Lab Matters Summer 2018 - Page 41

APHL 2018 Annual Meeting Poster Abstracts Presenter: Mimi Precit, Centers for Disease Control and Prevention/ Washington State Public Health Laboratories, Shoreline, WA, Email: A Semi-Automated Method to Analyze and Report Data from Neisseria gonorrheae Antimicrobial Susceptibility Testing J. Weiner, M. Khubbar and S. Bhattacharyya, City of Milwaukee Health Department Laboratory, Milwaukee, WI Introduction: The Milwaukee Health Department Laboratory (MHDL) uses the SoftLab/SoftMic platform from Soft Computer (SCC) for all LIS-based operations in the lab. The platform includes a front- end GUI interface for daily lab operations and a back-end database that can be queried via Microsoft Access. We designed a system of two MS Access queries coupled to three simple programs in the Python programming language that together accomplish the task of compiling, analyzing and validating lab test results for both NAATs, cultures and susceptibility testing for Neisseria gonorrheae monthly. Results: Implementation of the present method eliminated lab side data entry errors for specimen and test counts, incorrect date entry and miscalculation of turnaround times. The method also flags about 5 test orders per month with incorrect clinic or collector codes. By pulling LIS values directly from the database using an SQL query, transcription errors are eliminated. Similarly, by coding in the formulae needed to compute metrics such as turnaround time in working days and tallies of infection by specimen site by clinic, calculation errors are eliminated once the code is validated. Discussion: The task of compiling laboratory metrics was formerly done manually, requiring laboratorians to transcribe values from the LIS into an Excel spreadsheet, make calculations based on those values and report them on a separate form. This method introduced many sources of error, including data entry error, calculation errors and the propagation of errors through multiple calculations. The method also provided an unforeseen benefit by identifying test orders with incorrect clinic codes, which results in over- or underreporting of clinic metrics if left uncorrected. Overall, the method saves laboratorians’ time, reduces errors and helps validate data integrity within the LIS. Presenter: Sanjib Bhattacharyya, PhD, City of Milwaukee Health Department Laboratory, Milwaukee, WI, Phone: 414.286.5702, Email: PublicHealthLabs @APHL (complete abstract in Infectious Disease, p. 67) Identification of Carbapenem-Resistant Enterobacteriaceae from Rectal Swabs Using the ABI 7500 M. Bashore, M. Soehnlen and K. Jones, Michigan Department of Health and Human Services, Lansing, MI The emergence of Carbapenem-resistant Enterobacteriaceae (CRE) has become a serious concern for both clinicians and public health officials. These organisms confer antibiotic resistance through several mechanisms that often require advanced molecular methods to identify. The most common mechanism that is used to confer immunity is through the creation of extended-spectrum ß-lactamases (ESBLs). Common ESBLs that confer resistance of Carbapenems in the United States are Klebsiella pneumoniae carbapenemase (KPC), New Delhi metallo-beta-lactamase 1 (NDM- 1), oxacillinase carbapenemase (Oxa) and Verona integron-encoded metallo-ß-lactamase (VIM). State public health laboratories, in correlation with the Centers for Disease Control and Prevention (CDC) have develop molecular methods that can be W6VBFFVFgFW6RV秖W2FR77VRb'&B7V7G'VG'Vr&W67F6P5$R&v622W7V6ǒ66W&rg&V&Ɩ2VF7FGB&V6W6RG6ǒVFVRFB7V&RFW6PfV7F27B6ǒFRfV7F2&R7&VB7F0BrFW&6&Rf6ƗFW2FR66R&֗GbFVG2FW6Rf6ƗFW27&VFW276fR77VW2FW&2bfV7F6G&FFFǒ&BFVFf6FbFW6R&v622Fff7VB6Ɩ6&&F&W2GVRF6bWFFVBWVVBBFPW6RbVG2FW7FrVFrr7V6f6Gb&W7VG2&FW F6&BF277VR7FFRV&Ɩ2VF&&F&W2vW&RVB@FFWfVWFG2FG&7'B&V6VfRFW7BB&W'B7V6V0g&6Ɩ66FW2F6G&FR7&VBbFW6R&v62V7V"FW7Fr6&RWFƗVBFFVFgF֖7&&&W67F6PvVW2B&fFR67W&FRBFVǒ&W7VG2f"W6R'6Ɩ60BfV7F6G&FV2FR7FFRb֖6v2FWfVV@WFG2FFVFg&v626Fr&W67F6RvVW2&Fg&7VGW&R7V6V27V&֗GFVB'6Ɩ6&&F&W2@6Ɩ67V6V2FRf&b&V7F7v'27V&֗GFVBg&ЧFVB6&Rf6ƗFW2FR7FFRb֖6v'W&VRb&&F&W0V7V"WFB2&6VBfbbFRW6RbFR$sS&VFP5"7G'VVBvF&W'2&&W2B6G&2&fFVB'FP4D2FRfƖFFbF2WFBf"FVFgrvVR&W67F6PW6r&V7F7v'2W֖VB7V6V6FF2BGW2`7v'2FB6VB&RW6VBVffV7FfVǒf"FW7FrFR6FF0vW&RFW7FVBFW֖R66WF&R7V6V7&FW&f"FW7Fr`&VFR6Ɩ67V6V2FR'W&VRb&&F&W2&fFW06V7FFW&2F6&R6VFW'2BVFW'7FFrv66FF2ffV7BFR&W7VG2bFW7Fr&W7VG22fFFV7W&PFFW7Fr&W7VFrFFFV7G&FVBFRVffV7FfVW72`FRWFBB6FrDf"VFRV662b5$RVFW f&W26FF2vFWB67&f6r&W7VBVƗG&W6VFW#GFWr&6&R֖6vFW'FVBbVFBV6W'f6W26rԒSSr33R3s2Và&6&V֖6vv`7VW"#"EDU%03WFG3VW&W2vW&RFW6vVB266W72F&WGW&&W7VG0&6VB"FW7BGR&FW"2FVBB2FFW26V7FVB&V6VfVBBfƗVB6Ɩ2vW&R6Rv26V7FVB&W7V@6VFrԔ2fVW2f"5BǗ62bf&RBFW"FW&VƗGV7W&W2&W7VG2vW&RW'FVBF55bf&BBW6V@2WBf"F&w&2F&w&2vW&RFW6vV@F6WFRFRV&W"bV67V6VGRf"6FfR@VvFfR&W7VG2FRGW&&VBFRb7VGW&R6FfR6W0VFW&vrWFW7Frf"5BB&FW"V&W'2FBFBF6&WGvVVFRĕ2uTFW&f6RBFR266W72FF&6R4406gD֖2WFV֖w&W'G2vW&RvVW&FVBFRW7VW F7&726V6FW7B&FW"֗6F6W2B6gD֖2֖7&&W'G0vW&RW6VBFFv6RFR6W6Rb֗6F6W2F66fW&VB6WVF2W'Vv6FW'27F667W2W&WW06V6FfGFF&F70