Can Bar Code Scanning Improve Data Capture in a National Register? Findings from the Irish National Orthopaedic Register.

BACKGROUND: The Irish National Orthopaedic Register (INOR) provides a national mechanism for managing data on THA and TKA in Ireland, including a detailed implant record populated by intraoperative implant bar code scanning. It is critically important that implant details are recorded accurately for longitudinal outcome studies, implant recalls, and revision surgery planning. Before INOR's 2014 launch, Irish hospitals maintained separate, local institutional arthroplasty databases. These individual databases typically took the form of hardcopy operating room (OR) logbooks with handwritten patient details alongside the descriptive stickers from the implant packaging and/or individual institution electronic records using manual electronic implant data input. With the introduction of the INOR, a single, unifying national database was established with the ability to instead collect implant data using bar code scanning at time of implant unpackaging in the OR. We observed that bar code data entry represented a novel and potentially substantial change to implant recording methods at our institution and so sought to examine the potential effect on implant data quality. QUESTIONS/PURPOSES: We compared the new bar code scanning method of implant data collection used by the INOR to the previously employed recording methods at our institution (in our case, the previous methods included both an electronic operation note database [Bluespier software] and a duplicate hardcopy OR logbook) and asked (1) Does bar code scanning improve the completeness of implant records? (2) Does bar code scanning improve the accuracy of implant records? METHODS: Although the INOR was launched in 2014, our institution went live with it in 2019. To avoid any potential recording issues that may have occurred during the 2019 introduction of the novel system, a clear period before the introduction of INOR was selected at our institution to represent an era of manual data input to Bluespier software: July 2018. Although we initially aimed for 2 months of data from July 1, 2018, to August 31, 2018 (n = 247), we decided to proceed to 250 consecutive, primary THAs or TKAs for clarity of results. No procedure meeting these criteria was excluded. A second recent period, January 2021, was identified to represent an era of bar code data input; 250 consecutive, primary THAs or TKAs were also included from this date (to February 15, 2021). No case meeting these criteria was excluded. A total of 4244 implant parameters from these 500 primary THAs or TKAs were manually cross-referenced for missing or incorrect data. Eleven THA and six TKA parameters were chosen for comparison, including implant names and component sizes. For each case, either the 2018 Bluespier electronic record or the 2021 INOR electronic record was manually interrogated, and implant details were recorded by two authors before they were compared against the duplicate record for every case (the reference-standard OR logbook containing the corresponding implant product stickers) for both completeness and accuracy. Completeness was defined binarily as the implant parameter being either present or absent; we did likewise for accuracy, either that parameter was correct or incorrect. The OR logbooks were chosen as the reference standard because we felt the risk of product stickers containing errors (inaccuracies) was negligible, and in our collective experience, missing stickers (incompleteness) has not been encountered. Logbook case completeness was also confirmed by comparison to our inpatient management system. RESULTS: With the introduction of the automated bar code data entry in the INOR, the proportion of missing data declined from 7% (135 of 2051) to 0% (0 of 2193), and the proportion of incorrectly recorded implant parameters declined from 2% (45 of 2051) to 0% (0 of 2193). The proportion of procedures with entirely accurate implant records rose from 53% (133 of 250) to 100% (250 of 250). CONCLUSION: The completeness and accuracy of implant data capture was improved after the introduction of a contemporary electronic national arthroplasty registry that utilizes bar code data entry. CLINICAL RELEVANCE: Based on the results of this study, other local and national registers may consider bar code data entry in the OR to achieve excellent implant data quality. Future studies may examine implant data quality at a national level to validate the bar code-populated data of the INOR.

Effect of user puffing topography on total particulate matter, nicotine and volatile carbonyl emissions from narghile waterpipes.

OBJECTIVES: Puffing topographies of waterpipe users vary widely as does the puff-to-puff topography of an individual user. The aim of this study was to determine if puff duration and flow rate have an effect on the characteristics of the mainstream emission from waterpipes, including total particulate matter (TPM), mass ratio of nicotine and mass concentration of volatile carbonyls. METHODS: Puffing parameters were chosen to encompass a significant portion of the perimeter space observed from a natural environment study. Tested conditions were 150, 200 and 250 mL sec-1; each run at 2, 3.5 and 5 s durations; 25 s interpuff duration and ~100 puffs per session. Each session was run in quadruplicate using the Programmable Emissions System-2 (PES-2) emissions capture system under identical conditions. Particulate matter, for quantification of TPM and nicotine, was collected on filter pads every ~5 L of aerosol resulting in 6 to 25 samples per session. Volatile carbonyls were sampled using 2,4-Dinitrophenylhydrazine (DNPH)-coated silica. RESULTS: Mass concentration of TPM linearly decreased with increased flow rate, with no dependency on puff duration. Nicotine mass ratio was independent of topography, with average mass ratio of nicotine to TPM of 0.0027±0.0002 (mg/mg). The main carbonyls observed were acetaldehyde and formaldehyde. Puff duration increased emissions of some carbonyls (eg, formaldehyde) but not others (eg, acetaldehyde). CONCLUSIONS: The results presented here highlight that topographies influence the emissions generated from waterpipes including TPM, total nicotine and volatile carbonyls. For laboratory studies to be representative of user exposure, a range of topographies must be studied. Using a range of topographies within a controlled laboratory environment will better inform regulatory policy.

Framework to Estimate Total Particulate Mass and Nicotine Delivered to E-cig Users from Natural Environment Monitoring Data.

A framework describing the joint effect of user topography behavior and product characteristics of one exemplar device on the total particulate mass (TPM) and aerosol constituent yield delivered to a user is presented and validated against seven user-specific 'playback' emissions observations. A pen-style e-cig was used to collect emissions across puff flow rates and durations spanning the range observed in the natural environment. Emissions were analyzed with GC-MS and used to construct empirical correlations for TPM concentration and nicotine mass ratio. TPM concentration was demonstrated to depend upon both puff flow rate and duration, while nicotine mass ratio was not observed to be flow-dependent under the conditions presented. The empirical model for TPM and nicotine yield demonstrated agreement with experimental observations, with Pearson correlation coefficients of r = 0.79 and r = 0.86 respectively. The mass of TPM and nicotine delivered to the mouth of an e-cig user are dependent upon the puffing behavior of the user. Product-specific empirical models of emissions may be used in conjunction with participant-specific topography observations to accurately quantify the mass of TPM and nicotine delivered to a user.