We used TriNetX, a global federated research network providing access to statistics on EMR (diagnoses, procedures, medications, laboratory values, genomic information). The analytics subset allowed the analysis of approximately 38 million patients in 35 large Health Care Organizations predominately in the United States. As a federated network, TriNetX received a waiver from Western IRB, since only aggregated counts, statistical summaries of de-identified information, and no protected health information is received. In addition, no study-specific activities are performed in retrospective analyses. Details of the network have been described elsewhere[6 -8 ]. All analyses were done in the TriNetX “Analytics” network using the browser-based real-time analytics features. At the time of the analysis in June 2018, we analyzed the EMR of 46909 patients in the network who had an instance of any SGLT2 inhibitor (empagliflozin, dapagliflozin or canagliflozin) any time within the past ten years in their electronic medical record. As a comparison group, we chose patients who had taken dipeptidyl peptidase (DPP) 4 inhibitors (linagliptin, alogliptin, sitagliptin or saxagliptin) during the same time, and found 189120 patients. Using a Bayesian statistical approach[9 ] on demographics and pre-existing (baseline) comorbidities of the two groups, we identified five potential confounding factors and built strata with the following criteria: age ≥ 60 years, presence of hypertension [International Classification of Diseases (ICD)10 code I10], presence of CKD (ICD10 code N18), co-medication with insulin, and co-medication with metformin. Separately analyzing strata allowed us to address potential bias in the federated data model without direct access to the individual data sets on the patient level.
Cardiovascular events were counted by selecting any stroke (ICD10 code I63) or myocardial infarction (ICD10 code I21) occurring during a three-year observation period after the first instance of the above mentioned medications in the patients’ records.
The risks of experiencing an event in each stratum were calculated by dividing the number of patients with an event (numerator) by the total number of patients with the respective medication in each stratum (denominator). The risk ratios for SGLT2 inhibitors vs the comparison group were calculated by dividing the risk for each SGLT2 stratum by the risk in each corresponding DPP4 stratum.
Cardiovascular events were counted by selecting any stroke (ICD10 code I63) or myocardial infarction (ICD10 code I21) occurring during a three-year observation period after the first instance of the above mentioned medications in the patients’ records.
The risks of experiencing an event in each stratum were calculated by dividing the number of patients with an event (numerator) by the total number of patients with the respective medication in each stratum (denominator). The risk ratios for SGLT2 inhibitors vs the comparison group were calculated by dividing the risk for each SGLT2 stratum by the risk in each corresponding DPP4 stratum.