Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion

Karhade, Aditya V.; Ogink, Paul T.; Thio, Quirina C. B. S.; Broekman, Marike; Thomas, D.; Hershman, Stuart H.; Mao, Jianren; Peul, Wilco C.; Schoenfeld, Andrew J.; Bono, Christopher M.; Schwab, Joseph H.

doi:10.1016/j.spinee.2019.01.009

Cited by 103 publications

(94 citation statements)

References 33 publications

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“…A recent 2019 study by Karhade et al used machine learning to identify predictors of sustained opioid use in patients following ACDF. In this study of patients from a single-institutional health system, it was found that the most important risk factors were preoperative opioid use duration, antidepressant use, tobacco use, and Medicaid insurance [20]. Several of our results are corroborated by this study, such as tobacco use, preoperative opioid use, and depression, though we have also examined other specific comorbidities such as history of myocardial infarction and chronic obstructive pulmonary disease.…”

Section: Discussionsupporting

confidence: 72%

“…Several of our results are corroborated by this study, such as tobacco use, preoperative opioid use, and depression, though we have also examined other specific comorbidities such as history of myocardial infarction and chronic obstructive pulmonary disease. Additional perioperative risk factors identified in our study, such as 30-day readmission, have not been examined in previous studies [19,20,28].…”

Section: Discussionmentioning

confidence: 80%

“…Multiple organizations have proposed opioid prescribing guidelines for surgical patients, with the aim of ensuring adequate pain control while mitigating the potential adverse effects of excessive opioid supply and long-term opioid use [15−18]. Though prescription opioids are often necessary for adequate pain relief, and the majority of patients undergoing surgery are prescribed short courses of opioids without consequence [19], there is an elevated risk of spine patients progressing after surgery inadvertently into chronic opioid users postoperatively, as spine surgery is considered to be a "high-risk episode" for this transition to long-term opioid prescriptions [20].…”

Section: Introductionmentioning

confidence: 99%

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Chronic opioid use following anterior cervical discectomy and fusion surgery for degenerative cervical pathology

et al. 2020

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Section: Discussionsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

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Chronic opioid use following anterior cervical discectomy and fusion surgery for degenerative cervical pathology

et al. 2020

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“…Anterior cervical discectomy and fusion (ACDF) is a surgical procedure for DCM that involves removal of a diseased intervertebral disc and fusion of 2 adjacent vertebral bodies. 36 Karhade et al 37 developed 5 ML models to predict sustained opioid use after ACDF and found stochastic gradient boosting to be the highest-performing algorithm (AUC = 0.81 with good calibration). The authors determined that prolonged opioid use after ACDF was driven by preoperative opioid prescription, antidepressant use, tobacco use, and Medicaid insurance status.…”

Section: Machine Learning Algorithms In Nontraumatic Spinal Cord Injurymentioning

confidence: 99%

“…To overcome this issue, applications such as Shiny can be used to create web-based tools that incorporate ML models. The development of these web-based tools (e.g., as seen in the study of Karhade et al [37][38][39] ) is a necessary step in allowing ML models to be more easily applicable in the clinical setting.…”

Section: Future Directions and Recommendationsmentioning

confidence: 99%

Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions

et al. 2019

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Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over conventional regression techniques, such as a reduced requirement for a priori knowledge on predictors and better ability to manage large datasets. Current studies have made extensive strides in employing machine learning to a greater capacity in spinal cord injury (SCI). Analyses using machine learning algorithms have been done on both traumatic SCI and nontraumatic SCI, the latter of which typically represents degenerative spine disease resulting in spinal cord compression, such as degenerative cervical myelopathy. This article is a literature review of current studies published in traumatic and nontraumatic SCI that employ machine learning for the prediction of a host of outcomes. The studies described utilize machine learning in a variety of capacities, including imaging analysis and prediction in large epidemiological data sets. We discuss the performance of these machine learning-based clinical prognostic models relative to conventional statistical prediction models. Finally, we detail the future steps needed for machine learning to become a more common modality for statistical analysis in SCI.

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Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation

et al. 2020

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Key Points Question Are medication monitoring programs within a hospital associated with more accurate identification of patients with opioid use disorder through the use of proxy Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) ( DSM-5) criteria for opioid use disorder extracted from electronic health records? Findings This cross-sectional study demonstrated that DSM-5 criteria for opioid use disorder can be extracted through review of electronic health records and that patients who are part of a drug monitoring program had a higher mean prevalence of opiod use disorder and a higher mean number of psychiatric comorbidities associated with opioid use disorder. Meaning Proxy measures that rely on multiple sources of data, including prescription drug history and notes in the electronic health record, may help identify patients with opioid use disorder who have not received a diagnosis.

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Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion

Cited by 103 publications

References 33 publications

Chronic opioid use following anterior cervical discectomy and fusion surgery for degenerative cervical pathology

Chronic opioid use following anterior cervical discectomy and fusion surgery for degenerative cervical pathology

Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions

Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation

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