ABSTRACT
INTRODUCTION: This retrospective cohort study investigated the relation between different measures of glycemic exposure and micro- and macrovascular complications among patients with type 2 diabetes. METHODS: The analysis included patients receiving oral antihyperglycemic agents between 1 January 2006 and 31 December 2014 from the General Practitioner Database from the PHARMO Database Network. All recorded HbA1c levels during follow-up were used to express glycemic exposure in four ways: index HbA1c, time-dependent HbA1c, exponential moving average (EMA) and glycemic burden. Association between glycemic exposure and micro-/macrovascular complications was analyzed by estimating hazard ratios and 95% confidence intervals using an adjusted (time-dependent) Cox proportional hazards model. RESULTS: The analysis included 32,725 patients (median age, 65 years; 47% female). Median follow-up was 5.4 years; median number of HbA1c measurements per patient was 18.0. From all measures, HbA1c at index showed the weakest relation between all micro-/macrovascular complications, with coronary artery disease (CAD) having the highest HR (95% CI): 1.18 (1.04-1.34) for HbA1c ≥64 mmol/mol (8%). The time-dependent HbA1c model showed a significant association only for microvascular complications, with retinopathy having the highest HR (95% CI): 1.55 (1.40-1.73) for HbA1c ≥64 mmol/mol (8%). EMA-defined exposure showed similar findings, although the effect of retinopathy was more pronounced [HR (95% CI): 1.81 (1.63-2.02) for HbA1c ≥64 mmol/mol (8%)] and was also predictive for CAD [HR (95% CI): 1.29 (1.10-1.50) for HbA1c ≥64 mmol/mol (8%)]. A statistically significant relation with glycemic burden was found for all selected micro-/macrovascular complications, with retinopathy having the highest HR (95%): 2.60 (2.19-3.07) for glycemic burden years >3. CONCLUSION: This study shows that greater and more prolonged exposure to hyperglycemia increases the risk of micro- and macrovascular complications. FUNDING: Janssen Pharmaceutica NV.
ABSTRACT
Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93-100%), while drug-based components were the main contributors in RLDs (81-100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies.
