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OBJECTIVE: Electronic health records (EHRs) and big data tools offer the opportunity for surveillance of adverse events (patient harm associated with medical care). We used International Classification of Diseases, Ninth Revision, codes in electronic records to identify known, and potentially novel, adverse reactions to blood transfusion. METHODS: We used 49,331 adult admissions involving critical care at a major teaching hospital, 2001-2012, in the Medical Information Mart for Intensive Care III EHRs database. We formed a T (defined as packed red blood cells, platelets, or plasma) group of 21,443 admissions versus 25,468 comparison (C) admissions. The International Classification of Diseases, Ninth Revision, Clinical Modification , diagnosis codes were compared for T versus C, described, and tested with statistical tools. RESULTS: Transfusion adverse events (TAEs) such as transfusion-associated circulatory overload (TACO; 12 T cases; rate ratio [RR], 15.61; 95% confidence interval [CI], 2.49-98) were found. There were also potential TAEs similar to TAEs, such as fluid overload disorder (361 T admissions; RR, 2.24; 95% CI, 1.88-2.65), similar to TACO. Some diagnoses could have been sequelae of TAEs, including nontraumatic compartment syndrome of abdomen (52 T cases; RR, 6.76; 95% CI, 3.40-14.9) possibly being a consequence of TACO. CONCLUSIONS: Surveillance for diagnosis codes that could be TAE sequelae or unrecognized TAE might be useful supplements to existing medical product adverse event programs.

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BACKGROUND: Big data tools provide opportunities to monitor adverse events (patient harm associated with medical care) (AEs) in the unstructured text of electronic health care records (EHRs). Writers may explicitly state an apparent association between treatment and adverse outcome ("attributed") or state the simple treatment and outcome without an association ("unattributed"). Many methods for finding AEs in text rely on predefining possible AEs before searching for prespecified words and phrases or manual labeling (standardization) by investigators. We developed a method to identify possible AEs, even if unknown or unattributed, without any prespecifications or standardization of notes. Our method was inspired by word-frequency analysis methods used to uncover the true authorship of disputed works credited to William Shakespeare. We chose two use cases, "transfusion" and "time-based." Transfusion was chosen because new transfusion AE types were becoming recognized during the study data period; therefore, we anticipated an opportunity to find unattributed potential AEs (PAEs) in the notes. With the time-based case, we wanted to simulate near real-time surveillance. We chose time periods in the hope of detecting PAEs due to contaminated heparin from mid-2007 to mid-2008 that were announced in early 2008. We hypothesized that the prevalence of contaminated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin AEs, independent of clinicians' documentation of attributed AEs. OBJECTIVE: We aimed to develop a new method to identify attributed and unattributed PAEs using the unstructured text of EHRs. METHODS: We used EHRs for adult critical care admissions at a major teaching hospital (2001-2012). For each case, we formed a group of interest and a comparison group. We concatenated the text notes for each admission into one document sorted by date, and deleted replicate sentences and lists. We identified statistically significant words in the group of interest versus the comparison group. Documents in the group of interest were filtered to those words, followed by topic modeling on the filtered documents to produce topics. For each topic, the three documents with the maximum topic scores were manually reviewed to identify PAEs. RESULTS: Topics centered around medical conditions that were unique to or more common in the group of interest, including PAEs. In each use case, most PAEs were unattributed in the notes. Among the transfusion PAEs was unattributed evidence of transfusion-associated cardiac overload and transfusion-related acute lung injury. Some of the PAEs from mid-2007 to mid-2008 were increased unattributed events consistent with AEs related to heparin contamination. CONCLUSIONS: The Shakespeare method could be a useful supplement to AE reporting and surveillance of structured EHR data. Future improvements should include automation of the manual review process.

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BACKGROUND: Enzymatic and chemical reactions are key for understanding biological processes in cells. Curated databases of chemical reactions exist but these databases struggle to keep up with the exponential growth of the biomedical literature. Conventional text mining pipelines provide tools to automatically extract entities and relationships from the scientific literature, and partially replace expert curation, but such machine learning frameworks often require a large amount of labeled training data and thus lack scalability for both larger document corpora and new relationship types. RESULTS: We developed an application of Snorkel, a weakly supervised learning framework, for extracting chemical reaction relationships from biomedical literature abstracts. For this work, we defined a chemical reaction relationship as the transformation of chemical A to chemical B. We built and evaluated our system on small annotated sets of chemical reaction relationships from two corpora: curated bacteria-related abstracts from the MetaCyc database (MetaCyc_Corpus) and a more general set of abstracts annotated with MeSH (Medical Subject Headings) term Bacteria (Bacteria_Corpus; a superset of MetaCyc_Corpus). For the MetaCyc_Corpus, we obtained 84% precision and 41% recall (55% F1 score). Extending to the more general Bacteria_Corpus decreased precision to 62% with only a four-point drop in recall to 37% (46% F1 score). Overall, the Bacteria_Corpus contained two orders of magnitude more candidate chemical reaction relationships (nine million candidates vs 68,0000 candidates) and had a larger class imbalance (2.5% positives vs 5% positives) as compared to the MetaCyc_Corpus. In total, we extracted 6871 chemical reaction relationships from nine million candidates in the Bacteria_Corpus. CONCLUSIONS: With this work, we built a database of chemical reaction relationships from almost 900,000 scientific abstracts without a large training set of labeled annotations. Further, we showed the generalizability of our initial application built on MetaCyc documents enriched with chemical reactions to a general set of articles related to bacteria.

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Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response. Discovery and validation of drug metabolism via bacterial enzymes has yielded >50 drugs after nearly a century of experimental research. However, there are limited computational tools for screening drugs for potential metabolism by the gut microbiome. We developed a pipeline for comparing and characterizing chemical transformations using continuous vector representations of molecular structure learned using unsupervised representation learning. We applied this pipeline to chemical reaction data from MetaCyc to characterize the utility of vector representations for chemical reaction transformations. After clustering molecular and reaction vectors, we performed enrichment analyses and queries to characterize the space. We detected enriched enzyme names, Gene Ontology terms, and Enzyme Consortium (EC) classes within reaction clusters. In addition, we queried reactions against drug-metabolite transformations known to be metabolized by the human gut microbiome. The top results for these known drug transformations contained similar substructure modifications to the original drug pair. This work enables high throughput screening of drugs and their resulting metabolites against chemical reactions common to gut bacteria.

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OBJECTIVES: This article summarizes past and current data mining activities at the United States Food and Drug Administration (FDA). TARGET AUDIENCE: We address data miners in all sectors, anyone interested in the safety of products regulated by the FDA (predominantly medical products, food, veterinary products and nutrition, and tobacco products), and those interested in FDA activities. SCOPE: Topics include routine and developmental data mining activities, short descriptions of mined FDA data, advantages and challenges of data mining at the FDA, and future directions of data mining at the FDA.

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OBJECTIVE: The objective of openFDA is to facilitate access and use of big important Food and Drug Administration public datasets by developers, researchers, and the public through harmonization of data across disparate FDA datasets provided via application programming interfaces (APIs). MATERIALS AND METHODS: Using cutting-edge technologies deployed on FDA's new public cloud computing infrastructure, openFDA provides open data for easier, faster (over 300 requests per second per process), and better access to FDA datasets; open source code and documentation shared on GitHub for open community contributions of examples, apps and ideas; and infrastructure that can be adopted for other public health big data challenges. RESULTS: Since its launch on June 2, 2014, openFDA has developed four APIs for drug and device adverse events, recall information for all FDA-regulated products, and drug labeling. There have been more than 20 million API calls (more than half from outside the United States), 6000 registered users, 20,000 connected Internet Protocol addresses, and dozens of new software (mobile or web) apps developed. A case study demonstrates a use of openFDA data to understand an apparent association of a drug with an adverse event. CONCLUSION: With easier and faster access to these datasets, consumers worldwide can learn more about FDA-regulated products.

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BACKGROUND: The purpose of this study was to use 2003 nationwide United States data to determine the incidences of primary total hip replacement, partial hip replacement, and revision hip replacement and to assess the short-term patient outcomes and factors associated with the outcomes. METHODS: We screened more than eight million hospital discharge abstracts from the 2003 Healthcare Cost and Utilization Project Nationwide Inpatient Sample and approximately nine million discharge abstracts from five state inpatient databases. Patients who had undergone total, partial, or revision hip replacement were identified with use of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) procedure codes. In-hospital mortality, perioperative complications, readmissions, and the association between these outcomes and certain patient and hospital variables were analyzed. RESULTS: Approximately 200,000 total hip replacements, 100,000 partial hip replacements, and 36,000 revision hip replacements were performed in the United States in 2003. Approximately 60% of the patients were sixty-five years of age or older and at least 75% had one or more comorbid diseases. The in-hospital mortality rates associated with these three procedures were 0.33%, 3.04%, and 0.84%, respectively. The perioperative complication rates associated with the three procedures were 0.68%, 1.36%, and 1.08%, respectively, for deep vein thrombosis or pulmonary embolism; 0.28%, 1.88%, and 1.27% for decubitus ulcer; and 0.05%, 0.06%, and 0.25% for postoperative infection. The rates of readmission, for any cause, within thirty days were 4.91%, 12.15%, and 8.48%, respectively, and the rates of readmissions, within thirty days, that resulted in a surgical procedure on the affected hip were 0.79%, 0.91%, and 1.53%. The rates of readmission, for any cause, within ninety days were 8.94%, 21.14%, and 15.72%, and the rates of readmissions, within ninety days, that resulted in a surgical procedure on the affected hip were 2.15%, 1.61%, and 3.99%. Advanced age and comorbid diseases were associated with worse outcomes, while private insurance coverage and planned admissions were associated with better outcomes. No consistent association between outcomes and hospital characteristics, such as hip procedure volume, was identified. CONCLUSIONS: Total hip replacement, partial hip replacement, and revision hip replacement are associated with different rates of postoperative complications and readmissions. Advanced age, comorbidities, and nonelective admissions are associated with inferior outcomes.

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Breast pumps are medical devices used to express milk and maintain the milk supply. The purpose of this study was to characterize adverse events reported to the United States Food and Drug Administration (FDA) on breast pumps. Thirty-seven adverse event reports on breast pumps were identified from the Manufacturer and User Facility Device Experience database between 1992 and 2003. Four additional reports were found in the Device Experience Network database from 1992 to 1996. The most commonly reported adverse events for electric breast pumps were pain, soreness, or discomfort; the need for medical intervention; and breast tissue damage. Most frequently reported problems for manual breast pumps were breast tissue damage and infection. Contamination of breast milk during pumping was also reported. Breast pump adverse events are likely underreported to the FDA. Reporting adverse events is important for improving the design and manufacture of breast pumps and subsequently decreasing adverse events.

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CONTEXT: Although adverse drug events have been extensively evaluated by computer-based surveillance, medical device errors have no comparable surveillance techniques. OBJECTIVES: To determine whether computer-based surveillance can reliably identify medical device-related hazards (no known harm to patient) and adverse medical device events (AMDEs; patient experienced harm) and to compare alternative methods of detection of device-related problems. DESIGN, SETTING, AND PARTICIPANTS: This descriptive study was conducted from January through September 2000 at a 520-bed tertiary teaching institution in the United States with experience in using computer tools to detect and prevent adverse drug events. All 20 441 regular and short-stay patients (excluding obstetric and newborn patients) were included. MAIN OUTCOME MEASURES: Medical device events as detected by computer-based flags, telemetry problem checklists, International Classification of Diseases, Ninth Revision (ICD-9) discharge code (which could include AMDEs present at admission), clinical engineering work logs, and patient survey results were compared with each other and with routine voluntary incident reports to determine frequencies, proportions, positive predictive values, and incidence rates by each technique. RESULTS: Of the 7059 flags triggered, 552 (7.8%) indicate a device-related hazard or AMDE. The estimated 9-month incidence rates (number per 1000 admissions [95% confidence intervals]) for AMDEs were 1.6 (0.9-2.5) for incident reports, 27.7 (24.9-30.7) for computer flags, and 64.6 (60.4-69.1) for ICD-9 discharge codes. Few of these events were detected by more than 1 surveillance method, giving an overall incidence of AMDE detected by at least 1 of these methods of 83.7 per 1000 (95% confidence interval, 78.8-88.6) admissions. The positive predictive value of computer flags for detecting device-related hazards and AMDEs ranged from 0% to 38%. CONCLUSIONS: More intensive surveillance methods yielded higher rates of medical device problems than found with traditional voluntary reporting, with little overlap between methods. Several detection methods had low efficiency in detecting AMDEs. The high rate of AMDEs suggests that AMDEs are an important patient safety issue, but additional research is necessary to identify optimal AMDE detection strategies.

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Governments, manufacturers, and other entities are interested in adverse event surveillance of marketed medical products. FDA's Center for Drug Evaluation and Research redesigned the post-marketing adverse reaction surveillance process to use the advantages of new technology. As part of this effort, a 'Pharmacovigilance Working Group' designed a new strategy for the review and analyses of adverse event reports received by FDA. It created requirements which divided signal detection into five tiers: (1) Single 'urgent' reports would be sent to reviewers' workstations nightly for immediate attention. Reviewers would be able to customize definitions of 'urgent' (events that should not wait for aggregate review). (2) Single urgent reports would be placed in a context matrix containing historical counts of similar events to aid in initial interpretation. (3) In this first level of aggregate review, graphical displays would highlight patterns within all the reports, both urgent and non-urgent, and (4) periodic drug-specific tabled-based reports would display the newly received reports across a pre-defined variety of displays. These four tiers would produce passive and criteria-based results which would be presented to safety reviewers' electronic workstations. (5) Active query capabilities (routine, such as age, sex, and year distributions, as well as ad hoc) would be available for exploring alerted issues. The historical database would be migrated into the new format. All historical and new reaction data would be coded with the new MedDRA (Medical Dictionary for Regulatory Activities) scheme. The strategy was to design a full data capture system which effectively exploits current computing advances and technical performance to automate many aspects of initial adverse event review, supporting more efficient and effective clinical assessment of safety signals.

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