Sentinel Modular Program for Propensity Score-Matched Cohort Analyses: Application to Glyburide, Glipizide, and Serious Hypoglycemia.

Sentinel is a program sponsored by the US Food and Drug Administration to monitor the safety of medical products. We conducted a cohort assessment to evaluate the ability of the Sentinel Propensity Score Matching Tool to reproduce in an expedited fashion the known association between glyburide (vs. glipizide) and serious hypoglycemia. Thirteen data partners who contribute to the Sentinel Distributed Database participated in this analysis. A pretested and customizable analytic program was run at each individual site. De-identified summary results from each data partner were returned and aggregated at the Sentinel Operations Center. We identified a total of 198,550 and 379,507 new users of glyburide and glipizide, respectively. The incidence of emergency department visits and hospital admissions for serious hypoglycemia was 19 per 1000 person-years (95% confidence interval = 17.9, 19.7) for glyburide users and 22 (21.6, 22.7) for glipizide users. In cohorts matched by propensity score based on predefined variables, the hazard ratio (HR) for glyburide was 1.36 (1.24, 1.49) versus glipizide. In cohorts matched on a high-dimensional propensity score based on empirically selected variables, for which the program ran to completion in five data partners, the HR was 1.49 (1.31, 1.70). In cohorts matched on propensity scores based on both predefined and empirically selected variables via the high-dimensional propensity score algorithm (the same five data partners), the HR was 1.51 (1.32, 1.71). These findings are consistent with the literature, and demonstrate the ability of the Sentinel Propensity Score Matching Tool to reproduce this known association in an expedited fashion.See video abstract at, http://links.lww.com/EDE/B275.

Electronic clinical laboratory test results data tables: lessons from Mini-Sentinel.

PURPOSE: Developing electronic clinical data into a common data model posed substantial challenges unique from those encountered with administrative data. We present here the design, implementation, and use of the Mini-Sentinel Distributed Database laboratory results table (LRT). METHODS: We developed the LRT and guided Mini-Sentinel data partners (DPs) in populating it from their source data. Data sources included electronic health records and internal and contracted clinical laboratory systems databases. We employed the Logical Observation Identifiers, Names, and Codes (LOINC®) results reporting standards. We evaluated transformed results data using data checks and an iterative, ongoing characterization and harmonization process. RESULTS: Key LRT variables included test name, subcategory, specimen source, LOINC, patient location, specimen date and time, result unit, and unique person identifier. Selected blood and urine chemistry, hematology, coagulation, and influenza tests were included. Twelve DPs with outpatient test results participated; four also contributed inpatient test results. As of September 2013, the LRT included 385,516,239 laboratory test results; data are refreshed at least quarterly. LOINC availability and use varied across DP. Multiple data quality and content issues were identified and addressed. CONCLUSION: Developing the LRT brought together disparate data sources with no common coding structure. Clinical laboratory test results obtained during routine healthcare delivery are neither uniformly coded nor documented in a standardized manner. Applying a systematic approach with data harmonization efforts and ongoing oversight and management is necessary for a clinical laboratory results data table to remain valid and useful.

Design for validation of acute myocardial infarction cases in Mini-Sentinel.

PURPOSE: To describe the acute myocardial infarction (AMI) validation project, a test case for health outcome validation within the US Food and Drug Administration-funded Mini-Sentinel pilot program. METHODS: The project consisted of four parts: (i) case identification-developing an algorithm based on the International Classification of Diseases, Ninth Revision, to identify hospitalized AMI patients within the Mini-Sentinel Distributed Database; (ii) chart retrieval-establishing procedures that ensured patient privacy (collection and transfer of minimum necessary amount of information, and redaction of direct identifiers to validate potential cases of AMI); (iii) abstraction and adjudication-trained nurse abstractors gathered key data using a standardized form with cardiologist adjudication; and (iv) calculation of the positive predictive value of the constructed algorithm. RESULTS: Key decision points included (i) breadth of the AMI algorithm, (ii) centralized versus distributed abstraction, and (iii) approaches to maintaining patient privacy and to obtaining charts for public health purposes. We used an algorithm limited to International Classification of Diseases, Ninth Revision, codes 410.x0-410.x1. Centralized data abstraction was performed because of the modest number of charts requested (<155). The project's public health status accelerated chart retrieval in most instances. CONCLUSIONS: We have established a process to validate AMI within Mini-Sentinel, which may be used for other health outcomes. Challenges include the following: (i) ensuring that only minimum necessary data are transmitted by Data Partners for centralized chart review, (ii) establishing procedures to maintain data privacy while still allowing for timely access to medical charts, and (iii) securing access to charts for public health uses that do not require approval from an institutional review board while maintaining patient privacy.