Improving data quality in observational research studies: Report of the Cure Glomerulonephropathy (CureGN) network

Lessons learned from over a decade of data audits in international observational HIV cohorts in Latin America and East Africa

Introduction

Routine patient care data are increasingly used for biomedical research, but such "secondary use" data have known limitations, including their quality. When leveraging routine care data for observational research, developing audit protocols that can maximize informational return and minimize costs is paramount.

Methods

For more than a decade, the Latin America and East Africa regions of the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium have been auditing the observational data drawn from participating human immunodeficiency virus clinics. Since our earliest audits, where external auditors used paper forms to record audit findings from paper medical records, we have streamlined our protocols to obtain more efficient and informative audits that keep up with advancing technology while reducing travel obligations and associated costs.

Results

We present five key lessons learned from conducting data audits of secondary-use data from resource-limited settings for more than 10 years and share eight recommendations for other consortia looking to implement data quality initiatives.

Conclusion

After completing multiple audit cycles in both the Latin America and East Africa regions of the IeDEA consortium, we have established a rich reference for data quality in our cohorts, as well as large, audited analytical datasets that can be used to answer important clinical questions with confidence. By sharing our audit processes and how they have been adapted over time, we hope that others can develop protocols informed by our lessons learned from more than a decade of experience in these large, diverse cohorts.

CureGN-Diabetes Study: Rationale, Design, and Methods of a Prospective Observational Study of Glomerular Disease Patients with Diabetes

Glomerular diseases (GDs) represent the third leading cause of end-stage kidney disease (ESKD) in the US Diabetes was excluded from the CureGN Study, an NIH/NIDDK-sponsored observational cohort study of four leading primary GDs: IgA nephropathy (IgAN), membranous nephropathy (MN), focal segmental glomerulosclerosis (FSGS), and minimal change disease (MCD). CureGN-Diabetes, an ancillary study to CureGN, seeks to understand how diabetes influences the diagnosis, treatment, and outcomes of GD. It is a multicenter, prospective cohort study, targeting an enrollment of 300 adults with prevalent type 1 or type 2 diabetes and MCD, FSGS, MN, or IgAN, with first kidney biopsy obtained within 5 years of enrollment in 80% (20% allowed if biopsy after 2010). CureGN and Transformative Research in DiabEtic NephropaThy (TRIDENT) provide comparator cohorts. Retrospective and prospective clinical data and patient-reported outcomes are obtained. Blood and urine specimens are collected at study visits annually. Kidney biopsy reports and digital images are obtained, and standardized pathologic evaluations performed. Light microscopy images are uploaded to the NIH pathology repository. Outcomes include relapse and remission rates, changes in proteinuria and estimated glomerular filtration rate, infections, cardiovascular events, malignancy, ESKD, and death. Multiple analytical approaches will be used leveraging the baseline and longitudinal data to compare disease presentation and progression across subgroups of interest. With 300 patients and an average of 3 years of follow-up, the study has 80% power to detect a HR of 1.4-1.8 for time to complete remission of proteinuria, a rate ratio for hospitalizations of 1.18-1.56 and difference in eGFR slope of 6.0-8.6 mL/min/year between two groups of 300 participants each. CureGN-Diabetes will enhance our understanding of diabetes as a modifying factor of the pathology and outcomes of GDs and support studies to identify disease mechanisms and improve patient outcomes in this understudied patient population.

Immunosuppression Exposure and Risk of Infection-Related Acute Care Events in Patients With Glomerular Disease: An Observational Cohort Study

Rationale & Objective

Infections cause morbidity and mortality in patients with glomerular disease. The relative contributions from immunosuppression exposure and glomerular disease activity to infection risk are not well characterized. To address this unmet need, we characterized the relationship between time-varying combinations of immunosuppressant exposure and infection-related acute care events while controlling for disease activity, among individuals with glomerular disease.

Study Design

Prospective, multicenter, observational cohort study.

Setting & Participants

Adults and children with biopsy-proven minimal change disease, focal segmental glomerulosclerosis, membranous nephropathy, or immunoglobulin A nephropathy/vasculitis were enrolled at 71 clinical sites in North America and Europe. A total of 2,388 Cure Glomerulonephropathy Network participants (36% aged <18 years) had at least 1 follow-up visit and were included in the analysis.

Exposures

Immunosuppression exposure modeled on a weekly basis.

Outcome

Infections leading to an emergency department visit or hospitalization.

Analytical Approach

Marginal structural models were used to estimate the effect of time-varying immunosuppression exposure on hazard of first infection-related acute care event while accounting for baseline sociodemographic and clinical factors, and time-varying disease activity.

Results

A total of 2,388 participants were followed for a median of 3.2 years (interquartile range, 1.6-4.6), and 15% experienced at least 1 infection-related emergency department visit or hospitalization. Compared to no immunosuppression exposure, steroid exposure, steroid with any other immunosuppressant, and nonsteroid immunosuppressant exposure were associated with a 2.65-fold (95% CI, 1.83-3.86), 2.68-fold (95% CI, 1.95-3.68), and 1.7-fold (95% CI, 1.29-2.24) higher risk of first infection, respectively.

Limitations

Absence of medication dosing data, lack of a control group, and potential bias in ascertainment of outcome events secondary to the coronavirus 2 pandemic.

Conclusions

Corticosteroids with or without concomitant additional immunosuppression significantly increased risk of infection leading to acute care utilization in adults and children with glomerular disease.

Data Collection Theory in Healthcare Research: The Minimum Dataset in Quantitative Studies

There is considerable interest in data analytics because of its value in informing decisions in healthcare. Data variables can be derived from routinely collected records or from primary studies. The level of detail for individual variables in quantitative studies is often disregarded. In this work, we aim to present the concept of a minimum dataset for any variable. The most basic level of data collection is the value of a variable. In addition, there may be an indicator of severity and a measure of duration or how long the value has been present. The time course defines how the values for a variable fluctuated over time. The validity or accuracy of the values for a variable is also important to avoid spurious findings. Finally, there may be additional modifiers which drastically change the impact of a variable. In conclusion, the minimum dataset is a framework which can be used for the purposes of study design and appraisal of studies. Not all data requires full consideration of the minimum dataset framework for each variable, but the framework may be important if more detailed results are desired.

The promise of artificial intelligence for kidney pathophysiology

PURPOSE OF REVIEW

We seek to determine recent advances in kidney pathophysiology that have been enabled or enhanced by artificial intelligence. We describe some of the challenges in the field as well as future directions.

RECENT FINDINGS

We first provide an overview of artificial intelligence terminologies and methodologies. We then describe the use of artificial intelligence in kidney diseases to discover risk factors from clinical data for disease progression, annotate whole slide imaging and decipher multiomics data. We delineate key examples of risk stratification and prognostication in acute kidney injury (AKI) and chronic kidney disease (CKD). We contextualize these applications in kidney disease oncology, one of the subfields to benefit demonstrably from artificial intelligence using all if these approaches. We conclude by elucidating technical challenges and ethical considerations and briefly considering future directions.

SUMMARY

The integration of clinical data, patient derived data, histology and proteomics and genomics can enhance the work of clinicians in providing more accurate diagnoses and elevating understanding of disease progression. Implementation research needs to be performed to translate these algorithms to the clinical setting.

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative

OBJECTIVE

In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations.

MATERIALS AND METHODS

We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements.

RESULTS

Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback.

DISCUSSION

We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate.

CONCLUSION

By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.