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3 second-line type 2 diabetes therapies similarly lower HbA1c
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An international, collaborative analysis of data from more than 246 million patients with type 2 diabetes suggests that three second-line antidiabetes therapies similarly lower HbA1c when added to metformin, according to findings published in JAMA Network Open.
In a previous analysis from the Observational Health Data Sciences and Informatics (OHDSI) initiative, researchers found that metformin was the most commonly prescribed medication for diabetes; it was prescribed 75% of the time as a first therapy and remained the only medication 29% of the time, Nigam H. Shah, MBBS, PhD, of the Stanford Center for Biomedical Informatics Research, California, and colleagues wrote in the study background. However, second-line therapy choices for diabetes varied widely, they wrote, likely due to a lack of consensus around optimal second-line therapy choices. The latest OHDSI analysis examined the effectiveness of second-line treatments as add-ons to metformin therapy, analyzing eight data sources across multiple health care systems in three countries.
“The main takeaway is that this study is an example of a large, multinational, open, collaborative research network, which can produce evidence at scale, and is made feasible via the adoption of a common data model and open-source analytical tools,” Shah told Endocrine Today.
In a retrospective study, Shah and colleagues analyzed data from 246,558,805 patients (51.5% women) in the Truven Health MarketScan Commercial Claims and Encounters; Columbia University Medical Center; IQVIA Disease Analyzer France; Truven Health MarketScan Medicare; Mount Sinai Icahn School of Medicine; Optum Clinformatics Data Mart; Ajou University School of Medicine, South Korea; and Stanford University. Patient-level data from each site were transformed into the Observational Medical Outcomes Partnership Common Data Model schema to unify heterogeneous electronic medical records data and insurances claims sources. Researchers used specific combinations of drugs, diagnosis codes and laboratory test values to identify patients with type 2 diabetes who received a second-line treatment after metformin, limiting the analysis to three therapies: sulfonylureas, DPP-IV inhibitors and thiazolidinediones prescribed at least 90 days after metformin. Primary outcome was the first observation of an HbA1c level of 7% or less after the prescription of a second-line agent. Secondary outcomes included first occurrence of myocardial infarction, kidney disorders and eye disorders. Researchers performed three pairwise comparisons: sulfonylureas vs. DPP-IV inhibitors; sulfonylureas vs. TZDs; and DPP-IV inhibitors vs. TZDs, using propensity scoring to mitigate any biases from nonrandom treatment assignment at each site. Researchers then used a Cox proportional hazard model to calculate HRs for each outcome. Data analysis was conducted between 2015 and 2018.
In a random-effects meta-analysis across all data sites, performed due to study heterogeneity, researchers found no significant differences between sulfonylureas and DPP-IV inhibitors for the reduction of HbA1c levels to 7% or less (consensus HR = 0.99; 95% CI, 0.89-1.1).
In assessing secondary outcomes, in which study heterogeneity was low, researchers observed a small increased HR for MI with sulfonylureas vs. DPP-IV inhibitors (consensus HR = 1.12; 95% CI, 1.02-1.24) and eye disorders (consensus HR = 1.15; 95% CI, 1.11-1.19). However, recalibrated P values indicated that, individually, the association did not rise to significance at any one site, according to the researchers. There were no between-group differences with respect to kidney disorders (consensus HR = 1.09; 95% CI, 0.97-1.19).
“Comparisons of sulfonylureas with thiazolidinediones and of DPP-IV inhibitors with thiazolidinediones show no difference in reaching HbA1c levels of 7% of total hemoglobin or less, or in hazard of myocardial infarction, kidney disorders and eye disorders in patients with [type 2 diabetes] after recalibration of P values as well as after the meta-analysis,” the researchers wrote.
The researchers noted that it is possible that differences in clinical practice, patient populations or data standardization between study sites were, in part, responsible for the site-to-site variation.
“Now, we are at the next step to ask which treatment pathways are good,” Shah told Endocrine Today. “Most media stories will focus on the specific result around diabetes, but that is only part of the story. Having such a network to produce evidence is necessary for a learning health system.” – by Regina Schaffer
For more information:
Nigam H. Shah, MBBS, PhD, can be reached at the Center for Biomedical Informatics Research, Stanford University School of Medicine, 1265 Welch Road, Stanford, CA 94305; email: nigam@stanford.edu.
Disclosures: Shah reports no relevant financial disclosures. Please see the study for the other authors’ relevant financial disclosures.
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