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

Spine J. 2020 Oct;20(10):1602-1609. doi: 10.1016/j.spinee.2020.02.021. Epub 2020 Mar 4.


Background: 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.

Purpose: 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.

Patient sample: Adult patients undergoing discectomy at two academic and three community medical centers between January 1, 2000 and July 31, 2019 for lumbar disc herniation.

Outcome measures: 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.

Results: Overall, 5,860 patients were included in this study and 62 (1.1%) had a reoperation for wound infection. In patients who underwent surgery after January 1, 2016 (n=1,377), the NLP algorithm detected 15 of the 16 patients (sensitivity=0.94) who had reoperation for infection. In comparison, current procedural terminology and international classification of disease codes detected 12 of these 16 patients (sensitivity=0.75). At a threshold of 0.05, the NLP algorithm had positive predictive value of 0.83 and F1-score of 0.88.

Conclusion: Temporal validation of the algorithm developed in this study demonstrates a proof-of-concept application of NLP for automated reporting of adverse events after spine surgery. Adapting this methodology for other procedures and outcomes in spine and orthopedics has the potential to dramatically improve and automatize quality and safety reporting.

Keywords: Adverse events; Artificial intelligence; Complication; Disc herniation; Infection; Machine learning; Natural language processing; Prediction; Reoperation; Spine.

MeSH terms

  • Diskectomy* / adverse effects
  • Humans
  • Lumbar Vertebrae / surgery
  • Natural Language Processing*
  • Prospective Studies
  • Reoperation
  • Retrospective Studies