Can natural language processing provide accurate, automated reporting of wound infection requiring reoperation after lumbar discectomy?

The Spine Journal : Official Journal of the North American Spine Society
Aditya V KarhadeJoseph H Schwab

Abstract

Surgical site infections are a major driver of morbidity and increased costs in the postoperative period after spine surgery. Current tools for surveillance of these adverse events rely on prospective clinical tracking, manual retrospective chart review, or administrative procedural and diagnosis codes. The purpose of this study was to develop natural language processing (NLP) algorithms for automated reporting of postoperative wound infection requiring reoperation after lumbar discectomy. Adult patients undergoing discectomy at two academic and three community medical centers between January 1, 2000 and July 31, 2019 for lumbar disc herniation. Reoperation for wound infection within 90 days after surgery METHODS: Free-text notes of patients who underwent surgery from January 1, 2000 to December 31, 2015 were used for algorithm training. Free-text notes of patients who underwent surgery after January 1, 2016 were used for algorithm testing. Manual chart review was used to label which patients had reoperation for wound infection. An extreme gradient-boosting NLP algorithm was developed to detect reoperation for postoperative wound infection. Overall, 5,860 patients were included in this study and 62 (1.1%) had a reoperation for ...Continue Reading

Citations

Dec 1, 2020·Global Spine Journal·G Michael MallowDino Samartzis
Oct 20, 2020·Annals of Surgery·Joseph A MelliaJohn P Fischer
May 1, 2021·Clinical Orthopaedics and Related Research·Grace XiongAndrew J Schoenfeld
May 14, 2021·Clinical Orthopaedics and Related Research·Grace XiongAndrew J Schoenfeld
Aug 19, 2021·The Spine Journal : Official Journal of the North American Spine Society·Aditya V KarhadeJoseph H Schwab
Oct 12, 2021·Global Spine Journal·GuanRui RenXiaoTao Wu

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