Incidental durotomy: predictive risk model and external validation of natural language process identification algorithm

Jeff Ehresman, Zach Pennington, Aditya V. Karhade, Sakibul Huq, Ravi Medikonda, Andrew Schilling, James Feghali, Andrew Hersh, A. Karim Ahmed, Ethan Cottrill, Daniel Lubelski, Erick M. Westbroek, Joseph H. Schwab, Daniel M. Sciubba

Research output: Contribution to journalArticlepeer-review

Abstract

OBJECTIVE Incidental durotomy is a common complication of elective lumbar spine surgery seen in up to 11% of cases. Prior studies have suggested patient age and body habitus along with a history of prior surgery as being associated with an increased risk of dural tear. To date, no calculator has been developed for quantifying risk. Here, the authors’ aim was to identify independent predictors of incidental durotomy, present a novel predictive calculator, and externally validate a novel method to identify incidental durotomies using natural language processing (NLP). METHODS The authors retrospectively reviewed all patients who underwent elective lumbar spine procedures at a tertiary academic hospital for degenerative pathologies between July 2016 and November 2018. Data were collected regarding surgical details, patient demographic information, and patient medical comorbidities. The primary outcome was incidental durotomy, which was identified both through manual extraction and the NLP algorithm. Multivariable logistic regression was used to identify independent predictors of incidental durotomy. Bootstrapping was then employed to estimate optimism in the model, which was corrected for; this model was converted to a calculator and deployed online. RESULTS Of the 1279 elective lumbar surgery patients included in this study, incidental durotomy occurred in 108 (8.4%). Risk factors for incidental durotomy on multivariable logistic regression were increased surgical duration, older age, revision versus index surgery, and case starts after 4 pm. This model had an area under curve (AUC) of 0.73 in predicting incidental durotomies. The previously established NLP method was used to identify cases of incidental durotomy, of which it demonstrated excellent discrimination (AUC 0.97). CONCLUSIONS Using multivariable analysis, the authors found that increased surgical duration, older patient age, cases started after 4 pm, and a history of prior spine surgery are all independent positive predictors of incidental durotomy in patients undergoing elective lumbar surgery. Additionally, the authors put forth the first version of a clinical calculator for durotomy risk that could be used prospectively by spine surgeons when counseling patients about their surgical risk. Lastly, the authors presented an external validation of an NLP algorithm used to identify incidental durotomies through the review of free-text operative notes. The authors believe that these tools can aid clinicians and researchers in their efforts to prevent this costly complication in spine surgery.

Original languageEnglish (US)
Pages (from-to)342-348
Number of pages7
JournalJournal of Neurosurgery: Spine
Volume33
Issue number3
DOIs
StatePublished - Sep 2020

Keywords

  • Incidental durotomy
  • Intraoperative complication
  • Lumbar spine surgery
  • Natural language processing

ASJC Scopus subject areas

  • Surgery
  • Neurology
  • Clinical Neurology

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