[The COVID-19 Pandemic as an Opportunity and Challenge for Registries in Health Services Research: Lessons Learned from the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS)]

Role and benefits of infectious diseases specialists in the COVID-19 pandemic: Multilevel analysis of care provision in German hospitals using data from the Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS) cohort

PURPOSE

This study investigates the care provision and the role of infectious disease (ID) specialists during the coronavirus disease-2019 (COVID-19) pandemic.

METHODS

A survey was conducted at German study sites participating in the Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS). Hospitals certified by the German Society of Infectious diseases (DGI) were identified as ID centers. We compared care provision and the involvement of ID specialists between ID and non-ID hospitals. Then we applied a multivariable regression model to analyse how clinical ID care influenced the mortality of COVID-19 patients in the LEOSS cohort.

RESULTS

Of the 40 participating hospitals in the study, 35% (14/40) were identified as ID centers. Among those, clinical ID care structures were more commonly established, and ID specialists were always involved in pandemic management and the care of COVID-19 patients. Overall, 68% (27/40) of the hospitals involved ID specialists in the crisis management team, 78% (31/40) in normal inpatient care, and 80% (28/35) in intensive care. Multivariable analysis revealed that COVID-19 patients in ID centers had a lower mortality risk compared to those in non-ID centers (odds ratio: 0.61 (95% CI 0.40-0.93), p = 0.021).

CONCLUSION

ID specialists played a crucial role in pandemic management and inpatient care.

[The German Transplant Registry - An Analysis of Legacy Data 2006-2016]

INTRODUCTION

In 2018, medical transplant data from three institutions were merged to create a German transplant registry. Since June 2021, access to data of the registry has been available. It was planned to analyze the registry data in order to compare special allocation rules with regular allocation for heart, liver, lung, and kidney transplantation. Our approach led to a quality analysis of the registry.

METHODS

Upon request, legacy data (2006-2016) of the registry was provided, divided into 61 elements. From these elements, the user had to compile the required dataset. Data checks were performed for completeness, correct allocation of information, and consistency among different sources. Software used for these tasks included R, SQL, and Excel.

RESULTS

The initial elements ("waiting list" elements) of the four types of transplantations contained data from a total of 80,259 originally listed patients. However, these patients were only partially present in other elements resulting in complete datasets reflecting waiting time in only 23%, 30%, 50%, and 96%, and for post-transplantation outcomes in 14%, 11%, 38%, and 13% (heart, liver, lung, and kidney transplantation, respectively). The linking of urgency information with clinical data was successful in only a small proportion, with only 6% for heart transplantation. Incorrect and thus implausible allocations in the case of special allocation rules indicated incorrect entries in the registry. Data from different data providers were inconsistent.

DISCUSSION AND CONCLUSION

The incompleteness and incorrect data allocation raise doubts about the reliability of scientific studies based on the transplant registry. The complex structure also hinders the compilation of a reliable dataset, which is uncommon internationally. New data (acquisition since 2017) has only been available since December 2023. The transplant registry urgently needs restructuring. Competent clinical data management, involving transplant medical expertise, and continuous quality controls are essential in this process.

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping

BACKGROUND

It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets.

OBJECTIVE

This study's objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials.

METHODS

We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs.

RESULTS

Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date.

CONCLUSIONS

The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element's (variable's) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by "wrapping" them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium's Clinical Data Acquisition Standards Harmonization Model.

The Costs of Anonymization: Case Study Using Clinical Data

BACKGROUND

Sharing data from clinical studies can accelerate scientific progress, improve transparency, and increase the potential for innovation and collaboration. However, privacy concerns remain a barrier to data sharing. Certain concerns, such as reidentification risk, can be addressed through the application of anonymization algorithms, whereby data are altered so that it is no longer reasonably related to a person. Yet, such alterations have the potential to influence the data set's statistical properties, such that the privacy-utility trade-off must be considered. This has been studied in theory, but evidence based on real-world individual-level clinical data is rare, and anonymization has not broadly been adopted in clinical practice.

OBJECTIVE

The goal of this study is to contribute to a better understanding of anonymization in the real world by comprehensively evaluating the privacy-utility trade-off of differently anonymized data using data and scientific results from the German Chronic Kidney Disease (GCKD) study.

METHODS

The GCKD data set extracted for this study consists of 5217 records and 70 variables. A 2-step procedure was followed to determine which variables constituted reidentification risks. To capture a large portion of the risk-utility space, we decided on risk thresholds ranging from 0.02 to 1. The data were then transformed via generalization and suppression, and the anonymization process was varied using a generic and a use case-specific configuration. To assess the utility of the anonymized GCKD data, general-purpose metrics (ie, data granularity and entropy), as well as use case-specific metrics (ie, reproducibility), were applied. Reproducibility was assessed by measuring the overlap of the 95% CI lengths between anonymized and original results.

RESULTS

Reproducibility measured by 95% CI overlap was higher than utility obtained from general-purpose metrics. For example, granularity varied between 68.2% and 87.6%, and entropy varied between 25.5% and 46.2%, whereas the average 95% CI overlap was above 90% for all risk thresholds applied. A nonoverlapping 95% CI was detected in 6 estimates across all analyses, but the overwhelming majority of estimates exhibited an overlap over 50%. The use case-specific configuration outperformed the generic one in terms of actual utility (ie, reproducibility) at the same level of privacy.

CONCLUSIONS

Our results illustrate the challenges that anonymization faces when aiming to support multiple likely and possibly competing uses, while use case-specific anonymization can provide greater utility. This aspect should be taken into account when evaluating the associated costs of anonymized data and attempting to maintain sufficiently high levels of privacy for anonymized data.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00003971; https://drks.de/search/en/trial/DRKS00003971.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1093/ndt/gfr456.

The protective effect of tumor necrosis factor-alpha inhibitors in COVID-19 in patients with inflammatory rheumatic diseases compared to the general population-A comparison of two German registries

Objectives

To investigate, whether inflammatory rheumatic diseases (IRD) inpatients are at higher risk to develop a severe course of SARS-CoV-2 infections compared to the general population, data from the German COVID-19 registry for IRD patients and data from the Lean European Survey on SARS-CoV-2 (LEOSS) infected patients covering inpatients from the general population with SARS-CoV-2 infections were compared.

Methods

4310 (LEOSS registry) and 1139 cases (IRD registry) were collected in general. Data were matched for age and gender. From both registries, 732 matched inpatients (LEOSS registry: n = 366 and IRD registry: n = 366) were included for analyses in total.

Results

Regarding the COVID-19 associated lethality, no significant difference between both registries was observed. Age > 65°years, chronic obstructive pulmonary disease, diabetes mellitus, rheumatoid arthritis, spondyloarthritis and the use of rituximab were associated with more severe courses of COVID-19. Female gender and the use of tumor necrosis factor-alpha inhibitors (TNF-I) were associated with a better outcome of COVID-19.

Conclusion

Inflammatory rheumatic diseases (IRD) patients have the same risk factors for severe COVID-19 regarding comorbidities compared to the general population without any immune-mediated disease or immunomodulation. The use of rituximab was associated with an increased risk for severe COVID-19. On the other hand, the use of TNF-I was associated with less severe COVID-19 compared to the general population, which might indicate a protective effect of TNF-I against severe COVID-19 disease.

Use and effectiveness of remdesivir for the treatment of patients with covid-19 using data from the Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS): a multicentre cohort study

OBJECTIVES

The use of remdesivir (RDV) as the first drug approved for coronavirus disease 2019 (COVID-19) remains controversial. Based on the Lean European Open Survey on severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infected patients (LEOSS), we aim to contribute timing-focused complementary real-world insights to its evaluation.

METHODS

SARS-CoV-2 infected patients between January 2020 and December 2021 treated with RDV were matched 1:1 to controls considering sociodemographics, comorbidities and clinical status. Multiple imputations were used to account for missing data. Effects on fatal outcome were estimated using uni- and multivariable Cox regression models.

RESULTS

We included 9,687 patients. For those starting RDV administration in the complicated phase, Cox regression for fatal outcome showed an adjusted hazard ratio (aHR) of 0.59 (95%CI 0.41-0.83). Positive trends could be obtained for further scenarios: an aHR of 0.51 (95%CI 0.16-1.68) when RDV was initiated in uncomplicated and of 0.76 (95% CI 0.55-1.04) in a critical phase of disease. Patients receiving RDV with concomitant steroids exhibited a further reduction in aHR in both, the complicated (aHR 0.50, 95%CI 0.29-0.88) and critical phase (aHR 0.63, 95%CI 0.39-1.02).

CONCLUSION

Our study results elucidate that RDV use, in particular when initiated in the complicated phase and accompanied by steroids is associated with improved mortality. However, given the limitations of non-randomized trials in estimating the magnitude of the benefit of an intervention, further randomized trials focusing on the timing of therapy initiation seem warranted.

Disease severity in hospitalized COVID-19 patients: comparing routine surveillance with cohort data from the LEOSS study in 2020 in Germany

INTRODUCTION

Studies investigating risk factors for severe COVID-19 often lack information on the representativeness of the study population. Here, we investigate factors associated with severe COVID-19 and compare the representativeness of the dataset to the general population.

METHODS

We used data from the Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS) of hospitalized COVID-19 patients diagnosed in 2020 in Germany to identify associated factors for severe COVID-19, defined as progressing to a critical disease stage or death. To assess the representativeness, we compared the LEOSS cohort to cases of hospitalized patients in the German statutory notification data of the same time period. Descriptive methods and Poisson regression models were used.

RESULTS

Overall, 6672 hospitalized patients from LEOSS and 132,943 hospitalized cases from the German statutory notification data were included. In LEOSS, patients above 76 years were less likely represented (34.3% vs. 44.1%). Moreover, mortality was lower (14.3% vs. 21.5%) especially among age groups above 66 years. Factors associated with a severe COVID-19 disease course in LEOSS included increasing age, male sex (adjusted risk ratio (aRR) 1.69, 95% confidence interval (CI) 1.53-1.86), prior stem cell transplantation (aRR 2.27, 95% CI 1.53-3.38), and an elevated C-reactive protein at day of diagnosis (aRR 2.30, 95% CI 2.03-2.62).

CONCLUSION

We identified a broad range of factors associated with severe COVID-19 progression. However, the results may be less applicable for persons above 66 years since they experienced lower mortality in the LEOSS dataset compared to the statutory notification data.

SARS-CoV-2 infection in chronic kidney disease patients with pre-existing dialysis: description across different pandemic intervals and effect on disease course (mortality)

PURPOSE

Patients suffering from chronic kidney disease (CKD) are in general at high risk for severe coronavirus disease (COVID-19) but dialysis-dependency (CKD5D) is poorly understood. We aimed to describe CKD5D patients in the different intervals of the pandemic and to evaluate pre-existing dialysis dependency as a potential risk factor for mortality.

METHODS

In this multicentre cohort study, data from German study sites of the Lean European Open Survey on SARS-CoV-2-infected patients (LEOSS) were used. We multiply imputed missing data, performed subsequent analyses in each of the imputed data sets and pooled the results. Cases (CKD5D) and controls (CKD not requiring dialysis) were matched 1:1 by propensity-scoring. Effects on fatal outcome were calculated by multivariable logistic regression.

RESULTS

The cohort consisted of 207 patients suffering from CKD5D and 964 potential controls. Multivariable regression of the whole cohort identified age (> 85 years adjusted odds ratio (aOR) 7.34, 95% CI 2.45-21.99), chronic heart failure (aOR 1.67, 95% CI 1.25-2.23), coronary artery disease (aOR 1.41, 95% CI 1.05-1.89) and active oncological disease (aOR 1.73, 95% CI 1.07-2.80) as risk factors for fatal outcome. Dialysis-dependency was not associated with a fatal outcome-neither in this analysis (aOR 1.08, 95% CI 0.75-1.54) nor in the conditional multivariable regression after matching (aOR 1.34, 95% CI 0.70-2.59).

CONCLUSIONS

In the present multicentre German cohort, dialysis dependency is not linked to fatal outcome in SARS-CoV-2-infected CKD patients. However, the mortality rate of 26% demonstrates that CKD patients are an extreme vulnerable population, irrespective of pre-existing dialysis-dependency.

Statistical biases due to anonymization evaluated in an open clinical dataset from COVID-19 patients

Anonymization has the potential to foster the sharing of medical data. State-of-the-art methods use mathematical models to modify data to reduce privacy risks. However, the degree of protection must be balanced against the impact on statistical properties. We studied an extreme case of this trade-off: the statistical validity of an open medical dataset based on the German National Pandemic Cohort Network (NAPKON), which was prepared for publication using a strong anonymization procedure. Descriptive statistics and results of regression analyses were compared before and after anonymization of multiple variants of the original dataset. Despite significant differences in value distributions, the statistical bias was found to be small in all cases. In the regression analyses, the median absolute deviations of the estimated adjusted odds ratios for different sample sizes ranged from 0.01 [minimum = 0, maximum = 0.58] to 0.52 [minimum = 0.25, maximum = 0.91]. Disproportionate impact on the statistical properties of data is a common argument against the use of anonymization. Our analysis demonstrates that anonymization can actually preserve validity of statistical results in relatively low-dimensional data.