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Purpose: The Food and Drug Administration's Sentinel System developed parameterized, reusable analytic programs for evaluation of medical product safety. Research on outpatient antibiotic exposures, and Clostridium difficile infection (CDI) with non-user reference groups led us to expect a higher rate of CDI among outpatient clindamycin users vs penicillin users. We evaluated the ability of the Cohort Identification and Descriptive Analysis and Propensity Score Matching tools to identify a higher rate of CDI among clindamycin users., Methods: We matched new users of outpatient dispensings of oral clindamycin or penicillin from 13 Data Partners 1:1 on propensity score and followed them for up to 60 days for development of CDI. We used Cox proportional hazards regression stratified by Data Partner and matched pair to compare CDI incidence., Results: Propensity score models at 3 Data Partners had convergence warnings and a limited range of predicted values. We excluded these Data Partners despite adequate covariate balance after matching. From the 10 Data Partners where these models converged without warnings, we identified 807 919 new clindamycin users and 8 815 441 new penicillin users eligible for the analysis. The stratified analysis of 807 769 matched pairs included 840 events among clindamycin users and 290 among penicillin users (hazard ratio 2.90, 95% confidence interval 2.53, 3.31)., Conclusions: This evaluation produced an expected result and identified several potential enhancements to the Propensity Score Matching tool. This study has important limitations. CDI risk may have been related to factors other than the inherent properties of the drugs, such as duration of use or subsequent exposures., (Copyright © 2018 John Wiley & Sons, Ltd.)

Background: The Sentinel Distributed Database (SDD) is a large database of patient-level administrative health care records, primarily derived from insurance claims and electronic health records, and is sponsored by the US Food and Drug Administration for medical product safety evaluations. Acute myocardial infarction (AMI) is a common study endpoint for drug safety studies that rely on health records from the SDD and other administrative databases., Purpose: In this chart validation study, we report on the positive predictive value (PPV) of inpatient International Classification of Diseases, Ninth Revision, Clinical Modification AMI administrative diagnosis codes (410.x1 and 410.x0) in the SDD., Methods: As part of an assessment of thromboembolic adverse event risk following treatment with intravenous immune globulin, charts were obtained for 103 potential post-intravenous immune globulin AMI cases. Charts were abstracted by trained nurses and physician-adjudicated based on prespecified diagnostic criteria., Results: Acute myocardial infarction status could be determined for 89 potential cases. The PPVs for the inpatient AMI diagnoses recorded in the SDD were 75% overall (95% CI, 65-84%), 93% (95% CI, 78-99%) for principal-position diagnoses, 88% (95% CI, 72-97%) for secondary diagnoses, and 38% (95% CI, 20-59%) for position-unspecified diagnoses (eg, diagnoses originating from separate physician claims associated with an inpatient stay). Of the confirmed AMI cases, demand ischemia was the suspected etiology more often for those coded in secondary or unspecified positions (72% and 40%, respectively) than for principal-position AMI diagnoses (21%)., Conclusions: The PPVs for principal and secondary AMI diagnoses were high and similar to estimates from prior chart validation studies. Position-unspecified diagnosis codes were less likely to represent true AMI cases., (Copyright © 2018 John Wiley & Sons, Ltd.)

Purpose: The US Food and Drug Administration's Sentinel system developed tools for sequential surveillance., Methods: In patients with non-valvular atrial fibrillation, we sequentially compared outcomes for new users of rivaroxaban versus warfarin, employing propensity score matching and Cox regression. A total of 36 173 rivaroxaban and 79 520 warfarin initiators were variable-ratio matched within 2 monitoring periods., Results: Statistically significant signals were observed for ischemic stroke (IS) (first period) and intracranial hemorrhage (ICH) (second period) favoring rivaroxaban, and gastrointestinal bleeding (GIB) (second period) favoring warfarin. In follow-up analyses using primary position diagnoses from inpatient encounters for increased definition specificity, the hazard ratios (HR) for rivaroxaban vs warfarin new users were 0.61 (0.47, 0.79) for IS, 1.47 (1.29, 1.67) for GIB, and 0.71 (0.50, 1.01) for ICH. For GIB, the HR varied by age: <66 HR = 0.88 (0.60, 1.30) and 66+ HR = 1.49 (1.30, 1.71)., Conclusions: This study demonstrates the capability of Sentinel to conduct prospective safety monitoring and raises no new concerns about rivaroxaban safety., (Copyright © 2018 John Wiley & Sons, Ltd.)

Purpose: To systematically review algorithms to identify seizure, convulsion, or epilepsy cases in administrative and claims data, with a focus on studies that have examined the validity of the algorithms., Methods: A literature search was conducted using PubMed and the Iowa Drug Information Service database. Reviews were conducted by two investigators to identify studies using data sources from the USA or Canada because these data sources were most likely to reflect the coding practices of Mini-Sentinel data partners., Results: Eleven studies that validated seizure, convulsion, or epilepsy cases were identified. All algorithms included International Classification of Diseases, Ninth Revision, Clinical Modification code 345.X (epilepsy) and either code 780.3 (convulsions) or code 780.39 (other convulsions). Six studies included 333.2 (myoclonus). In populations that included children, 779.0 (convulsions in newborn) was also fairly common. Positive predictive values (PPVs) ranged from 21% to 98%. Studies that used nonspecific indicators such as presence of an electroencephalogram or anti-epileptic drug (AED) level monitoring had lower PPVs. In studies focusing exclusively on epilepsy as opposed to isolated seizure events, sensitivity ranged from 70% to 99%., Conclusions: Algorithm performance was highly variable, so it is difficult to draw any strong conclusions. However, the PPVs were generally best in studies where epilepsy diagnoses were required. Using procedure codes for electroencephalograms or prescription claims for drugs possibly used for epilepsy or convulsions in the absence of a diagnostic code is not recommended. Many newer AEDs require no drug level monitoring, so requiring an AED level monitoring procedure in algorithms to identify epilepsy is not recommended., (Copyright © 2012 John Wiley & Sons, Ltd.)

Purpose: This paper aimed to systematically review algorithms to identify transfusion-related ABO incompatibility reactions in administrative data, with a focus on studies that have examined the validity of the algorithms., Methods: A literature search was conducted using PubMed, Iowa Drug Information Service database, and Embase. A Google Scholar search was also conducted because of the difficulty identifying relevant studies. Reviews were conducted by two investigators to identify studies using data sources from the USA or Canada because these data sources were most likely to reflect the coding practices of Mini-Sentinel data sources., Results: One study was found that validated International Classification of Diseases (ICD-9-CM) codes representing transfusion reactions. None of these cases were ABO incompatibility reactions. Several studies consistently used ICD-9-CM code 999.6, which represents ABO incompatibility reactions, and a technical report identified the ICD-10 code for these reactions. One study included the E-code E8760 for mismatched blood in transfusion in the algorithm. Another study reported finding no ABO incompatibility reaction codes in the Healthcare Cost and Utilization Project Nationwide Inpatient Sample database, which contains data of 2.23 million patients who received transfusions, raising questions about the sensitivity of administrative data for identifying such reactions. Two studies reported perfect specificity, with sensitivity ranging from 21% to 83%, for the code identifying allogeneic red blood cell transfusions in hospitalized patients., Conclusions: There is no information to assess the validity of algorithms to identify transfusion-related ABO incompatibility reactions. Further information on the validity of algorithms to identify transfusions would also be useful., (Copyright © 2012 John Wiley & Sons, Ltd.)

Purpose: The validity of findings from surveillance activities, which use administrative and claims data to link exposures to adverse events, depends in part on the validity of algorithms to identify health outcomes using these data. This review provides a high level overview of the findings of 19 systematic reviews of studies, which have examined the validity of algorithms to identify health outcomes using these data. The author categorized outcomes on the basis of the strength of evidence supporting valid algorithms to identify acute or incident events and suggested priorities for future validation studies., Methods: The 19 reviews were evaluated, and key findings and suggestions for future research were summarized by a single reviewer. Outcomes with algorithms that consistently identified acute events or incident conditions with positive predictive values of greater than 70% across multiple studies and populations are described as low priority for future algorithm validation studies., Results: Algorithms to identify cerebrovascular accidents, transient ischemic attacks, congestive heart failure, deep vein thrombosis, pulmonary embolism, angioedema, and total hip arthroplasty revision performed well across multiple studies and are considered low priority for future validation studies. Other outcomes were generally thought to require additional validation studies or algorithm refinement to be confident in algorithms. Few studies examined the validity of International Classification of Diseases, 10th Revision, codes., Conclusion: Users of these reviews need to consider the generalizability of findings to their study populations. For some outcomes with poorly performing codes, it may always be necessary to validate cases., (Copyright © 2012 John Wiley & Sons, Ltd.)

The Mini-Sentinel is a pilot program that is developing methods, tools, resources, policies, and procedures to facilitate the use of routinely collected electronic healthcare data to perform active surveillance of the safety of marketed medical products, including drugs, biologics, and medical devices. The U.S. Food and Drug Administration (FDA) initiated the program in 2009 as part of its Sentinel Initiative, in response to a Congressional mandate in the FDA Amendments Act of 2007. After two years, Mini-Sentinel includes 31 academic and private organizations. It has developed policies, procedures, and technical specifications for developing and operating a secure distributed data system comprised of separate data sets that conform to a common data model covering enrollment, demographics, encounters, diagnoses, procedures, and ambulatory dispensing of prescription drugs. The distributed data sets currently include administrative and claims data from 2000 to 2011 for over 300 million person-years, 2.4 billion encounters, 38 million inpatient hospitalizations, and 2.9 billion dispensings. Selected laboratory results and vital signs data recorded after 2005 are also available. There is an active data quality assessment and characterization program, and eligibility for medical care and pharmacy benefits is known. Systematic reviews of the literature have assessed the ability of administrative data to identify health outcomes of interest, and procedures have been developed and tested to obtain, abstract, and adjudicate full-text medical records to validate coded diagnoses. Mini-Sentinel has also created a taxonomy of study designs and analytical approaches for many commonly occurring situations, and it is developing new statistical and epidemiologic methods to address certain gaps in analytic capabilities. Assessments are performed by distributing computer programs that are executed locally by each data partner. The system is in active use by FDA, with the majority of assessments performed using customizable, reusable queries (programs). Prospective and retrospective assessments that use customized protocols are conducted as well. To date, several hundred unique programs have been distributed and executed. Current activities include active surveillance of several drugs and vaccines, expansion of the population, enhancement of the common data model to include additional types of data from electronic health records and registries, development of new methodologic capabilities, and assessment of methods to identify and validate additional health outcomes of interest., (Copyright © 2012 John Wiley & Sons, Ltd.)

Purpose: To systematically review algorithms to identify transfusion-related sepsis or septicemia in administrative data, with a focus on studies that have examined the validity of the algorithms., Methods: A literature search was conducted using PubMed, the database of the Iowa Drug Information Service (IDIS/Web), and Embase. A Google Scholar search was conducted because of difficulty identifying relevant studies. Reviews were conducted by two investigators to identify studies using data sources from the USA or Canada, because these data sources were most likely to reflect the coding practices of Mini-Sentinel data sources., Results: No studies that were identified that used administrative data to identify sepsis or septicemia related to transfusion of blood products. Thus, four studies that studied the validity of algorithms to identify sepsis and two that studied algorithms to identify allogeneic blood transfusion are described in this review. Two studies found acceptable positive predictive values of 80% and 89% for algorithms to identify sepsis in hospitalized patients. One study reported a negative predictive value of 80% in hospitalized patients, and another, a sensitivity of 75%. One study of veterans receiving surgery reported much worse performance characteristics. Two studies reported near-perfect specificity of codes for allogeneic red blood cell transfusion, but sensitivity ranged from 21% to 83%., Conclusions: There is no information to assess the validity of algorithms to identify transfusion-related sepsis or septicemia. Codes to identify sepsis performed well in most studies. Algorithms to identify transfusions need further research that includes a broader range of transfusion types., (Copyright © 2012 John Wiley & Sons, Ltd.)

Purpose: To systematically review algorithms to identify infections related to blood products, tissue grafts, or organ transplants in administrative and claims data, focusing on studies that have examined the validity of the algorithms., Methods: A literature search was conducted using PubMed and the database of the Iowa Drug Information Service. Reviews were conducted by two investigators to identify studies using data sources from the USA or Canada because these data sources were most likely to reflect the coding practices of Mini-Sentinel data sources., Results: Searches identified one study that examined the validity of an algorithm to identify aspergillosis in transplant recipients and 16 studies that used nonvalidated algorithms to identify infections in recipients of blood products, tissue grafts, or organ transplants. Transfusion was studied as a risk factor for infection, but no studies attempted to identify infection transmitted by any of the exposures under review. Two studies reported sensitivity ranging from 21% to 83% and specificity of 100% of codes to identify allogeneic blood transfusion. No validation studies of algorithms to identify tissue grafts or organ transplant were identified., Conclusions: There is little evidence to support the validity of algorithms to identify infections related to blood products, tissue grafts, or organ transplants in administrative data or algorithms to identify the exposures. Although it may be possible to validate algorithms to identify the exposures and infectious outcomes, the use of administrative data to identify infections transmitted by these exposures may be challenging. Codes indicating infections acquired through medical care may be useful., (Copyright © 2012 John Wiley & Sons, Ltd.)

Purpose: To overview the methods used in the Mini-Sentinel systematic reviews of validation studies of algorithms to identify health outcomes in administrative and claims data and to describe lessons learned in the development of search strategies, including their ability to identify articles from previous systematic reviews which used different search strategies., Methods: Literature searches were conducted using PubMed and the citation database of the Iowa Drug Information Service. Embase was searched for some outcomes. The searches were based on a strategy developed by the Observational Medical Outcomes Partnership (OMOP) researchers. All citations were reviewed by two investigators. Exclusion criteria were applied at abstract and full-text review stages to ultimately identify algorithm validation studies that used data sources from the USA or Canada, as the results of these studies were considered most likely to generalize to Mini-Sentinel data. Nonvalidated algorithms were reviewed if fewer than five algorithm validation studies were identified., Results: The results of this project are described in the separate articles and reports written on algorithms to identify each outcome of interest., Conclusions: The Mini-Sentinel systematic reviews of algorithms to identify health outcomes in administrative and claims data are expected to be relatively complete, despite some limitations. Algorithm validation studies are inconsistently indexed in PubMed, creating challenges in conducting systematic reviews of these studies. Google Scholar searches, which can perform text word searches of electronically available articles, are suggested as a strategy to identify studies that are not captured through searches of standard citation databases., (Copyright © 2012 John Wiley & Sons, Ltd.)