Quantifying Discharge Medication Reconciliation Errors at 2 Pediatric Hospitals.

INTRODUCTION: Medication reconciliation errors (MREs) are common and can lead to significant patient harm. Quality improvement efforts to identify and reduce these errors typically rely on resource-intensive chart reviews or adverse event reporting. Quantifying these errors hospital-wide is complicated and rarely done. The purpose of this study is to define a set of 6 MREs that can be easily identified across an entire healthcare organization and report their prevalence at 2 pediatric hospitals. METHODS: An algorithmic analysis of discharge medication lists and confirmation by clinician reviewers was used to find the prevalence of the 6 discharge MREs at 2 pediatric hospitals. These errors represent deviations from the standards for medication instruction completeness, clarity, and safety. The 6 error types are Duplication, Missing Route, Missing Dose, Missing Frequency, Unlisted Medication, and See Instructions errors. RESULTS: This study analyzed 67,339 discharge medications and detected MREs commonly at both hospitals. For Institution A, a total of 4,234 errors were identified, with 29.9% of discharges containing at least one error and an average of 0.7 errors per discharge. For Institution B, a total of 5,942 errors were identified, with 42.2% of discharges containing at least 1 error and an average of 1.6 errors per discharge. The most common error types were Duplication and See Instructions errors. CONCLUSION: The presented method shows these MREs to be a common finding in pediatric care. This work offers a tool to strengthen hospital-wide quality improvement efforts to reduce pediatric medication errors.

Using Probabilistic Record Linkage of Structured and Unstructured Data to Identify Duplicate Cases in Spontaneous Adverse Event Reporting Systems.

INTRODUCTION: Duplicate case reports in spontaneous adverse event reporting systems pose a challenge for medical reviewers to efficiently perform individual and aggregate safety analyses. Duplicate cases can bias data mining by generating spurious signals of disproportional reporting of product-adverse event pairs. OBJECTIVE: We have developed a probabilistic record linkage algorithm for identifying duplicate cases in the US Vaccine Adverse Event Reporting System (VAERS) and the US Food and Drug Administration Adverse Event Reporting System (FAERS). METHODS: In addition to using structured field data, the algorithm incorporates the non-structured narrative text of adverse event reports by examining clinical and temporal information extracted by the Event-based Text-mining of Health Electronic Records system, a natural language processing tool. The final component of the algorithm is a novel duplicate confidence value that is calculated by a rule-based empirical approach that looks for similarities in a number of criteria between two case reports. RESULTS: For VAERS, the algorithm identified 77% of known duplicate pairs with a precision (or positive predictive value) of 95%. For FAERS, it identified 13% of known duplicate pairs with a precision of 100%. The textual information did not improve the algorithm's automated classification for VAERS or FAERS. The empirical duplicate confidence value increased performance on both VAERS and FAERS, mainly by reducing the occurrence of false-positives. CONCLUSIONS: The algorithm was shown to be effective at identifying pre-linked duplicate VAERS reports. The narrative text was not shown to be a key component in the automated detection evaluation; however, it is essential for supporting the semi-automated approach that is likely to be deployed at the Food and Drug Administration, where medical reviewers will perform some manual review of the most highly ranked reports identified by the algorithm.

Post-Marketing Surveillance of Human Rabies Diploid Cell Vaccine (Imovax) in the Vaccine Adverse Event Reporting System (VAERS) in the United States, 1990‒2015.

BACKGROUND: In 1980, human diploid cell vaccine (HDCV, Imovax Rabies, Sanofi Pasteur), was licensed for use in the United States. OBJECTIVE: To assess adverse events (AEs) after HDCV reported to the US Vaccine Adverse Event Reporting System (VAERS), a spontaneous reporting surveillance system. METHODS: We searched VAERS for US reports after HDCV among persons vaccinated from January 1, 1990-July 31, 2015. Medical records were requested for reports classified as serious (death, hospitalization, prolonged hospitalization, disability, life-threatening-illness), and those suggesting anaphylaxis and Guillain-Barré syndrome (GBS). Physicians reviewed available information and assigned a primary clinical category to each report using MedDRA system organ classes. Empirical Bayesian (EB) data mining was used to identify disproportional AE reporting after HDCV. RESULTS: VAERS received 1,611 reports after HDCV; 93 (5.8%) were serious. Among all reports, the three most common AEs included pyrexia (18.2%), headache (17.9%), and nausea (16.5%). Among serious reports, four deaths appeared to be unrelated to vaccination. CONCLUSIONS: This 25-year review of VAERS did not identify new or unexpected AEs after HDCV. The vast majority of AEs were non-serious. Injection site reactions, hypersensitivity reactions, and non-specific constitutional symptoms were most frequently reported, similar to findings in pre-licensure studies.

CD34⁺ collection efficiency as a function of blood volumes processed in pediatric autologous peripheral blood stem cell collection.

PURPOSE: To characterize the relationship between CD34(+) collection efficiency and blood volumes processed in pediatric patients undergoing autologous peripheral blood stem cell (PBSC) collection. METHODS: Retrospective 8-year (2001-2009) study of pediatric patients (n = 79) with neuroblastoma and central nervous system (CNS) tumors undergoing first day of autologous PBSC harvest using MNC program on the COBE Spectra (Caridian BCT, Lakewood, CO) was performed. Patients undergoing 0 to 2.9 BV (standard volume), 3 to 6 BV (large volume), and greater than 6 BV (ultra large volume) harvests were evaluated for CD34(+) collection efficiency, diagnosis (neuroblastoma vs. nonneuroblastoma), disease type (primary vs. relapse), mobilization regimen, granulocyte colony stimulating factor (GCSF) dose, and apheresis complications. RESULTS: CD34(+) collection efficiencies (CE) for neuroblastoma patients were 67%, 50%, and 53% for standard (n = 14), large (n = 9), and ultra large (n = 5) volume harvests, respectively. Similarly, patients with nonneuroblastoma diagnoses had CD34(+) CE of 63%, 55%, and 65% for low (n = 19), large (n = 27), and ultra large (n = 5) volume harvests, respectively. Weight, granulocyte colony stimulating factor (G-CSF) stimulation, type of mobilization, and apheresis complications (normalized by run time) were similar between the standard, large, and ultra large volume groups in patients with either neuroblastoma or nonneuroblastoma diagnoses. CONCLUSIONS: CD34(+) collection efficiency in pediatric autologous PBSC collection on the first day of harvest does not decrease with higher numbers of blood volumes processed in patients with either neuroblastoma or nonneuroblastoma primary disease. These results indirectly indicate bone marrow CD34(+) cell mobilization occurs with longer apheresis procedures in pediatric patients.