annotation software in the biomedical field and in the context of the FAIR principles. Ten relevant software systems were summarized and aligned with those requirements. We concluded that despite active research on semantic annotation systems, no system meets all requirements. Consequently, further research and software development in this area is needed, as interoperability of data dictionaries, questionnaires and data collection tools is key to reusing and combining results from independent research studies.

A Secure and Reusable Software Architecture for Supporting Online Data Harmonization

Retrospective data harmonization across multiple research cohorts and studies is frequently done to increase statistical power, provide comparison analysis, and create a richer data source for data mining. However, when combining disparate data sources, harmonization projects face data management and analysis challenges. These include differences in the data dictionaries and variable definitions, privacy concerns surrounding health data representing sensitive populations, and lack of properly defined data models. With the availability of mature open-source web-based database technologies, developing a complete software architecture to overcome the challenges associated with the harmonization process can alleviate many roadblocks. By leveraging state-of-the-art software engineering and database principles, we can ensure data quality and enable cross-center online access and collaboration. This paper outlines a complete software architecture developed and customized using the Django web framework, leveraged to harmonize sensitive data collected from three NIH-support birth cohorts. We describe our framework and show how we successfully overcame challenges faced when harmonizing data from these cohorts. We discuss our efforts in data cleaning, data sharing, data transformation, data visualization, and analytics, while reflecting on what we have learned to date from these harmonized datasets.

Unravelling data for rapid evidence-based response to COVID-19: a summary of the unCoVer protocol

INTRODUCTION

unCoVer-Unravelling data for rapid evidence-based response to COVID-19-is a Horizon 2020-funded network of 29 partners from 18 countries capable of collecting and using real-world data (RWD) derived from the response and provision of care to patients with COVID-19 by health systems across Europe and elsewhere. unCoVer aims to exploit the full potential of this information to rapidly address clinical and epidemiological research questions arising from the evolving pandemic.

METHODS AND ANALYSIS

From the onset of the COVID-19 pandemic, partners are gathering RWD from electronic health records currently including information from over 22 000 hospitalised patients with COVID-19, and national surveillance and screening data, and registries with over 1 900 000 COVID-19 cases across Europe, with continuous updates. These heterogeneous datasets will be described, harmonised and integrated into a multi-user data repository operated through Opal-DataSHIELD, an interoperable open-source server application. Federated data analyses, without sharing or disclosing any individual-level data, will be performed with the objective to reveal patients' baseline characteristics, biomarkers, determinants of COVID-19 prognosis, safety and effectiveness of treatments, and potential strategies against COVID-19, as well as epidemiological patterns. These analyses will complement evidence from efficacy/safety clinical trials, where vulnerable, more complex/heterogeneous populations and those most at risk of severe COVID-19 are often excluded.

ETHICS AND DISSEMINATION

After strict ethical considerations, databases will be available through a federated data analysis platform that allows processing of available COVID-19 RWD without disclosing identification information to analysts and limiting output to data aggregates. Dissemination of unCoVer's activities will be related to the access and use of dissimilar RWD, as well as the results generated by the pooled analyses. Dissemination will include training and educational activities, scientific publications and conference communications.

Advancing tools for human early lifecourse exposome research and translation (ATHLETE): Project overview

Early life stages are vulnerable to environmental hazards and present important windows of opportunity for lifelong disease prevention. This makes early life a relevant starting point for exposome studies. The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project aims to develop a toolbox of exposome tools and a Europe-wide exposome cohort that will be used to systematically quantify the effects of a wide range of community- and individual-level environmental risk factors on mental, cardiometabolic, and respiratory health outcomes and associated biological pathways, longitudinally from early pregnancy through to adolescence. Exposome tool and data development include as follows: (1) a findable, accessible, interoperable, reusable (FAIR) data infrastructure for early life exposome cohort data, including 16 prospective birth cohorts in 11 European countries; (2) targeted and nontargeted approaches to measure a wide range of environmental exposures (urban, chemical, physical, behavioral, social); (3) advanced statistical and toxicological strategies to analyze complex multidimensional exposome data; (4) estimation of associations between the exposome and early organ development, health trajectories, and biological (metagenomic, metabolomic, epigenetic, aging, and stress) pathways; (5) intervention strategies to improve early life urban and chemical exposomes, co-produced with local communities; and (6) child health impacts and associated costs related to the exposome. Data, tools, and results will be assembled in an openly accessible toolbox, which will provide great opportunities for researchers, policymakers, and other stakeholders, beyond the duration of the project. ATHLETE's results will help to better understand and prevent health damage from environmental exposures and their mixtures from the earliest parts of the life course onward.

Imputation strategies for missing baseline neurological assessment covariates after traumatic brain injury: A CENTER-TBI study

Statistical models for outcome prediction are central to traumatic brain injury research and critical to baseline risk adjustment. Glasgow coma score (GCS) and pupil reactivity are crucial covariates in all such models but may be measured at multiple time points between the time of injury and hospital and are subject to a variable degree of unreliability and/or missingness. Imputation of missing data may be undertaken using full multiple imputation or by simple substitution of measurements from other time points. However, it is unknown which strategy is best or which time points are more predictive. We evaluated the pseudo-R² of logistic regression models (dichotomous survival) and proportional odds models (Glasgow Outcome Score—extended) using different imputation strategies on the The Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study dataset. Substitution strategies were easy to implement, achieved low levels of missingness (<< 10%) and could outperform multiple imputation without the need for computationally costly calculations and pooling multiple final models. While model performance was sensitive to imputation strategy, this effect was small in absolute terms and clinical relevance. A strategy of using the emergency department discharge assessments and working back in time when these were missing generally performed well. Full multiple imputation had the advantage of preserving time-dependence in the models: the pre-hospital assessments were found to be relatively unreliable predictors of survival or outcome. The predictive performance of later assessments was model-dependent. In conclusion, simple substitution strategies for imputing baseline GCS and pupil response can perform well and may be a simple alternative to full multiple imputation in many cases.

[Making COVID-19 research data more accessible-building a nationwide information infrastructure]

Public-Health-Forschung, epidemiologische und klinische Studien sind erforderlich, um die COVID-19-Pandemie besser zu verstehen und geeignete Maßnahmen zu ergreifen. Daher wurden auch in Deutschland zahlreiche Forschungsprojekte initiiert. Zum heutigen Zeitpunkt ist es ob der Fülle an Informationen jedoch kaum noch möglich, einen Überblick über die vielfältigen Forschungsaktivitäten und deren Ergebnisse zu erhalten. Im Rahmen der Initiative „Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten“ (NFDI4Health) schafft die „Task Force COVID-19“ einen leichteren Zugang zu SARS-CoV-2- und COVID-19-bezogenen klinischen, epidemiologischen und Public-Health-Forschungsdaten. Dabei werden die sogenannten FAIR-Prinzipien (Findable, Accessible, Interoperable, Reusable) berücksichtigt, die eine schnellere Kommunikation von Ergebnissen befördern sollen. Zu den wesentlichen Arbeitsinhalten der Taskforce gehören die Erstellung eines Studienportals mit Metadaten, Erhebungsinstrumenten, Studiendokumenten, Studienergebnissen und Veröffentlichungen sowie einer Suchmaschine für Preprint-Publikationen. Weitere Inhalte sind ein Konzept zur Verknüpfung von Forschungs- und Routinedaten, Services zum verbesserten Umgang mit Bilddaten und die Anwendung standardisierter Analyseroutinen für harmonisierte Qualitätsbewertungen. Die im Aufbau befindliche Infrastruktur erleichtert die Auffindbarkeit von und den Umgang mit deutscher COVID-19-Forschung. Die im Rahmen der NFDI4Health Task Force COVID-19 begonnenen Entwicklungen sind für weitere Forschungsthemen nachnutzbar, da die adressierten Herausforderungen generisch für die Auffindbarkeit von und den Umgang mit Forschungsdaten sind.

Deep generative models in DataSHIELD

Background

The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients.

Methods

The DataSHIELD software provides an infrastructure and a set of statistical methods for joint, privacy-preserving analyses of distributed data. The contained algorithms are reformulated to work with aggregated data from the participating sites instead of the individual data. If a desired algorithm is not implemented in DataSHIELD or cannot be reformulated in such a way, using artificial data is an alternative. Generating artificial data is possible using so-called generative models, which are able to capture the distribution of given data. Here, we employ deep Boltzmann machines (DBMs) as generative models. For the implementation, we use the package “BoltzmannMachines” from the Julia programming language and wrap it for use with DataSHIELD, which is based on R.

Results

We present a methodology together with a software implementation that builds on DataSHIELD to create artificial data that preserve complex patterns from distributed individual patient data. Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses. As an exemplary application, we conduct a distributed analysis with DBMs on a synthetic data set, which simulates genetic variant data. Patterns from the original data can be recovered in the artificial data using hierarchical clustering of the virtual patients, demonstrating the feasibility of the approach. Additionally, we compare DBMs, variational autoencoders, generative adversarial networks, and multivariate imputation as generative approaches by assessing the utility and disclosure of synthetic data generated from real genetic variant data in a distributed setting with data of a small sample size.

Conclusions

Our implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e.g., for pattern recognition with deep learning. This also demonstrates more generally how DataSHIELD can be flexibly extended with advanced algorithms from languages other than R.

Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R

Background

No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments.

Methods

Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP).

Results

The data quality framework comprises 34 data quality indicators. These target four aspects of data quality: compliance with pre-specified structural and technical requirements (integrity); presence of data values (completeness); inadmissible or uncertain data values and contradictions (consistency); unexpected distributions and associations (accuracy). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website.

Conclusions

The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses but the developments are also of relevance beyond research.

Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD

Combined analysis of multiple, large datasets is a common objective in the health- and biosciences. Existing methods tend to require researchers to physically bring data together in one place or follow an analysis plan and share results. Developed over the last 10 years, the DataSHIELD platform is a collection of R packages that reduce the challenges of these methods. These include ethico-legal constraints which limit researchers' ability to physically bring data together and the analytical inflexibility associated with conventional approaches to sharing results. The key feature of DataSHIELD is that data from research studies stay on a server at each of the institutions that are responsible for the data. Each institution has control over who can access their data. The platform allows an analyst to pass commands to each server and the analyst receives results that do not disclose the individual-level data of any study participants. DataSHIELD uses Opal which is a data integration system used by epidemiological studies and developed by the OBiBa open source project in the domain of bioinformatics. However, until now the analysis of big data with DataSHIELD has been limited by the storage formats available in Opal and the analysis capabilities available in the DataSHIELD R packages. We present a new architecture ("resources") for DataSHIELD and Opal to allow large, complex datasets to be used at their original location, in their original format and with external computing facilities. We provide some real big data analysis examples in genomics and geospatial projects. For genomic data analyses, we also illustrate how to extend the resources concept to address specific big data infrastructures such as GA4GH or EGA, and make use of shell commands. Our new infrastructure will help researchers to perform data analyses in a privacy-protected way from existing data sharing initiatives or projects. To help researchers use this framework, we describe selected packages and present an online book (https://isglobal-brge.github.io/resource_bookdown).

Use and impact of high intensity treatments in patients with traumatic brain injury across Europe: a CENTER-TBI analysis

PURPOSE

To study variation in, and clinical impact of high Therapy Intensity Level (TIL) treatments for elevated intracranial pressure (ICP) in patients with traumatic brain injury (TBI) across European Intensive Care Units (ICUs).

METHODS

We studied high TIL treatments (metabolic suppression, hypothermia (< 35 °C), intensive hyperventilation (PaCO2 < 4 kPa), and secondary decompressive craniectomy) in patients receiving ICP monitoring in the ICU stratum of the CENTER-TBI study. A random effect logistic regression model was used to determine between-centre variation in their use. A propensity score-matched model was used to study the impact on outcome (6-months Glasgow Outcome Score-extended (GOSE)), whilst adjusting for case-mix severity, signs of brain herniation on imaging, and ICP.

RESULTS

313 of 758 patients from 52 European centres (41%) received at least one high TIL treatment with significant variation between centres (median odds ratio = 2.26). Patients often transiently received high TIL therapies without escalation from lower tier treatments. 38% of patients with high TIL treatment had favourable outcomes (GOSE ≥ 5). The use of high TIL treatment was not significantly associated with worse outcome (285 matched pairs, OR 1.4, 95% CI [1.0-2.0]). However, a sensitivity analysis excluding high TIL treatments at day 1 or use of metabolic suppression at any day did reveal a statistically significant association with worse outcome.

CONCLUSION

Substantial between-centre variation in use of high TIL treatments for TBI was found and treatment escalation to higher TIL treatments were often not preceded by more conventional lower TIL treatments. The significant association between high TIL treatments after day 1 and worse outcomes may reflect aggressive use or unmeasured confounders or inappropriate escalation strategies.

TAKE HOME MESSAGE

Substantial variation was found in the use of highly intensive ICP-lowering treatments across European ICUs and a stepwise escalation strategy from lower to higher intensity level therapy is often lacking. Further research is necessary to study the impact of high therapy intensity treatments.

TRIAL REGISTRATION

The core study was registered with ClinicalTrials.gov, number NCT02210221, registered 08/06/2014, https://clinicaltrials.gov/ct2/show/NCT02210221?id=NCT02210221&draw=1&rank=1 and with Resource Identification Portal (RRID: SCR_015582).

Systemic Markers of Injury and Injury Response Are Not Associated with Impaired Cerebrovascular Reactivity in Adult Traumatic Brain Injury: A Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study

The role of extra-cranial injury burden and systemic injury response on cerebrovascular response in traumatic brain injury (TBI) is poorly documented. This study preliminarily assesses the association between admission features of extra-cranial injury burden on cerebrovascular reactivity. Using the Collaborative European Neurotrauma Effectiveness Research in TBI High-Resolution ICU (HR ICU) sub-study cohort, we evaluated those patients with both archived high-frequency digital intra-parenchymal intra-cranial pressure monitoring data of a minimum of 6 h in duration, and the presence of a digital copy of their admission computed tomography (CT) scan. Digital physiologic signals were processed for pressure reactivity index (PRx) and both the percent time above defined PRx thresholds and mean hourly dose above threshold. This was conducted for both the first 72 h and entire duration of recording. Admission extra-cranial injury characteristics and CT injury scores were obtained from the database, with quantitative contusion, edema, intraventricular hemorrhage, and extra-axial lesion volumes were obtained via semi-automated segmentation. Comparison between admission extra-cranial markers of injury and PRx metrics was conducted using Mann-Whitney U testing, and logistic regression techniques, adjusting for known CT injury metrics associated with impaired PRx. A total of 165 patients were included. Evaluating the entire ICU recording period, there was limited association between metrics of extra-cranial injury burden and impaired cerebrovascular reactivity. Using the first 72 h of recording, admission temperature (p = 0.042) and white blood cell % (WBC %; p = 0.013) were statistically associated with impaired cerebrovascular reactivity on Mann-Whitney U and univariate logistic regression. After adjustment for admission age, pupillary status, GCS motor score, pre-hospital hypoxia/hypotension, and intra-cranial CT characteristics associated with impaired reactivity, temperature (p = 0.021) and WBC % (p = 0.013) remained significantly associated with mean PRx values above +0.25 and +0.35, respectively. Markers of extra-cranial injury burden and systemic injury response do not appear to be strongly associated with impaired cerebrovascular reactivity in TBI during both the initial and entire ICU stay.

Overview of retrospective data harmonisation in the MINDMAP project: process and results

Background

The MINDMAP project implemented a multinational data infrastructure to investigate the direct and interactive effects of urban environments and individual determinants of mental well-being and cognitive function in ageing populations. Using a rigorous process involving multiple teams of experts, longitudinal data from six cohort studies were harmonised to serve MINDMAP objectives. This article documents the retrospective data harmonisation process achieved based on the Maelstrom Research approach and provides a descriptive analysis of the harmonised data generated.

Methods

A list of core variables (the DataSchema) to be generated across cohorts was first defined, and the potential for cohort-specific data sets to generate the DataSchema variables was assessed. Where relevant, algorithms were developed to process cohort-specific data into DataSchema format, and information to be provided to data users was documented. Procedures and harmonisation decisions were thoroughly documented.

Results

The MINDMAP DataSchema (v2.0, April 2020) comprised a total of 2841 variables (993 on individual determinants and outcomes, 1848 on environmental exposures) distributed across up to seven data collection events. The harmonised data set included 220 621 participants from six cohorts (10 subpopulations). Harmonisation potential, participant distributions and missing values varied across data sets and variable domains.

Conclusion

The MINDMAP project implemented a collaborative and transparent process to generate a rich integrated data set for research in ageing, mental well-being and the urban environment. The harmonised data set supports a range of research activities and will continue to be updated to serve ongoing and future MINDMAP research needs.

Descriptive analysis of low versus elevated intracranial pressure on cerebral physiology in adult traumatic brain injury: a CENTER-TBI exploratory study

Background

To date, the cerebral physiologic consequences of persistently elevated intracranial pressure (ICP) have been based on either low-resolution physiologic data or retrospective high-frequency data from single centers. The goal of this study was to provide a descriptive multi-center analysis of the cerebral physiologic consequences of ICP, comparing those with normal ICP to those with elevated ICP.

Methods

The Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) High-Resolution Intensive Care Unit (HR-ICU) sub-study cohort was utilized. The first 3 days of physiologic recording were analyzed, evaluating and comparing those patients with mean ICP < 15 mmHg versus those with mean ICP > 20 mmHg. Various cerebral physiologic parameters were derived and evaluated, including ICP, brain tissue oxygen (PbtO₂), cerebral perfusion pressure (CPP), pulse amplitude of ICP (AMP), cerebrovascular reactivity, and cerebral compensatory reserve. The percentage time and dose above/below thresholds were also assessed. Basic descriptive statistics were employed in comparing the two cohorts.

Results

185 patients were included, with 157 displaying a mean ICP below 15 mmHg and 28 having a mean ICP above 20 mmHg. For admission demographics, only admission Marshall and Rotterdam CT scores were statistically different between groups (p = 0.017 and p = 0.030, respectively). The high ICP group displayed statistically worse CPP, PbtO₂, cerebrovascular reactivity, and compensatory reserve. The high ICP group displayed worse 6-month mortality (p < 0.0001) and poor outcome (p = 0.014), based on the Extended Glasgow Outcome Score.

Conclusions

Low versus high ICP during the first 72 h after moderate/severe TBI is associated with significant disparities in CPP, AMP, cerebrovascular reactivity, cerebral compensatory reserve, and brain tissue oxygenation metrics. Such ICP extremes appear to be strongly related to 6-month patient outcomes, in keeping with previous literature. This work provides multi-center validation for previously described single-center retrospective results.

Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p < 0.0001 for each patient). Thus, these particular L-PRx variants appear closest in nature to standard PRx. ICP and MAP derived via 10-s or minute based averaging display similar statistical time-series structure and co-variance patterns. PRx and L-PRx based on shorter windows also behave similarly over time. These results imply certain L-PRx variants may carry similar information to PRx in TBI.

Association between Physiological Signal Complexity and Outcomes in Moderate and Severe Traumatic Brain Injury: A CENTER-TBI Exploratory Analysis of Multi-Scale Entropy

In traumatic brain injury (TBI), preliminary retrospective work on signal entropy suggests an association with global outcome. The goal of this study was to provide multi-center validation of the association between multi-scale entropy (MSE) of cardiovascular and cerebral physiological signals, with six-month outcome. Using the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) high-resolution intensive care unit (ICU) cohort, we selected patients with a minimum of 72 h of physiological recordings and a documented six-month Glasgow Outcome Scale Extended (GOSE) score. The 10-sec summary data for heart rate (HR), mean arterial pressure (MAP), intracranial pressure (ICP), and pulse amplitude of ICP (AMP) were derived across the first 72 h of data. The MSE complexity index (MSE-Ci) was determined for HR, MAP, ICP, and AMP, with the association between MSE and dichotomized six-month outcomes assessed using Mann-Whitney U testing and logistic regression analysis. A total of 160 patients had a minimum of 72 h of recording and a documented outcome. Decreased HR MSE-Ci (7.3 [interquartile range (IQR) 5.4 to 10.2] vs. 5.1 [IQR 3.1 to 7.0]; p = 0.002), lower ICP MSE-Ci (11.2 [IQR 7.5 to 14.2] vs. 7.3 [IQR 6.1 to 11.0]; p = 0.009), and lower AMP MSE-Ci (10.9 [IQR 8.0 to 13.7] vs. 8.7 [IQR 6.6 to 11.0]; p = 0.022), were associated with death. Similarly, lower HR MSE-Ci (8.0 [IQR 6.2 to 10.9] vs. 6.2 [IQR 3.9 to 8.7]; p = 0.003) and lower ICP MSE-Ci (11.4 [IQR 8.6 to 14.4)] vs. 9.2 [IQR 6.0 to 13.5]), were associated with unfavorable outcome. Logistic regression analysis confirmed that lower HR MSE-Ci and ICP MSE-Ci were associated with death and unfavorable outcome at six months. These findings suggest that a reduction in cardiovascular and cerebrovascular system entropy is associated with worse outcomes. Further work in the field of signal complexity in TBI multi-modal monitoring is required.

Brain Tissue Oxygen and Cerebrovascular Reactivity in Traumatic Brain Injury: A Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury Exploratory Analysis of Insult Burden

Pressure reactivity index (PRx) and brain tissue oxygen (PbtO2) are associated with outcome in traumatic brain injury (TBI). This study explores the relationship between PRx and PbtO2 in adult moderate/severe TBI. Using the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) high resolution intensive care unit (ICU) sub-study cohort, we evaluated those patients with archived high-frequency digital intraparenchymal intracranial pressure (ICP) and PbtO2 monitoring data of, a minimum of 6 h in duration, and the presence of a 6 month Glasgow Outcome Scale -Extended (GOSE) score. Digital physiological signals were processed for ICP, PbtO2, and PRx, with the % time above/below defined thresholds determined. The duration of ICP, PbtO2, and PRx derangements was characterized. Associations with dichotomized 6-month GOSE (alive/dead, and favorable/unfavorable outcome; ≤ 4 = unfavorable), were assessed. A total of 43 patients were included. Severely impaired cerebrovascular reactivity was seen during elevated ICP and low PbtO2 episodes. However, most of the acute ICU physiological derangements were impaired cerebrovascular reactivity, not ICP elevations or low PbtO2 episodes. Low PbtO2 without PRx impairment was rarely seen. % time spent above PRx threshold was associated with mortality at 6 months for thresholds of 0 (area under the curve [AUC] 0.734, p = 0.003), > +0.25 (AUC 0.747, p = 0.002) and > +0.35 (AUC 0.745, p = 0.002). Similar relationships were not seen for % time with ICP >20 mm Hg, and PbtO2 < 20 mm Hg in this cohort. Extreme impairment in cerebrovascular reactivity is seen during concurrent episodes of elevated ICP and low PbtO2. However, the majority of the deranged cerebral physiology seen during the acute ICU phase is impairment in cerebrovascular reactivity, with most impairment occurring in the presence of normal PbtO2 levels. Measures of cerebrovascular reactivity appear to display the most consistent associations with global outcome in TBI, compared with ICP and PbtO2.

Diffuse Intracranial Injury Patterns Are Associated with Impaired Cerebrovascular Reactivity in Adult Traumatic Brain Injury: A CENTER-TBI Validation Study

Recent single-center retrospective analysis displayed the association between admission computed tomography (CT) markers of diffuse intracranial injury and worse cerebrovascular reactivity. The goal of this study was to further explore these associations using the prospective multi-center Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) high-resolution intensive care unit (HR ICU) data set. Using the CENTER-TBI HR ICU sub-study cohort, we evaluated those patients with both archived high-frequency digital physiology (100 Hz or higher) and the presence of a digital admission CT scan. Physiological signals were processed for pressure reactivity index (PRx) and both the percent (%) time above defined PRx thresholds and mean hourly dose above threshold. Admission CT injury scores were obtained from the database. Quantitative contusion, edema, intraventricular hemorrhage (IVH), and extra-axial lesion volumes were obtained via semi-automated segmentation. Comparison between admission CT characteristics and PRx metrics was conducted using Mann-U, Jonckheere-Terpstra testing, with a combination of univariate linear and logistic regression techniques. A total of 165 patients were included. Cisternal compression and high admission Rotterdam and Helsinki CT scores, and Marshall CT diffuse injury sub-scores were associated with increased percent (%) time and hourly dose above PRx threshold of 0, +0.25, and +0.35 (p < 0.02 for all). Logistic regression analysis displayed an association between deep peri-contusional edema and mean PRx above a threshold of +0.25. These results suggest that diffuse injury patterns, consistent with acceleration/deceleration forces, are associated with impaired cerebrovascular reactivity. Diffuse admission intracranial injury patterns appear to be consistently associated with impaired cerebrovascular reactivity, as measured through PRx. This is in keeping with the previous single-center retrospective literature on the topic. This study provides multi-center validation for those results, and provides preliminary data to support potential risk stratification for impaired cerebrovascular reactivity based on injury pattern.

Statistical Cerebrovascular Reactivity Signal Properties after Secondary Decompressive Craniectomy in Traumatic Brain Injury: A CENTER-TBI Pilot Analysis

Decompressive craniectomy (DC) in traumatic brain injury (TBI) has been suggested to influence cerebrovascular reactivity. We aimed to determine if the statistical properties of vascular reactivity metrics and slow-wave relationships were impacted after DC, as such information would allow us to comment on whether vascular reactivity monitoring remains reliable after craniectomy. Using the CENTER-TBI High Resolution Intensive Care Unit (ICU) Sub-Study cohort, we selected those secondary DC patients with high-frequency physiological data for both at least 24 h pre-DC, and more than 48 h post-DC. Data for all physiology measures were separated into the 24 h pre-DC, the first 48 h post-DC, and beyond 48 h post-DC. We produced slow-wave data sheets for intracranial pressure (ICP) and mean arterial pressure (MAP) per patient. We also derived a Pressure Reactivity Index (PRx) as a continuous cerebrovascular reactivity metric updated every minute. The time-series behavior of the PRx was modeled for each time period per patient. Finally, the relationship between ICP and MAP during these three time periods was assessed using time-series vector autoregressive integrative moving average (VARIMA) models, impulse response function (IRF) plots, and Granger causality testing. Ten patients were included in this study. Mean PRx and proportion of time above PRx thresholds were not affected by craniectomy. Similarly, PRx time-series structure was not affected by DC, when assessed in each individual patient. This was confirmed with Granger causality testing, and VARIMA IRF plotting for the MAP/ICP slow-wave relationship. PRx metrics and statistical time-series behavior appear not to be substantially influenced by DC. Similarly, there is little change in the relationship between slow waves of ICP and MAP before and after DC. This may suggest that cerebrovascular reactivity monitoring in the setting of DC may still provide valuable information regarding autoregulation.

Evaluation of the relationship between slow-waves of intracranial pressure, mean arterial pressure and brain tissue oxygen in TBI: a CENTER-TBI exploratory analysis

Brain tissue oxygen (PbtO₂) monitoring in traumatic brain injury (TBI) has demonstrated strong associations with global outcome. Additionally, PbtO₂ signals have been used to derive indices thought to be associated with cerebrovascular reactivity in TBI. However, their true relationship to slow-wave vasogenic fluctuations associated with cerebral autoregulation remains unclear. The goal of this study was to investigate the relationship between slow-wave fluctuations of intracranial pressure (ICP), mean arterial pressure (MAP) and PbtO₂ over time. Using the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) high resolution ICU sub-study cohort, we evaluated those patients with recorded high-frequency digital intra-parenchymal ICP and PbtO₂ monitoring data of a minimum of 6 h in duration. Digital physiologic signals were processed for ICP, MAP, and PbtO₂ slow-waves using a moving average filter to decimate the high-frequency signal. The first 5 days of recording were analyzed. The relationship between ICP, MAP and PbtO₂ slow-waves over time were assessed using autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) modelling, as well as Granger causality testing. A total of 47 patients were included. The ARIMA structure of ICP and MAP were similar in time, where PbtO₂ displayed different optimal structure. VARIMA modelling and IRF plots confirmed the strong directional relationship between MAP and ICP, demonstrating an ICP response to MAP impulse. PbtO₂ slow-waves, however, failed to demonstrate a definite response to ICP and MAP slow-wave impulses. These results raise questions as to the utility of PbtO₂ in the derivation of cerebrovascular reactivity measures in TBI. There is a reproducible relationship between slow-wave fluctuations of ICP and MAP, as demonstrated across various time-series analytic techniques. PbtO₂ does not appear to reliably respond in time to slow-wave fluctuations in MAP, as demonstrated on various VARIMA models across all patients. These findings suggest that PbtO₂ should not be utilized in the derivation of cerebrovascular reactivity metrics in TBI, as it does not appear to be responsive to changes in MAP in the slow-waves. These findings corroborate previous results regarding PbtO₂ based cerebrovascular reactivity indices.

Association between Cerebrovascular Reactivity Monitoring and Mortality Is Preserved When Adjusting for Baseline Admission Characteristics in Adult Traumatic Brain Injury: A CENTER-TBI Study

Cerebral autoregulation, as measured using the pressure reactivity index (PRx), has been related to global patient outcome in adult patients with traumatic brain injury (TBI). To date, this has been documented without accounting for standard baseline admission characteristics and intracranial pressure (ICP). We evaluated this association, adjusting for baseline admission characteristics and ICP, in a multi-center, prospective cohort. We derived PRx as the correlation between ICP and mean arterial pressure in prospectively collected multi-center data from the High-Resolution Intensive Care Unit (ICU) cohort of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study. Multi-variable logistic regression models were analyzed to assess the association between global outcome (measured as either mortality or dichotomized Glasgow Outcome Score-Extended [GOSE]) and a range of covariates (IMPACT [International Mission for Prognosis and Analysis of Clinical Trials] Core and computed tomography [CT] variables, ICP, and PRx). Performance of these models in outcome association was compared using area under the receiver operating curve (AUC) and Nagelkerke's pseudo-R2. One hundred ninety-three patients had a complete data set for analysis. The addition of percent time above threshold for PRx improved AUC and displayed statistically significant increases in Nagelkerke's pseudo-R2 over the IMPACT Core and IMPACT Core + CT models for mortality. The addition of PRx monitoring to IMPACT Core ± CT + ICP models accounted for additional variance in mortality, when compared to models with IMPACT Core ± CT + ICP alone. The addition of cerebrovascular reactivity monitoring, through PRx, provides a statistically significant increase in association with mortality at 6 months. Our data suggest that cerebrovascular reactivity monitoring may provide complementary information regarding outcomes in TBI.

Cerebrovascular reactivity is not associated with therapeutic intensity in adult traumatic brain injury: a CENTER-TBI analysis

BACKGROUND

Impaired cerebrovascular reactivity in adult traumatic brain injury (TBI) is known to be associated with poor outcome. However, there has yet to be an analysis of the association between the comprehensively assessed intracranial hypertension therapeutic intensity level (TIL) and cerebrovascular reactivity.

METHODS

Using the Collaborative European Neuro Trauma Effectiveness Research in TBI (CENTER-TBI) high-resolution intensive care unit (ICU) cohort, we derived pressure reactivity index (PRx) as the moving correlation coefficient between slow-wave in ICP and mean arterial pressure, updated every minute. Mean daily PRx, and daily % time above PRx of 0 were calculated for the first 7 days of injury and ICU stay. This data was linked with the daily TIL-Intermediate scores, including total and individual treatment sub-scores. Daily mean PRx variable values were compared for each TIL treatment score via mean, standard deviation, and the Mann U test (Bonferroni correction for multiple comparisons). General fixed effects and mixed effects models for total TIL versus PRx were created to display the relation between TIL and cerebrovascular reactivity.

RESULTS

A total of 249 patients with 1230 ICU days of high frequency physiology matched with daily TIL, were assessed. Total TIL was unrelated to daily PRx. Most TIL sub-scores failed to display a significant relationship with the PRx variables. Mild hyperventilation (p < 0.0001), mild hypothermia (p = 0.0001), high levels of sedation for ICP control (p = 0.0001), and use vasopressors for CPP management (p < 0.0001) were found to be associated with only a modest decrease in mean daily PRx or % time with PRx above 0.

CONCLUSIONS

Cerebrovascular reactivity remains relatively independent of intracranial hypertension therapeutic intensity, suggesting inadequacy of current TBI therapies in modulating impaired autoregulation. These findings support the need for investigation into the molecular mechanisms involved, or individualized physiologic targets (ICP, CPP, or Co2) in order to treat dysautoregulation actively.

Compensatory-reserve-weighted intracranial pressure versus intracranial pressure for outcome association in adult traumatic brain injury: a CENTER-TBI validation study

Background

Compensatory-reserve-weighted intracranial pressure (wICP) has recently been suggested as a supplementary measure of intracranial pressure (ICP) in adult traumatic brain injury (TBI), with a single-center study suggesting an association with mortality at 6 months. No multi-center studies exist to validate this relationship. The goal was to compare wICP to ICP for association with outcome in a multi-center TBI cohort.

Methods

Using the Collaborative European Neuro Trauma Effectiveness Research in TBI (CENTER-TBI) high-resolution intensive care unit (ICU) cohort, we derived ICP and wICP (calculated as wICP = (1 − RAP) × ICP; where RAP is the compensatory reserve index derived from the moving correlation between pulse amplitude of ICP and ICP). Various univariate logistic regression models were created comparing ICP and wICP to dichotomized outcome at 6 to 12 months, based on Glasgow Outcome Score—Extended (GOSE) (alive/dead—GOSE ≥ 2/GOSE = 1; favorable/unfavorable—GOSE 5 to 8/GOSE 1 to 4, respectively). Models were compared using area under the receiver operating curves (AUC) and p values.

Results

wICP displayed higher AUC compared to ICP on univariate regression for alive/dead outcome compared to mean ICP (AUC 0.712, 95% CI 0.615–0.810, p = 0.0002, and AUC 0.642, 95% CI 0.538–746, p < 0.0001, respectively; no significant difference on Delong’s test), and for favorable/unfavorable outcome (AUC 0.627, 95% CI 0.548–0.705, p = 0.015, and AUC 0.495, 95% CI 0.413–0.577, p = 0.059; significantly different using Delong’s test p = 0.002), with lower wICP values associated with improved outcomes (p < 0.05 for both). These relationships on univariate analysis held true even when comparing the wICP models with those containing both ICP and RAP integrated area under the curve over time (p < 0.05 for all via Delong’s test).

Conclusions

Compensatory-reserve-weighted ICP displays superior outcome association for both alive/dead and favorable/unfavorable dichotomized outcomes in adult TBI, through univariate analysis. Lower wICP is associated with better global outcomes. The results of this study provide multi-center validation of those seen in a previous single-center study.

Electronic supplementary material

The online version of this article (10.1007/s00701-019-03915-3) contains supplementary material, which is available to authorized users.

Patient-specific ICP Epidemiologic Thresholds in Adult Traumatic Brain Injury: A CENTER-TBI Validation Study

BACKGROUND

Patient-specific epidemiologic intracranial pressure (ICP) thresholds in adult traumatic brain injury (TBI) have emerged, using the relationship between pressure reactivity index (PRx) and ICP, displaying stronger association with outcome over existing guideline thresholds. The goal of this study was to explore this relationship in a multi-center cohort in order to confirm the previous finding.

METHODS

Using the Collaborative European Neuro Trauma Effectiveness Research in TBI (CENTER-TBI) high-resolution intensive care unit cohort, we derived individualized epidemiologic ICP thresholds for each patient using the relationship between PRx and ICP. Mean hourly dose of ICP was calculated for every patient for the following thresholds: 20, 22 mm Hg and the patient's individual ICP threshold. Univariate logistic regression models were created comparing mean hourly dose of ICP above thresholds to dichotomized outcome at 6 to 12 months, based on Glasgow Outcome Score-Extended (GOSE) (alive/dead-GOSE≥2/GOSE=1; favorable/unfavorable-GOSE 5 to 8/GOSE 1 to 4, respectively).

RESULTS

Individual thresholds were identified in 65.3% of patients (n=128), in keeping with previous results (23.0±11.8 mm Hg [interquartile range: 14.9 to 29.8 mm Hg]). Mean hourly dose of ICP above individual threshold provides superior discrimination (area under the receiver operating curve [AUC]=0.678, P=0.029) over mean hourly dose above 20 mm Hg (AUC=0.509, P=0.03) or above 22 mm Hg (AUC=0.492, P=0.035) on univariate analysis for alive/dead outcome at 6 to 12 months. The AUC for mean hourly dose above individual threshold trends to higher values for favorable/unfavorable outcome, but fails to reach statistical significance (AUC=0.610, P=0.060). This was maintained when controlling for baseline admission characteristics.

CONCLUSIONS

Mean hourly dose of ICP above individual epidemiologic ICP threshold has stronger associations with mortality compared with the dose above Brain Trauma Foundation defined thresholds of 20 or 22 mm Hg, confirming prior findings. Further studies on patient-specific epidemiologic ICP thresholds are required.

Univariate comparison of performance of different cerebrovascular reactivity indices for outcome association in adult TBI: a CENTER-TBI study

BACKGROUND

Monitoring cerebrovascular reactivity in adult traumatic brain injury (TBI) has been linked to global patient outcome. Three intra-cranial pressure (ICP)-derived indices have been described. It is unknown which index is superior for outcome association in TBI outside previous single-center evaluations. The goal of this study is to evaluate indices for 6- to 12-month outcome association using uniform data harvested in multiple centers.

METHODS

Using the prospectively collected data from the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study, the following indices of cerebrovascular reactivity were derived: PRx (correlation between ICP and mean arterial pressure (MAP)), PAx (correlation between pulse amplitude of ICP (AMP) and MAP), and RAC (correlation between AMP and cerebral perfusion pressure (CPP)). Univariate logistic regression models were created to assess the association between vascular reactivity indices with global dichotomized outcome at 6 to 12 months, as assessed by Glasgow Outcome Score-Extended (GOSE). Models were compared via area under the receiver operating curve (AUC) and Delong's test.

RESULTS

Two separate patient groups from this cohort were assessed: the total population with available data (n = 204) and only those without decompressive craniectomy (n = 159), with identical results. PRx, PAx, and RAC perform similar in outcome association for both dichotomized outcomes, alive/dead and favorable/unfavorable, with RAC trending towards higher AUC values. There were statistically higher mean values for the index, % time above threshold, and hourly dose above threshold for each of PRx, PAx, and RAC in those patients with poor outcomes.

CONCLUSIONS

PRx, PAx, and RAC appear similar in their associations with 6- to 12-month outcome in moderate/severe adult TBI, with RAC showing tendency to achieve stronger associations. Further work is required to determine the role for each of these cerebrovascular indices in monitoring of TBI patients.

Harmonization of the Health and Risk Factor Questionnaire data of the Canadian Partnership for Tomorrow Project: a descriptive analysis

BACKGROUND

The Canadian Partnership for Tomorrow Project is a multistudy platform integrating the British Columbia Generations Project, Alberta's Tomorrow Project, the Ontario Health Study, CARTaGENE (Quebec) and the Atlantic Partnership for Tomorrow's Health. This paper describes the process used to harmonize the Health and Risk Factor Questionnaire data and provides an overview of the key information required to properly use the core data set generated.

METHODS

This is a descriptive analysis of the harmonization process that was developed on the basis of the Maelstrom Research guidelines for retrospective harmonization. Core variables (DataSchema) to be generated across cohorts were defined and the potential for cohort-specific data sets to generate the DataSchema variables was assessed. Where relevant, algorithms were developed and applied to process cohort-specific data into the DataSchema format, and information to be provided to data users was documented.

RESULTS

The Health and Risk Factor Questionnaire DataSchema (version 2.0, October 2017) comprised 694 variables. The assessment of harmonization potential for the variables over 12 cohort-specific data sets resulted in 6799 (81.6%) of the variables being considered as harmonizable. A total of 307 017 participants were included in the harmonized data set. Through the cohort data portal, researchers can find information about the definitions of variables, harmonization potential, algorithms applied to generate harmonized variables and participant distributions.

INTERPRETATION

The harmonization process enabled the creation of a unique data set including data on health and risk factors from over 307 000 Canadians. These data, in combination with complementary data sets, can be used to investigate the impact of biological, environmental and behavioural factors on cancer and chronic diseases.

Rationale for a Swedish cohort consortium

We herein outline the rationale for a Swedish cohort consortium, aiming to facilitate greater use of Swedish cohorts for world-class research. Coordination of all Swedish prospective population-based cohorts in a common infrastructure would enable more precise research findings and facilitate research on rare exposures and outcomes, leading to better utilization of study participants' data, better return of funders' investments, and higher benefit to patients and populations. We motivate the proposed infrastructure partly by lessons learned from a pilot study encompassing data from 21 cohorts. We envisage a standing Swedish cohort consortium that would drive development of epidemiological research methods and strengthen the Swedish as well as international epidemiological competence, community, and competitiveness.