Can natural language processing provide accurate,automated reporting of wound infection requiring reoperation after lumbar discectomy? |
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| Institution: | 1. Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;2. Department of Orthopedic Surgery, Newton Wellesley Hospital, Newton, MA, USA;3. Department of Neurosurgery, North Shore Medical Center, Boston, MA, USA;4. Department of Orthopedic Surgery, Brigham and Women''s Hospital, Harvard Medical School, Boston, MA, USA;5. Department of Orthopedic Surgery, Brigham and Women''s Faulkner Hospital, Boston, MA, USA;1. Department of Orthopaedic Surgery, Brigham and Women''s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA;2. Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02214, USA;3. Department of Radiation Oncology, Brigham and Women''s Hospital/Dana Farber Cancer Institute, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA;4. Department of Neurosurgery, Brigham and Women''s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA;5. Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02214, USA;1. Cleveland Clinic, Machine Learning Arthroplasty Lab, Cleveland, OH, USA;2. Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA;3. Department of Orthopedic Surgery, Baylor College of Medicine, Houston, TX, USA;4. Said Business School, University of Oxford, Oxford, UK;5. Cleveland Clinic Center for Spine Health, Cleveland, OH, USA;1. Department of Orthopedic Surgery, Brigham and Women''s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA;2. Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02214, USA;3. Department of Neurosurgery, Brigham and Women''s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA;4. Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02214, USA;1. Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;2. Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA;1. Department of Orthopedics, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei, Taiwan;2. School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan;3. Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, USA |
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| Abstract: | BACKGROUNDSurgical 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.PURPOSEThe 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 SAMPLEAdult patients undergoing discectomy at two academic and three community medical centers between January 1, 2000 and July 31, 2019 for lumbar disc herniation.OUTCOME MEASURESReoperation for wound infection within 90 days after surgeryMETHODSFree-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.RESULTSOverall, 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.CONCLUSIONTemporal 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. |
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