• Spine J · Nov 2019

    Multicenter Study

    Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation.

    • Aditya V Karhade, Paul T Ogink, Quirina C B S Thio, Thomas D Cha, William B Gormley, Stuart H Hershman, Timothy R Smith, Jianren Mao, Andrew J Schoenfeld, Christopher M Bono, and Joseph H Schwab.
    • Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
    • Spine J. 2019 Nov 1; 19 (11): 1764-1771.

    Background ContextSpine surgery has been identified as a risk factor for prolonged postoperative opioid use. Preoperative prediction of opioid use could improve risk stratification, shared decision-making, and patient counseling before surgery.PurposeThe primary purpose of this study was to develop algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation.Study Design/SettingRetrospective, case-control study at five medical centers.Patient SampleChart review was conducted for patients undergoing surgery for lumbar disc herniation between January 1, 2000 and March 1, 2018.Outcome MeasuresThe primary outcome of interest was sustained opioid prescription after surgery to at least 90 to 180 days postoperatively.MethodsFive models (elastic-net penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed to predict prolonged opioid prescription. Explanations of predictions were provided globally (averaged across all patients) and locally (for individual patients).ResultsOverall, 5,413 patients were identified, with sustained postoperative opioid prescription of 416 (7.7%) at 90 to 180 days after surgery. The elastic-net penalized logistic regression model had the best discrimination (c-statistic 0.81) and good calibration and overall performance; the three most important predictors were: instrumentation, duration of preoperative opioid prescription, and comorbidity of depression. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms. The application can be found here: https://sorg-apps.shinyapps.io/lumbardiscopioid/ CONCLUSION: Preoperative prediction of prolonged postoperative opioid prescription can help identify candidates for increased surveillance after surgery. Patient-centered explanations of predictions can enhance both shared decision-making and quality of care.Copyright © 2019. Published by Elsevier Inc.

      Pubmed     Full text   Copy Citation     Plaintext  

      Add institutional full text...

    Notes

     
    Knowledge, pearl, summary or comment to share?
    300 characters remaining
    help        
    You can also include formatting, links, images and footnotes in your notes
    • Simple formatting can be added to notes, such as *italics*, _underline_ or **bold**.
    • Superscript can be denoted by <sup>text</sup> and subscript <sub>text</sub>.
    • Numbered or bulleted lists can be created using either numbered lines 1. 2. 3., hyphens - or asterisks *.
    • Links can be included with: [my link to pubmed](http://pubmed.com)
    • Images can be included with: ![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
    • For footnotes use [^1](This is a footnote.) inline.
    • Or use an inline reference [^1] to refer to a longer footnote elseweher in the document [^1]: This is a long footnote..

    hide…

What will the 'Medical Journal of You' look like?

Start your free 21 day trial now.

We guarantee your privacy. Your email address will not be shared.