Dataset Information

Adverse Drug Event Reporting From Clinical Care: Mixed-Methods Analysis for a Minimum Required Dataset.

ABSTRACT:

Background

Patients commonly transition between health care settings, requiring care providers to transfer medication utilization information. Yet, information sharing about adverse drug events (ADEs) remains nonstandardized.

Objective

The objective of our study was to describe a minimum required dataset for clinicians to document and communicate ADEs to support clinical decision making and improve patient safety.

Methods

We used mixed-methods analysis to design a minimum required dataset for ADE documentation and communication. First, we completed a systematic review of the existing ADE reporting systems. After synthesizing reporting concepts and data fields, we conducted fieldwork to inform the design of a preliminary reporting form. We presented this information to clinician end-user groups to establish a recommended dataset. Finally, we pilot-tested and refined the dataset in a paper-based format.

Results

We evaluated a total of 1782 unique data fields identified in our systematic review that describe the reporter, patient, ADE, and suspect and concomitant drugs. Of these, clinicians requested that 26 data fields be integrated into the dataset. Avoiding the need to report information already available electronically, reliance on prospective rather than retrospective causality assessments, and omitting fields deemed irrelevant to clinical care were key considerations.

Conclusions

By attending to the information needs of clinicians, we developed a standardized dataset for adverse drug event reporting. This dataset can be used to support communication between care providers and integrated into electronic systems to improve patient safety. If anonymized, these standardized data may be used for enhanced pharmacovigilance and research activities.

SUBMITTER: Peddie D

PROVIDER: S-EPMC6043729 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

Adverse Drug Event Reporting From Clinical Care: Mixed-Methods Analysis for a Minimum Required Dataset.

Peddie David D Small Serena S SS Badke Katherin K Bailey Chantelle C Balka Ellen E Hohl Corinne M CM

JMIR medical informatics 20180628 2

<h4>Background</h4>Patients commonly transition between health care settings, requiring care providers to transfer medication utilization information. Yet, information sharing about adverse drug events (ADEs) remains nonstandardized.<h4>Objective</h4>The objective of our study was to describe a minimum required dataset for clinicians to document and communicate ADEs to support clinical decision making and improve patient safety.<h4>Methods</h4>We used mixed-methods analysis to design a minimum r ...[more]

PMID: 29954724

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Project description:UnlabelledBackgroundAdverse events (AEs) frequently occur in intensive care units (ICUs) and affect negatively patient outcomes. Targeted improvement strategies for patient safety are difficult to evaluate because of the intrinsic limitations of reporting crude AE rates. Single interventions influence positively the quality of care, but a multifaceted approach has been tested only in selected cases. The present study was designed to evaluate the rate, types, and contributing factors of emerging AEs and test the hypothesis that a multifaceted intervention on medication might reduce drug-related AEs.MethodsThis is a prospective, multicenter, before-and-after study of adult patients admitted to four ICUs during a 24-month period. Voluntary, anonymous, self-reporting of AEs was performed using a detailed, locally designed questionnaire. The temporal impact of a multifaceted implementation strategy to reduce drug-related AEs was evaluated using the risk-index scores methodology.ResultsA total of 2,047 AEs were reported (32 events per 100 ICU patient admissions and 117.4 events per 1,000 ICU patient days) from 6,404 patients, totaling 17,434 patient days. Nurses submitted the majority of questionnaires (n = 1,781, 87%). AEs were eye-witnessed in 49% (n = 1,003) of cases and occurred preferentially during an elective procedure (n = 1,597, 78%) and on morning shifts (n = 1,003, 49%), with a peak rate occurring around 10 a.m. Drug-related AEs were the most prevalent (n = 984, 48%), mainly as a consequence of incorrect prescriptions. Poor communication among caregivers (n = 776) and noncompliance with internal guidelines (n = 525) were the most prevalent contributing factors for AE occurrence. The majority of AEs (n = 1155, 56.4%) was associated with minimal, temporary harm. Risk-index scores for drug-related AEs decreased from 10.01 ± 2.7 to 8.72 ± 3.52 (absolute risk difference 1.29; 95% confidence interval, 0.88-1.7; p < 0.01) following the introduction of the intervention.ConclusionsAEs occurred in the ICU with a typical diurnal frequency distribution. Medication-related AEs were the most prevalent. By applying the risk-index scores methodology, we were able to demonstrate that our multifaceted implementation strategy focused on medication-related adverse events allowed to decrease drug related incidents.

Project description:BackgroundExisting systems to document adverse drug events often use free text data entry, which produces nonstandardized and unstructured data that are prone to misinterpretation. Standardized terminology may improve data quality; however, it is unclear which data standard is most appropriate for documenting adverse drug event symptoms and diagnoses.ObjectiveThis study aims to compare the utility, strengths, and weaknesses of different data standards for documenting adverse drug event symptoms and diagnoses.MethodsWe performed a mixed methods substudy of a multicenter retrospective chart review. We reviewed the research records of prospectively diagnosed adverse drug events at 5 Canadian hospitals. A total of 2 pharmacy research assistants independently entered the symptoms and diagnoses for the adverse drug events using four standards: Medical Dictionary for Regulatory Activities (MedDRA), Systematized Nomenclature of Medicine (SNOMED) Clinical Terms, SNOMED Adverse Reaction (SNOMED ADR), and International Classification of Diseases (ICD) 11th Revision. Disagreements between research assistants regarding the case-specific utility of data standards were discussed until a consensus was reached. We used consensus ratings to determine the proportion of adverse drug events covered by a data standard and coded and analyzed field notes from the consensus sessions.ResultsWe reviewed 573 adverse drug events and found that MedDRA and ICD-11 had excellent coverage of adverse drug event symptoms and diagnoses. MedDRA had the highest number of matches between the research assistants, whereas ICD-11 had the fewest. SNOMED ADR had the lowest proportion of adverse drug event coverage. The research assistants were most likely to encounter terminological challenges with SNOMED ADR and usability challenges with ICD-11, whereas least likely to encounter challenges with MedDRA.ConclusionsUsability, comprehensiveness, and accuracy are important features of data standards for documenting adverse drug event symptoms and diagnoses. On the basis of our results, we recommend the use of MedDRA.

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Project description:BackgroundThere is a high degree of variability in assessing the preventability of adverse drug events, limiting the ability to compare rates of preventable adverse drug events across different studies. We compared three methods for determining preventability of adverse drug events in emergency department patients and explored their strengths and weaknesses.MethodsThis mixed-methods study enrolled emergency department patients diagnosed with at least one adverse drug event from three prior prospective studies. A clinical pharmacist and physician reviewed the medical and research records of all patients, and independently rated each event's preventability using a "best practice-based" approach, an "error-based" approach, and an "algorithm-based" approach. Raters discussed discordant ratings until reaching consensus. We assessed the inter-rater agreement between clinicians using the same assessment method, and between different assessment methods using Cohen's kappa with 95% confidence intervals (95% CI). Qualitative researchers observed discussions, took field notes, and reviewed free text comments made by clinicians in a "comment" box in the data collection form. We developed a coding structure and iteratively analyzed qualitative data for emerging themes regarding the application of each preventability assessment method using NVivo.ResultsAmong 1356 adverse drug events, a best practice-based approach rated 64.1% (95% CI: 61.5-66.6%) of events as preventable, an error-based approach rated 64.3% (95% CI: 61.8-66.9%) of events as preventable, and an algorithm-based approach rated 68.8% (95% CI: 66.1-71.1%) of events as preventable. When applying the same method, the inter-rater agreement between clinicians was 0.53 (95% CI: 0.48-0.59), 0.55 (95%CI: 0.50-0.60) and 0.55 (95% CI: 0.49-0.55) for the best practice-, error-, and algorithm-based approaches, respectively. The inter-rater agreement between different assessment methods using consensus ratings for each ranged between 0.88 (95% CI 0.85-0.91) and 0.99 (95% CI 0.98-1.00). Compared to a best practice-based assessment, clinicians believed the algorithm-based assessment was too rigid. It did not account for the complexities of and variations in clinical practice, and frequently was too definitive when assigning preventability ratings.ConclusionThere was good agreement between all three methods of determining the preventability of adverse drug events. However, clinicians found the algorithmic approach constraining, and preferred a best practice-based assessment method.

Project description:ObjectivesPaclitaxel is a highly effective antitumor agent with notable adverse events, including hypersensitivity reactions, peripheral neuropathy, arthralgia, myalgias, and neutropenia. Solvent-based paclitaxel causes severe allergic, hypersensitivity, and anaphylactic reactions. Nanoparticle albumin-bound paclitaxel was recently developed and provides an advantage over solvent-based paclitaxel in avoiding solvent/surfactant-related adverse events. The aim of this study was to assess the adverse event profiles of solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel formulations using data from the spontaneous adverse event reporting system of the US Food and Drug Administration Adverse Event Reporting System database.MethodsThis study relied on Medical Dictionary for Regulatory Activities preferred terms and standardized queries, and calculated the reporting ratio and reporting odds ratios of paclitaxel formulations.ResultsOf 8,867,135 reports recorded in the US Food and Drug Administration Adverse Event Reporting System database from January 2004 to December 2016, 3469 and 4447 adverse events corresponded to solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel, respectively. Reporting odds ratios (95% confidence interval) for anaphylactic reaction (standardized MedDRA query code: 20000021) associated with the use of solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel were 1.69 (1.56-1.84) and 0.75 (0.68-0.83), respectively. Reporting odds ratio signal for anaphylactic reaction was not detected for nanoparticle albumin-bound paclitaxel. Reporting odds ratios (95% confidence interval) for acute renal failure (standardized MedDRA query code: 20000003) associated with the use of solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel were 0.75 (0.58-0.98) and 1.60 (1.37-1.89), respectively.ConclusionThis is the first study to evaluate the adverse event profile of nanoparticle albumin-bound paclitaxel using US Food and Drug Administration Adverse Event Reporting System data. Considering that the US Food and Drug Administration Adverse Event Reporting System database does not allow to infer causality or risk ranking, the different reporting frequencies of anaphylactic reaction and acute renal failure between solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel must be further investigated via analytical observational research.

Dataset Information

Adverse Drug Event Reporting From Clinical Care: Mixed-Methods Analysis for a Minimum Required Dataset.

Background

Objective

Methods

Results

Conclusions

Publications

Adverse Drug Event Reporting From Clinical Care: Mixed-Methods Analysis for a Minimum Required Dataset.

Similar Datasets

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