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Please use this identifier to cite or link to this item:
http://ir.nhri.org.tw/handle/3990099045/8616
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| Title: | Development and evaluation of a hospital-wide healthcare-associated surgical site infection detection algorithm |
| Authors: | Tseng, YJ;Huang, BC;Lin, HC;Chen, MY;Shang, RJ;Sheng, WH;Chen, YC;Lai, F;Chang, SC |
| Contributors: | Division of Infectious Diseases |
| Abstract: | Purpose: Healthcare-associated surgical site infections (HASSIs) are important adverse events associated with health care. Surveillance of healthcare-associated infections (HAI) is a cornerstone of infection prevention programs, but labor intensive and performance variable. Recent studies have identified inter-institutional variability of surveillance techniques and these inconsistencies affect the validity of publicly reported HAI data. This study aims to develop a reliable and objective HASSI detection system. Methods: We developed five HASSI detection rules based on US Centers for Disease Control and Prevention National Healthcare Safety Network definition of healthcare–associated infection and modified according to local practice and the limitation of electronic medical records (EMR). We established a system systematically surveys EMR of patients receiving clean surgical procedures at a 2200-bed teaching hospital. The System uses discrete data, such as antibiotic prescriptions, microbiology results, and surgical site infections (SSI) related diagnosis, searching for SSI related texts in procedure notes and nurse EMR. The performance of each detection rule was evaluated based on EMR during Sep. to Dec., 2013. Then, we developed the detection algorithm based on logistic regression analysis of the detection rules performance. We validate the performance of the detection algorithm based on EMR data during Jan. to Feb., 2014. The performance was determined based on the reference standard generated by retrospectively reviewed and verified by one of the authors. Results: Among 12,032 in-patients receiving clean surgical procedures, there were 68 ICP-detected HASSIs. The area under ROC curve of detection algorithm is 0.957. When the sensitivity is 100%, the specificity of the model is 78.4%. The positive predictive value and negative predictive value are 1.4% and 100%, respectively. Conclusions: We established a HASSI screening system with high sensitivity and reduced by 78.1% the number of possible HASSI candidates needed to be reviewed by ICP. |
| Date: | 2015-04 |
| Relation: | Journal of Microbiology, Immunology and Infection. 2015 Apr;48(2, Suppl. 1):S43. |
| Link to: | http://dx.doi.org/10.1016/j.jmii.2015.02.074 |
| JIF/Ranking 2023: | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1684-1182&DestApp=IC2JCR |
| Appears in Collections: | [陳宜君] 會議論文/會議摘要
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| SDO1684118215001073.pdf | | 45Kb | Adobe PDF | 754 | View/Open |
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