Improving Consistency in Audits with Human Research Participants: Determining the Optimal Percent of Research Records to Review (P3-7.004)

Abstract

<h3>Objective:</h3> The objective of this study was to gain additional understanding about the optimal percentage of subject records needed to review to confidently identify events of non-compliance during an audit. <h3>Background:</h3> Applying current quality assurance guidelines to research studies has a risk of either overestimating the incidence of non-compliance events and potentially wasting study resources or underestimating and missing events which may cause unnecessary harm to patients. This study evaluates the variability found while auditing protocols according to current practices in NINDS. <h3>Design/Methods:</h3> A 100% audit was performed for a NINDS natural history protocol. Events identified were categorized by nature and severity. Simulations were performed to visualize how varying the size of the audit impacts the accuracy of identifying non-compliance: 1) Data were randomly sampled from 5% of total participants and the number of deviations per person was calculated; 2) Resampling, with replacement, was performed 99 more times; and 3) The above procedures were repeated for 10% of participants, 15%, 20%, etc., until 100%, where 100% is the true deviation event rate. <h3>Results:</h3> This protocol enrolled 52 participants who experienced 371 total events of non-compliance, 249 (67%) of which were procedural non-compliance and 30 (8%) of which were major non-compliance. Results indicated that the current guidelines of reviewing 10% of charts for large studies may result in excessive variability in gaining an understanding of the study team’s compliance. Quality assurance teams should review more than 10% of charts to provide additional accuracy to protect patient safety and data integrity. <h3>Conclusions:</h3> This study demonstrates the importance of understanding the variability that may result from reviewing a standard percentage of charts during an audit and its subsequent effects on potentially impacting patient safety or data integrity. Future studies are needed to determine if the results are consistent with larger studies and with other trial types. <b>Disclosure:</b> Matthew Gooden has nothing to disclose. Gina Norato has nothing to disclose. Sandra Martin has nothing to disclose. Dr. Nath has received personal compensation in the range of $10,000-$49,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Elsevier. The institution of Dr. Nath has received research support from National Institutes of Health. The institution of Dr. Nath has received research support from ALS Association. Dr. Reoma has received research support from NIH. Dr. Reoma has a non-compensated relationship as a Vice Chair with AAN Experimental Neurotherapeutics Section that is relevant to AAN interests or activities. Dr. Reoma has a non-compensated relationship as a Federal Employee with NINDS/NIH that is relevant to AAN interests or activities. Dr. Reoma has a non-compensated relationship as a Member with ASENT Program Committee that is relevant to AAN interests or activities.