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Purpose Identification of hospitalizations for infection is important for post-marketing surveillance of drugs, but the validity of using diagnosis codes to identify these events is unknown. Differentiating between hospitalization for and with infection is important, as the latter is common and less likely to arise from pre-admission exposure to drugs. We determined positive predictive values (PPVs) of diagnostic coding-based algorithms to identify hospitalization for infection among patients prescribed oral anti-diabetic drugs (OADs). Methods We identified patients initiating OADs within 2 United States claims databases (Medicare, HealthCore Integrated Research DatabaseSM [HIRDSM]) and 2 United Kingdom electronic medical record databases (Clinical Practice Research Datalink [CPRD], The Health Improvement Network [THIN]) from 2009 to 2014. To identify potential hospitalizations for infection, we selected patients with a hospital diagnosis of infection and, within 7 days prior to hospitalization, either an outpatient/emergency department visit with an infection diagnosis or outpatient antimicrobial treatment. Hospital records were reviewed by infectious disease specialists to adjudicate hospital admissions for infection. PPVs for confirmed outcomes were determined for each database. Results Code-based algorithms to identify hospitalization for infection had PPVs exceeding 80% within Medicare (PPV, 83% [90/109]; 95% CI, 74–89%), HIRDSM (PPV, 89% [73/82]; 95% CI, 80–95%), and THIN (PPV, 86% [12/14]; 95% CI, 57–98%) but not within CPRD (PPV, 67% [14/21]; 95% CI, 43–85%). Conclusions Algorithms identifying hospitalization for infection utilizing hospital diagnoses along with antecedent outpatient/emergency infection diagnoses or antimicrobial therapy had sufficiently high PPVs for confirmed events within Medicare, HIRDSM, and THIN to enable their use for pharmacoepidemiologic research.

The extent to which days’ supply data are missing in pharmacoepidemiologic databases and effective methods for estimation is unknown. We determined the percentage of missing days’ supply on prescription and patient levels for oral anti-diabetic drugs (OADs) and evaluated three methods for estimating days’ supply within the Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN). We estimated the percentage of OAD prescriptions and patients with missing days’ supply in each database from 2009 to 2013. Within a random sample of prescriptions with known days’ supply, we measured the accuracy of three methods to estimate missing days’ supply by imputing the following: (1) 28 days’ supply, (2) mode number of tablets/day by drug strength and number of tablets/prescription, and (3) number of tablets/day via a machine learning algorithm. We determined incidence rates (IRs) of acute myocardial infarction (AMI) using each method to evaluate the impact on ascertainment of exposure time and outcomes. Days’ supply was missing for 24 % of OAD prescriptions in CPRD and 33 % in THIN (affecting 48 and 57 % of patients, respectively). Methods 2 and 3 were very accurate in estimating days’ supply for OADs prescribed at a consistent number of tablets/day. Method 3 was more accurate for OADs prescribed at varying number of tablets/day. IRs of AMI were similar across methods for most OADs. Missing days’ supply is a substantial problem in both databases. Method 2 is easy and very accurate for most OADs and results in IRs comparable to those from method 3.

There has been increased interest in using multiple observational databases to understand the safety profile of medical products during the postmarketing period. However, it is challenging to perform analyses across these heterogeneous data sources. The Observational Medical Outcome Partnership (OMOP) provides a Common Data Model (CDM) for organizing and standardizing databases. OMOP’s work with the CDM has primarily focused on US databases. As a participant in the OMOP Extended Consortium, we implemented the OMOP CDM on the UK Electronic Healthcare Record database—The Health Improvement Network (THIN). The aim of the study was to evaluate the implementation of the THIN database in the OMOP CDM and explore its use for active drug safety surveillance. Following the OMOP CDM specification, the raw THIN database was mapped into a CDM THIN database. Ten Drugs of Interest (DOI) and nine Health Outcomes of Interest (HOI), defined and focused by the OMOP, were created using the CDM THIN database. Quantitative comparison of raw THIN to CDM THIN was performed by execution and analysis of OMOP standardized reports and additional analyses. The practical value of CDM THIN for drug safety and pharmacoepidemiological research was assessed by implementing three analysis methods: Proportional Reporting Ratio (PRR), Univariate Self-Case Control Series (USCCS) and High-Dimensional Propensity Score (HDPS). A published study using raw THIN data was selected to examine the external validity of CDM THIN. Overall demographic characteristics were the same in both databases. Mapping medical and drug codes into the OMOP terminology dictionary was incomplete: 25 % medical codes and 55 % drug codes in raw THIN were not listed in the OMOP terminology dictionary, representing 6 % condition occurrence counts, 4 % procedure occurrence counts and 7 % drug exposure counts in raw THIN. Seven DOIs had

Pharmacoepidemiology researchers often utilize data from two UK electronic medical record databases, the Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN), and may choose to combine the two in an effort to increase sample size. To minimize duplication of data, previous studies examined the practice-level overlap between these databases. However, the proportion of overlapping patients remains unknown. We developed a method using demographic and pharmacy variables to identify patients included in both CPRD and THIN, and applied this method to measure the proportion of overlapping patients who initiated the oral anti-diabetic drug saxagliptin.We conducted a cross-sectional study among patients initiating saxagliptin in CPRD and THIN between October 2009 and September 2012. Within both databases, we identified patients: (i) ≥18 years, (ii) newly prescribed saxagliptin, and (iii) with ≥180 days enrollment prior to saxagliptin initiation. Demographic data (birth year, sex, patient registration date, family number, and marital status) and prescriptions (including dates) for the first two oral anti-diabetic drugs prescribed within the study period were used to identify matching patients.Among 4202 CPRD and 3641 THIN patients initiating saxagliptin, 2574 overlapping patients (61% of CPRD saxagliptin initiators; 71% of THIN saxagliptin initiators) were identified. Among these patients, 2474 patients (96%) perfectly matched on all demographic and prescription data.Within each database, over 60% of patients initiating saxagliptin were included within both CPRD and THIN. Combined demographic and prescription data can be used to identify patients included in both CPRD and THIN.

The patterns and determinants of saxagliptin use among patients with type 2 diabetes mellitus (T2DM) are unknown in real-world settings. We compared the characteristics of T2DM patients who were new initiators of saxagliptin to those who were new initiators of non-dipeptidyl peptidase-4 (DPP-4) inhibitor oral anti-diabetic drugs (OADs) and identified factors associated with saxagliptin use. We conducted a cross-sectional study within the Clinical Practice Research Datalink (CPRD), The Health Improvement Network (THIN), US Medicare, and the HealthCore Integrated Research Database (HIRDSM) across the first 36 months of saxagliptin availability (29 months for US Medicare). Patients were included if they were: 1) ≥18 years old, 2) newly prescribed saxagliptin or a non-DPP-4 inhibitor OAD, and 3) enrolled in their respective database for 180 days. For each saxagliptin initiator, we randomly selected up to ten non-DPP-4 inhibitor OAD initiators matched on age, sex, and geographic region. Conditional logistic regression was used to identify determinants of saxagliptin use. We identified 64,079 saxagliptin initiators (CPRD: 1,962; THIN: 2,084; US Medicare: 51,976; HIRDSM: 8,057) and 610,660 non-DPP-4 inhibitor OAD initiators (CPRD: 19,484; THIN: 19,936; US Medicare: 493,432; HIRDSM: 77,808). Across all four data sources, prior OAD use, hypertension, and hyperlipidemia were associated with saxagliptin use. Saxagliptin initiation was also associated with hemoglobin A1c results >8% within the UK data sources, and a greater number of hemoglobin A1c measurements in the US data sources. In these UK and US data sources, initiation of saxagliptin was associated with prior poor glycemic control, prior OAD use, and diagnoses of hypertension and hyperlipidemia. ClinicalTrials.gov identifiers NCT01086280 , NCT01086293 , NCT01086319 , NCT01086306 , and NCT01377935

To describe the design and rationale of a series of postmarketing studies to examine the safety of saxagliptin, an oral dipeptidyl peptidase-4 inhibitor for the treatment of type 2 diabetes mellitus, in real-world settings.We are conducting a series of retrospective cohort studies using two UK (General Practice Research Database, and The Health Improvement Network) and two US (Medicare, HealthCore Integrated Research Database(SM) ) data sources. The primary outcomes of interest will include (i) hospitalization with acute liver failure, (ii) hospitalization for acute kidney injury, (iii) hospitalization for severe hypersensitivity reactions, (iv) hospitalization for severe infections, (v) hospitalization with infections associated with T-lymphocyte dysfunction (i.e., herpes zoster, tuberculosis, or nontuberculous mycobacteria), and (vi) major cardiovascular events. Diagnosis codes for the outcomes of interest will be validated by medical record review within each data source. Projected use and estimated incidence rates of outcomes of interest suggest there will be at least 80% statistical power to detect a minimum hazard ratio of 1.5 for major cardiovascular events, 2.0 for acute kidney injury and severe infections, 2.4 for acute liver failure, and 4.0 for severe hypersensitivity reactions.Forthcoming.This postmarketing safety assessment will provide important information regarding the safety of saxagliptin and could potentially identify important dipeptidyl peptidase-4 inhibitor class effects. The methods described may be useful to others planning similar evaluations.