Frank J. DeFalco

Characterizing treatment pathways at scale using the OHDSI network

Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component. In this study, the Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients. All data were mapped to common data standards, patient privacy was maintained by using a distributed model, and results were aggregated centrally. Treatment pathways were elucidated for type 2 diabetes mellitus, hypertension, and depression. The pathways revealed that the world is moving toward more consistent therapy over time across diseases and across locations, but significant heterogeneity remains among sources, pointing to challenges in generalizing clinical trial results. Diabetes favored a single first-line medication, metformin, to a much greater extent than hypertension or depression. About 10% of diabetes and depression patients and almost 25% of hypertension patients followed a treatment pathway that was unique within the cohort. Aside from factors such as sample size and underlying population (academic medical center versus general population), electronic health records data and administrative claims data revealed similar results. Large-scale international observational research is feasible.

Feasibility and utility of applications of the common data model to multiple, disparate observational health databases.

Objectives To evaluate the utility of applying the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) across multiple observational databases within an organization and to apply standardized analytics tools for conducting observational research. Materials and methods Six deidentified patient-level datasets were transformed to the OMOP CDM. We evaluated the extent of information loss that occurred through the standardization process. We developed a standardized analytic tool to replicate the cohort construction process from a published epidemiology protocol and applied the analysis to all 6 databases to assess time-to-execution and comparability of results. Results Transformation to the CDM resulted in minimal information loss across all 6 databases. Patients and observations excluded were due to identified data quality issues in the source system, 96% to 99% of condition records and 90% to 99% of drug records were successfully mapped into the CDM using the standard vocabulary. The full cohort replication and descriptive baseline summary was executed for 2 cohorts in 6 databases in less than 1 hour. Discussion The standardization process improved data quality, increased efficiency, and facilitated cross-database comparisons to support a more systematic approach to observational research. Comparisons across data sources showed consistency in the impact of inclusion criteria, using the protocol and identified differences in patient characteristics and coding practices across databases. Conclusion Standardizing data structure (through a CDM), content (through a standard vocabulary with source code mappings), and analytics can enable an institution to apply a network-based approach to observational research across multiple, disparate observational health databases.

Risk of lower extremity amputations in people with type 2 diabetes mellitus treated with sodium‐glucose co‐transporter‐2 inhibitors in the USA: A retrospective cohort study

To examine the incidence of amputation in patients with type 2 diabetes mellitus (T2DM) treated with sodium glucose co‐transporter 2 (SGLT2) inhibitors overall, and canagliflozin specifically, compared with non‐SGLT2 inhibitor antihyperglycaemic agents (AHAs).

Incidence of diabetic ketoacidosis among patients with type 2 diabetes mellitus treated with SGLT2 inhibitors and other antihyperglycemic agents

AIMS To estimate and compare incidence of diabetes ketoacidosis (DKA) among patients with type 2 diabetes who are newly treated with SGLT2 inhibitors (SGLT2i) versus non-SGLT2i antihyperglycemic agents (AHAs) in actual clinical practice. METHODS A new-user cohort study design using a large insurance claims database in the US. DKA incidence was compared between new users of SGLT2i and new users of non-SGLT2i AHAs pair-matched on exposure propensity scores (EPS) using Cox regression models. RESULTS Overall, crude incidence rates (95% CI) per 1000 patient-years for DKA were 1.69 (1.22-2.30) and 1.83 (1.58-2.10) among new users of SGLT2i (n=34,442) and non-SGLT2i AHAs (n=126,703). These rates more than doubled among patients with prior insulin prescriptions but decreased by more than half in analyses that excluded potential autoimmune diabetes (PAD). The hazard ratio (95% CI) for DKA comparing new users of SGLT2i to new users of non-SGLT2i AHAs was 1.91 (0.94-4.11) (p=0.09) among the 30,196 EPS-matched pairs overall, and 1.13 (0.43-3.00) (p=0.81) among the 27,515 EPS-matched pairs that excluded PAD. CONCLUSIONS This was the first observational study that compared DKA risk between new users of SGLT2i and non-SGLT2i AHAs among patients with type 2 diabetes, and overall no statistically significant difference was detected.

Applying standardized drug terminologies to observational healthcare databases: a case study on opioid exposure

Observational healthcare databases represent a valuable resource for health economics, outcomes research, quality of care, drug safety, epidemiology and comparative effectiveness research. The methods used to identify a population for study in an observational healthcare database with the desired drug exposures of interest are complex and not consistent nor apparent in the published literature. Our research evaluates three drug classification systems and their impact on prevalence in the analysis of observational healthcare databases using opioids as a case in point. The standard terminologies compiled in the Observational Medical Outcomes Partnership’s Common Data Model vocabulary were used to facilitate the identification of populations with opioid exposures. This study analyzed three distinct observational healthcare databases and identified patients with at least one exposure to an opioid as defined by drug codes derived through the application of three classification systems. Opioid code sets were created for each of the three classification systems and the number of identified codes was summarized. We estimated the prevalence of opioid exposure in three observational healthcare databases using the three defined code sets. In addition we compared the number of drug codes and distinct ingredients that were identified using these classification systems. We found substantial variation in the prevalence of opioid exposure identified using an individual classification system versus a composite method using multiple classification systems. To ensure transparent and reproducible research publications should include a description of the process used to develop code sets and the complete code set used in studies.

Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Datasets

Introduction: Data quality and fitness for analysis are crucial if outputs of analyses of electronic health record data or administrative claims data should be trusted by the public and the research community. Methods: We describe a data quality analysis tool (called Achilles Heel) developed by the Observational Health Data Sciences and Informatics Collaborative (OHDSI) and compare outputs from this tool as it was applied to 24 large healthcare datasets across seven different organizations. Results: We highlight 12 data quality rules that identified issues in at least 10 of the 24 datasets and provide a full set of 71 rules identified in at least one dataset. Achilles Heel is a freely available software that provides a useful starter set of data quality rules with the ability to add additional rules. We also present results of a structured email-based interview of all participating sites that collected qualitative comments about the value of Achilles Heel for data quality evaluation. Discussion: Our analysis represents the first comparison of outputs from a data quality tool that implements a fixed (but extensible) set of data quality rules. Thanks to a common data model, we were able to compare quickly multiple datasets originating from several countries in America, Europe and Asia.

Comment on “Comparative effectiveness of canagliflozin, SGLT2 inhibitors and non‐SGLT2 inhibitors on the risk of hospitalization for heart failure and amputation in patients with type 2 diabetes mellitus: A real‐world meta‐analysis of 4 observational databases (OBSERVE‐4D)”

Sodium glucose co‐transporter 2 inhibitors (SGLT2i) are indicated for treatment of type 2 diabetes mellitus (T2DM); some SGLT2i have reported cardiovascular benefit, and some have reported risk of below‐knee lower extremity (BKLE) amputation. This study examined the real‐world comparative effectiveness within the SGLT2i class and compared with non‐SGLT2i antihyperglycaemic agents.