Am J Infect Control. 2023 Apr 24:S0196-6553(23)00335-8. doi: 10.1016/j.ajic.2023.04.165. Online ahead of print.
ABSTRACT
BACKGROUND: Surgical site infection (SSI) surveillance is a labor-intensive endeavor. We present the design and validation of an algorithm for SSI detection after hip replacement surgery, and a report of its successful implementation in four public hospitals in Madrid, Spain.
METHODS: We designed a multivariable algorithm, AI-HPRO, using natural language processing (NLP) and extreme gradient-boosting to screen for SSI in patients undergoing hip replacement surgery. The development and validation cohorts included data from 19661 healthcare episodes from four hospitals in Madrid, Spain.
RESULTS: Positive microbiological cultures, the text variable “infection”, and prescription of clindamycin were strong markers of SSI. Statistical analysis of the final model indicated high sensitivity (99.18%) and specificity (91.01%) with a F1-score of 0.32, AUC of 0.989, accuracy of 91.27% and NPV of 99.98%.
DISCUSSION: Implementation of the AI-HPRO algorithm has reduced surveillance time from 975 person/hours to 63.5 person/hours and has permitted an 88.95% reduction in total volume of clinical records to be reviewed manually. The model presents a higher NPV (99.98%) than algorithms relying on NLP alone (94%) or NLP and logistic regression (97%).
CONCLUSIONS: This is the first report of an algorithm combining NLP and extreme gradient-boosting to permit accurate, real-time orthopedic SSI surveillance.
PMID:37100291 | DOI:10.1016/j.ajic.2023.04.165
